diff --git a/docs/modelarts/sdk-ref/ALL_META.TXT.json b/docs/modelarts/sdk-ref/ALL_META.TXT.json
new file mode 100644
index 00000000..70aaf559
--- /dev/null
+++ b/docs/modelarts/sdk-ref/ALL_META.TXT.json
@@ -0,0 +1,702 @@
+[
+ {
+ "uri":"modelarts_04_0001.html",
+ "product_code":"modelarts",
+ "code":"1",
+ "des":"This document describes how to install and configure a development environment and call functions provided by ModelArts SDK for secondary development.",
+ "doc_type":"sdkreference",
+ "kw":"Before You Start,SDK Reference",
+ "title":"Before You Start",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0002.html",
+ "product_code":"modelarts",
+ "code":"2",
+ "des":"ModelArts Software Development Kit (ModelArts SDK) encapsulates the ModelArts RESTful APIs in Python language to simplify application development. You can directly call M",
+ "doc_type":"sdkreference",
+ "kw":"SDK Overview,SDK Reference",
+ "title":"SDK Overview",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0003.html",
+ "product_code":"modelarts",
+ "code":"3",
+ "des":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "doc_type":"sdkreference",
+ "kw":"Preparations",
+ "title":"Preparations",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0005.html",
+ "product_code":"modelarts",
+ "code":"4",
+ "des":"ModelArts SDK can be used in the following environments:ModelArts SDK has been integrated into ModelArts Notebook and can be directly used without session authentication.",
+ "doc_type":"sdkreference",
+ "kw":"Environment Preparations,Preparations,SDK Reference",
+ "title":"Environment Preparations",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0004.html",
+ "product_code":"modelarts",
+ "code":"5",
+ "des":"Download the ModelArts SDK software package of the latest version.After the SDK is downloaded, you can use pip to install it. For details about how to install pip, see th",
+ "doc_type":"sdkreference",
+ "kw":"Downloading and Installing the SDK,Preparations,SDK Reference",
+ "title":"Downloading and Installing the SDK",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0044.html",
+ "product_code":"modelarts",
+ "code":"6",
+ "des":"When calling APIs, you need to specify the project ID in certain URLs. To do so, you need to obtain the project ID first. To obtain a project ID, perform the following op",
+ "doc_type":"sdkreference",
+ "kw":"Viewing the Project ID,Preparations,SDK Reference",
+ "title":"Viewing the Project ID",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0300.html",
+ "product_code":"modelarts",
+ "code":"7",
+ "des":"Using the SDK in non-notebook environments needs to call IAM, OBS, and ModelArts. Therefore, the endpoints of these services are required. Therefore, the endpoints of the",
+ "doc_type":"sdkreference",
+ "kw":"Configuring a Service Endpoint,Preparations,SDK Reference",
+ "title":"Configuring a Service Endpoint",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0153.html",
+ "product_code":"modelarts",
+ "code":"8",
+ "des":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "doc_type":"sdkreference",
+ "kw":"Session Authentication",
+ "title":"Session Authentication",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0123.html",
+ "product_code":"modelarts",
+ "code":"9",
+ "des":"The session module authenticates in-cloud resources and initializes ModelArts SDK Client and OBS Client. After a session is set up, you can directly call the ModelArts SD",
+ "doc_type":"sdkreference",
+ "kw":"Overview of Session Authentication,Session Authentication,SDK Reference",
+ "title":"Overview of Session Authentication",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0154.html",
+ "product_code":"modelarts",
+ "code":"10",
+ "des":"This authentication method is available for OBS Management, Training Management, Model Management, and Service Management.Set account to your domain name and username to ",
+ "doc_type":"sdkreference",
+ "kw":"Authentication Using the Username and Password,Session Authentication,SDK Reference",
+ "title":"Authentication Using the Username and Password",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0155.html",
+ "product_code":"modelarts",
+ "code":"11",
+ "des":"This authentication method is available for OBS Management, Training Management, Model Management, and Service Management.Parameters in this command are described as foll",
+ "doc_type":"sdkreference",
+ "kw":"AK/SK-based Authentication,Session Authentication,SDK Reference",
+ "title":"AK/SK-based Authentication",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0006.html",
+ "product_code":"modelarts",
+ "code":"12",
+ "des":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "doc_type":"sdkreference",
+ "kw":"OBS Management (Recommended)",
+ "title":"OBS Management (Recommended)",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0217.html",
+ "product_code":"modelarts",
+ "code":"13",
+ "des":"ModelArts SDK 1.1.3 supports OBS management, including uploading and downloading files and folders. The operations are as follows:Uploading a File to OBSUploading a Folde",
+ "doc_type":"sdkreference",
+ "kw":"Overview of OBS Management,OBS Management (Recommended),SDK Reference",
+ "title":"Overview of OBS Management",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0218.html",
+ "product_code":"modelarts",
+ "code":"14",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Uploading a File to OBS,OBS Management (Recommended),SDK Reference",
+ "title":"Uploading a File to OBS",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0219.html",
+ "product_code":"modelarts",
+ "code":"15",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Uploading a Folder to OBS,OBS Management (Recommended),SDK Reference",
+ "title":"Uploading a Folder to OBS",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0220.html",
+ "product_code":"modelarts",
+ "code":"16",
+ "des":"If the size of a file in a folder exceeds 5 GB, the file cannot be downloaded in this mode.In the ModelArts notebook instance, you do not need to enter authentication par",
+ "doc_type":"sdkreference",
+ "kw":"Downloading a File from OBS,OBS Management (Recommended),SDK Reference",
+ "title":"Downloading a File from OBS",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0221.html",
+ "product_code":"modelarts",
+ "code":"17",
+ "des":"If the size of a file in a folder exceeds 5 GB, the file cannot be downloaded in this mode. However, other files whose size is less than 5 GB in the folder can be downloa",
+ "doc_type":"sdkreference",
+ "kw":"Downloading a Folder from OBS,OBS Management (Recommended),SDK Reference",
+ "title":"Downloading a Folder from OBS",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0157.html",
+ "product_code":"modelarts",
+ "code":"18",
+ "des":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "doc_type":"sdkreference",
+ "kw":"Training Management",
+ "title":"Training Management",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0158.html",
+ "product_code":"modelarts",
+ "code":"19",
+ "des":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "doc_type":"sdkreference",
+ "kw":"Training Jobs",
+ "title":"Training Jobs",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0131.html",
+ "product_code":"modelarts",
+ "code":"20",
+ "des":"For training on the training platform, if the training fails, you can view the detailed log information on the platform or by calling the API in Querying Training Job Log",
+ "doc_type":"sdkreference",
+ "kw":"Creating a Training Job,Training Jobs,SDK Reference",
+ "title":"Creating a Training Job",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0160.html",
+ "product_code":"modelarts",
+ "code":"21",
+ "des":"In a ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other develop",
+ "doc_type":"sdkreference",
+ "kw":"Querying the List of Training Jobs,Training Jobs,SDK Reference",
+ "title":"Querying the List of Training Jobs",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0161.html",
+ "product_code":"modelarts",
+ "code":"22",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Querying the Details About a Training Job,Training Jobs,SDK Reference",
+ "title":"Querying the Details About a Training Job",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0162.html",
+ "product_code":"modelarts",
+ "code":"23",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Modifying the Description of a Training Job,Training Jobs,SDK Reference",
+ "title":"Modifying the Description of a Training Job",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0163.html",
+ "product_code":"modelarts",
+ "code":"24",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Obtaining the Name of a Training Job Log File,Training Jobs,SDK Reference",
+ "title":"Obtaining the Name of a Training Job Log File",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0164.html",
+ "product_code":"modelarts",
+ "code":"25",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Querying Training Job Logs,Training Jobs,SDK Reference",
+ "title":"Querying Training Job Logs",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0166.html",
+ "product_code":"modelarts",
+ "code":"26",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Deleting a Training Job,Training Jobs,SDK Reference",
+ "title":"Deleting a Training Job",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0167.html",
+ "product_code":"modelarts",
+ "code":"27",
+ "des":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "doc_type":"sdkreference",
+ "kw":"Training Job Versions",
+ "title":"Training Job Versions",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0168.html",
+ "product_code":"modelarts",
+ "code":"28",
+ "des":"A training job must exist before you create a version for it. You can create a training job version based on Creating a Training Job or job_id and version_id of the objec",
+ "doc_type":"sdkreference",
+ "kw":"Creating a Training Job Version,Training Job Versions,SDK Reference",
+ "title":"Creating a Training Job Version",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0169.html",
+ "product_code":"modelarts",
+ "code":"29",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Querying the List of Training Job Versions,Training Job Versions,SDK Reference",
+ "title":"Querying the List of Training Job Versions",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0170.html",
+ "product_code":"modelarts",
+ "code":"30",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Querying the Details About a Training Job Version,Training Job Versions,SDK Reference",
+ "title":"Querying the Details About a Training Job Version",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0171.html",
+ "product_code":"modelarts",
+ "code":"31",
+ "des":"You can stop a training job version that is being created only when the job is running.In the ModelArts notebook instance, you do not need to enter authentication paramet",
+ "doc_type":"sdkreference",
+ "kw":"Stopping a Training Job Version,Training Job Versions,SDK Reference",
+ "title":"Stopping a Training Job Version",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0172.html",
+ "product_code":"modelarts",
+ "code":"32",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Deleting a Training Job Version,Training Job Versions,SDK Reference",
+ "title":"Deleting a Training Job Version",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0173.html",
+ "product_code":"modelarts",
+ "code":"33",
+ "des":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "doc_type":"sdkreference",
+ "kw":"Training Job Parameter Configuration",
+ "title":"Training Job Parameter Configuration",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0174.html",
+ "product_code":"modelarts",
+ "code":"34",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Creating a Training Job Configuration,Training Job Parameter Configuration,SDK Reference",
+ "title":"Creating a Training Job Configuration",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0175.html",
+ "product_code":"modelarts",
+ "code":"35",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Querying the List of Training Job Parameter Configuration Objects,Training Job Parameter Configurati",
+ "title":"Querying the List of Training Job Parameter Configuration Objects",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0176.html",
+ "product_code":"modelarts",
+ "code":"36",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Querying the List of Training Job Configurations,Training Job Parameter Configuration,SDK Reference",
+ "title":"Querying the List of Training Job Configurations",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0177.html",
+ "product_code":"modelarts",
+ "code":"37",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Querying the Details About a Training Job Configuration,Training Job Parameter Configuration,SDK Ref",
+ "title":"Querying the Details About a Training Job Configuration",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0178.html",
+ "product_code":"modelarts",
+ "code":"38",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Modifying a Training Job Configuration,Training Job Parameter Configuration,SDK Reference",
+ "title":"Modifying a Training Job Configuration",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0179.html",
+ "product_code":"modelarts",
+ "code":"39",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Deleting a Training Job Configuration,Training Job Parameter Configuration,SDK Reference",
+ "title":"Deleting a Training Job Configuration",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0180.html",
+ "product_code":"modelarts",
+ "code":"40",
+ "des":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "doc_type":"sdkreference",
+ "kw":"Visualization Jobs",
+ "title":"Visualization Jobs",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0181.html",
+ "product_code":"modelarts",
+ "code":"41",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Creating a Visualization Job,Visualization Jobs,SDK Reference",
+ "title":"Creating a Visualization Job",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0182.html",
+ "product_code":"modelarts",
+ "code":"42",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Querying the List of Visualization Job Objects,Visualization Jobs,SDK Reference",
+ "title":"Querying the List of Visualization Job Objects",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0183.html",
+ "product_code":"modelarts",
+ "code":"43",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Querying the List of Visualization Jobs,Visualization Jobs,SDK Reference",
+ "title":"Querying the List of Visualization Jobs",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0184.html",
+ "product_code":"modelarts",
+ "code":"44",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Querying the Details About a Visualization Job,Visualization Jobs,SDK Reference",
+ "title":"Querying the Details About a Visualization Job",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0185.html",
+ "product_code":"modelarts",
+ "code":"45",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Modifying the Description of a Visualization Job,Visualization Jobs,SDK Reference",
+ "title":"Modifying the Description of a Visualization Job",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0186.html",
+ "product_code":"modelarts",
+ "code":"46",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Stopping a Visualization Job,Visualization Jobs,SDK Reference",
+ "title":"Stopping a Visualization Job",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0187.html",
+ "product_code":"modelarts",
+ "code":"47",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Restarting a Visualization Job,Visualization Jobs,SDK Reference",
+ "title":"Restarting a Visualization Job",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0188.html",
+ "product_code":"modelarts",
+ "code":"48",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Deleting a Visualization Job,Visualization Jobs,SDK Reference",
+ "title":"Deleting a Visualization Job",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0189.html",
+ "product_code":"modelarts",
+ "code":"49",
+ "des":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "doc_type":"sdkreference",
+ "kw":"Resource and Engine Specifications",
+ "title":"Resource and Engine Specifications",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0190.html",
+ "product_code":"modelarts",
+ "code":"50",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Querying a Built-in Algorithm,Resource and Engine Specifications,SDK Reference",
+ "title":"Querying a Built-in Algorithm",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0191.html",
+ "product_code":"modelarts",
+ "code":"51",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Querying the List of Resource Flavors,Resource and Engine Specifications,SDK Reference",
+ "title":"Querying the List of Resource Flavors",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0192.html",
+ "product_code":"modelarts",
+ "code":"52",
+ "des":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "doc_type":"sdkreference",
+ "kw":"Querying the List of Engine Types,Resource and Engine Specifications,SDK Reference",
+ "title":"Querying the List of Engine Types",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0077.html",
+ "product_code":"modelarts",
+ "code":"53",
+ "des":"Table 1 describes the job statuses.",
+ "doc_type":"sdkreference",
+ "kw":"Job Statuses,Training Management,SDK Reference",
+ "title":"Job Statuses",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0193.html",
+ "product_code":"modelarts",
+ "code":"54",
+ "des":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "doc_type":"sdkreference",
+ "kw":"Model Management",
+ "title":"Model Management",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0194.html",
+ "product_code":"modelarts",
+ "code":"55",
+ "des":"The model import function covers the following aspects:Initialize the existing model and create a model object based on the model ID.Create a model. For details about the",
+ "doc_type":"sdkreference",
+ "kw":"Importing a Model,Model Management,SDK Reference",
+ "title":"Importing a Model",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0195.html",
+ "product_code":"modelarts",
+ "code":"56",
+ "des":"In ModelArts notebook, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development enviro",
+ "doc_type":"sdkreference",
+ "kw":"Obtaining the Model List,Model Management,SDK Reference",
+ "title":"Obtaining the Model List",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0196.html",
+ "product_code":"modelarts",
+ "code":"57",
+ "des":"In ModelArts notebook, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development enviro",
+ "doc_type":"sdkreference",
+ "kw":"Obtaining the Model Object List,Model Management,SDK Reference",
+ "title":"Obtaining the Model Object List",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0197.html",
+ "product_code":"modelarts",
+ "code":"58",
+ "des":"You can use the API to query the information about a model object.In the ModelArts notebook instance, you do not need to enter authentication parameters for session authe",
+ "doc_type":"sdkreference",
+ "kw":"Querying the Details About a Model,Model Management,SDK Reference",
+ "title":"Querying the Details About a Model",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0198.html",
+ "product_code":"modelarts",
+ "code":"59",
+ "des":"You can use the API to delete a model object.In a ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details ",
+ "doc_type":"sdkreference",
+ "kw":"Deleting a Model,Model Management,SDK Reference",
+ "title":"Deleting a Model",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0199.html",
+ "product_code":"modelarts",
+ "code":"60",
+ "des":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "doc_type":"sdkreference",
+ "kw":"Service Management",
+ "title":"Service Management",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0200.html",
+ "product_code":"modelarts",
+ "code":"61",
+ "des":"Service management indicates deploying a model that has been successfully created as a real-time. This feature provides functions such as real-time prediction, service de",
+ "doc_type":"sdkreference",
+ "kw":"Service Management Overview,Service Management,SDK Reference",
+ "title":"Service Management Overview",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0201.html",
+ "product_code":"modelarts",
+ "code":"62",
+ "des":"Real-time service deployment covers the following aspects:Initialize a real-time service.Deploy a real-time service predictor.Deploy a batch service transformer.The servi",
+ "doc_type":"sdkreference",
+ "kw":"Deploying a Real-Time Service,Service Management,SDK Reference",
+ "title":"Deploying a Real-Time Service",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0203.html",
+ "product_code":"modelarts",
+ "code":"63",
+ "des":"You can use the API to query details about a service object.In the ModelArts notebook instance, you do not need to enter authentication parameters for session authenticat",
+ "doc_type":"sdkreference",
+ "kw":"Querying the Details of a Service,Service Management,SDK Reference",
+ "title":"Querying the Details of a Service",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0205.html",
+ "product_code":"modelarts",
+ "code":"64",
+ "des":"You can use the API to obtain the service list of a user.In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication",
+ "doc_type":"sdkreference",
+ "kw":"Querying the Service List,Service Management,SDK Reference",
+ "title":"Querying the Service List",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0206.html",
+ "product_code":"modelarts",
+ "code":"65",
+ "des":"You can use the API to obtain the service object list of a user.In the ModelArts notebook instance, you do not need to enter authentication parameters for session authent",
+ "doc_type":"sdkreference",
+ "kw":"Querying the List of Service Objects,Service Management,SDK Reference",
+ "title":"Querying the List of Service Objects",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0207.html",
+ "product_code":"modelarts",
+ "code":"66",
+ "des":"You can use the API to update the configurations of a service object.In ModelArts notebook, you do not need to enter authentication parameters for session authentication.",
+ "doc_type":"sdkreference",
+ "kw":"Updating Service Configurations,Service Management,SDK Reference",
+ "title":"Updating Service Configurations",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0208.html",
+ "product_code":"modelarts",
+ "code":"67",
+ "des":"You can use the API to query the monitoring information about a service.In the ModelArts notebook instance, you do not need to enter authentication parameters for session",
+ "doc_type":"sdkreference",
+ "kw":"Querying Service Monitoring Details,Service Management,SDK Reference",
+ "title":"Querying Service Monitoring Details",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0209.html",
+ "product_code":"modelarts",
+ "code":"68",
+ "des":"You can use the API to query the logs of a service object.In the ModelArts notebook instance, you do not need to enter authentication parameters for session authenticatio",
+ "doc_type":"sdkreference",
+ "kw":"Querying Service Logs,Service Management,SDK Reference",
+ "title":"Querying Service Logs",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0211.html",
+ "product_code":"modelarts",
+ "code":"69",
+ "des":"You can delete a service in either of the following ways:Delete the service created in Deploying a Real-Time Service.Delete the service object returned in Querying the Li",
+ "doc_type":"sdkreference",
+ "kw":"Delete a Service,Service Management,SDK Reference",
+ "title":"Delete a Service",
+ "githuburl":""
+ },
+ {
+ "uri":"modelarts_04_0099.html",
+ "product_code":"modelarts",
+ "code":"70",
+ "des":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "doc_type":"sdkreference",
+ "kw":"Change History,SDK Reference",
+ "title":"Change History",
+ "githuburl":""
+ }
+]
\ No newline at end of file
diff --git a/docs/modelarts/sdk-ref/CLASS.TXT.json b/docs/modelarts/sdk-ref/CLASS.TXT.json
new file mode 100644
index 00000000..5fd57222
--- /dev/null
+++ b/docs/modelarts/sdk-ref/CLASS.TXT.json
@@ -0,0 +1,632 @@
+[
+ {
+ "desc":"This document describes how to install and configure a development environment and call functions provided by ModelArts SDK for secondary development.",
+ "product_code":"modelarts",
+ "title":"Before You Start",
+ "uri":"modelarts_04_0001.html",
+ "doc_type":"sdkreference",
+ "p_code":"",
+ "code":"1"
+ },
+ {
+ "desc":"ModelArts Software Development Kit (ModelArts SDK) encapsulates the ModelArts RESTful APIs in Python language to simplify application development. You can directly call M",
+ "product_code":"modelarts",
+ "title":"SDK Overview",
+ "uri":"modelarts_04_0002.html",
+ "doc_type":"sdkreference",
+ "p_code":"",
+ "code":"2"
+ },
+ {
+ "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "product_code":"modelarts",
+ "title":"Preparations",
+ "uri":"modelarts_04_0003.html",
+ "doc_type":"sdkreference",
+ "p_code":"",
+ "code":"3"
+ },
+ {
+ "desc":"ModelArts SDK can be used in the following environments:ModelArts SDK has been integrated into ModelArts Notebook and can be directly used without session authentication.",
+ "product_code":"modelarts",
+ "title":"Environment Preparations",
+ "uri":"modelarts_04_0005.html",
+ "doc_type":"sdkreference",
+ "p_code":"3",
+ "code":"4"
+ },
+ {
+ "desc":"Download the ModelArts SDK software package of the latest version.After the SDK is downloaded, you can use pip to install it. For details about how to install pip, see th",
+ "product_code":"modelarts",
+ "title":"Downloading and Installing the SDK",
+ "uri":"modelarts_04_0004.html",
+ "doc_type":"sdkreference",
+ "p_code":"3",
+ "code":"5"
+ },
+ {
+ "desc":"When calling APIs, you need to specify the project ID in certain URLs. To do so, you need to obtain the project ID first. To obtain a project ID, perform the following op",
+ "product_code":"modelarts",
+ "title":"Viewing the Project ID",
+ "uri":"modelarts_04_0044.html",
+ "doc_type":"sdkreference",
+ "p_code":"3",
+ "code":"6"
+ },
+ {
+ "desc":"Using the SDK in non-notebook environments needs to call IAM, OBS, and ModelArts. Therefore, the endpoints of these services are required. Therefore, the endpoints of the",
+ "product_code":"modelarts",
+ "title":"Configuring a Service Endpoint",
+ "uri":"modelarts_04_0300.html",
+ "doc_type":"sdkreference",
+ "p_code":"3",
+ "code":"7"
+ },
+ {
+ "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "product_code":"modelarts",
+ "title":"Session Authentication",
+ "uri":"modelarts_04_0153.html",
+ "doc_type":"sdkreference",
+ "p_code":"",
+ "code":"8"
+ },
+ {
+ "desc":"The session module authenticates in-cloud resources and initializes ModelArts SDK Client and OBS Client. After a session is set up, you can directly call the ModelArts SD",
+ "product_code":"modelarts",
+ "title":"Overview of Session Authentication",
+ "uri":"modelarts_04_0123.html",
+ "doc_type":"sdkreference",
+ "p_code":"8",
+ "code":"9"
+ },
+ {
+ "desc":"This authentication method is available for OBS Management, Training Management, Model Management, and Service Management.Set account to your domain name and username to ",
+ "product_code":"modelarts",
+ "title":"Authentication Using the Username and Password",
+ "uri":"modelarts_04_0154.html",
+ "doc_type":"sdkreference",
+ "p_code":"8",
+ "code":"10"
+ },
+ {
+ "desc":"This authentication method is available for OBS Management, Training Management, Model Management, and Service Management.Parameters in this command are described as foll",
+ "product_code":"modelarts",
+ "title":"AK/SK-based Authentication",
+ "uri":"modelarts_04_0155.html",
+ "doc_type":"sdkreference",
+ "p_code":"8",
+ "code":"11"
+ },
+ {
+ "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "product_code":"modelarts",
+ "title":"OBS Management (Recommended)",
+ "uri":"modelarts_04_0006.html",
+ "doc_type":"sdkreference",
+ "p_code":"",
+ "code":"12"
+ },
+ {
+ "desc":"ModelArts SDK 1.1.3 supports OBS management, including uploading and downloading files and folders. The operations are as follows:Uploading a File to OBSUploading a Folde",
+ "product_code":"modelarts",
+ "title":"Overview of OBS Management",
+ "uri":"modelarts_04_0217.html",
+ "doc_type":"sdkreference",
+ "p_code":"12",
+ "code":"13"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Uploading a File to OBS",
+ "uri":"modelarts_04_0218.html",
+ "doc_type":"sdkreference",
+ "p_code":"12",
+ "code":"14"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Uploading a Folder to OBS",
+ "uri":"modelarts_04_0219.html",
+ "doc_type":"sdkreference",
+ "p_code":"12",
+ "code":"15"
+ },
+ {
+ "desc":"If the size of a file in a folder exceeds 5 GB, the file cannot be downloaded in this mode.In the ModelArts notebook instance, you do not need to enter authentication par",
+ "product_code":"modelarts",
+ "title":"Downloading a File from OBS",
+ "uri":"modelarts_04_0220.html",
+ "doc_type":"sdkreference",
+ "p_code":"12",
+ "code":"16"
+ },
+ {
+ "desc":"If the size of a file in a folder exceeds 5 GB, the file cannot be downloaded in this mode. However, other files whose size is less than 5 GB in the folder can be downloa",
+ "product_code":"modelarts",
+ "title":"Downloading a Folder from OBS",
+ "uri":"modelarts_04_0221.html",
+ "doc_type":"sdkreference",
+ "p_code":"12",
+ "code":"17"
+ },
+ {
+ "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "product_code":"modelarts",
+ "title":"Training Management",
+ "uri":"modelarts_04_0157.html",
+ "doc_type":"sdkreference",
+ "p_code":"",
+ "code":"18"
+ },
+ {
+ "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "product_code":"modelarts",
+ "title":"Training Jobs",
+ "uri":"modelarts_04_0158.html",
+ "doc_type":"sdkreference",
+ "p_code":"18",
+ "code":"19"
+ },
+ {
+ "desc":"For training on the training platform, if the training fails, you can view the detailed log information on the platform or by calling the API in Querying Training Job Log",
+ "product_code":"modelarts",
+ "title":"Creating a Training Job",
+ "uri":"modelarts_04_0131.html",
+ "doc_type":"sdkreference",
+ "p_code":"19",
+ "code":"20"
+ },
+ {
+ "desc":"In a ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other develop",
+ "product_code":"modelarts",
+ "title":"Querying the List of Training Jobs",
+ "uri":"modelarts_04_0160.html",
+ "doc_type":"sdkreference",
+ "p_code":"19",
+ "code":"21"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Querying the Details About a Training Job",
+ "uri":"modelarts_04_0161.html",
+ "doc_type":"sdkreference",
+ "p_code":"19",
+ "code":"22"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Modifying the Description of a Training Job",
+ "uri":"modelarts_04_0162.html",
+ "doc_type":"sdkreference",
+ "p_code":"19",
+ "code":"23"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Obtaining the Name of a Training Job Log File",
+ "uri":"modelarts_04_0163.html",
+ "doc_type":"sdkreference",
+ "p_code":"19",
+ "code":"24"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Querying Training Job Logs",
+ "uri":"modelarts_04_0164.html",
+ "doc_type":"sdkreference",
+ "p_code":"19",
+ "code":"25"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Deleting a Training Job",
+ "uri":"modelarts_04_0166.html",
+ "doc_type":"sdkreference",
+ "p_code":"19",
+ "code":"26"
+ },
+ {
+ "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "product_code":"modelarts",
+ "title":"Training Job Versions",
+ "uri":"modelarts_04_0167.html",
+ "doc_type":"sdkreference",
+ "p_code":"18",
+ "code":"27"
+ },
+ {
+ "desc":"A training job must exist before you create a version for it. You can create a training job version based on Creating a Training Job or job_id and version_id of the objec",
+ "product_code":"modelarts",
+ "title":"Creating a Training Job Version",
+ "uri":"modelarts_04_0168.html",
+ "doc_type":"sdkreference",
+ "p_code":"27",
+ "code":"28"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Querying the List of Training Job Versions",
+ "uri":"modelarts_04_0169.html",
+ "doc_type":"sdkreference",
+ "p_code":"27",
+ "code":"29"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Querying the Details About a Training Job Version",
+ "uri":"modelarts_04_0170.html",
+ "doc_type":"sdkreference",
+ "p_code":"27",
+ "code":"30"
+ },
+ {
+ "desc":"You can stop a training job version that is being created only when the job is running.In the ModelArts notebook instance, you do not need to enter authentication paramet",
+ "product_code":"modelarts",
+ "title":"Stopping a Training Job Version",
+ "uri":"modelarts_04_0171.html",
+ "doc_type":"sdkreference",
+ "p_code":"27",
+ "code":"31"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Deleting a Training Job Version",
+ "uri":"modelarts_04_0172.html",
+ "doc_type":"sdkreference",
+ "p_code":"27",
+ "code":"32"
+ },
+ {
+ "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "product_code":"modelarts",
+ "title":"Training Job Parameter Configuration",
+ "uri":"modelarts_04_0173.html",
+ "doc_type":"sdkreference",
+ "p_code":"18",
+ "code":"33"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Creating a Training Job Configuration",
+ "uri":"modelarts_04_0174.html",
+ "doc_type":"sdkreference",
+ "p_code":"33",
+ "code":"34"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Querying the List of Training Job Parameter Configuration Objects",
+ "uri":"modelarts_04_0175.html",
+ "doc_type":"sdkreference",
+ "p_code":"33",
+ "code":"35"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Querying the List of Training Job Configurations",
+ "uri":"modelarts_04_0176.html",
+ "doc_type":"sdkreference",
+ "p_code":"33",
+ "code":"36"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Querying the Details About a Training Job Configuration",
+ "uri":"modelarts_04_0177.html",
+ "doc_type":"sdkreference",
+ "p_code":"33",
+ "code":"37"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Modifying a Training Job Configuration",
+ "uri":"modelarts_04_0178.html",
+ "doc_type":"sdkreference",
+ "p_code":"33",
+ "code":"38"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Deleting a Training Job Configuration",
+ "uri":"modelarts_04_0179.html",
+ "doc_type":"sdkreference",
+ "p_code":"33",
+ "code":"39"
+ },
+ {
+ "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "product_code":"modelarts",
+ "title":"Visualization Jobs",
+ "uri":"modelarts_04_0180.html",
+ "doc_type":"sdkreference",
+ "p_code":"18",
+ "code":"40"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Creating a Visualization Job",
+ "uri":"modelarts_04_0181.html",
+ "doc_type":"sdkreference",
+ "p_code":"40",
+ "code":"41"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Querying the List of Visualization Job Objects",
+ "uri":"modelarts_04_0182.html",
+ "doc_type":"sdkreference",
+ "p_code":"40",
+ "code":"42"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Querying the List of Visualization Jobs",
+ "uri":"modelarts_04_0183.html",
+ "doc_type":"sdkreference",
+ "p_code":"40",
+ "code":"43"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Querying the Details About a Visualization Job",
+ "uri":"modelarts_04_0184.html",
+ "doc_type":"sdkreference",
+ "p_code":"40",
+ "code":"44"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Modifying the Description of a Visualization Job",
+ "uri":"modelarts_04_0185.html",
+ "doc_type":"sdkreference",
+ "p_code":"40",
+ "code":"45"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Stopping a Visualization Job",
+ "uri":"modelarts_04_0186.html",
+ "doc_type":"sdkreference",
+ "p_code":"40",
+ "code":"46"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Restarting a Visualization Job",
+ "uri":"modelarts_04_0187.html",
+ "doc_type":"sdkreference",
+ "p_code":"40",
+ "code":"47"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Deleting a Visualization Job",
+ "uri":"modelarts_04_0188.html",
+ "doc_type":"sdkreference",
+ "p_code":"40",
+ "code":"48"
+ },
+ {
+ "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "product_code":"modelarts",
+ "title":"Resource and Engine Specifications",
+ "uri":"modelarts_04_0189.html",
+ "doc_type":"sdkreference",
+ "p_code":"18",
+ "code":"49"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Querying a Built-in Algorithm",
+ "uri":"modelarts_04_0190.html",
+ "doc_type":"sdkreference",
+ "p_code":"49",
+ "code":"50"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Querying the List of Resource Flavors",
+ "uri":"modelarts_04_0191.html",
+ "doc_type":"sdkreference",
+ "p_code":"49",
+ "code":"51"
+ },
+ {
+ "desc":"In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other devel",
+ "product_code":"modelarts",
+ "title":"Querying the List of Engine Types",
+ "uri":"modelarts_04_0192.html",
+ "doc_type":"sdkreference",
+ "p_code":"49",
+ "code":"52"
+ },
+ {
+ "desc":"Table 1 describes the job statuses.",
+ "product_code":"modelarts",
+ "title":"Job Statuses",
+ "uri":"modelarts_04_0077.html",
+ "doc_type":"sdkreference",
+ "p_code":"18",
+ "code":"53"
+ },
+ {
+ "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "product_code":"modelarts",
+ "title":"Model Management",
+ "uri":"modelarts_04_0193.html",
+ "doc_type":"sdkreference",
+ "p_code":"",
+ "code":"54"
+ },
+ {
+ "desc":"The model import function covers the following aspects:Initialize the existing model and create a model object based on the model ID.Create a model. For details about the",
+ "product_code":"modelarts",
+ "title":"Importing a Model",
+ "uri":"modelarts_04_0194.html",
+ "doc_type":"sdkreference",
+ "p_code":"54",
+ "code":"55"
+ },
+ {
+ "desc":"In ModelArts notebook, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development enviro",
+ "product_code":"modelarts",
+ "title":"Obtaining the Model List",
+ "uri":"modelarts_04_0195.html",
+ "doc_type":"sdkreference",
+ "p_code":"54",
+ "code":"56"
+ },
+ {
+ "desc":"In ModelArts notebook, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development enviro",
+ "product_code":"modelarts",
+ "title":"Obtaining the Model Object List",
+ "uri":"modelarts_04_0196.html",
+ "doc_type":"sdkreference",
+ "p_code":"54",
+ "code":"57"
+ },
+ {
+ "desc":"You can use the API to query the information about a model object.In the ModelArts notebook instance, you do not need to enter authentication parameters for session authe",
+ "product_code":"modelarts",
+ "title":"Querying the Details About a Model",
+ "uri":"modelarts_04_0197.html",
+ "doc_type":"sdkreference",
+ "p_code":"54",
+ "code":"58"
+ },
+ {
+ "desc":"You can use the API to delete a model object.In a ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details ",
+ "product_code":"modelarts",
+ "title":"Deleting a Model",
+ "uri":"modelarts_04_0198.html",
+ "doc_type":"sdkreference",
+ "p_code":"54",
+ "code":"59"
+ },
+ {
+ "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "product_code":"modelarts",
+ "title":"Service Management",
+ "uri":"modelarts_04_0199.html",
+ "doc_type":"sdkreference",
+ "p_code":"",
+ "code":"60"
+ },
+ {
+ "desc":"Service management indicates deploying a model that has been successfully created as a real-time. This feature provides functions such as real-time prediction, service de",
+ "product_code":"modelarts",
+ "title":"Service Management Overview",
+ "uri":"modelarts_04_0200.html",
+ "doc_type":"sdkreference",
+ "p_code":"60",
+ "code":"61"
+ },
+ {
+ "desc":"Real-time service deployment covers the following aspects:Initialize a real-time service.Deploy a real-time service predictor.Deploy a batch service transformer.The servi",
+ "product_code":"modelarts",
+ "title":"Deploying a Real-Time Service",
+ "uri":"modelarts_04_0201.html",
+ "doc_type":"sdkreference",
+ "p_code":"60",
+ "code":"62"
+ },
+ {
+ "desc":"You can use the API to query details about a service object.In the ModelArts notebook instance, you do not need to enter authentication parameters for session authenticat",
+ "product_code":"modelarts",
+ "title":"Querying the Details of a Service",
+ "uri":"modelarts_04_0203.html",
+ "doc_type":"sdkreference",
+ "p_code":"60",
+ "code":"63"
+ },
+ {
+ "desc":"You can use the API to obtain the service list of a user.In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication",
+ "product_code":"modelarts",
+ "title":"Querying the Service List",
+ "uri":"modelarts_04_0205.html",
+ "doc_type":"sdkreference",
+ "p_code":"60",
+ "code":"64"
+ },
+ {
+ "desc":"You can use the API to obtain the service object list of a user.In the ModelArts notebook instance, you do not need to enter authentication parameters for session authent",
+ "product_code":"modelarts",
+ "title":"Querying the List of Service Objects",
+ "uri":"modelarts_04_0206.html",
+ "doc_type":"sdkreference",
+ "p_code":"60",
+ "code":"65"
+ },
+ {
+ "desc":"You can use the API to update the configurations of a service object.In ModelArts notebook, you do not need to enter authentication parameters for session authentication.",
+ "product_code":"modelarts",
+ "title":"Updating Service Configurations",
+ "uri":"modelarts_04_0207.html",
+ "doc_type":"sdkreference",
+ "p_code":"60",
+ "code":"66"
+ },
+ {
+ "desc":"You can use the API to query the monitoring information about a service.In the ModelArts notebook instance, you do not need to enter authentication parameters for session",
+ "product_code":"modelarts",
+ "title":"Querying Service Monitoring Details",
+ "uri":"modelarts_04_0208.html",
+ "doc_type":"sdkreference",
+ "p_code":"60",
+ "code":"67"
+ },
+ {
+ "desc":"You can use the API to query the logs of a service object.In the ModelArts notebook instance, you do not need to enter authentication parameters for session authenticatio",
+ "product_code":"modelarts",
+ "title":"Querying Service Logs",
+ "uri":"modelarts_04_0209.html",
+ "doc_type":"sdkreference",
+ "p_code":"60",
+ "code":"68"
+ },
+ {
+ "desc":"You can delete a service in either of the following ways:Delete the service created in Deploying a Real-Time Service.Delete the service object returned in Querying the Li",
+ "product_code":"modelarts",
+ "title":"Delete a Service",
+ "uri":"modelarts_04_0211.html",
+ "doc_type":"sdkreference",
+ "p_code":"60",
+ "code":"69"
+ },
+ {
+ "desc":"HUAWEI CLOUD Help Center presents technical documents to help you quickly get started with HUAWEI CLOUD services. The technical documents include Service Overview, Price Details, Purchase Guide, User Guide, API Reference, Best Practices, FAQs, and Videos.",
+ "product_code":"modelarts",
+ "title":"Change History",
+ "uri":"modelarts_04_0099.html",
+ "doc_type":"sdkreference",
+ "p_code":"",
+ "code":"70"
+ }
+]
\ No newline at end of file
diff --git a/docs/modelarts/sdk-ref/PARAMETERS.txt b/docs/modelarts/sdk-ref/PARAMETERS.txt
new file mode 100644
index 00000000..6da8d5f0
--- /dev/null
+++ b/docs/modelarts/sdk-ref/PARAMETERS.txt
@@ -0,0 +1,3 @@
+version=""
+language="en-us"
+type=""
\ No newline at end of file
diff --git a/docs/modelarts/sdk-ref/en-us_image_0000001455263253.jpg b/docs/modelarts/sdk-ref/en-us_image_0000001455263253.jpg
new file mode 100644
index 00000000..b0e263d9
Binary files /dev/null and b/docs/modelarts/sdk-ref/en-us_image_0000001455263253.jpg differ
diff --git a/docs/modelarts/sdk-ref/modelarts_04_0001.html b/docs/modelarts/sdk-ref/modelarts_04_0001.html
new file mode 100644
index 00000000..dbc471ce
--- /dev/null
+++ b/docs/modelarts/sdk-ref/modelarts_04_0001.html
@@ -0,0 +1,46 @@
+
+
+Before You Start
+This document describes how to install and configure a development environment and call functions provided by ModelArts SDK for secondary development.
+
+
+
SDK Overview
+ModelArts Software Development Kit (ModelArts SDK) encapsulates the ModelArts RESTful APIs in Python language to simplify application development. You can directly call ModelArts SDK to easily manage datasets, start AI training, generate models, and deploy the models.
+
ModelArts SDK supports only the Python language, including Python 2.7, Python 3.6, Python 3.7, and later versions.
+
Preparations
+
+
+
diff --git a/docs/modelarts/sdk-ref/modelarts_04_0004.html b/docs/modelarts/sdk-ref/modelarts_04_0004.html
new file mode 100644
index 00000000..aaf6e9ed
--- /dev/null
+++ b/docs/modelarts/sdk-ref/modelarts_04_0004.html
@@ -0,0 +1,31 @@
+
+
+Downloading and Installing the SDK
+
+
Installing the SDK
After the SDK is downloaded, you can use pip to install it. For details about how to install pip, see the pip official website. To install the ModelArts SDK, run the following command:
+
pip install modelarts-latest-py2.py3-none-any.whl
+
The required dependency packages are installed by default during SDK installation. The details are as follows:
certifi >= 14.05.14
+six >= 1.10
+python_dateutil >= 2.5.3
+setuptools >= 21.0.0
+urllib3 >= 1.15.1
+requests >= 2.19.1
+esdk-obs-python == 3.0.5
+Flask==1.0.2
+Flask-Cors==3.0.4
+gunicorn==19.8.1
+mxnet-model-server==0.3
+psutil==5.4.6
+prometheus_client==0.3.1
+
+
If an error message is displayed during the installation, indicating that a required dependency package is missing, run the following command to install the dependency package as prompted. In the command, xxxx indicates the name of the dependency package.
+
pip install xxxx
+
+
Environment Preparations
+ModelArts SDK can be used in the following environments:
+
- ModelArts SDK has been integrated into ModelArts Notebook and can be directly used without session authentication.
On the ModelArts management console, access the DevEnviron module, create a notebook instance, and directly call the ModelArts SDK APIs in the Terminal or Ipynb file. You can call the SDK in a notebook instance to perform operations such as OBS management, job management, model management, and service management by referring to the API reference.
+ - ModelArts SDK can be installed and configured in the local Windows- or Linux-based environment.
Download and install Python 2 or Python 3 from the official website of Python (Python 2.7, Python 3.6, Python 3.7, or later is recommended), and configure a Python runtime environment.
+
- After Python is installed, you can enter python or python3 in the command line interface (CLI) and press Enter to view the Python version and enter the Python environment.
- In the Windows-based environment, if a message is displayed indicating that the command is not an internal or external command, add the Python and pip installation paths to Path in the environment variable. The pip installation path is usually the Scripts folder in the directory where Python is located.
+
+
OBS Management (Recommended)
+
+
+
diff --git a/docs/modelarts/sdk-ref/modelarts_04_0044.html b/docs/modelarts/sdk-ref/modelarts_04_0044.html
new file mode 100644
index 00000000..0fe12f5e
--- /dev/null
+++ b/docs/modelarts/sdk-ref/modelarts_04_0044.html
@@ -0,0 +1,14 @@
+
+
+Viewing the Project ID
+When calling APIs, you need to specify the project ID in certain URLs. To do so, you need to obtain the project ID first. To obtain a project ID, perform the following operations:
+
- Click the username and choose My Credential from the drop-down list.
On the My Credential page, view project IDs in the project list.
+
+
Figure 1 Viewing project ID
+
Job Statuses
+Table 1 describes the job statuses.
+
+
Table 1 Job statusesStatus Value
+ |
+Description
+ |
+
|---|
+
+0
+ |
+JOBSTAT_UNKNOWN: Unknown status.
+ |
+
+1
+ |
+JOBSTAT_INIT: The job is being initialized.
+ |
+
+2
+ |
+JOBSTAT_IMAGE_CREATING: The job image is being created.
+ |
+
+3
+ |
+JOBSTAT_IMAGE_FAILED: Failed to create the job image.
+ |
+
+4
+ |
+JOBSTAT_SUBMIT_TRYING: The job is being submitted.
+ |
+
+5
+ |
+JOBSTAT_SUBMIT_FAILED: Failed to submit the job.
+ |
+
+6
+ |
+JOBSTAT_DELETE_FAILED: Failed to delete the job.
+ |
+
+7
+ |
+JOBSTAT_WAITING: The job is queuing.
+ |
+
+8
+ |
+JOBSTAT_RUNNING: The job is running.
+ |
+
+9
+ |
+JOBSTAT_KILLING: The job is being canceled.
+ |
+
+10
+ |
+JOBSTAT_COMPLETED: The job has been completed.
+ |
+
+11
+ |
+JOBSTAT_FAILED: Failed to run the job.
+ |
+
+12
+ |
+JOBSTAT_KILLED: Job canceled successfully.
+ |
+
+13
+ |
+JOBSTAT_CANCELED: Job canceled.
+ |
+
+14
+ |
+JOBSTAT_LOST: Job lost.
+ |
+
+15
+ |
+JOBSTAT_SCALING: The job is being scaled.
+ |
+
+16
+ |
+JOBSTAT_SUBMIT_MODEL_FAILED: Failed to submit the model.
+ |
+
+17
+ |
+JOBSTAT_DEPLOY_SERVICE_FAILED: Failed to deploy the service.
+ |
+
+18
+ |
+JOBSTAT_CHECK_INIT: The job review is being initialized.
+ |
+
+19
+ |
+JOBSTAT_CHECK_RUNNING: The job is being reviewed.
+ |
+
+20
+ |
+JOBSTAT_CHECK_RUNNING_COMPLETED: The approval job is completed.
+ |
+
+21
+ |
+JOBSTAT_CHECK_FAILED: Failed to review the job.
+ |
+
+22
+ |
+MOUNT_FAILED: Failed to mount.
+ |
+
+
+
+
+
Change History
+
+
Released On
+ |
+Description
+ |
+
|---|
+
+2022-11-17
+ |
+This is the first official release.
+ |
+
+
+
+
+
Overview of Session Authentication
+The session module authenticates in-cloud resources and initializes ModelArts SDK Client and OBS Client. After a session is set up, you can directly call the ModelArts SDK APIs.
+
- ModelArts notebook can be directly used without configuring session authentication parameters. The sample code is as follows:
| from modelarts.session import Session
+session = Session()
+ |
+
+
- When using the ModelArts SDK in other development environments, select one of the following authentication methods for session authentication:
+
+
Creating a Training Job
+For training on the training platform, if the training fails, you can view the detailed log information on the platform or by calling the API in Querying Training Job Logs.
+
Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Example 1: Create a training job using the data stored on OBS.
1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21 | from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(
+ modelarts_session=session,
+ framework_type='PyTorch', # AI engine name
+ framework_version='PyTorch-1.0.0-python3.6', # AI engine version
+ code_dir='/bucket/src/', # Training script directory
+ boot_file='/bucket/src/pytorch_sentiment.py', # Training boot script directory
+ log_url='/bucket/log/', # Training log directory
+ hyperparameters=[
+ {"label":"classes",
+ "value": "10"},
+ {"label":"lr",
+ "value": "0.001"}
+ ],
+ output_path='/bucket/output/', # Training output directory
+ train_instance_type='modelarts.vm.cpu.2u', # Training environment flavor
+ train_instance_count=1, # Number of training nodes
+ job_description='pytorch-sentiment with ModelArts SDK') # Training job description
+job_instance = estimator.fit(inputs='/bucket/data/train/', wait=False, job_name='my_training_job')
+ |
+
+
+
- Example 2: Create a training job using a dataset.
1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21 | from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(
+ modelarts_session=session,
+ framework_type='PyTorch', # AI engine name
+ framework_version='PyTorch-1.0.0-python3.6', # AI engine version
+ code_dir='/bucket/src/', # Training script directory
+ boot_file='/bucket/src/pytorch_sentiment.py', # Training boot script directory
+ log_url='/bucket/log/', # Training log directory
+ hyperparameters=[
+ {"label":"classes",
+ "value": "10"},
+ {"label":"lr",
+ "value": "0.001"}
+ ],
+ output_path='/bucket/output/', # Training output directory
+ train_instance_type='modelarts.vm.cpu.2u', # Training environment flavor
+ train_instance_count=1, # Number of training nodes
+ job_description='pytorch-sentiment with ModelArts SDK') # Training job description
+job_instance = estimator.fit(dataset_id='4AZNvFkN7KYr5EdhFkH', dataset_version_id='UOF9BIeSGArwVt0oI6T', wait=False, job_name='my_training_job')
+ |
+
+
- Example 3: Create a training job using a custom image.
1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19 | from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(
+ modelarts_session=session,
+ log_url='/bucket/log/', # Training log directory
+ hyperparameters=[
+ {"label":"classes",
+ "value": "10"},
+ {"label":"lr",
+ "value": "0.001"}
+ ],
+ output_path='/bucket/output/', # Training output directory
+ train_instance_type='modelarts.vm.cpu.2u', # Training environment flavor
+ train_instance_count=1, # Number of training nodes
+ user_command='bash -x /home/work/run_train.sh python /home/work/user-job-dir/app/mnist/mnist_softmax.py --data_url /home/work/user-job-dir/app/mnist_data', # Boot command of the custom image
+ user_image_url='100.125.5.235:20202/jobmng/cpu-base:1.0', # Address for downloading the custom image
+ job_description='pytorch-sentiment with ModelArts SDK') # Training job description
+job_instance = estimator.fit(inputs='/bucket/data/train/', wait=False, job_name='my_training_job')
+ |
+
+
+
+
Parameter Description
+
Table 1 Estimator request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+train_instance_count
+ |
+Yes
+ |
+Long
+ |
+Number of compute nodes in a training job
+ |
+
+code_dir
+ |
+No
+ |
+String
+ |
+Code directory of a training job, for example, /bucket/src/. Leave this parameter blank when model_name is set.
+ |
+
+boot_file
+ |
+No
+ |
+String
+ |
+Boot file of a training job, which needs to be stored in the code directory. For example, /bucket/src/boot.py. Leave this parameter blank when model_name is set.
+ |
+
+output_path
+ |
+Yes
+ |
+String
+ |
+Output path of a training job
+ |
+
+hyperparameters
+ |
+No
+ |
+JSON Array
+ |
+Running parameters of a training job. It is a collection of label-value pairs of the string type. This parameter is a container environment variable when a job uses a custom image.
+ |
+
+log_url
+ |
+No
+ |
+String
+ |
+OBS URL of the logs of a training job. By default, this parameter is left blank. Example value: /usr/log/
+ |
+
+train_instance_type
+ |
+Yes
+ |
+Long
+ |
+Resource flavor selected for a training job. If you choose to train on the training platform, obtain the value by calling the API described in Querying the List of Resource Flavors.
+ |
+
+framework_type
+ |
+No
+ |
+String
+ |
+Engine selected for a training job. Obtain the value by calling the API described in Querying the List of Engine Types. Leave this parameter blank when model_name is set.
+ |
+
+framework_version
+ |
+No
+ |
+String
+ |
+Engine version selected for a training job. Obtain the value by calling the API described in Querying the List of Engine Types. Leave this parameter blank when model_name is set.
+ |
+
+job_description
+ |
+No
+ |
+String
+ |
+Description of a training job
+ |
+
+user_image_url
+ |
+No
+ |
+String
+ |
+SWR URL of the custom image used by a training job. Example value: 100.125.5.235:20202/jobmng/custom-cpu-base:1.0
+ |
+
+user_command
+ |
+No
+ |
+String
+ |
+Boot command used to start the container of the custom image of a training job. The format is bash /home/work/run_train.sh python /home/work/user-job-dir/app/train.py {python_file_parameter}.
+ |
+
+
+
+
+
+
Table 2 fit request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+inputs
+ |
+Yes
+ |
+String
+ |
+Data storage location of a training job.
+inputs cannot be used with dataset_id and dataset_version_id, or with data_source at the same time. However, one of the parameters must exist.
+Only this parameter is supported in local training.
+ |
+
+dataset_id
+ |
+No
+ |
+String
+ |
+Dataset ID of a training job. To obtain the dataset ID, view basic information about the dataset.
+This parameter must be used together with dataset_version_id, but cannot be used together with inputs.
+ |
+
+dataset_version_id
+ |
+No
+ |
+String
+ |
+Dataset version ID of a training job. To obtain the dataset version ID, view basic information about the dataset.
+This parameter must be used together with dataset_id, but cannot be used together with inputs.
+ |
+
+wait
+ |
+No
+ |
+Boolean
+ |
+Whether to wait for the completion of a training job. Default value: False
+ |
+
+job_name
+ |
+No
+ |
+String
+ |
+Name of a training job, consisting of 1 to 64 alphanumeric characters. If this parameter is left blank, a job name is generated randomly.
+ |
+
+
+
+
+
+
Table 3 Parameters in the successful response to trainingParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+TrainingJob
+ |
+Object
+ |
+Training object. This object contains attributes such as job_id and version_id, and operations on a training job, such as querying, modifying, or deleting the training job. For example, you can use job_instance.job_id to obtain the ID of a training job.
+ |
+
+
+
+
+
+
Session Authentication
+
+
+
diff --git a/docs/modelarts/sdk-ref/modelarts_04_0154.html b/docs/modelarts/sdk-ref/modelarts_04_0154.html
new file mode 100644
index 00000000..f862c236
--- /dev/null
+++ b/docs/modelarts/sdk-ref/modelarts_04_0154.html
@@ -0,0 +1,29 @@
+
+
+Authentication Using the Username and Password
+This authentication method is available for OBS Management, Training Management, Model Management, and Service Management.
+
Sample Code
Set
account to your domain name and
username to your username.
| from modelarts.session import Session
+# Set endpoint
+Session.set_endpoint(iam_endpoint='***',
+ obs_endpoint='***',
+ modelarts_endpoint='***',
+ region_name='***')
+session = Session(account='***', username='***', password='***', region_name='***', project_id='***')
+ |
+
+
+
+
+
AK/SK-based Authentication
+This authentication method is available for OBS Management, Training Management, Model Management, and Service Management.
+
Sample Code
| from modelarts.session import Session
+# Set endpoint
+Session.set_endpoint(iam_endpoint='***',
+ obs_endpoint='***',
+ modelarts_endpoint='***',
+ region_name='***')
+session = Session(access_key='***',secret_key='***', project_id='***', region_name='***')
+ |
+
+
+
Parameters in this command are described as follows:
+
- For details about how to obtain the values of access_key and secret_key, see Obtaining an Access Key.
- project_id indicates the project ID.
- region_name indicates the region ID. Obtain the region ID from the system administrator.
+
+
Training Management
+
+
+
diff --git a/docs/modelarts/sdk-ref/modelarts_04_0158.html b/docs/modelarts/sdk-ref/modelarts_04_0158.html
new file mode 100644
index 00000000..eed81b7e
--- /dev/null
+++ b/docs/modelarts/sdk-ref/modelarts_04_0158.html
@@ -0,0 +1,28 @@
+
+
+Training Jobs
+
+
+
diff --git a/docs/modelarts/sdk-ref/modelarts_04_0160.html b/docs/modelarts/sdk-ref/modelarts_04_0160.html
new file mode 100644
index 00000000..97321e00
--- /dev/null
+++ b/docs/modelarts/sdk-ref/modelarts_04_0160.html
@@ -0,0 +1,232 @@
+
+
+Querying the List of Training Jobs
+Sample Code
In a ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+job_list_info = Estimator.get_job_list(modelarts_session=session, status=8, per_page=10, page=1, order="asc", search_content="job")
+ |
+
+
+
+
Parameter Description
+
Table 1 get_job_list request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+status
+ |
+No
+ |
+Integer
+ |
+Job status to be queried. By default, jobs of all statuses are queried. For example, to view jobs that fail to be created, set this parameter to 3, 5, 6, or 13. For details about the job statuses, see Job Statuses.
+ |
+
+per_page
+ |
+No
+ |
+Integer
+ |
+Number of jobs displayed on each page. The value range is [1, 1000]. Default value: 10
+ |
+
+page
+ |
+No
+ |
+Integer
+ |
+Index of the page to be queried. Default value: 1
+ |
+
+sortBy/sort_by
+ |
+No
+ |
+String
+ |
+When AK/SK-based authentication is used, the parameter name is sortBy. When account-based authentication is used, the parameter name is sort_by. The parameter specifies the sorting mode of the query. The value can be job_name, job_desc, status, duration, engine_type, or create_time. Default value: job_name
+ |
+
+order
+ |
+No
+ |
+String
+ |
+The options are as follows:
+- asc: ascending order
- desc: descending order. The default value is desc.
+ |
+
+search_content
+ |
+No
+ |
+String
+ |
+Search content, for example, a training job name. The value is a string of 0 to 100 characters. By default, this parameter is left blank.
+ |
+
+
+
+
+
+
Table 2 get_job_list response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+job_total_count
+ |
+Integer
+ |
+Total number of created jobs
+ |
+
+job_count_limit
+ |
+Integer
+ |
+Number of training jobs that can be created
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+quotas
+ |
+Integer
+ |
+Maximum number of training jobs
+ |
+
+jobs
+ |
+JSON Array
+ |
+Attributes of a training job. For details, see Table 3.
+ |
+
+
+
+
+
+
Table 3 jobs parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+job_id
+ |
+Long
+ |
+Training job ID
+ |
+
+job_name
+ |
+String
+ |
+Training job name
+ |
+
+version_id
+ |
+Long
+ |
+Version ID of a training job
+ |
+
+status
+ |
+Byte
+ |
+Status of a training job. For details about the job statuses, see Job Statuses.
+ |
+
+create_time
+ |
+Long
+ |
+Timestamp when a training job is created
+ |
+
+duration
+ |
+Long
+ |
+Training job running duration, in milliseconds
+ |
+
+job_desc
+ |
+String
+ |
+Description of a training job
+ |
+
+version_count
+ |
+Long
+ |
+Number of versions of a training job
+ |
+
+
+
+
+
+
Querying the Details About a Training Job
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Use the specified job_id and version_id.
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(modelarts_session=session, job_id="182626", version_id="278813")
+job_info = estimator.get_job_info()
+ |
+
+
+
+
+
+
Parameter Description
+
Table 1 Estimator request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+job_id
+ |
+Yes
+ |
+String
+ |
+ID of a training job. You can query job_id using the training job object generated in Creating a Training Job, for example, job_instance.job_id, or from the response obtained in Querying the List of Training Jobs.
+ |
+
+version_id
+ |
+Yes
+ |
+String
+ |
+Version ID of a training job. You can query version_id using the training job object generated in Creating a Training Job, for example, job_instance.version_id, or from the response obtained in Querying the List of Training Jobs.
+ |
+
+
+
+
+
+
Table 2 get_job_info response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+job_id
+ |
+Long
+ |
+Training job ID
+ |
+
+job_name
+ |
+String
+ |
+Training job name
+ |
+
+job_desc
+ |
+String
+ |
+Description of a training job
+ |
+
+version_id
+ |
+Long
+ |
+Version ID of a training job
+ |
+
+version_name
+ |
+String
+ |
+Version name of a training job
+ |
+
+pre_version_id
+ |
+Long
+ |
+Name of the previous version of a training job
+ |
+
+engine_type
+ |
+Short
+ |
+Engine type of a training job. The mapping between engine_type and engine_name is as follows:
+- engine_type: 1, engine_name: TensorFlow
- engine_type: 2, engine_name: MXNet
- engine_type: 3, engine_name: Ray
- engine_type: 4, engine_name: Caffe
- engine_type: 5, engine_name: Spark_MLlib
- engine_type: 9, engine_name: XGBoost-Sklearn
- engine_type: 10, engine_name: PyTorch
- engine_type: 12, engine_name: Horovod
+ |
+
+engine_name
+ |
+String
+ |
+Name of the engine selected for a training job. Currently, the following engines are supported:
+- Caffe
- Horovod
- MXNet
- PyTorch
- Ray
- Spark_MLlib
- TensorFlow
- XGBoost-Sklearn
+ |
+
+engine_id
+ |
+Long
+ |
+ID of the engine selected for a training job
+ |
+
+engine_version
+ |
+String
+ |
+Version of the engine selected for a training job
+ |
+
+status
+ |
+Integer
+ |
+Status of a training job. For details about the job statuses, see Job Statuses.
+ |
+
+app_url
+ |
+String
+ |
+Code directory of a training job
+ |
+
+boot_file_url
+ |
+String
+ |
+Boot file of a training job
+ |
+
+create_time
+ |
+Long
+ |
+Time when a training job is created
+ |
+
+parameter
+ |
+JSON Array
+ |
+Running parameters of a training job. It is a collection of label-value pairs. This parameter is a container environment variable when a job uses a custom image.
+ |
+
+duration
+ |
+Long
+ |
+Training job running duration, in milliseconds
+ |
+
+spec_id
+ |
+Long
+ |
+ID of the resource specifications selected for a training job
+ |
+
+core
+ |
+String
+ |
+Number of cores of the resource specifications
+ |
+
+cpu
+ |
+String
+ |
+CPU memory of the resource specifications
+ |
+
+gpu_num
+ |
+Integer
+ |
+Number of GPUs of the resource specifications
+ |
+
+gpu_type
+ |
+String
+ |
+GPU type of the resource specifications
+ |
+
+worker_server_num
+ |
+Integer
+ |
+Number of workers in a training job
+ |
+
+data_url
+ |
+String
+ |
+Dataset of a training job
+ |
+
+train_url
+ |
+String
+ |
+OBS path to the training job output file
+ |
+
+dataset_version_id
+ |
+String
+ |
+Dataset version ID of a training job
+ |
+
+dataset_id
+ |
+String
+ |
+Dataset ID of a training job
+ |
+
+data_source
+ |
+JSON Array
+ |
+Datasets of a training job
+ |
+
+model_id
+ |
+Long
+ |
+Model ID of a training job
+ |
+
+model_metric_list
+ |
+JSON Array
+ |
+Model metrics of a training job
+ |
+
+system_metric_list
+ |
+JSON Array
+ |
+System monitoring metrics of a training job
+ |
+
+user_image_url
+ |
+String
+ |
+SWR URL of the custom image used by a training job
+ |
+
+user_command
+ |
+String
+ |
+Boot command used to start the container of the custom image of a training job
+ |
+
+
+
+
+
+
Table 3 data_source parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+dataset_id
+ |
+String
+ |
+Dataset ID of a training job
+ |
+
+dataset_version
+ |
+String
+ |
+Dataset version ID of a training job
+ |
+
+type
+ |
+String
+ |
+Dataset type
+obs: Data from OBS is used.
+dataset: Data from a specified dataset is used.
+ |
+
+data_url
+ |
+String
+ |
+OBS bucket path
+ |
+
+
+
+
+
+
Table 4 model_metric_list parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+metric
+ |
+JSON Array
+ |
+Validation metrics of a class of a training job
+ |
+
+total_metric
+ |
+JSON Array
+ |
+All validation metrics of a training job
+ |
+
+
+
+
+
+
Table 5 system_metric_list parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+cpuUsage
+ |
+JSON Array
+ |
+CPU usage of a training job
+ |
+
+memUsage
+ |
+JSON Array
+ |
+Memory usage of a training job
+ |
+
+gpuUtil
+ |
+JSON Array
+ |
+GPU usage of a training job
+ |
+
+
+
+
+
+
Table 6 metric parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+metric_values
+ |
+JSON Array
+ |
+Validation metrics of a class of a training job
+ |
+
+reserved_data
+ |
+JSON Array
+ |
+Reserved parameter
+ |
+
+metric_meta
+ |
+JSON Array
+ |
+A class of a training job, including the class ID and name
+ |
+
+
+
+
+
+
Table 7 metric_values parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+recall
+ |
+JSON Array
+ |
+Recall of a class of a training job
+ |
+
+precision
+ |
+JSON Array
+ |
+Precision of a class of a training job
+ |
+
+accuracy
+ |
+JSON Array
+ |
+Accuracy of a class of a training job
+ |
+
+
+
+
+
+
Table 8 total_metric parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+total_metric_meta
+ |
+JSON Array
+ |
+Reserved parameter
+ |
+
+total_reserved_data
+ |
+JSON Array
+ |
+Reserved parameter
+ |
+
+total_metric_values
+ |
+JSON Array
+ |
+All validation metrics of a training job
+ |
+
+
+
+
+
+
Table 9 total_metric_values parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+f1_score
+ |
+Float
+ |
+F1 score of a training job
+ |
+
+recall
+ |
+Float
+ |
+Total recall of a training job
+ |
+
+precision
+ |
+Float
+ |
+Total precision of a training job
+ |
+
+accuracy
+ |
+Float
+ |
+Total accuracy of a training job
+ |
+
+
+
+
+
+
Modifying the Description of a Training Job
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Modify the description of a training job based on the specified job_id.
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(modelarts_session=session, job_id="182626")
+job_description = estimator.update_job_description(description='update description')
+ |
+
+
+
- Method 2: Modify the description of the training job created in Creating a Training Job.
| job_description = job_instance.update_job_description(description='update description')
+ |
+
+
+
- Method 3: Modify the description of a training job version object returned in Querying the List of Training Job Versions.
| job_description = job_version_instance_list[0].update_job_description(description='update description')
+ |
+
+
+
+
Parameter Description
+
Table 1 Estimator request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+job_id
+ |
+Yes
+ |
+String
+ |
+ID of a training job. You can query job_id using the training job object generated in Creating a Training Job, for example, job_instance.job_id, or from the response obtained in Querying the List of Training Jobs.
+ |
+
+
+
+
+
+
Table 2 update_job_description request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+description
+ |
+Yes
+ |
+String
+ |
+Description of the training job to be modified
+ |
+
+
+
+
+
+
Table 3 update_job_description response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Obtaining the Name of a Training Job Log File
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Obtain the file based on the specified job_id and version_id.
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(modelarts_session=session, job_id="182626", version_id="278813")
+job_log_list = estimator.get_job_log_file_list()
+ |
+
+
+
- Method 2: Obtain the file based on the training job created in Creating a Training Job.
| job_log_list = job_instance.get_job_log_file_list()
+ |
+
+
+
+
+
Parameter Description
+
Table 1 Estimator request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+job_id
+ |
+Yes
+ |
+String
+ |
+ID of a training job. You can query job_id using the training job object generated in Creating a Training Job, for example, job_instance.job_id, or from the response obtained in Querying the List of Training Jobs.
+ |
+
+version_id
+ |
+Yes
+ |
+String
+ |
+Version ID of a training job. You can query version_id using the training job object generated in Creating a Training Job, for example, job_instance.version_id, or from the response obtained in Querying the List of Training Jobs.
+ |
+
+
+
+
+
+
Table 2 get_job_log_file_list response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+log_file_list
+ |
+List
+ |
+Log file name of a training job. A single-node job has only one log file, and a distributed job has multiple log files.
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Querying Training Job Logs
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Use the specified job_id and version_id.
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(modelarts_session=session, job_id="182626", version_id="278813")
+job_log = estimator.get_job_log(log_file='job-job-0713-191758.0')
+ |
+
+
+
- Method 2: Use the training job created in Creating a Training Job.
| job_log = job_instance.get_job_log(log_file='job-job-0713-191758.0')
+ |
+
+
+
+
+
Parameter Description
+
Table 1 Estimator request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+job_id
+ |
+Yes
+ |
+String
+ |
+ID of a training job. You can query job_id using the training job object generated in Creating a Training Job, for example, job_instance.job_id, or from the response obtained in Querying the List of Training Jobs.
+ |
+
+version_id
+ |
+Yes
+ |
+String
+ |
+Version ID of a training job. You can query version_id using the training job object generated in Creating a Training Job, for example, job_instance.version_id, or from the response obtained in Querying the List of Training Jobs.
+ |
+
+
+
+
+
+
Table 2 get_job_log request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+log_file
+ |
+Yes
+ |
+String
+ |
+Name of a training job log file
+ |
+
+start_byte
+ |
+No
+ |
+Long
+ |
+Start position for obtaining the log. The default value is 0. The value range is [-1, +∞]. If the value is -1, the log with the latest offset is obtained.
+ |
+
+offset
+ |
+No
+ |
+Long
+ |
+Length of the obtained log. The default value is 2048. The value range is [-2048, 2048].
+ |
+
+
+
+
+
+
Table 3 get_job_log response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+content
+ |
+String
+ |
+Content of the requested log
+ |
+
+lines
+ |
+Integer
+ |
+Number of lines in the log
+ |
+
+start_line
+ |
+String
+ |
+Start position of the obtained log
+ |
+
+end_line
+ |
+String
+ |
+End position of the obtained log
+ |
+
+
+
+
+
+
Deleting a Training Job
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
Method 1: Delete a training job based on the specified
job_id.
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+Estimator.delete_job_by_id(modelarts_session=session, job_id="155500")
+ |
+
+
+
+
Method 2: Delete the training job created in Creating a Training Job.
+
| status = job_instance.delete_job()
+ |
+
+
+
+
Parameter Description
+
Table 1 delete_job_by_id request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+job_id
+ |
+Yes
+ |
+String
+ |
+ID of a training job. You can query job_id using the training job object generated in Creating a Training Job, for example, job_instance.job_id, or from the response obtained in Querying the List of Training Jobs.
+ |
+
+
+
+
+
+
Table 2 delete_job_by_id response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Training Job Versions
+
+
+
diff --git a/docs/modelarts/sdk-ref/modelarts_04_0168.html b/docs/modelarts/sdk-ref/modelarts_04_0168.html
new file mode 100644
index 00000000..151c58eb
--- /dev/null
+++ b/docs/modelarts/sdk-ref/modelarts_04_0168.html
@@ -0,0 +1,361 @@
+
+
+Creating a Training Job Version
+A training job must exist before you create a version for it. You can create a training job version based on Creating a Training Job or job_id and version_id of the object returned by Querying the List of Training Job Versions.
+
Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Example 1: Create a training job version using the data stored on OBS.
1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20 | from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(
+ modelarts_session=session,
+ framework_type='PyTorch', # AI engine name
+ framework_version='PyTorch-1.0.0-python3.6', # AI engine version
+ code_dir='/bucket/src/', # Training script directory
+ boot_file='/bucket/src/pytorch_sentiment.py', # Training boot script directory
+ log_url='/bucket/log/', # Training log directory
+ hyperparameters=[
+ {"label":"classes",
+ "value": "10"},
+ {"label":"lr",
+ "value": "0.001"}
+ ],
+ output_path='/bucket/output/', # Training output directory
+ train_instance_type='modelarts.vm.gpu.p100', # Training environment flavor
+ train_instance_count=1)
+job_version_instance = estimator.create_job_version(job_id='182626', pre_version_id=278813, inputs='/bucket/data/train/', wait=False, job_desc='create a job version')
+ |
+
+
+
- Example 2: Create a training job version using a dataset.
1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21 | from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(
+ modelarts_session=session,
+ framework_type='PyTorch', # AI engine name
+ framework_version='PyTorch-1.0.0-python3.6', # AI engine version
+ code_dir='/bucket/src/', # Training script directory
+ boot_file='/bucket/src/pytorch_sentiment.py', # Training boot script directory
+ log_url='/bucket/log/', # Training log directory
+ hyperparameters=[
+ {"label":"classes",
+ "value": "10"},
+ {"label":"lr",
+ "value": "0.001"}
+ ],
+ output_path='/bucket/output/', # Training output directory
+ train_instance_type='modelarts.vm.gpu.p100', # Training environment flavor
+ train_instance_count=1, # Number of training nodes
+ job_description='pytorch-sentiment with ModelArts SDK') # Training job description
+job_version_instance = estimator.create_job_version(job_id='182626', pre_version_id=278813, inputs='/bucket/data/train/', wait=False, job_desc='create a job version')
+ |
+
+
+
- Example 3: Create a training job version using a custom image.
1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19 | from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(
+ modelarts_session=session,
+ log_url='/bucket/log/', # Training log directory
+ hyperparameters=[
+ {"label":"classes",
+ "value": "10"},
+ {"label":"lr",
+ "value": "0.001"}
+ ],
+ output_path='/bucket/output/', # Training output directory
+ train_instance_type='modelarts.vm.gpu.p100', # Training environment flavor
+ train_instance_count=1, # Number of training nodes
+ user_command='bash -x /home/work/run_train.sh python /home/work/user-job-dir/app/mnist/mnist_softmax.py --data_url /home/work/user-job-dir/app/mnist_data', # Boot command of the custom image
+ user_image_url='100.125.5.235:20202/jobmng/cpu-base:1.0', # Address for downloading the custom image
+ job_description='pytorch-sentiment with ModelArts SDK') # Training job description
+job_version_instance = estimator.create_job_version(job_id='182626', pre_version_id=278813, inputs='/bucket/data/train/', wait=False, job_desc='create a job version')
+ |
+
+
+
+
Parameter Description
+
Table 1 Estimator request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+train_instance_count
+ |
+Yes
+ |
+Long
+ |
+Number of workers in a training job
+ |
+
+code_dir
+ |
+No
+ |
+String
+ |
+Code directory of a training job, for example, /bucket/src/. Leave this parameter blank when model_name is set.
+ |
+
+boot_file
+ |
+No
+ |
+String
+ |
+Boot file of a training job, which needs to be stored in the code directory. For example, /bucket/src/boot.py. Leave this parameter blank when model_name is set.
+ |
+
+output_path
+ |
+Yes
+ |
+String
+ |
+Output path of a training job
+ |
+
+hyperparameters
+ |
+No
+ |
+JSON Array
+ |
+Running parameters of a training job. It is a collection of label-value pairs of the string type. This parameter is a container environment variable when a job uses a custom image.
+ |
+
+log_url
+ |
+No
+ |
+String
+ |
+OBS URL of the logs of a training job. By default, this parameter is left blank. Example value: /usr/log/
+ |
+
+train_instance_type
+ |
+Yes
+ |
+Long
+ |
+Resource flavor selected for a training job. If you choose to train on the training platform, obtain the value by calling the API described in Querying the List of Resource Flavors.
+ |
+
+framework_type
+ |
+No
+ |
+String
+ |
+Engine selected for a training job. Obtain the value by calling the API described in Querying the List of Engine Types. Leave this parameter blank when model_name is set.
+ |
+
+framework_version
+ |
+No
+ |
+String
+ |
+Engine version selected for a training job. Obtain the value by calling the API described in Querying the List of Engine Types. Leave this parameter blank when model_name is set.
+ |
+
+user_image_url
+ |
+No
+ |
+String
+ |
+SWR URL of the custom image used by a training job. Example value: 100.125.5.235:20202/jobmng/custom-cpu-base:1.0
+ |
+
+user_command
+ |
+No
+ |
+String
+ |
+Boot command used to start the container of the custom image of a training job. The format is bash /home/work/run_train.sh python /home/work/user-job-dir/app/train.py {python_file_parameter}.
+ |
+
+
+
+
+
+
Table 2 create_job_version request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+job_id
+ |
+Yes
+ |
+String
+ |
+ID of a training job. You can query job_id using the training job object generated in Creating a Training Job, for example, job_instance.job_id, or from the response obtained in Querying the List of Training Jobs.
+ |
+
+pre_version_id
+ |
+Yes
+ |
+Long
+ |
+ID of the previous version of a training job. You can query pre_version_id using the training job object generated in Creating a Training Job, for example, job_instance.version_id, or from the response obtained in Querying the List of Training Jobs.
+ |
+
+inputs
+ |
+Yes
+ |
+String
+ |
+Data storage location of a training job. inputs cannot be used with dataset_id and dataset_version_id, or with data_source at the same time. However, one of the parameters must exist. Only this parameter is supported in local training.
+ |
+
+dataset_id
+ |
+No
+ |
+String
+ |
+Dataset ID of a training job. This parameter must be used together with dataset_version_id, but cannot be used together with inputs. To obtain the dataset ID, view basic information about the dataset.
+ |
+
+dataset_version_id
+ |
+No
+ |
+String
+ |
+Dataset version ID of a training job. This parameter must be used together with dataset_id, but cannot be used together with inputs. To obtain the dataset version ID, view basic information about the dataset.
+ |
+
+wait
+ |
+No
+ |
+Boolean
+ |
+Whether to wait for the completion of creating a training job version. Default value: False
+ |
+
+job_desc
+ |
+No
+ |
+String
+ |
+Description of a training job
+ |
+
+
+
+
+
+
Table 3 create_job_version response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+TrainingJob
+ |
+Object
+ |
+Training object. This object contains attributes such as job_id and version_id, and operations on a training job, such as querying, modifying, or deleting the training job. For example, you can use job_version_instance.job_id to obtain the ID of a training job.
+ |
+
+
+
+
+
+
Querying the List of Training Job Versions
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(session, job_id="182626")
+job_version_instance_list = estimator.get_job_version_object_list()
+ |
+
+
+
+
Parameter Description
+
Table 1 Estimator request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+job_id
+ |
+Yes
+ |
+String
+ |
+ID of a training job. You can query job_id using the training job object generated in Creating a Training Job, for example, job_instance.job_id, or from the response obtained in Querying the List of Training Jobs.
+ |
+
+
+
+
+
+
Table 2 get_job_version_object_list request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+is_show
+ |
+No
+ |
+Boolean
+ |
+Whether to print the training job version details. Default value: True
+ |
+
+
+
+
+
A training object list is returned in the successful response to get_job_version_object_list. For details, see Table 3.
+
+
Table 3 TrainingJob object descriptionParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+TrainingJob
+ |
+Object
+ |
+Training object. This object contains attributes such as job_id and version_id, and operations on a training job, such as querying, modifying, or deleting the training job. For example, you can use job_version_instance.job_id to obtain the ID of a training job.
+ |
+
+
+
+
+
+
Querying the Details About a Training Job Version
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Use the specified job_id.
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(session, job_id="182626")
+job_version_info = estimator.get_job_version_info()
+ |
+
+
+
+
+
Parameter Description
+
Table 1 Estimator request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+job_id
+ |
+Yes
+ |
+String
+ |
+ID of a training job. You can query job_id using the training job object generated in Creating a Training Job, for example, job_instance.job_id, or from the response obtained in Querying the List of Training Jobs.
+ |
+
+
+
+
+
+
Table 2 get_job_version_info response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+job_id
+ |
+Long
+ |
+Training job ID
+ |
+
+job_name
+ |
+String
+ |
+Training job name
+ |
+
+job_desc
+ |
+String
+ |
+Description of a training job
+ |
+
+version_count
+ |
+Long
+ |
+Number of versions of a training job
+ |
+
+versions
+ |
+JSON Array
+ |
+Version parameters of a training job
+ |
+
+
+
+
+
+
Table 3 versions parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+version_id
+ |
+Long
+ |
+Version ID of a training job
+ |
+
+version_name
+ |
+String
+ |
+Version name of a training job
+ |
+
+pre_version_id
+ |
+Long
+ |
+ID of the previous version of a training job
+ |
+
+engine_type
+ |
+Long
+ |
+Engine type of a training job
+ |
+
+engine_id
+ |
+Long
+ |
+ID of the engine selected for a training job
+ |
+
+engine_version
+ |
+String
+ |
+Version of the engine selected for a training job
+ |
+
+status
+ |
+Integer
+ |
+Status of a training job
+ |
+
+app_url
+ |
+String
+ |
+Code directory of a training job
+ |
+
+boot_file_url
+ |
+String
+ |
+Boot file of a training job
+ |
+
+create_time
+ |
+Long
+ |
+Time when a training job is created
+ |
+
+parameter
+ |
+JSON Array
+ |
+Running parameters of a training job. It is a collection of label-value pairs. This parameter is a container environment variable when a job uses a custom image.
+ |
+
+duration
+ |
+Long
+ |
+Training job running duration, in milliseconds
+ |
+
+spec_id
+ |
+Long
+ |
+ID of the resource specifications selected for a training job
+ |
+
+core
+ |
+String
+ |
+Number of cores of the resource specifications
+ |
+
+cpu
+ |
+String
+ |
+CPU memory of the resource specifications
+ |
+
+gpu_num
+ |
+Integer
+ |
+Number of GPUs of the resource specifications
+ |
+
+gpu_type
+ |
+String
+ |
+GPU type of the resource specifications
+ |
+
+worker_server_num
+ |
+Integer
+ |
+Number of workers in a training job
+ |
+
+data_url
+ |
+String
+ |
+Dataset of a training job
+ |
+
+train_url
+ |
+String
+ |
+OBS path to the training job output file
+ |
+
+log_url
+ |
+String
+ |
+OBS URL of the logs of a training job. By default, this parameter is left blank. Example value: /usr/log/
+ |
+
+dataset_version_id
+ |
+String
+ |
+Dataset version ID of a training job
+ |
+
+dataset_id
+ |
+String
+ |
+Dataset ID of a training job
+ |
+
+data_source
+ |
+JSON Array
+ |
+Datasets of a training job
+ |
+
+model_id
+ |
+String
+ |
+Model ID of a training job
+ |
+
+model_metric_list
+ |
+JSON Array
+ |
+Model metrics of a training job
+ |
+
+system_metric_list
+ |
+JSON Array
+ |
+System monitoring metrics of a training job
+ |
+
+user_image_url
+ |
+String
+ |
+SWR URL of the custom image used by a training job
+ |
+
+user_command
+ |
+String
+ |
+Boot command used to start the container of the custom image of a training job
+ |
+
+
+
+
+
+
Table 4 data_source parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+dataset_id
+ |
+String
+ |
+Dataset ID of a training job
+ |
+
+dataset_version
+ |
+String
+ |
+Dataset version ID of a training job
+ |
+
+type
+ |
+String
+ |
+Dataset type
+obs: Data from OBS is used.
+dataset: Data from a specified dataset is used.
+ |
+
+data_url
+ |
+String
+ |
+OBS bucket path
+ |
+
+
+
+
+
+
Table 5 model_metric_list parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+metric
+ |
+JSON Array
+ |
+Validation metrics of a class of a training job
+ |
+
+total_metric
+ |
+JSON Array
+ |
+All validation metrics of a training job
+ |
+
+
+
+
+
+
Table 6 system_metric_list parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+cpuUsage
+ |
+JSON Array
+ |
+CPU usage of a training job
+ |
+
+memUsage
+ |
+JSON Array
+ |
+Memory usage of a training job
+ |
+
+gpuUtil
+ |
+JSON Array
+ |
+GPU usage of a training job
+ |
+
+
+
+
+
+
Table 7 metric parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+metric_values
+ |
+JSON Array
+ |
+Validation metrics of a class of a training job
+ |
+
+reserved_data
+ |
+JSON Array
+ |
+Reserved parameter
+ |
+
+metric_meta
+ |
+JSON Array
+ |
+A class of a training job, including the class ID and name
+ |
+
+
+
+
+
+
Table 8 metric_values parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+recall
+ |
+JSON Array
+ |
+Recall of a class of a training job
+ |
+
+precision
+ |
+JSON Array
+ |
+Precision of a class of a training job
+ |
+
+accuracy
+ |
+JSON Array
+ |
+Accuracy of a class of a training job
+ |
+
+
+
+
+
+
Table 9 total_metric parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+total_metric_meta
+ |
+JSON Array
+ |
+Reserved parameter
+ |
+
+total_reserved_data
+ |
+JSON Array
+ |
+Reserved parameter
+ |
+
+total_metric_values
+ |
+JSON Array
+ |
+All validation metrics of a training job
+ |
+
+
+
+
+
+
Table 10 total_metric_values parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+f1_score
+ |
+Float
+ |
+F1 score of a training job
+ |
+
+recall
+ |
+Float
+ |
+Total recall of a training job
+ |
+
+precision
+ |
+Float
+ |
+Total precision of a training job
+ |
+
+accuracy
+ |
+Float
+ |
+Total accuracy of a training job
+ |
+
+
+
+
+
+
Stopping a Training Job Version
+You can stop a training job version that is being created only when the job is running.
+
Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Stop a training job version based on the specified job_id and version_id.
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(session, job_id="182626", version_id="278813")
+status = estimator.stop_job_version()
+ |
+
+
+
+
+
+
Parameter Description
+
Table 1 Estimator request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+job_id
+ |
+Yes
+ |
+String
+ |
+ID of a training job. You can query job_id using the training job object generated in Creating a Training Job, for example, job_instance.job_id, or from the response obtained in Querying the List of Training Jobs.
+ |
+
+version_id
+ |
+Yes
+ |
+String
+ |
+Version ID of a training job. You can query version_id from the response obtained in Querying the List of Training Job Versions.
+ |
+
+
+
+
+
+
Table 2 stop_job_version response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Deleting a Training Job Version
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Delete a training job version based on the specified job_id and version_id.
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(session, job_id="182626", version_id="278813")
+status = estimator.delete_job_version()
+ |
+
+
+
+
+
+
Parameter Description
+
Table 1 Estimator request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+job_id
+ |
+Yes
+ |
+String
+ |
+ID of a training job. You can query job_id using the training job object generated in Creating a Training Job, for example, job_instance.job_id, or from the response obtained in Querying the List of Training Jobs.
+ |
+
+version_id
+ |
+Yes
+ |
+String
+ |
+Version ID of a training job. You can query version_id from the response obtained in Querying the List of Training Job Versions.
+ |
+
+
+
+
+
+
Table 2 delete_job_version response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Training Job Parameter Configuration
+
+
+
diff --git a/docs/modelarts/sdk-ref/modelarts_04_0174.html b/docs/modelarts/sdk-ref/modelarts_04_0174.html
new file mode 100644
index 00000000..57923391
--- /dev/null
+++ b/docs/modelarts/sdk-ref/modelarts_04_0174.html
@@ -0,0 +1,317 @@
+
+
+Creating a Training Job Configuration
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Example 1: Create a training job parameter configuration using the data stored on OBS.
1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20 | from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(
+ modelarts_session=session,
+ framework_type='PyTorch', # AI engine name
+ framework_version='PyTorch-1.0.0-python3.6', # AI engine version
+ code_dir='/bucket/src/', # Training script directory
+ boot_file='/bucket/src/pytorch_sentiment.py', # Training boot script directory
+ log_url='/bucket/log/', # Training log directory
+ hyperparameters=[
+ {"label":"classes",
+ "value": "10"},
+ {"label":"lr",
+ "value": "0.001"}
+ ],
+ output_path='/bucket/output/', # Training output directory
+ train_instance_type='modelarts.vm.gpu.p100', # Training environment flavor
+ train_instance_count=1) # Number of training nodes
+job_config_instance = estimator.create_job_configs(config_name='my_job_config', inputs='/bucket/data/train/', config_desc='my job config')
+ |
+
+
+
- Example 2: Create a training job parameter configuration using a dataset.
1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20 | from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(
+ modelarts_session=session,
+ framework_type='PyTorch', # AI engine name
+ framework_version='PyTorch-1.0.0-python3.6', # AI engine version
+ code_dir='/bucket/src/', # Training script directory
+ boot_file='/bucket/src/pytorch_sentiment.py', # Training boot script directory
+ log_url='/bucket/log/', # Training log directory
+ hyperparameters=[
+ {"label":"classes",
+ "value": "10"},
+ {"label":"lr",
+ "value": "0.001"}
+ ],
+ output_path='/bucket/output/', # Training output directory
+ train_instance_type='modelarts.vm.gpu.p100', # Training environment flavor
+ train_instance_count=1) # Number of training nodes
+job_config_instance = estimator.create_job_configs(config_name='my_job_config', dataset_id='4AZNvFkN7KYr5EdhFkH', dataset_version_id='UOF9BIeSGArwVt0oI6T', config_desc='my job config')
+ |
+
+
+
+
Parameter Description
+
Table 1 Estimator request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+train_instance_count
+ |
+Yes
+ |
+Long
+ |
+Number of workers in a training job
+ |
+
+code_dir
+ |
+No
+ |
+String
+ |
+Code directory of a training job, for example, /bucket/src/. Leave this parameter blank when model_name is set.
+ |
+
+boot_file
+ |
+No
+ |
+String
+ |
+Boot file of a training job, which needs to be stored in the code directory. For example, /bucket/src/boot.py. Leave this parameter blank when model_name is set.
+ |
+
+model_name
+ |
+No
+ |
+Long
+ |
+Name of the built-in algorithm used by a training job. If you have configured model_name, you do not need to configure app_url, boot_file_url, framework_type, and framework_version.
+ |
+
+output_path
+ |
+Yes
+ |
+String
+ |
+Output path of a training job
+ |
+
+hyperparameters
+ |
+No
+ |
+JSON Array
+ |
+Running parameters of a training job. It is a collection of label-value pairs. This parameter is a container environment variable when a job uses a custom image.
+ |
+
+log_url
+ |
+No
+ |
+String
+ |
+OBS URL of the logs of a training job. By default, this parameter is left blank. Example value: /usr/log/
+ |
+
+train_instance_type
+ |
+Yes
+ |
+Long
+ |
+Resource flavor selected for a training job. If you choose to train on the training platform, obtain the value by calling the API described in Querying the List of Resource Flavors.
+ |
+
+framework_type
+ |
+No
+ |
+String
+ |
+Engine selected for a training job. Obtain the value by calling the API described in Querying the List of Engine Types. Leave this parameter blank when model_name is set.
+ |
+
+framework_version
+ |
+No
+ |
+String
+ |
+Engine version selected for a training job. Obtain the value by calling the API described in Querying the List of Engine Types. Leave this parameter blank when model_name is set.
+ |
+
+job_description
+ |
+No
+ |
+String
+ |
+Description of a training job
+ |
+
+user_image_url
+ |
+No
+ |
+String
+ |
+SWR URL of the custom image used by a training job. Example value: 100.125.5.235:20202/jobmng/custom-cpu-base:1.0
+ |
+
+user_command
+ |
+No
+ |
+String
+ |
+Boot command used to start the container of the custom image of a training job. The format is bash /home/work/run_train.sh python /home/work/user-job-dir/app/train.py {python_file_parameter}.
+ |
+
+
+
+
+
+
+
Table 2 create_job_configs request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+config_name
+ |
+No
+ |
+String
+ |
+Name of a training job parameter configuration. The value is a string of 1 to 20 characters consisting of only digits, letters, underscores (_), and hyphens (-). By default, if this parameter is left blank, the value is dynamically generated by date.
+ |
+
+config_desc
+ |
+No
+ |
+String
+ |
+Description of a training job parameter configuration. The value is a string of 0 to 256 characters. By default, this parameter is left blank.
+ |
+
+inputs
+ |
+No
+ |
+String
+ |
+OBS storage path of a training job
+ |
+
+dataset_id
+ |
+No
+ |
+String
+ |
+Dataset ID of a training job. This parameter must be used together with dataset_version_id, but cannot be used together with inputs.
+ |
+
+dataset_version_id
+ |
+No
+ |
+String
+ |
+Dataset version ID of a training job. This parameter must be used together with dataset_id, but cannot be used together with inputs.
+ |
+
+
+
+
+
+
Table 3 create_job_configs response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+TrainingJob
+ |
+Object
+ |
+Training object. This object contains attributes such as config_name, and operations on a training job parameter configuration, such as querying or deleting the training job parameter configuration. For example, you can use job_config_instance.config_name to obtain the name of a training job parameter configuration.
+ |
+
+
+
+
+
Querying the List of Training Job Parameter Configuration Objects
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+job_config_instance_list = Estimator.get_job_configs_object_list(modelarts_session=session, is_show=True, per_page=10, page=1, sort_by="create_time", order="asc", search_content="configname")
+ |
+
+
+
+
Parameter Description
+
Table 1 get_job_configs_object_list request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+per_page
+ |
+No
+ |
+Integer
+ |
+Number of job parameters displayed on each page. The value range is [1, 1000]. Default value: 10
+ |
+
+page
+ |
+No
+ |
+Integer
+ |
+Index of the page to be queried. Default value: 1
+ |
+
+sortBy/sort_by
+ |
+No
+ |
+String
+ |
+When AK/SK-based authentication is used, the parameter name is sortBy. When the username and password are used for authentication, the parameter name is sort_by. The parameter specifies the sorting mode of the query. The value can be job_name, job_desc, status, duration, engine_type, or create_time. Default value: job_name
+ |
+
+order
+ |
+No
+ |
+String
+ |
+Sorting order. The options are as follows:
+- asc: ascending order. It is the default value.
- desc: descending order
+ |
+
+search_content
+ |
+No
+ |
+String
+ |
+Search content, for example, a parameter name. By default, this parameter is left blank.
+ |
+
+is_show
+ |
+No
+ |
+Boolean
+ |
+Whether to print the training job parameter configuration list. Default value: True
+ |
+
+
+
+
+
A training object list is returned in the successful response to get_job_configs_object_list. For details, see Table 2.
+
+
Table 2 TrainingJob object descriptionParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+TrainingJob
+ |
+Object
+ |
+Training object. This object contains attributes such as config_name, and operations on a training job parameter configuration, such as querying or deleting the training job parameter configuration. For example, you can use job_config_instance.config_name to obtain the name of a training job parameter configuration.
+ |
+
+
+
+
+
+
Querying the List of Training Job Configurations
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+job_paras_list = Estimator.get_job_configs_list(modelarts_session=session, per_page=10, page=1, sort_by="create_time", order="asc", search_content="configname")
+ |
+
+
+
+
Parameter Description
+
Table 1 get_job_configs_list request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+per_page
+ |
+No
+ |
+Integer
+ |
+Number of job parameters displayed on each page. The value range is [1, 1000]. Default value: 10
+ |
+
+page
+ |
+No
+ |
+Integer
+ |
+Index of the page to be queried. Default value: 1
+ |
+
+sortBy/sort_by
+ |
+No
+ |
+String
+ |
+When AK/SK-based authentication is used, the parameter name is sortBy. When account-based authentication is used, the parameter name is sort_by. The parameter specifies the sorting mode of the query. The value can be job_name, job_desc, status, duration, engine_type, or create_time. Default value: job_name
+ |
+
+order
+ |
+No
+ |
+String
+ |
+Sorting order. The options are as follows:
+- asc: ascending order. It is the default value.
- desc: descending order
+ |
+
+search_content
+ |
+No
+ |
+String
+ |
+Search content, for example, a parameter name. By default, this parameter is left blank.
+ |
+
+
+
+
+
+
Table 2 get_job_configs_list response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+config_total_count
+ |
+Integer
+ |
+Total number of the queried training job configurations
+ |
+
+configs
+ |
+JSON Array
+ |
+configs parameters
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Table 3 configs parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+config_name
+ |
+String
+ |
+Name of a training job parameter configuration
+ |
+
+config_desc
+ |
+String
+ |
+Description of a training job parameter configuration
+ |
+
+create_time
+ |
+Long
+ |
+Time when a training job is created
+ |
+
+engine_type
+ |
+Short
+ |
+Engine type of a training job
+ |
+
+engine_name
+ |
+String
+ |
+Name of the engine selected for a training job
+ |
+
+engine_id
+ |
+Long
+ |
+ID of the engine selected for a training job
+ |
+
+engine_version
+ |
+String
+ |
+Version of the engine selected for a training job
+ |
+
+
+
+
+
+
Querying the Details About a Training Job Configuration
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Use the specified config_name.
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(modelarts_session=session, config_name="my_job_config")
+job_paras_info = estimator.get_job_configs_info()
+ |
+
+
+
+
+
+
Parameter Description
+
Table 1 Estimator request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+config_name
+ |
+Yes
+ |
+String
+ |
+Name of a training job parameter configuration
+ |
+
+
+
+
+
+
Table 2 get_job_configs_info response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+config_name
+ |
+String
+ |
+Name of a training job parameter configuration
+ |
+
+config_desc
+ |
+String
+ |
+Description of a training job parameter configuration
+ |
+
+worker_server_num
+ |
+Integer
+ |
+Number of workers in a training job
+ |
+
+app_url
+ |
+String
+ |
+Code directory of a training job
+ |
+
+boot_file_url
+ |
+String
+ |
+Boot file of a training job
+ |
+
+model_id
+ |
+Long
+ |
+Model ID of a training job
+ |
+
+parameter
+ |
+JSON Array
+ |
+Running parameters of a training job. It is a collection of label-value pairs. This parameter is a container environment variable when a job uses a custom image.
+ |
+
+spec_id
+ |
+Long
+ |
+ID of the resource specifications selected for a training job
+ |
+
+data_url
+ |
+String
+ |
+Dataset of a training job
+ |
+
+dataset_id
+ |
+String
+ |
+Dataset ID of a training job
+ |
+
+dataset_version_id
+ |
+String
+ |
+Dataset version ID of a training job
+ |
+
+engine_type
+ |
+Short
+ |
+Engine type of a training job
+ |
+
+engine_name
+ |
+String
+ |
+Name of the engine selected for a training job
+ |
+
+engine_id
+ |
+Long
+ |
+ID of the engine selected for a training job
+ |
+
+engine_version
+ |
+String
+ |
+Version of the engine selected for a training job
+ |
+
+train_url
+ |
+String
+ |
+OBS URL of the output file of a training job. By default, this parameter is left blank. Example value: /usr/train/
+ |
+
+log_url
+ |
+String
+ |
+OBS URL of the logs of a training job. By default, this parameter is left blank. Example value: /usr/train/
+ |
+
+user_image_url
+ |
+String
+ |
+SWR URL of the custom image used by a training job
+ |
+
+user_command
+ |
+String
+ |
+Boot command used to start the container of the custom image of a training job
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Modifying a Training Job Configuration
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Example 1: Modify a training job parameter configuration using the data stored on OBS.
1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20 | from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(
+ modelarts_session=session,
+ framework_type='PyTorch', # AI engine name
+ framework_version='PyTorch-1.0.0-python3.6', # AI engine version
+ code_dir='/bucket/src/', # Training script directory
+ boot_file='/bucket/src/pytorch_sentiment.py', # Training boot script directory
+ log_url='/bucket/log/', # Training log directory
+ hyperparameters=[
+ {"label":"classes",
+ "value": "10"},
+ {"label":"lr",
+ "value": "0.001"}
+ ],
+ output_path='/bucket/output/', # Training output directory
+ train_instance_type='modelarts.vm.gpu.p100', # Training environment flavor
+ train_instance_count=1) # Number of training nodes
+update_info = estimator.update_job_configs(config_name='my_job_config', inputs='/bucket/dataset/', config_desc='update')
+ |
+
+
+
- Example 2: Modify a training job parameter configuration using a dataset.
1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20 | from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(
+ modelarts_session=session,
+ framework_type='PyTorch', # AI engine name
+ framework_version='PyTorch-1.0.0-python3.6', # AI engine version
+ code_dir='/bucket/src/', # Training script directory
+ boot_file='/bucket/src/pytorch_sentiment.py', # Training boot script directory
+ log_url='/bucket/log/', # Training log directory
+ hyperparameters=[
+ {"label":"classes",
+ "value": "10"},
+ {"label":"lr",
+ "value": "0.001"}
+ ],
+ output_path='/bucket/output/', # Training output directory
+ train_instance_type='modelarts.vm.gpu.p100', # Training environment flavor
+ train_instance_count=1) # Number of training nodes
+update_info = estimator.update_job_configs(config_name='my_job_config', dataset_id='4AZNvFkN7KYr5EdhFkH', dataset_version_id='UOF9BIeSGArwVt0oI6T', config_desc='update')
+ |
+
+
+
+
Parameter Description
+
Table 1 Estimator request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+train_instance_count
+ |
+Yes
+ |
+Long
+ |
+Number of workers in a training job
+ |
+
+code_dir
+ |
+No
+ |
+String
+ |
+Code directory of a training job, for example, /bucket/src/. Leave this parameter blank when model_name is set.
+ |
+
+boot_file
+ |
+No
+ |
+String
+ |
+Boot file of a training job, which needs to be stored in the code directory. For example, /bucket/src/boot.py. Leave this parameter blank when model_name is set.
+ |
+
+model_name
+ |
+No
+ |
+Long
+ |
+Name of the built-in algorithm used by a training job. If you have configured model_name, you do not need to configure app_url, boot_file_url, framework_type, and framework_version.
+ |
+
+output_path
+ |
+Yes
+ |
+String
+ |
+Output path of a training job
+ |
+
+hyperparameters
+ |
+No
+ |
+JSON Array
+ |
+Running parameters of a training job. It is a collection of label-value pairs. This parameter is a container environment variable when a job uses a custom image.
+ |
+
+log_url
+ |
+No
+ |
+String
+ |
+OBS URL of the logs of a training job. By default, this parameter is left blank. Example value: /usr/log/
+ |
+
+train_instance_type
+ |
+Yes
+ |
+Long
+ |
+Resource flavor selected for a training job. If you choose to train on the training platform, obtain the value by calling the API described in Querying the List of Resource Flavors.
+ |
+
+framework_type
+ |
+No
+ |
+String
+ |
+Engine selected for a training job. Obtain the value by calling the API described in Querying the List of Engine Types. Leave this parameter blank when model_name is set.
+ |
+
+framework_version
+ |
+No
+ |
+String
+ |
+Engine version selected for a training job. Obtain the value by calling the API described in Querying the List of Engine Types. Leave this parameter blank when model_name is set.
+ |
+
+job_description
+ |
+No
+ |
+String
+ |
+Description of a training job
+ |
+
+user_image_url
+ |
+No
+ |
+String
+ |
+SWR URL of the custom image used by a training job. Example value: 100.125.5.235:20202/jobmng/custom-cpu-base:1.0
+ |
+
+user_command
+ |
+No
+ |
+String
+ |
+Boot command used to start the container of the custom image of a training job. The format is bash /home/work/run_train.sh python /home/work/user-job-dir/app/train.py {python_file_parameter}.
+ |
+
+
+
+
+
+
Table 2 update_job_configs request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+config_name
+ |
+Yes
+ |
+String
+ |
+Name of a training job parameter configuration. The value is a string of 1 to 20 characters consisting of only digits, letters, underscores (_), and hyphens (-). By default, if this parameter is left blank, the value is dynamically generated by date.
+ |
+
+config_desc
+ |
+No
+ |
+String
+ |
+Description of a training job parameter configuration. The value is a string of 0 to 256 characters. By default, this parameter is left blank.
+ |
+
+inputs
+ |
+No
+ |
+String
+ |
+OBS storage path of a training job
+ |
+
+dataset_id
+ |
+No
+ |
+String
+ |
+Dataset ID of a training job. This parameter must be used together with dataset_version_id, but cannot be used together with inputs.
+ |
+
+dataset_version_id
+ |
+No
+ |
+String
+ |
+Dataset version ID of a training job. This parameter must be used together with dataset_id, but cannot be used together with inputs.
+ |
+
+data_source
+ |
+No
+ |
+JSON Array
+ |
+Dataset of a training job. This parameter cannot be used together with inputs, dataset_id, or dataset_version_id.
+ |
+
+
+
+
+
+
Table 3 data_source parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+dataset_id
+ |
+No
+ |
+String
+ |
+Dataset ID of a training job
+ |
+
+dataset_version
+ |
+No
+ |
+String
+ |
+Dataset version ID of a training job
+ |
+
+type
+ |
+Yes
+ |
+String
+ |
+Dataset type. The value can be obs or dataset.
+ |
+
+data_url
+ |
+No
+ |
+String
+ |
+OBS bucket path. This parameter cannot be used together with dataset_id or dataset_version.
+ |
+
+
+
+
+
+
Table 4 update_job_configs response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Deleting a Training Job Configuration
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Delete a training job parameter configuration based on the specified config_name.
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+estimator = Estimator(modelarts_session=session, config_name="my_job_config")
+status = estimator.delete_job_configs()
+ |
+
+
+
+
+
+
Parameter Description
+
Table 1 Estimator request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+config_name
+ |
+Yes
+ |
+String
+ |
+Name of a training job parameter configuration
+ |
+
+
+
+
+
+
Table 2 delete_job_configs response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Visualization Jobs
+
+
+
diff --git a/docs/modelarts/sdk-ref/modelarts_04_0181.html b/docs/modelarts/sdk-ref/modelarts_04_0181.html
new file mode 100644
index 00000000..ca75528f
--- /dev/null
+++ b/docs/modelarts/sdk-ref/modelarts_04_0181.html
@@ -0,0 +1,132 @@
+
+
+Creating a Visualization Job
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+from modelarts.estimator import VisualizationJob
+session = Session()
+job = VisualizationJob(modelarts_session=session)
+job_visualization_instance = job.create_visualization_job(train_url='/bucket/train/', job_name='visualization_job', job_desc='my visualization job')
+ |
+
+
+
+
Parameter Description
+
Table 1 create_visualization_job request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+job_name
+ |
+No
+ |
+String
+ |
+Name of a visualization job. The value is a string of 1 to 20 characters consisting of only digits, letters, underscores (_), and hyphens (-).
+ |
+
+job_desc
+ |
+No
+ |
+String
+ |
+Description of a visualization job. The value is a string of 0 to 256 characters. By default, this parameter is left blank.
+ |
+
+train_url
+ |
+Yes
+ |
+String
+ |
+OBS path to the visualization file. The visualization file is provided for the visualization job to read and display, and is usually located in the training output path. The visualization file is generated by the tf.summary or tensorboardx.SummaryWriter module in the training code, and the file name usually starts with events.out.tfevents.
+ |
+
+
+
+
+
+
Table 2 create_visualization_job response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+VisualizationJob
+ |
+Object
+ |
+Visualization job object. This object contains attributes such as visualization_id, create_time, job_name, and status, and operations on a visualization job, such as querying, modifying, stopping, restarting, or deleting the visualization job.
+ |
+
+
+
+
+
+
Table 3 VisualizationJob parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+create_time
+ |
+Long
+ |
+Time when a visualization job is created
+ |
+
+job_name
+ |
+String
+ |
+Name of a visualization job
+ |
+
+status
+ |
+Byte
+ |
+Status of a visualization job. For details about the job statuses, see Job Statuses.
+ |
+
+job_id
+ |
+String
+ |
+ID of a visualization job
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Querying the List of Visualization Job Objects
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+from modelarts.estimator import VisualizationJob
+session = Session()
+job_visualization_instance_list = VisualizationJob.get_visualization_job_object_list(modelarts_session=session, is_show=True, status=8, per_page=10, page=1, sort_by="create_time", order="asc", search_content="job")
+ |
+
+
+
+
Parameter Description
+
Table 1 get_visualization_job_object_list request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+status
+ |
+No
+ |
+String
+ |
+Status of a visualization job. For details about the job statuses, see Job Statuses.
+ |
+
+per_page
+ |
+No
+ |
+Integer
+ |
+Number of jobs displayed on each page. The value range is [1, 100]. Default value: 10
+ |
+
+page
+ |
+No
+ |
+Integer
+ |
+Index of the page to be queried. Default value: 1
+ |
+
+sortBy/sort_by
+ |
+No
+ |
+String
+ |
+When AK/SK-based authentication is used, the parameter name is sortBy. When the username and password are used for authentication, the parameter name is sort_by. The parameter specifies the sorting mode of the query. The value can be job_name, job_desc, status, duration, create_time, or log_dir. Default value: job_name
+ |
+
+order
+ |
+No
+ |
+String
+ |
+Sorting order. The options are as follows:
+- asc: ascending order. It is the default value.
- desc: descending order
+ |
+
+search_content
+ |
+No
+ |
+String
+ |
+Search content, for example, a visualization job name. The value is a string of 0 to 100 characters. By default, this parameter is left blank.
+ |
+
+is_show
+ |
+No
+ |
+Boolean
+ |
+Whether to print the visualization job list. Default value: True
+ |
+
+
+
+
+
+
Table 2 get_visualization_job_object_list response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+VisualizationJob
+ |
+Object
+ |
+Visualization job object. This object contains attributes such as visualization_id, create_time, job_name, and status, and operations on a visualization job, such as querying, modifying, stopping, restarting, or deleting the visualization job.
+ |
+
+
+
+
+
+
Table 3 VisualizationJob parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+create_time
+ |
+Long
+ |
+Time when a visualization job is created
+ |
+
+job_name
+ |
+String
+ |
+Name of a visualization job
+ |
+
+status
+ |
+Byte
+ |
+Status of a visualization job. For details about the job statuses, see Job Statuses.
+ |
+
+job_id
+ |
+String
+ |
+ID of a visualization job
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Querying the List of Visualization Jobs
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+from modelarts.estimator import VisualizationJob
+session = Session()
+job_list = VisualizationJob.get_visualization_job_list(modelarts_session=session, status=8, per_page=10, page=1, sort_by="create_time", order="asc", search_content="job")
+ |
+
+
+
+
Parameter Description
+
Table 1 get_visualization_job_list request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+status
+ |
+No
+ |
+String
+ |
+Status of a visualization job. For details about the job statuses, see Job Statuses.
+ |
+
+per_page
+ |
+No
+ |
+Integer
+ |
+Number of jobs displayed on each page. The value range is [1, 100]. Default value: 10
+ |
+
+page
+ |
+No
+ |
+Integer
+ |
+Index of the page to be queried. Default value: 1
+ |
+
+sortBy/sort_by
+ |
+No
+ |
+String
+ |
+When AK/SK-based authentication is used, the parameter name is sortBy. When the username and password are used for authentication, the parameter name is sort_by. The parameter specifies the sorting mode of the query. The value can be job_name, job_desc, status, duration, create_time, or log_dir. Default value: job_name
+ |
+
+order
+ |
+No
+ |
+String
+ |
+Sorting order. The options are as follows:
+- asc: ascending order. It is the default value.
- desc: descending order
+ |
+
+search_content
+ |
+No
+ |
+String
+ |
+Search content, for example, a visualization job name. The value is a string of 0 to 100 characters. By default, this parameter is left blank.
+ |
+
+
+
+
+
+
Table 2 get_visualization_job_list response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+job_total_count
+ |
+Integer
+ |
+Total number of the queried visualization jobs
+ |
+
+job_count_limit
+ |
+Integer
+ |
+Number of visualization jobs that can be created
+ |
+
+jobs
+ |
+JSON Array
+ |
+Visualization job attributes. For details, see Table 3.
+ |
+
+
+
+
+
+
Table 3 jobs parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+job_id
+ |
+Integer
+ |
+ID of a visualization job
+ |
+
+job_name
+ |
+String
+ |
+Name of a visualization job
+ |
+
+status
+ |
+Integer
+ |
+Status of a visualization job. For details about the job statuses, see Job Statuses.
+ |
+
+create_time
+ |
+Long
+ |
+Time when a visualization job is created
+ |
+
+duration
+ |
+Long
+ |
+Running duration of a visualization job, in milliseconds
+ |
+
+job_desc
+ |
+String
+ |
+Description of a visualization job
+ |
+
+service_url
+ |
+String
+ |
+Endpoint of a visualization job
+ |
+
+train_url
+ |
+String
+ |
+Path to visualization job logs
+ |
+
+
+
+
+
+
+
Querying the Details About a Visualization Job
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Use the specified visualization_id.
| from modelarts.session import Session
+from modelarts.estimator import VisualizationJob
+session = Session()
+job = VisualizationJob(modelarts_session=session, visualization_id='8992')
+job_info = job.get_visualization_job_info()
+ |
+
+
+
- Method 2: Use the visualization job created in Creating a Visualization Job.
| job_info = job_visualization_instance.get_visualization_job_info()
+ |
+
+
+
+
+
Parameter Description
+
Table 1 VisualizationJob request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+visualization_id
+ |
+Yes
+ |
+String
+ |
+ID of a visualization job
+ |
+
+
+
+
+
+
Table 2 get_visualization_job_info response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+job_name
+ |
+String
+ |
+Name of a visualization job
+ |
+
+service_url
+ |
+String
+ |
+Endpoint of a visualization job
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+duration
+ |
+Long
+ |
+Running duration of a visualization job
+ |
+
+create_time
+ |
+Long
+ |
+Time when a visualization job is created
+ |
+
+train_url
+ |
+String
+ |
+OBS path to the visualization job output file
+ |
+
+job_id
+ |
+Long
+ |
+ID of a visualization job
+ |
+
+job_desc
+ |
+String
+ |
+Description of a visualization job
+ |
+
+resource_id
+ |
+String
+ |
+Resource ID of a visualization job
+ |
+
+status
+ |
+Integer
+ |
+Status of a visualization job. For details about the job statuses, see Job Statuses.
+ |
+
+
+
+
+
+
Modifying the Description of a Visualization Job
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Modify the description of a visualization job based on the specified visualization_id.
| from modelarts.session import Session
+from modelarts.estimator import VisualizationJob
+session = Session()
+job = VisualizationJob(modelarts_session=session, visualization_id='8992')
+job_description = job.update_visualization_job(job_desc='update visualization job')
+ |
+
+
+
- Method 2: Modify the description of the visualization job created in Creating a Visualization Job.
| job_description = job_visualization_instance.update_visualization_job(job_desc='update visualization job')
+ |
+
+
+
- Method 3: Modify the description of a visualization job object returned in Querying the List of Visualization Job Objects.
| job_description = job_visualization_instance_list[0].update_visualization_job(job_desc='update visualization job')
+ |
+
+
+
+
Parameter Description
+
Table 1 VisualizationJob request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+visualization_id
+ |
+Yes
+ |
+String
+ |
+ID of a visualization job
+ |
+
+
+
+
+
+
Table 2 update_visualization_job request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+job_desc
+ |
+Yes
+ |
+String
+ |
+Description of a visualization job. The value is a string of 0 to 256 characters.
+ |
+
+
+
+
+
+
Table 3 update_visualization_job response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Stopping a Visualization Job
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Stop a visualization job based on the specified visualization_id.
| from modelarts.session import Session
+from modelarts.estimator import VisualizationJob
+session = Session()
+job = VisualizationJob(modelarts_session=session, visualization_id='8992')
+status = job.stop_visualization_job()
+ |
+
+
+
- Method 2: Stop the visualization job created in Creating a Visualization Job.
| status = job_visualization_instance.stop_visualization_job()
+ |
+
+
+
+
+
Parameter Description
+
Table 1 VisualizationJob request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+visualization_id
+ |
+Yes
+ |
+String
+ |
+ID of a visualization job
+ |
+
+
+
+
+
+
Table 2 stop_visualization_job response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Restarting a Visualization Job
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Restart a visualization job based on the specified visualization_id.
| from modelarts.session import Session
+from modelarts.estimator import VisualizationJob
+session = Session()
+job = VisualizationJob(modelarts_session=session, visualization_id='8992')
+resp = job.restart_visualization_job()
+ |
+
+
+
- Method 2: Restart the visualization job created in Creating a Visualization Job.
| status = job_visualization_instance.restart_visualization_job()
+ |
+
+
+
+
+
Parameter Description
+
Table 1 VisualizationJob request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+visualization_id
+ |
+Yes
+ |
+String
+ |
+ID of a visualization job
+ |
+
+
+
+
+
+
Table 2 restart_visualization_job response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Deleting a Visualization Job
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Delete a visualization job based on the specified visualization_id.
| from modelarts.session import Session
+from modelarts.estimator import VisualizationJob
+session = Session()
+job = VisualizationJob(modelarts_session=session, visualization_id='8992')
+status = job.delete_visualization_job()
+ |
+
+
+
- Method 2: Delete the visualization job created in Creating a Visualization Job.
| status = job_visualization_instance.delete_visualization_job()
+ |
+
+
+
+
+
Parameter Description
+
Table 1 VisualizationJob request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+visualization_id
+ |
+Yes
+ |
+String
+ |
+ID of a visualization job
+ |
+
+
+
+
+
+
Table 2 delete_visualization_job response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Resource and Engine Specifications
+
+
+
diff --git a/docs/modelarts/sdk-ref/modelarts_04_0190.html b/docs/modelarts/sdk-ref/modelarts_04_0190.html
new file mode 100644
index 00000000..9e660b07
--- /dev/null
+++ b/docs/modelarts/sdk-ref/modelarts_04_0190.html
@@ -0,0 +1,199 @@
+
+
+Querying a Built-in Algorithm
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+algo_info = Estimator.get_built_in_algorithms(modelarts_session=session)
+ |
+
+
+
+
Parameter Description
+
Table 1 get_built_in_algorithms request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+
+
+
+
+
Table 2 get_built_in_algorithms response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_code
+ |
+String
+ |
+Error code when the API fails to be called. For details, see .
+This parameter is not included when the API call succeeds.
+ |
+
+model_total_count
+ |
+Integer
+ |
+Number of models
+ |
+
+models
+ |
+JSON Array
+ |
+Parameter list of a model
+ |
+
+is_success
+ |
+Boolean
+ |
+Whether the API call succeeds
+ |
+
+
+
+
+
+
Table 3 models parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_id
+ |
+Integer
+ |
+Model ID
+ |
+
+model_name
+ |
+String
+ |
+Model name
+ |
+
+model_usage
+ |
+Integer
+ |
+Model usage. The options are as follows:
+- 1: image classification
- 2: object class and location
- 3: image semantic segmentation
- 4: natural language processing
+ |
+
+model_precision
+ |
+String
+ |
+Model precision
+ |
+
+model_size
+ |
+Long
+ |
+Model size, in bytes
+ |
+
+model_train_dataset
+ |
+String
+ |
+Model training dataset
+ |
+
+model_dataset_format
+ |
+String
+ |
+Dataset format required by a model
+ |
+
+model_description_url
+ |
+String
+ |
+URL of the model description
+ |
+
+parameter
+ |
+JSON Array
+ |
+Running parameters of a model. It is a collection of label-value pairs. This parameter is a container environment variable when a job uses a custom image. For details, see the sample request.
+ |
+
+create_time
+ |
+Long
+ |
+Time when a model is created
+ |
+
+engine_id
+ |
+Long
+ |
+Engine ID of a model
+ |
+
+engine_name
+ |
+String
+ |
+Engine name of a model
+ |
+
+engine_version
+ |
+String
+ |
+Engine version of a model
+ |
+
+
+
+
+
+
Querying the List of Resource Flavors
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+algo_info = Estimator.get_train_instance_types(modelarts_session=session)
+ |
+
+
+
+
Parameter Description
+
Table 1 Request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+
+
+
+
+
+
Table 2 Successful response parametersType
+ |
+Description
+ |
+
|---|
+
+List
+ |
+List of resource flavor attributes
+ |
+
+
+
+
+
+
Table 3 Failed response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_code
+ |
+String
+ |
+Error code when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+
+
+
+
Querying the List of Engine Types
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+from modelarts.estimator import Estimator
+session = Session()
+engine_list = Estimator.get_framework_list(modelarts_session=session)
+ |
+
+
+
+
Parameter Description
+
Table 1 get_framework_list parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+modelarts_session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+
+
+
+
+
+
Table 2 Successful response parameters of get_framework_listType
+ |
+Description
+ |
+
|---|
+
+List
+ |
+List of engine flavor attributes. For details, see Table 3.
+ |
+
+
+
+
+
+
Table 3 framework_list parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+framework_type
+ |
+String
+ |
+Engine type
+ |
+
+framework_version
+ |
+String
+ |
+Engine version
+ |
+
+
+
+
+
+
Table 4 Failed response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_code
+ |
+String
+ |
+Error code when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+
+
+
+
Model Management
+
+
+
diff --git a/docs/modelarts/sdk-ref/modelarts_04_0194.html b/docs/modelarts/sdk-ref/modelarts_04_0194.html
new file mode 100644
index 00000000..da5fd907
--- /dev/null
+++ b/docs/modelarts/sdk-ref/modelarts_04_0194.html
@@ -0,0 +1,522 @@
+
+
+Importing a Model
+The model import function covers the following aspects:
+
- Initialize the existing model and create a model object based on the model ID.
- Create a model. For details about the attributes of the created model, see Querying the Details About a Model.
+
Sample Code
In ModelArts notebook, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+from modelarts.model import Model
+from modelarts.config.model_config import ServiceConfig,Params,Dependencies,Packages
+session = Session()
+ |
+
+
+
- Method 1: Initialize an existing model.
| model_instance = Model(session, model_id="input your model id")
+ |
+
+
+
- Method 2: Create a model.
1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12 | model_instance = Model(
+ session,
+ model_name="input model name", # Model name
+ model_version="1.0.0", # Model version
+ source_location=model_location, # OBS path to a model file, for example, obs://your_obs_bucket/mode_file_path
+ model_type="MXNet", # Model type
+ model_algorithm="image_classification", # Model algorithm
+ execution_code="OBS_PATH",
+ input_params=input_params, # For details, see the input_params format description.
+ output_params=output_params, # For details, see the output_params format description.
+ dependencies=dependencies, # For details, see the dependencies format description.
+ apis=apis)
+ |
+
+
- Definition formats of input_params and output_params parameter groups used in method 2
The SDK provides the definition of input_params and output_params parameter groups. The types of input_params and output_params are list, and those of the tuple objects in the list are Params.
+The following uses input_params as an example:
+1
+2
+3
+4
+5
+6
+7
+8
+9 | input_params = [] # The type of input_params is list. Multiple objects of the Params type can be stored.
+input_params1 = Params(
+ url='url', # URL
+ param_name='param_name', # Parameter name
+ param_type='param_type', # Parameter type
+ min='min',
+ max='max',
+ param_desc='param_desc')
+input_params.append(input_params1)
+ |
+
+
- Definition formats of the dependencies parameter group used in method 2
The SDK provides the definition of the dependencies parameter group. The type of dependencies is list, and those of the tuple objects in the list are Dependencies.
+The code is as follows:
+ | dependencies = []
+dependency1 = Dependencies(
+ installer="pip", # Installation mode. pip is supported.
+ packages=packages) # Collection of dependency packages. For details about the definition format, see the definition of packages.
+dependencies.append(dependency1)
+ |
+
+
- Definition formats of the package parameter group used in method 2
The SDK provides the definition of the packages parameter group. The type of packages is list, and those of the tuple objects in the list are Packages.
+The code is as follows:
+ | packages = []
+package1 = Packages(
+ package_name="package_name", # Package name
+ package_version="version", # Package version
+ restraint="restraint")
+packages.append(package1)
+ |
+
+
The following is an example of creating a dependencies parameter group:
+
dependencies = []
+packages = [{
+ "package_name": "numpy",
+ "package_version": "1.15.0",
+ "restraint": "EXACT"},
+ {
+ "package_name": "h5py",
+ "package_version": "2.8.0",
+ "restraint": "EXACT"}
+ ]
+dependency = Dependencies(installer="pip", packages=packages)
+dependencies.append(dependency)
+
+
+
+
+
Parameter Description
+
Table 1 Parameters for initializing a modelParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+model_id
+ |
+Yes
+ |
+String
+ |
+Model ID
+ |
+
+
+
+
+
+
Table 2 Parameters for creating a modelParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+model_name
+ |
+No
+ |
+String
+ |
+Name of a model, which contains 1 to 64 characters that consist of only letters, digits, underscores (_), and hyphens (-). It must start with a letter. If this parameter is not specified, the system automatically generates a model name.
+ |
+
+model_version
+ |
+Yes
+ |
+String
+ |
+Model version in the format of Digit.Digit.Digit. The value range of the digits is [0, 99]. The version number cannot start with 0, for example, 01.01.01.
+ |
+
+publish
+ |
+No
+ |
+Bool
+ |
+Whether to publish a model. The options are as follows:
+- True: Publish the model. (Default value)
- False: Do not publish the model.
+ |
+
+source_location_type
+ |
+No
+ |
+String
+ |
+Model location type. The options are as follows:
+- OBS_SOURCE: OBS path. (Default value)
- LOCAL_SOURCE: local path.
+ |
+
+source_location
+ |
+Yes
+ |
+String
+ |
+Path (parent directory) of the model file
+- If source_location_type is set to OBS_SOURCE, the model file path is an OBS path in the format of /obs_bucketname/.../model_file_parent_dir/.
- If source_location_type is set to LOCAL_SOURCE, the model file path is a local path in the format of /local_path/.../model_file_parent_dir/.
+ |
+
+environment
+ |
+No
+ |
+Environment instance
+ |
+Environment required for normal model running, such as the Python or TensorFlow version
+ |
+
+source_job_id
+ |
+No
+ |
+String
+ |
+ID of the source training job. If the model is generated from a training job, specify this parameter for source tracing. If the model is imported from a third-party meta model, leave this parameter blank. By default, this parameter is left blank.
+ |
+
+source_job_version
+ |
+No
+ |
+String
+ |
+Version of the source training job. If the model is generated from a training job, specify this parameter for source tracing. If the model is imported from a third-party meta model, leave this parameter blank. By default, this parameter is left blank.
+ |
+
+source_type
+ |
+No
+ |
+String
+ |
+Model source type. Currently, the value can only be auto, which indicates an ExeML model (model download is not allowed). If the model is deployed by a training job, leave this parameter blank. By default, this parameter is left blank.
+ |
+
+model_type
+ |
+Yes
+ |
+String
+ |
+Model type. The value can be TensorFlow, MXNet, Spark_MLlib, Scikit_Learn, XGBoost, MindSpore, Image, or PyTorch.
+ |
+
+model_algorithm
+ |
+No
+ |
+String
+ |
+Model algorithm. If the algorithm has been configured in the model configuration file, this parameter can be left blank. For example, predict_analysis, object_detection, or image_classification.
+ |
+
+description
+ |
+No
+ |
+String
+ |
+Model description, which contains a maximum of 100 characters and cannot contain the following special characters: !<>=&'"
+ |
+
+execution_code
+ |
+No
+ |
+String
+ |
+OBS path to the execution script. The inference script must be stored in the model directory in the path where the model is located. For details, see the source_location parameter. The script name is fixed to customize_service.py.
+ |
+
+input_params
+ |
+No
+ |
+params array
+ |
+List of input parameters for model inference. By default, this parameter is left blank. If the apis information has been configured in the model configuration file, you do not need to set this parameter. The backend automatically reads the input parameters from the apis field in the configuration file.
+ |
+
+output_params
+ |
+No
+ |
+params array
+ |
+List of output parameters for model inference. By default, this parameter is left blank. If the apis information has been configured in the model configuration file, you do not need to set this parameter. The backend automatically reads the output parameters from the apis field in the configuration file.
+ |
+
+dependencies
+ |
+No
+ |
+dependency array
+ |
+Dependency package required for running the code and model. By default, this parameter is left blank. If the dependencies information has been configured in the model configuration file, you do not need to set this parameter. The backend automatically reads the dependencies to be installed from the dependencies field in the configuration file.
+ |
+
+apis
+ |
+No
+ |
+String
+ |
+List of inference APIs provided by a model. By default, this parameter is left blank. If the apis information has been configured in the model configuration file, you do not need to set this parameter. The backend automatically reads the configured inference API information from the apis field in the configuration file.
+ |
+
+
+
+
+
+
Table 3 params parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+url
+ |
+Yes
+ |
+String
+ |
+Request path of a model inference API
+ |
+
+param_name
+ |
+Yes
+ |
+String
+ |
+Parameter name, which contains a maximum of 64 characters
+ |
+
+param_type
+ |
+Yes
+ |
+String
+ |
+Basic parameter types of JSON schema, including string, object, array, boolean, number, and integer
+ |
+
+min
+ |
+No
+ |
+Double
+ |
+This parameter is optional when param_type is set to int or float. By default, this parameter is left blank.
+ |
+
+max
+ |
+No
+ |
+Double
+ |
+This parameter is optional when param_type is set to int or float. By default, this parameter is left blank.
+ |
+
+param_desc
+ |
+No
+ |
+String
+ |
+Parameter description, which contains a maximum of 100 characters. By default, this parameter is left blank.
+ |
+
+
+
+
+
+
Table 4 dependency parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+installer
+ |
+Yes
+ |
+String
+ |
+Installation mode. Only pip is supported.
+ |
+
+packages
+ |
+Yes
+ |
+package array
+ |
+Collection of dependency packages
+ |
+
+
+
+
+
+
Table 5 package parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+package_name
+ |
+Yes
+ |
+String
+ |
+Name of a dependency package
+ |
+
+package_version
+ |
+No
+ |
+String
+ |
+Version of a dependency package
+ |
+
+restraint
+ |
+No
+ |
+String
+ |
+Version filtering condition. This parameter is mandatory only when package_version exists. Possible values are as follows:
+- EXACT: the specified version
- ATLEAST: not earlier than the specified version
- ATMOST: not later than the specified version
+ |
+
+
+
+
+
+
Table 6 create_model response parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_instance
+ |
+Yes
+ |
+Model object
+ |
+Model object, which can be any of the APIs described in this chapter
+ |
+
+
+
+
+
Example of creating a model in a handwritten digit recognition project using MXNet:
1
+2
+3
+4
+5
+6
+7
+8
+9 | from modelarts.session import Session
+from modelarts.model import Model
+session = Session()
+model_instance = Model(session,
+ model_name = "digit recognition",
+ model_version = "1.0.0",
+ source_location = model_location,
+ model_type = "MXNet",
+ model_algorithm = "image_classification")
+ |
+
+
+
+
+
+
Obtaining the Model List
+Sample Code
In ModelArts notebook, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Scenario 1: Query all models of a user.
+
| from modelarts.session import Session
+from modelarts.model import Model
+session = Session()
+model_list = Model.get_model_list(session)
+ |
+
+
+
- Scenario 2: Query a model based on the search criteria.
+
| from modelarts.session import Session
+from modelarts.model import Model
+session = Session()
+model_list = Model.get_model_list(session, model_status="published", model_name="digit", order="desc")
+ |
+
+
+
+
Parameter Description
+
Table 1 Query parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_name
+ |
+No
+ |
+String
+ |
+Model name. Fuzzy match is supported.
+ |
+
+model_version
+ |
+No
+ |
+String
+ |
+Model version
+ |
+
+model_status
+ |
+No
+ |
+String
+ |
+Model status. The value can be publishing, published, or failed. You can query jobs based on their statuses.
+ |
+
+description
+ |
+No
+ |
+String
+ |
+Description. Fuzzy match is supported.
+ |
+
+offset
+ |
+No
+ |
+Integer
+ |
+Index of the page to be queried. Default value: 0
+ |
+
+limit
+ |
+No
+ |
+Integer
+ |
+Maximum number of records returned on each page. Default value: 280
+ |
+
+sort_by
+ |
+No
+ |
+String
+ |
+Sorting mode. The value can be create_at, model_version, or model_size. Default value: create_at
+ |
+
+order
+ |
+No
+ |
+String
+ |
+Sorting order. The value can be asc or desc, indicating the ascending or descending order. Default value: desc
+ |
+
+workspace_id
+ |
+No
+ |
+String
+ |
+Workspace ID. Default value: 0
+ |
+
+
+
+
+
+
Table 2 get_model_list parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+total_count
+ |
+Integer
+ |
+Total number of models that meet the search criteria when no paging is implemented
+ |
+
+count
+ |
+Integer
+ |
+Number of models
+ |
+
+models
+ |
+model array
+ |
+Model metadata
+ |
+
+
+
+
+
+
+
Table 3 model parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_id
+ |
+String
+ |
+Model ID
+ |
+
+model_name
+ |
+String
+ |
+Model name
+ |
+
+model_version
+ |
+String
+ |
+Model version
+ |
+
+model_type
+ |
+String
+ |
+Model type. The value can be TensorFlow, MXNet, Spark_MLlib, Scikit_Learn, XGBoost, MindSpore, Image, or PyTorch.
+ |
+
+model_size
+ |
+Long
+ |
+Model size, in bytes
+ |
+
+tenant
+ |
+String
+ |
+Tenant to whom a model belongs
+ |
+
+project
+ |
+String
+ |
+Project to which a model belongs
+ |
+
+owner
+ |
+String
+ |
+User to whom a model belongs
+ |
+
+create_at
+ |
+Long
+ |
+Time when a model is created, in milliseconds calculated from 1970.1.1 0:0:0 UTC
+ |
+
+description
+ |
+String
+ |
+Model description
+ |
+
+source_type
+ |
+String
+ |
+Model source type. This parameter is valid only when the model is deployed by an ExeML project. The value is auto.
+ |
+
+
+
+
+
Obtaining the Model Object List
+Sample Code
In ModelArts notebook, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Scenario 1: Obtain all model objects of a user.
| from modelarts.session import Session
+from modelarts.model import Model
+session = Session()
+model_object_list = Model.get_model_object_list(session)
+ |
+
+
+
- Scenario 2: Obtain a model object based on the search criteria.
| from modelarts.session import Session
+from modelarts.model import Model
+session = Session()
+model_object_list = Model.get_model_object_list(session, model_status="published", model_name="digit", order="desc")
+ |
+
+
+
+
Parameters
- After you obtain a model list, the size of the list is equal to the number of models that have been deployed by the current user. Each element in the list is a model object. The object attributes are the same as those in Querying the Details About a Model. For example, in model_list = [model_instance1, model_instance2, model_instance3 ...], each model_instance in the list is a model API that can be called.
- The model list can be obtained based on the query parameters. Table 1 describes the query parameters.
- When you obtain a model list, details about the models are returned. See Table 2 and Table 3.
- A maximum of 150 model objects can be obtained.
+
+
Table 1 Query parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_name
+ |
+No
+ |
+String
+ |
+Model name. Fuzzy match is supported.
+ |
+
+model_version
+ |
+No
+ |
+String
+ |
+Model version
+ |
+
+model_status
+ |
+No
+ |
+String
+ |
+Model status. The value can be publishing, published, or failed. You can obtain models based on their statuses.
+ |
+
+description
+ |
+No
+ |
+String
+ |
+Description. Fuzzy match is supported.
+ |
+
+offset
+ |
+No
+ |
+Integer
+ |
+Index of the page to be obtained. Default value: 0
+ |
+
+limit
+ |
+No
+ |
+Integer
+ |
+Maximum number of records returned on each page. Default value: 280
+ |
+
+sort_by
+ |
+No
+ |
+String
+ |
+Sorting mode. The value can be create_at, model_version, or model_size. Default value: create_at
+ |
+
+order
+ |
+No
+ |
+String
+ |
+Sorting order. The value can be asc or desc, indicating the ascending or descending order. Default value: desc
+ |
+
+workspace_id
+ |
+No
+ |
+String
+ |
+Workspace ID. Default value: 0
+ |
+
+
+
+
+
+
Table 2 get_model_list parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+total_count
+ |
+Integer
+ |
+Total number of models that meet the search criteria when no paging is implemented
+ |
+
+count
+ |
+Integer
+ |
+Number of models
+ |
+
+models
+ |
+model array
+ |
+Model metadata
+ |
+
+
+
+
+
+
+
Table 3 model parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_id
+ |
+String
+ |
+Model ID
+ |
+
+model_name
+ |
+String
+ |
+Model name
+ |
+
+model_version
+ |
+String
+ |
+Model version
+ |
+
+model_type
+ |
+String
+ |
+Model type. The value can be TensorFlow, MXNet, Spark_MLlib, Scikit_Learn, XGBoost, MindSpore, Image, or PyTorch.
+ |
+
+model_size
+ |
+Long
+ |
+Model size, in bytes
+ |
+
+tenant
+ |
+String
+ |
+Tenant to whom a model belongs
+ |
+
+project
+ |
+String
+ |
+Project to which a model belongs
+ |
+
+owner
+ |
+String
+ |
+User to which a model belongs
+ |
+
+create_at
+ |
+Long
+ |
+Time when a model is created, in milliseconds calculated from 1970.1.1 0:0:0 UTC
+ |
+
+description
+ |
+String
+ |
+Model description
+ |
+
+source_type
+ |
+String
+ |
+Model source type. This parameter is valid only when the model is deployed by an ExeML project. The value is auto.
+ |
+
+
+
+
+
Querying the Details About a Model
+You can use the API to query the information about a model object.
+
Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Query the details about the model created in Importing a Model.
| from modelarts.session import Session
+from modelarts.model import Model
+session = Session()
+model_instance = Model(session, model_id="input your model_id")
+model_info = model_instance.get_model_info()
+ |
+
+
- Method 2: Query the details about a model object returned in Obtaining the Model Object List.
| from modelarts.session import Session
+from modelarts.model import Model
+session = Session()
+model_object_list = Model.get_model_object_list(session)
+model_instance = model_object_list[0]
+model_info = model_instance.get_model_info()
+ |
+
+
+
+
Parameter Description
+
Table 1 get_model_info response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_id
+ |
+String
+ |
+Model ID
+ |
+
+model_name
+ |
+String
+ |
+Model name
+ |
+
+model_version
+ |
+String
+ |
+Model version
+ |
+
+tenant
+ |
+String
+ |
+Tenant
+ |
+
+project
+ |
+String
+ |
+Project
+ |
+
+owner
+ |
+String
+ |
+User
+ |
+
+create_at
+ |
+Long
+ |
+Time when a model is created, in milliseconds calculated from 1970.1.1 0:0:0 UTC
+ |
+
+source_location
+ |
+String
+ |
+OBS path where a model resides
+ |
+
+source_job_id
+ |
+String
+ |
+ID of the source training job
+ |
+
+source_job_version
+ |
+String
+ |
+Version of the source training job
+ |
+
+source_type
+ |
+String
+ |
+Type of a model source
+- If a model is deployed by an ExeML project, the value is auto.
- If a model is deployed by a training job or OBS model file, this parameter is left blank.
+ |
+
+model_type
+ |
+String
+ |
+Model type. The value can be TensorFlow, MXNet, Spark_MLlib, Scikit_Learn, XGBoost, MindSpore, Image, or PyTorch.
+ |
+
+model_size
+ |
+Long
+ |
+Model size, in bytes
+ |
+
+model_status
+ |
+String
+ |
+Model status. The value can be publishing, published, or failed.
+ |
+
+description
+ |
+String
+ |
+Model description
+ |
+
+execution_code
+ |
+String
+ |
+OBS path for storing the execution code. The name of the execution code file is fixed to customize_service.py.
+ |
+
+schema_doc
+ |
+String
+ |
+Download address of the model schema file
+ |
+
+image_address
+ |
+String
+ |
+Execution image path of a model. Before the image is built, that is, before a model has been published as a service, this parameter is left blank.
+ |
+
+input_params
+ |
+params array
+ |
+Collection of input parameters of a model. By default, this parameter is left blank.
+ |
+
+output_params
+ |
+params array
+ |
+Collection of output parameters of a model. By default, this parameter is left blank.
+ |
+
+dependencies
+ |
+dependency array
+ |
+Package required for running the code and model
+ |
+
+model_metrics
+ |
+String
+ |
+Model evaluation parameter. This parameter is returned only when source_job_id and source_job_version are assigned values and the corresponding training job has evaluation results.
+ |
+
+apis
+ |
+String
+ |
+All apis input and output parameters of the model
+ |
+
+
+
+
+
+
Table 2 params parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+url
+ |
+String
+ |
+API URL
+ |
+
+param_name
+ |
+String
+ |
+Parameter name, which contains a maximum of 64 characters
+ |
+
+param_type
+ |
+String
+ |
+Parameter type. The value can be int, string, float, timestamp, date, or file.
+ |
+
+min
+ |
+Number
+ |
+When param_type is set to int or float and min is set during model creation, the value will be returned. By default, this parameter is left blank.
+ |
+
+max
+ |
+Number
+ |
+When param_type is set to int or float and max is set during model creation, the value will be returned. By default, this parameter is left blank.
+ |
+
+param_desc
+ |
+String
+ |
+Parameter description, which contains a maximum of 100 characters. By default, this parameter is left blank.
+ |
+
+
+
+
+
+
Table 3 dependency parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+installer
+ |
+String
+ |
+Installer
+ |
+
+packages
+ |
+package array
+ |
+Collection of dependency packages
+ |
+
+
+
+
+
+
Table 4 package parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+package_name
+ |
+String
+ |
+Name of a dependency package
+ |
+
+package_version
+ |
+String
+ |
+Version of a dependency package
+ |
+
+restraint
+ |
+String
+ |
+Version filtering criterion. The options are as follows:
+- EXACT: the specified version
- ATLEAST: not earlier than the specified version
- ATMOST: not later than the specified version
+ |
+
+
+
+
+
+
Table 5 metric parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+f1
+ |
+Yes
+ |
+Double
+ |
+Mean
+ |
+
+recall
+ |
+Yes
+ |
+Double
+ |
+Recall
+ |
+
+precision
+ |
+Yes
+ |
+Double
+ |
+Precision
+ |
+
+accuracy
+ |
+Yes
+ |
+Double
+ |
+Accuracy
+ |
+
+
+
+
+
+
Deleting a Model
+You can use the API to delete a model object.
+
Sample Code
In a ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Delete the model created in Importing a Model.
| from modelarts.session import Session
+from modelarts.model import Model
+session = Session()
+model_instance = Model(session, model_id="input your model_id")
+model_instance.delete_model()
+ |
+
+
+
- Method 2: Delete the model object returned in Obtaining the Model Object List.
| from modelarts.session import Session
+from modelarts.model import Model
+session = Session()
+model_object_list = Model.get_model_object_list(session)
+model_instance = model_object_list[0]
+model_instance.delete_model()
+ |
+
+
+
+
Service Management
+
+
+
diff --git a/docs/modelarts/sdk-ref/modelarts_04_0200.html b/docs/modelarts/sdk-ref/modelarts_04_0200.html
new file mode 100644
index 00000000..d5c7ee27
--- /dev/null
+++ b/docs/modelarts/sdk-ref/modelarts_04_0200.html
@@ -0,0 +1,14 @@
+
+
+Service Management Overview
+Service management indicates deploying a model that has been successfully created as a real-time. This feature provides functions such as real-time prediction, service details query, and service log query.
+
The real-time services include predictor and transformer, both of which provide the functions described in the following sections. This chapter uses predictor as an example.
+
The sample code in this chapter is implemented in ModelArts notebook instances. If the code is used in other development environments, the session needs to be authenticated. For details about session authentication, see Session Authentication.
+
+
Deploying a Real-Time Service
+Real-time service deployment covers the following aspects:
+
- Initialize a real-time service.
- Deploy a real-time service predictor.
- Deploy a batch service transformer.
+
The service object predictor is returned after deployment. The attributes of the service object include all functions described in this chapter.
+
Sample Code
In ModelArts notebook, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Initialize the predictor that has been deployed as a real-time service.
| from modelarts.session import Session
+from modelarts.model import Predictor
+session = Session()
+predictor_instance = Predictor(session, service_id="input your service_id")
+ |
+
+
+
+
- Method 3: Deploy a batch service transformer.
| transformer = model_instance.deploy_transformer(
+ service_name="input service transformer name",
+ infer_type="batch",
+ vpc_id="vpc_id",
+ subnet_network_id="subnet_network_id ",
+ security_group_id="security_group_id",
+ configs=configs) # transformer configuration parameter. For details, see the format description of the configs parameter.
+ |
+
+
+
- Definition formats of the configs parameter group used for deploying a real-time service predictor and a batch service transformer
- Deploying a real-time service predictor:
The SDK provides the definition of the configs parameter. The type of configs is list, and those of the tuple objects in the list are ServiceConfig. The code is as follows:
+ 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14 | configs = []
+service_config1 = ServiceConfig(
+ model_id="model_id1",
+ weight="70",
+ specification="specification",
+ instance_count=2,
+ envs=envs) # Environment variable value, for example, envs = {"model_name":"mxnet-model-1", "load_epoch":"0"}
+service_config2 = ServiceConfig(
+ model_id="model_id2",
+ weight="30",
+ specification="specification",
+ instance_count=2,
+ envs=envs) # Environment variable value, for example, envs = {"model_name":"mxnet-model-1", "load_epoch":"0"}
+configs.append(service_config1, service_config2)
+ |
+
+
- Deploying a batch service transformer:
The SDK provides the definition of the configs parameter. The type of configs is list, and those of the tuple objects in the list are TransformerConfig. The code is as follows:
+ 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12 | configs = []
+transformer_config1 = TransformerConfig(
+ model_id="model_id",
+ specification="specification",
+ instance_count=2,
+ src_path="src_path",
+ dest_path="dest_path",
+ req_uri="req_uri",
+ mapping_type="mapping_type",
+ mapping_rule="mapping_rule",
+ envs=envs) # Environment variable value, for example, envs = {"model_name":"mxnet-model-1", "load_epoch":"0"}
+configs.append(transformer_config1)
+ |
+
+
+
+
+
Parameter Description
+
Table 1 Parameter descriptionParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+service_id
+ |
+Yes
+ |
+String
+ |
+Service ID, which can be obtained from the real-time service on the ModelArts management console
+ |
+
+session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+
+
+
+
+
Table 2 Parameters for deploying the predictor and transformerParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+service_name
+ |
+No
+ |
+String
+ |
+Service name, which consists of 1 to 64 characters. It must start with a letter. Only letters, digits, hyphens (-), and underscores (_) are allowed.
+ |
+
+description
+ |
+No
+ |
+String
+ |
+Service description, which contains a maximum of 100 characters. By default, this parameter is left blank.
+ |
+
+infer_type
+ |
+No
+ |
+String
+ |
+Inference mode. The value can be real-time or batch. The default value is real-time.
+- real-time: real-time service. A model is deployed as a web service and provides real-time test UI and monitoring capabilities. The service keeps running.
- batch: batch service. A batch service can perform inference on batch data and automatically stops after data processing is completed.
+ |
+
+vpc_id
+ |
+No
+ |
+String
+ |
+ID of the VPC to which a real-time service instance is deployed. By default, this parameter is left blank. In this case, ModelArts allocates a dedicated VPC to each user, and users are isolated from each other. If you need to access other service components in the VPC of the service instance, set this parameter to the ID of the corresponding VPC.
+Once a VPC is configured, it cannot be modified. When vpc_id and cluster_id are configured, only the dedicated cluster parameter takes effect.
+ |
+
+subnet_network_id
+ |
+No
+ |
+String
+ |
+ID of a subnet. By default, this parameter is left blank. This parameter is mandatory when vpc_id is configured. Enter the network ID displayed in the subnet details on the VPC management console. A subnet provides dedicated network resources that are isolated from other networks.
+ |
+
+security_group_id
+ |
+No
+ |
+String
+ |
+Security group. By default, this parameter is left blank. This parameter is mandatory when vpc_id is configured. A security group is a virtual firewall that provides secure network access control policies for service instances. A security group must contain at least one inbound rule to permit the requests whose protocol is TCP, source address is 0.0.0.0/0, and port number is 8080.
+ |
+
+configs
+ |
+Yes
+ |
+configs parameters of predictor and transformer
+ |
+Model running configurations
+- When infer_type is set to batch, only one model can be configured.
- When infer_type is set to real-time, you can configure multiple models and assign traffic weights based on service requirements. The version numbers of the models must be different.
+ |
+
+schedule
+ |
+No
+ |
+schedule array
+ |
+Service scheduling configuration, which can be configured only for real-time services. By default, this parameter is not used. Services run for a long time. For details, see Table 6.
+ |
+
+
+
+
+
+
Table 3 configs parameters of predictorParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_id
+ |
+Yes
+ |
+String
+ |
+Model ID. You can obtain the value by calling the API described in Obtaining the Model List or from the ModelArts management console.
+ |
+
+weight
+ |
+Yes
+ |
+Integer
+ |
+Weight of traffic allocated to a model. This parameter is mandatory only when infer_type is set to real-time. The sum of multiple weights must be equal to 100. If multiple model versions are configured in a real-time service and different traffic weights are set, ModelArts continuously accesses the prediction API of the service and forwards prediction requests to the model instances of the corresponding versions based on the weights.
+{
+"service_name": "mnist",
+"description": "mnist service",
+"infer_type": "real-time",
+"config": [
+{
+"model_id": "xxxmodel-idxxx",
+"weight": "70",
+"specification": "modelarts.vm.cpu.2u",
+"instance_count": 1,
+"envs":
+{
+"model_name": "mxnet-model-1",
+"load_epoch": "0"
+}
+},
+{
+"model_id": "xxxxxx",
+"weight": "30",
+"specification": "modelarts.vm.cpu.2u",
+"instance_count": 1
+}
+]
+}
+ |
+
+specification
+ |
+Yes
+ |
+String
+ |
+Resource specifications.
+ |
+
+instance_count
+ |
+Yes
+ |
+Integer
+ |
+Number of instances deployed in a model. The maximum number of instances is 5. To use more instances, submit a service ticket.
+ |
+
+envs
+ |
+No
+ |
+Map<String, String>
+ |
+(Optional) Environment variable key-value pair required for running a model. By default, this parameter is left blank.
+ |
+
+
+
+
+
+
Table 4 configs parameters of transformerParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_id
+ |
+Yes
+ |
+String
+ |
+Model ID
+ |
+
+specification
+ |
+Yes
+ |
+String
+ |
+Resource flavor. Currently, modelarts.vm.cpu.2u and modelarts.vm.gpu.p4 are available.
+ |
+
+instance_count
+ |
+Yes
+ |
+Integer
+ |
+Number of instances deployed in a model. The value range during the closed beta test is [1, 2].
+ |
+
+envs
+ |
+No
+ |
+Map<String, String>
+ |
+(Optional) Environment variable key-value pair required for running a model. By default, this parameter is left blank.
+ |
+
+src_path
+ |
+Yes
+ |
+String
+ |
+OBS path of the input data of a batch job
+ |
+
+dest_path
+ |
+Yes
+ |
+String
+ |
+OBS path of the output data of a batch job
+ |
+
+req_uri
+ |
+Yes
+ |
+String
+ |
+Inference API called in a batch task, that is, the RESTful API exposed in the model image. You must select an API URL from the config.json file of the model for inference. If a built-in inference image of ModelArts is used, the API is displayed as /.
+ |
+
+mapping_type
+ |
+Yes
+ |
+String
+ |
+Mapping type of the input data. The value can be file or csv.
+- If you select file, each inference request corresponds to a file in the input data path. When this mode is used, req_uri of a model can have only one input parameter and the type of this parameter is file.
- If you select csv, each inference request corresponds to a row of data in the CSV file. When this mode is used, the files in the input data path can only be in CSV format and mapping_rule needs to be configured to map the index of each parameter in the inference request body to the CSV file.
+The following shows how to create a batch service whose mapping_type is set to file:
+{
+"service_name": "batchservicetest",
+"description": "",
+"infer_type": "batch",
+"config": [{
+"model_id": "598b913a-af3e-41ba-a1b5-bf065320f1e2",
+"specification": "modelarts.vm.cpu.2u",
+"instance_count": 1,
+"src_path": "https://infers-data.obs.xxx.com/xgboosterdata/",
+"dest_path": "https://infers-data.obs.xxx.com/output/",
+"req_uri": "/",
+"mapping_type": "file"
+}]
+}
+The following shows how to create a batch service whose mapping_type is set to csv:
+{
+"service_name": "batchservicetest",
+"description": "",
+"infer_type": "batch",
+"config": [{
+"model_id": "598b913a-af3e-41ba-a1b5-bf065320f1e2",
+"specification": "modelarts.vm.cpu.2u",
+"instance_count": 1,
+"src_path": "https://infers-data.obs.xxx.com/xgboosterdata/",
+"dest_path": "https://infers-data.obs.xxx.com/output/",
+"req_uri": "/",
+"mapping_type": "csv",
+"mapping_rule": {
+"type": "object",
+"properties": {
+"data": {
+"type": "object",
+"properties": {
+"req_data": {
+"type": "array",
+"items": [{
+"type": "object",
+"properties": {
+"input5": {
+"type": "number",
+"index": 0
+},
+"input4": {
+"type": "number",
+"index": 1
+},
+"input3": {
+"type": "number",
+"index": 2
+},
+"input2": {
+"type": "number",
+"index": 3
+},
+"input1": {
+"type": "number",
+"index": 4
+}
+}
+}]
+}
+}
+}
+}
+}
+}]
+}
+ |
+
+mapping_rule
+ |
+No
+ |
+Map
+ |
+Mapping between input parameters and CSV data. This parameter is mandatory only when mapping_type is set to csv. The mapping rule is similar to the input parameter definition in the config.json model configuration file. You only need to configure the index parameters under each parameter of the string, number, integer, or boolean type, and the value of this parameter to the values of the index parameters in the CSV file to send an inference request. Use commas (,) to separate multiple pieces of CSV data. The values of the index parameters start from 0. If the value of the index parameter is -1, ignore this parameter. For details, see the sample code of deploying transformer.
+The format of the inference request body described in mapping_rule is as follows:
+{
+"data": {
+"req_data": [{
+"input1": 1,
+"input2": 2,
+"input3": 3,
+"input4": 4,
+"input5": 5
+}]
+}
+}
+ |
+
+
+
+
+
+
Table 5 Parameters in the response to the request for deploying predictor and transformerParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+predictor
+ |
+Yes
+ |
+Predictor object
+ |
+Predictor object. Its attributes include all functions described in this chapter.
+ |
+
+
+
+
+
+
Table 6 schedule parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+op_type
+ |
+Yes
+ |
+String
+ |
+Scheduling type. Currently, only the value stop is supported.
+ |
+
+time_unit
+ |
+Yes
+ |
+String
+ |
+Scheduling time unit. The options are as follows:
+
+ |
+
+duration
+ |
+Yes
+ |
+Integer
+ |
+Value that maps to the time unit. For example, if the task stops after two hours, set time_unit to HOURS and duration to 2.
+ |
+
+
+
+
+
- Example of deploying a real-time predictor instance in the handwritten digit recognition project implemented by MXNet:
1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14 | from modelarts.session import Session
+from modelarts.model import Model
+from modelarts.config.model_config import ServiceConfig,TransformerConfig
+model_instance = Model(session, model_id = "input you model id")
+configs = []
+config1 = ServiceConfig(model_id="input you model id",
+ weight="100",
+ instance_count=1,
+ specification="modelarts.vm.cpu.2u",
+ envs={"input_data_name":"images",
+ "input_data_shape":"0,1,28,28",
+ "output_data_shape":"0,10"})
+configs.append(config1)
+predictor = model_instance.deploy_predictor(service_name="DigitRecognition", configs=configs)
+ |
+
+
- Example of deploying a transformer instance (batch processing) in a handwritten digit recognition project implemented by MXNet:
1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15 | from modelarts.session import Session
+from modelarts.model import Model
+from modelarts.config.model_config import ServiceConfig,TransformerConfig
+model_instance = Model(session, model_id = "input your model id")
+configs = []
+config1 = TransformerConfig(model_id="input your model id",
+ specification="modelarts.vm.cpu.2u",
+ instance_count=1,
+ envs={"input_data_name":"images","input_data_shape":"0,1,28,28","output_data_shape":"0,10"},
+ src_path="/w0403/testdigitrecognition/inferimages/",
+ dest_path="/w0403/testdigitrecognition/" ,
+ req_uri = "/",
+ mapping_type = "file")
+configs.append(config1)
+predictor = model_instance.deploy_transformer(service_name="DigitRecognition", infer_type="batch", configs=configs)
+ |
+
+
+
+
+
Querying the Details of a Service
+You can use the API to query details about a service object.
+
Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Query details about the service created in Deploying a Real-Time Service.
| from modelarts.session import Session
+from modelarts.model import Predictor
+session = Session()
+predictor_instance = Predictor(session, service_id="input your service_id")
+predictor_info = predictor_instance.get_service_info()
+ |
+
+
+
- Method 2: Query details about the service object returned in Querying the List of Service Objects.
| from modelarts.session import Session
+from modelarts.model import Predictor
+session = Session()
+predictor_object_list = Predictor.get_service_object_list(session)
+predictor_instance = predictor_object_list[0]
+predictor_info = predictor_instance.get_service_info()
+ |
+
+
+
+
Parameter Description
+
Table 1 get_service_info response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+service_id
+ |
+String
+ |
+Service ID
+ |
+
+service_name
+ |
+String
+ |
+Service name
+ |
+
+description
+ |
+String
+ |
+Service description
+ |
+
+tenant
+ |
+String
+ |
+Tenant to whom a service belongs
+ |
+
+project
+ |
+String
+ |
+Project to which a service belongs
+ |
+
+owner
+ |
+String
+ |
+User to whom a service belongs
+ |
+
+publish_at
+ |
+Number
+ |
+Latest service publishing time, in milliseconds calculated from 1970.1.1 0:0:0 UTC
+ |
+
+infer_type
+ |
+String
+ |
+Inference mode. The value can be real-time or batch.
+ |
+
+vpc_id
+ |
+String
+ |
+ID of the VPC to which a service instance belongs. This parameter is returned when the network configuration is customized.
+ |
+
+subnet_network_id
+ |
+String
+ |
+ID of the subnet where a service instance resides. This parameter is returned when the network configuration is customized.
+ |
+
+security_group_id
+ |
+String
+ |
+Security group to which a service instance belongs. This parameter is returned when the network configuration is customized.
+ |
+
+status
+ |
+String
+ |
+Service status. The value can be running, deploying, concerning, failed, stopped, or finished.
+ |
+
+error_msg
+ |
+String
+ |
+Error message. When status is failed, the deployment failure cause is returned.
+ |
+
+config
+ |
+config array corresponding to infer_type
+ |
+config array corresponding to infer_type
+Service configurations (If a service is shared, only model_id, model_name, and model_version are returned.)
+ |
+
+access_address
+ |
+String
+ |
+Access address of an inference request. This parameter is returned when infer_type is set to real-time.
+ |
+
+invocation_times
+ |
+Number
+ |
+Total number of service calls
+ |
+
+failed_times
+ |
+Number
+ |
+Number of failed service calls
+ |
+
+is_shared
+ |
+Boolean
+ |
+Whether a service is subscribed
+ |
+
+shared_count
+ |
+Number
+ |
+Number of subscriptions
+ |
+
+progress
+ |
+Integer
+ |
+Deployment progress. This parameter is returned when status is deploying.
+ |
+
+
+
+
+
+
Table 2 config parameters corresponding to real-timeParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_id
+ |
+String
+ |
+Model ID. You can obtain the value by calling the API described in Obtaining the Model List or from the ModelArts management console.
+ |
+
+model_name
+ |
+String
+ |
+Model name
+ |
+
+model_version
+ |
+String
+ |
+Model version
+ |
+
+source_type
+ |
+String
+ |
+Model source. This parameter is returned when a model is created by an ExeML project. The value is auto.
+ |
+
+status
+ |
+String
+ |
+Running status of a model instance. Possible values are as follows:
+- ready: ready (All instances have been started.)
- concerning: partially ready (Some instances are started but some are not.)
- notReady: not ready (All instances are not started.)
+ |
+
+weight
+ |
+Integer
+ |
+Traffic weight allocated to a model
+ |
+
+specification
+ |
+String
+ |
+Resource flavor. The value can be modelarts.vm.cpu.2u, modelarts.vm.gpu.p4, or modelarts.vm.ai1.a310.
+ |
+
+envs
+ |
+Map<String, String>
+ |
+Environment variable key-value pair required for running a model
+ |
+
+instance_count
+ |
+Integer
+ |
+Number of instances deployed in a model
+ |
+
+scaling
+ |
+Boolean
+ |
+Whether auto scaling is enabled
+ |
+
+
+
+
+
+
Table 3 config parameters corresponding to batchParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_id
+ |
+String
+ |
+Model ID. You can obtain the value by calling the API described in Obtaining the Model List or from the ModelArts management console.
+ |
+
+model_name
+ |
+String
+ |
+Model name
+ |
+
+model_version
+ |
+String
+ |
+Model version
+ |
+
+specification
+ |
+String
+ |
+Resource flavor. The value can be modelarts.vm.cpu.2u or modelarts.vm.gpu.p4.
+ |
+
+envs
+ |
+Map<String, String>
+ |
+Environment variable key-value pair required for running a model
+ |
+
+instance_count
+ |
+Integer
+ |
+Number of instances deployed in a model
+ |
+
+src_path
+ |
+String
+ |
+OBS path of the input data of a batch job
+ |
+
+dest_path
+ |
+String
+ |
+OBS path of the output data of a batch job
+ |
+
+req_uri
+ |
+String
+ |
+Inference path of a batch job
+ |
+
+mapping_type
+ |
+String
+ |
+Mapping type of the input data. The value can be file or csv.
+ |
+
+mapping_rule
+ |
+Map
+ |
+Mapping between input parameters and CSV data. This parameter is returned only when mapping_type is set to csv.
+ |
+
+
+
+
+
+
Querying the Service List
+You can use the API to obtain the service list of a user.
+
Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Scenario 1: Query all services of a user.
| from modelarts.session import Session
+from modelarts.model import Predictor
+session = Session()
+predictor_list = Predictor.get_service_list(session)
+ |
+
+
+
- Scenario 2: Query a service based on the search criteria.
| from modelarts.session import Session
+from modelarts.model import Predictor
+session = Session()
+predictor_list = Predictor.get_service_list(session, service_name="digit", order="asc", offset="0", infer_type="real-time")
+ |
+
+
+
+
Parameter Description
+
Table 1 Query parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+service_id
+ |
+No
+ |
+String
+ |
+Service ID. By default, the service ID is not filtered.
+ |
+
+service_name
+ |
+No
+ |
+String
+ |
+Service name. By default, the service name is not filtered.
+ |
+
+infer_type
+ |
+No
+ |
+String
+ |
+Inference mode. The value can be real-time or batch. By default, this parameter is left blank.
+ |
+
+offset
+ |
+No
+ |
+Integer
+ |
+Start page of the paging list. Default value: 0
+ |
+
+limit
+ |
+No
+ |
+Integer
+ |
+Maximum number of records returned on each page. Default value: 1000
+ |
+
+service_status
+ |
+No
+ |
+String
+ |
+Service status. By default, the service status is not filtered. The service list can be queried based on the service status. Possible values are as follows:
+- running: The service is running properly and is being billed.
- deploying: The service is being deployed or scheduling resources are being deployed.
- concerning: An alarm is generated, indicating that the backend instance is abnormal and may be billed. For example, in the case of multiple instances, some instances are normal, but some are not. A normal instance is billed but is in the concerning status.
- failed: The service fails to be deployed. For details about the failure cause, see the event and log.
- stopped: The service has been stopped.
- finished: This status is displayed only for the batch service, indicating that the service running is completed.
+ |
+
+sort_by
+ |
+No
+ |
+String
+ |
+Sorting mode. The value can be publish_at or service_name. Default value: publish_at
+ |
+
+order
+ |
+No
+ |
+String
+ |
+Sorting order. The value can be asc or desc, indicating the ascending or descending order. Default value: desc
+ |
+
+model_id
+ |
+No
+ |
+String
+ |
+Model ID. By default, the model ID is not filtered.
+ |
+
+
+
+
+
+
+
Table 2 get_service_list response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+total_count
+ |
+Integer
+ |
+Total number of services that meet the search criteria when no paging is implemented
+ |
+
+count
+ |
+Integer
+ |
+Number of services in the query result. If offset and limit are not set, the values of count and total are the same.
+ |
+
+services
+ |
+service array
+ |
+Collection of the queried services
+ |
+
+
+
+
+
+
Table 3 service parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+service_id
+ |
+String
+ |
+Service ID
+ |
+
+service_name
+ |
+String
+ |
+Service name
+ |
+
+description
+ |
+String
+ |
+Service description
+ |
+
+tenant
+ |
+String
+ |
+Tenant to whom a service belongs
+ |
+
+project
+ |
+String
+ |
+Project to which a service belongs
+ |
+
+owner
+ |
+String
+ |
+User to whom a service belongs
+ |
+
+publish_at
+ |
+Number
+ |
+Latest service publishing time, in milliseconds calculated from 1970.1.1 0:0:0 UTC
+ |
+
+infer_type
+ |
+String
+ |
+Inference mode. The value can be real-time or batch.
+ |
+
+status
+ |
+String
+ |
+Service status. The value can be running, deploying, concerning, failed, stopped, or finished.
+ |
+
+progress
+ |
+Integer
+ |
+Deployment progress. This parameter is returned when status is deploying.
+ |
+
+invocation_times
+ |
+Number
+ |
+Total number of service calls
+ |
+
+failed_times
+ |
+Number
+ |
+Number of failed service calls
+ |
+
+is_shared
+ |
+Boolean
+ |
+Whether a service is subscribed
+ |
+
+shared_count
+ |
+Number
+ |
+Number of subscriptions
+ |
+
+
+
+
+
Querying the List of Service Objects
+You can use the API to obtain the service object list of a user.
+
Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Scenario 1: Query all service objects of a user.
| from modelarts.session import Session
+from modelarts.model import Predictor
+session = Session()
+predictor_list_object_resp = Predictor.get_service_object_list(session)
+ |
+
+
+
- Scenario 2: Query a service object based on the search criteria.
| from modelarts.session import Session
+from modelarts.model import Predictor
+session = Session()
+predictor_object_list = Predictor.get_service_object_list(session, service_name="digit", order="asc", offset="0", infer_type="real-time")
+ |
+
+
+
+
Parameter Description
- You can use the API to query the service list. The list size is equal to the number of services deployed by the user. Each element in the list is a predictor object. The object attributes are the same as those in service initialization.
For example, in service_list_resp = [service_instance1, service_instance2, service_instance3 ...], each service_instance in the list is a service API that can be called in the service management section.
+ - The service list can be queried based on the query parameters. Table 1 describes the query parameters.
- When the model list is queried, details about the services are returned. See Table 2 and Table 3.
+
+
Table 1 Query parameter descriptionParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+session
+ |
+Yes
+ |
+Object
+ |
+Session object. For details about the initialization method, see Session Authentication.
+ |
+
+is_show
+ |
+No
+ |
+Boolean
+ |
+Whether to print service object information. Default value: True
+ |
+
+service_id
+ |
+No
+ |
+String
+ |
+Service ID. By default, the service ID is not filtered.
+ |
+
+service_name
+ |
+No
+ |
+String
+ |
+Service name. By default, the service name is not filtered.
+ |
+
+infer_type
+ |
+No
+ |
+String
+ |
+Inference mode. The value can be real-time or batch. By default, this parameter is left blank.
+ |
+
+offset
+ |
+No
+ |
+Integer
+ |
+Start page of the paging list. Default value: 0
+ |
+
+limit
+ |
+No
+ |
+Integer
+ |
+Maximum number of records returned on each page. Default value: 1000
+ |
+
+sort_by
+ |
+No
+ |
+String
+ |
+Sorting mode. The value can be publish_at or service_name. Default value: publish_at
+ |
+
+order
+ |
+No
+ |
+String
+ |
+Sorting order. The value can be asc or desc, indicating the ascending or descending order. Default value: desc
+ |
+
+model_id
+ |
+No
+ |
+String
+ |
+Model ID. By default, the model ID is not filtered.
+ |
+
+
+
+
+
+
+
Table 2 get_service_list response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+total_count
+ |
+Integer
+ |
+Total number of services that meet the search criteria when no paging is implemented
+ |
+
+count
+ |
+Integer
+ |
+Number of services in the query result. If offset and limit are not set, the values of count and total are the same.
+ |
+
+services
+ |
+service array
+ |
+Collection of the queried services
+ |
+
+
+
+
+
+
Table 3 service parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+service_id
+ |
+String
+ |
+Service ID
+ |
+
+service_name
+ |
+String
+ |
+Service name
+ |
+
+description
+ |
+String
+ |
+Service description
+ |
+
+tenant
+ |
+String
+ |
+Tenant to whom a service belongs
+ |
+
+project
+ |
+String
+ |
+Project to which a service belongs
+ |
+
+owner
+ |
+String
+ |
+User to whom a service belongs
+ |
+
+publish_at
+ |
+Number
+ |
+Latest service publishing time, in milliseconds calculated from 1970.1.1 0:0:0 UTC
+ |
+
+infer_type
+ |
+String
+ |
+Inference mode. The value can be real-time or batch.
+ |
+
+status
+ |
+String
+ |
+Service status. The value can be running, deploying, concerning, failed, stopped, or finished.
+ |
+
+progress
+ |
+Integer
+ |
+Deployment progress. This parameter is returned when status is deploying.
+ |
+
+invocation_times
+ |
+Number
+ |
+Total number of service calls
+ |
+
+failed_times
+ |
+Number
+ |
+Number of failed service calls
+ |
+
+is_shared
+ |
+Boolean
+ |
+Whether a service is subscribed
+ |
+
+shared_count
+ |
+Number
+ |
+Number of subscriptions
+ |
+
+
+
+
+
Updating Service Configurations
+You can use the API to update the configurations of a service object.
+
Sample Code
In ModelArts notebook, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Update the configurations of the service object created in Deploying a Real-Time Service.
1
+2
+3
+4
+5
+6
+7
+8
+9 | from modelarts.session import Session
+from modelarts.model import Predictor
+from modelarts.config.model_config import ServiceConfig
+session = Session()
+predictor_instance = Predictor(session, service_id="input your service_id")
+configs = [ServiceConfig(weight="100", instance_count=1, specification="modelarts.vm.cpu.2u",model_id="input your model_id")]
+service_config = predictor_instance.update_service_config(description="description",
+ status="running",
+ configs=configs)
+ |
+
+
- Method 2: Update the configurations of the service object returned in Querying the List of Service Objects.
1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10 | from modelarts.session import Session
+from modelarts.model import Predictor
+from modelarts.config.model_config import ServiceConfig
+session = Session()
+predictor_object_list = Predictor.get_service_object_list(session)
+predictor_instance = predictor_object_list[0]
+configs = [ServiceConfig(weight="100", instance_count=1, specification="modelarts.vm.cpu.2u",model_id="input your model_id")]
+predictor_config = predictor_instance.update_service_config(description="description",
+ status="running",
+ configs=configs)
+ |
+
+
+
+
Parameter Description
+
Table 1 Parameters for deploying predictorParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+description
+ |
+No
+ |
+String
+ |
+Service description, which contains a maximum of 100 characters. If this parameter is not set, the service description is not updated.
+ |
+
+status
+ |
+No
+ |
+String
+ |
+Service status. The value can be running or stopped. If this parameter is not set, the service status is not changed. status and configs cannot be modified at the same time. If both parameters exist, modify only the status parameter.
+ |
+
+configs
+ |
+No
+ |
+predictor configs and transformer configs
+ |
+Service configurations. If this parameter is not set, the service is not updated. For details about how to generate configs, see Deploying a Real-Time Service.
+ |
+
+
+
+
+
The restrictions on updating service configurations are as follows:
+
- The specified status cannot be the same as the current service status.
- If the service status is deploying, stopping, or deleting, status cannot be set to running or configs is not allowed to configure.
- If the service status is waiting, status cannot be set to running.
- If the service status is concerning, status cannot be set to running.
+
+
+
+
Table 2 configs parameters of predictorParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_id
+ |
+Yes
+ |
+String
+ |
+Model ID. You can obtain the value by calling the API described in Obtaining the Model List or from the ModelArts management console.
+ |
+
+weight
+ |
+Yes
+ |
+Integer
+ |
+Weight of traffic allocated to a model. This parameter is mandatory only when infer_type is set to real-time. The sum of multiple weights must be equal to 100. If multiple model versions are configured in a real-time service and different traffic weights are set, ModelArts continuously accesses the prediction API of the service and forwards prediction requests to the model instances of the corresponding versions based on the weights.
+ |
+
+specification
+ |
+Yes
+ |
+String
+ |
+Resource flavor.
+ |
+
+instance_count
+ |
+Yes
+ |
+Integer
+ |
+Number of instances deployed in a model. The maximum number of instances is 5. To use more instances, submit a service ticket.
+ |
+
+envs
+ |
+No
+ |
+Map<String, String>
+ |
+(Optional) Environment variable key-value pair required for running a model. By default, this parameter is left blank.
+ |
+
+
+
+
+
+
Table 3 configs parameters of transformerParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_id
+ |
+Yes
+ |
+String
+ |
+Model ID. You can obtain the value by calling the API described in Obtaining the Model List or from the ModelArts management console.
+ |
+
+specification
+ |
+Yes
+ |
+String
+ |
+Resource flavor. Currently, modelarts.vm.cpu.2u and modelarts.vm.gpu.p4 are available.
+ |
+
+instance_count
+ |
+Yes
+ |
+Integer
+ |
+Number of instances deployed in a model. The maximum number of instances is 5. To use more instances, submit a service ticket.
+ |
+
+envs
+ |
+No
+ |
+Map<String, String>
+ |
+(Optional) Environment variable key-value pair required for running a model. By default, this parameter is left blank.
+ |
+
+src_path
+ |
+Yes
+ |
+String
+ |
+OBS path of the input data of a batch job
+ |
+
+dest_path
+ |
+Yes
+ |
+String
+ |
+OBS path of the output data of a batch job
+ |
+
+req_uri
+ |
+Yes
+ |
+String
+ |
+Inference API called in batch tasks. You must select an API URL from the config.json file of the model for inference.
+ |
+
+mapping_type
+ |
+Yes
+ |
+String
+ |
+Mapping type of the input data. The value can be file or csv.
+- If you select file, each inference request corresponds to a file in the input data path. When this mode is used, req_uri of a model can have only one input parameter and the type of this parameter is file.
- If you select csv, each inference request corresponds to a row of data in the CSV file. When this mode is used, the files in the input data path can only be in CSV format and mapping_rule needs to be configured to map the index of each parameter in the inference request body to the CSV file.
+ |
+
+mapping_rule
+ |
+No
+ |
+Map
+ |
+Mapping between input parameters and CSV data. This parameter is mandatory only when mapping_type is set to csv. The mapping rule is similar to the definition of the input parameters in the config.json file. You only need to configure the index parameters under each parameter of the string, number, integer, or boolean type, and the value of this parameter to the values of the index parameters in the CSV file to send an inference request. Use commas (,) to separate multiple pieces of CSV data. The values of the index parameters start from 0. If the value of the index parameter is -1, ignore this parameter.
+ |
+
+
+
+
+
+
Table 4 update_service_config response parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_code
+ |
+Yes
+ |
+String
+ |
+Error code when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_msg
+ |
+Yes
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+
+
+
+
Querying Service Monitoring Details
+You can use the API to query the monitoring information about a service.
+
Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Query the monitoring information about the service created in Deploying a Real-Time Service.
| from modelarts.session import Session
+from modelarts.model import Predictor
+session = Session()
+predictor_instance = Predictor(session, service_id="input your service_id")
+predictor_monitor = predictor_instance.get_service_monitor()
+ |
+
+
- Method 2: Query the monitoring information about the service object returned in Querying the List of Service Objects.
| from modelarts.session import Session
+from modelarts.model import Predictor
+session = Session()
+predictor_object_list = Predictor.get_service_object_list(session)
+predictor_instance = predictor_object_list[0]
+predictor_monitor = predictor_instance.get_service_monitor()
+ |
+
+
+
+
Parameter Description
+
Table 1 get_service_monitor response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+service_id
+ |
+String
+ |
+Service ID
+ |
+
+service_name
+ |
+String
+ |
+Service name
+ |
+
+monitors
+ |
+monitor array corresponding to infer_type of a service
+ |
+Monitoring details
+ |
+
+
+
+
+
+
+
Table 2 monitor parameters corresponding to real-timeParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_id
+ |
+String
+ |
+Model ID
+ |
+
+model_name
+ |
+String
+ |
+Model name
+ |
+
+model_version
+ |
+String
+ |
+Model version
+ |
+
+invocation_times
+ |
+Number
+ |
+Total number of model instance calls
+ |
+
+failed_times
+ |
+Number
+ |
+Number of failed model instance calls
+ |
+
+cpu_core_usage
+ |
+Float
+ |
+Number of used CPUs
+ |
+
+cpu_core_total
+ |
+Float
+ |
+Total number of CPUs
+ |
+
+cpu_memory_usage
+ |
+Integer
+ |
+Used memory, in MBs
+ |
+
+cpu_memory_total
+ |
+Integer
+ |
+Total memory, in MBs
+ |
+
+gpu_usage
+ |
+Float
+ |
+Number of used GPUs
+ |
+
+gpu_total
+ |
+Float
+ |
+Total number of GPUs
+ |
+
+
+
+
+
Querying Service Logs
+You can use the API to query the logs of a service object.
+
Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Query service logs based on the service created in Deploying a Real-Time Service.
| from modelarts.session import Session
+from modelarts.model import Predictor
+session = Session()
+predictor_instance = Predictor(session, service_id="input your service_id")
+predictor_log = predictor_instance.get_service_logs()
+ |
+
+
- Method 2: Query service logs based on the service object returned in Querying the List of Service Objects.
| from modelarts.session import Session
+from modelarts.model import Predictor
+session = Session()
+predictor_object_list = Predictor.get_service_object_list(session)
+predictor_instance = predictor_object_list[0]
+predictor_log = predictor_instance.get_service_logs()
+ |
+
+
+
+
Parameter Description
+
Table 1 get_service_logs response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+service_id
+ |
+String
+ |
+Service ID
+ |
+
+service_name
+ |
+String
+ |
+Service name
+ |
+
+logs
+ |
+log array
+ |
+Service update logs
+ |
+
+
+
+
+
+
+
Table 2 log parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+update_time
+ |
+Long
+ |
+Time when a service is updated, in milliseconds calculated from 1970.1.1 0:0:0 UTC
+ |
+
+result
+ |
+String
+ |
+Update result. The value can be SUCCESS, FAIL, or RUNNING.
+ |
+
+config
+ |
+config array
+ |
+Updated service configurations. This parameter is returned when infer_type is set to real-time.
+ |
+
+
+
+
+
+
Table 3 config parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+model_id
+ |
+String
+ |
+Model ID
+ |
+
+model_name
+ |
+String
+ |
+Model name
+ |
+
+model_version
+ |
+String
+ |
+Model version
+ |
+
+weight
+ |
+Integer
+ |
+Traffic weight allocated to a model
+ |
+
+specification
+ |
+String
+ |
+Resource flavor
+ |
+
+instance_count
+ |
+Integer
+ |
+Number of instances deployed in a model
+ |
+
+envs
+ |
+Map<String, String>
+ |
+Environment variable key-value pair required for running a model
+ |
+
+
+
+
+
+
Table 4 result parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+node_name
+ |
+String
+ |
+Name of an edge node
+ |
+
+operation
+ |
+String
+ |
+Operation type. The value can be deploy or delete.
+ |
+
+result
+ |
+Boolean
+ |
+Operation result. true indicates a successful operation, and false indicates a failed operation.
+ |
+
+
+
+
+
Delete a Service
+You can delete a service in either of the following ways:
+
+
Sample Code
In a ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
- Method 1: Delete the service created in Deploying a Real-Time Service.
| from modelarts.session import Session
+from modelarts.model import Predictor
+session = Session()
+predictor_instance = Predictor(session, service_id="input your service_id")
+predictor_instance.delete_service()
+ |
+
+
- Method 2: Delete the service object returned in Querying the List of Service Objects.
| from modelarts.session import Session
+from modelarts.model import Predictor
+session = Session()
+predictor_object_list = Predictor.get_service_object_list(session)
+predictor_instance = predictor_object_list[0]
+predictor_instance.delete_service()
+ |
+
+
+
+
Overview of OBS Management
+ModelArts SDK 1.1.3 supports OBS management, including uploading and downloading files and folders. The operations are as follows:
+
+
Uploading a File to OBS
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+session = Session()
+session.obs.upload_file(src_local_file='/home/ma-user/file1.txt', dst_obs_dir='obs://bucket-name/dir1/')
+ |
+
+
+
+
After the sample code is executed, the local source file file1.txt is uploaded to the dir1 folder in the bucket-name bucket. The path is obs://bucket-name/dir1/file1.txt. The bucket name and folder name are user-defined.
+
Parameter Description
+
Table 1 Request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+session
+ |
+Yes
+ |
+Object
+ |
+Session object
+ |
+
+src_local_file
+ |
+Yes
+ |
+String
+ |
+Path to the local file to be uploaded
+ |
+
+dst_obs_dir
+ |
+Yes
+ |
+String
+ |
+Path to the target OBS bucket. The path must start with obs:// and end with a slash (/).
+ |
+
+
+
+
+
+
+
Table 2 Failed response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_code
+ |
+String
+ |
+Error code when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+
+
+
+
Uploading a Folder to OBS
+Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+session = Session()
+session.obs.upload_dir(src_local_dir='/home/ma-user/', dst_obs_dir='obs://bucket-name/dir1/')
+ |
+
+
+
+
After the sample code is executed, the local source folder /ma-user/ is uploaded to the dir1 folder in the bucket-name bucket. The path is obs://bucket-name/dir1/ma-user/. The bucket name and folder name are user-defined.
+
Parameter Description
+
Table 1 Request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+session
+ |
+Yes
+ |
+Object
+ |
+Session object
+ |
+
+src_local_dir
+ |
+Yes
+ |
+String
+ |
+Path to the local folder to be uploaded.
+If the folder to be uploaded is empty or contains multiple empty folders, no empty folders are created in the corresponding OBS path.
+ |
+
+dst_obs_dir
+ |
+Yes
+ |
+String
+ |
+Path to the target OBS bucket. The path must start with obs:// and end with a slash (/).
+ |
+
+
+
+
+
+
+
Table 2 Failed response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_code
+ |
+String
+ |
+Error code when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+
+
+
+
Downloading a File from OBS
+ If the size of a file in a folder exceeds 5 GB, the file cannot be downloaded in this mode.
+
+
Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+session = Session()
+session.obs.download_file(src_obs_file="obs://bucket-name/dir1/file1.txt", dst_local_dir="/home/ma-user/")
+ |
+
+
+
+
After the sample code is executed, source file file1.txt is downloaded from OBS to /home/ma-user/file1.txt.
+
Parameter Description
+
Table 1 Request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+session
+ |
+Yes
+ |
+Object
+ |
+Session object
+ |
+
+src_obs_file
+ |
+Yes
+ |
+String
+ |
+Path to the source file to be downloaded from OBS. The path must start with obs://.
+ |
+
+dst_local_dir
+ |
+Yes
+ |
+String
+ |
+Path to the target local folder. The path must end with a slash (/).
+ |
+
+
+
+
+
+
+
Table 2 Failed response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_code
+ |
+String
+ |
+Error code when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+
+
+
+
Downloading a Folder from OBS
+ If the size of a file in a folder exceeds 5 GB, the file cannot be downloaded in this mode. However, other files whose size is less than 5 GB in the folder can be downloaded.
+
+
Sample Code
In the ModelArts notebook instance, you do not need to enter authentication parameters for session authentication. For details about session authentication of other development environments, see Session Authentication.
+
| from modelarts.session import Session
+session = Session()
+session.obs.download_dir(src_obs_dir="obs://bucket-name/dir1/", dst_local_dir="/home/ma-user/work/")
+ |
+
+
+
+
After the sample code is executed, source folder dir1 is downloaded from OBS to /home/ma-user/work/dir1/.
+
You must have the write permission on the local path.
+
+
Parameter Description
+
Table 1 Request parametersParameter
+ |
+Mandatory
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+session
+ |
+Yes
+ |
+Object
+ |
+Session object
+ |
+
+src_obs_dir
+ |
+Yes
+ |
+String
+ |
+Path to the source folder to be downloaded from OBS. The path must start with obs:// and end with a slash (/). If the downloaded folder contains empty folders, no empty folders are created in the corresponding local path.
+ |
+
+dst_local_dir
+ |
+Yes
+ |
+String
+ |
+Path to the target local folder. The path must end with a slash (/).
+ |
+
+
+
+
+
+
+
Table 2 Failed response parametersParameter
+ |
+Type
+ |
+Description
+ |
+
|---|
+
+error_code
+ |
+String
+ |
+Error code when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+error_msg
+ |
+String
+ |
+Error message when the API call fails.
+This parameter is not included when the API call succeeds.
+ |
+
+
+
+
+
Configuring a Service Endpoint
+
+
Using the SDK in non-notebook environments needs to call IAM, OBS, and ModelArts. Therefore, the endpoints of these services are required. Therefore, the endpoints of these services are required. To obtain the endpoints, configure as follows:
+
from modelarts.session import Session
+Session.set_endpoint(iam_endpoint="***", obs_endpoint="***", modelarts_endpoint="***", region_name="***")
+
+
Table 1 ParametersParameter
+ |
+Description
+ |
+
|---|
+
+iam_endpoint
+ |
+IAM endpoint
+ |
+
+obs_endpoint
+ |
+OBS endpoint
+ |
+
+modelarts_endpoint
+ |
+ModelArts endpoint
+ |
+
+region_name
+ |
+Region name
+ |
+
+
+
+
+