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Changes to ma_umn from docs/doc-exports#385 (Skip escaping inside of bold

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Reviewed-by: gtema <artem.goncharov@gmail.com>
Co-authored-by: proposalbot <proposalbot@otc-service.com>
Co-committed-by: proposalbot <proposalbot@otc-service.com>
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proposalbot 2022-10-25 14:30:46 +00:00 committed by zuul
parent 7c5b3f5e25
commit 59cc5f44db
8 changed files with 48 additions and 48 deletions
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2
umn/source/exeml/predictive_analytics/preparing_data.rst
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@ -21,7 +21,7 @@ Requirements on Datasets
------------------------
- The name of files in a dataset consists of letters, digits, hyphens (-), and underscores (_), and the file name extension is CSV. The files cannot be stored in the root directory of an OBS bucket, but in a folder in the OBS bucket, for example, **/obs-xxx/data/input.csv**.
- The files are saved in CSV format. Use newline characters (\n) to separate lines and commas (,) to separate columns of the file content. The file content cannot contain Chinese characters. The column content cannot contain special characters such as commas (,) and newline characters. The quotation marks are not supported. It is recommended that the column content consist of letters and digits.
- The files are saved in CSV format. Use newline characters (\\n) to separate lines and commas (,) to separate columns of the file content. The file content cannot contain Chinese characters. The column content cannot contain special characters such as commas (,) and newline characters. The quotation marks are not supported. It is recommended that the column content consist of letters and digits.
- The number of training columns is the same. There are at least 100 different data records (a feature with different values is considered as different data) in total. The training columns cannot contain the data of the timestamp format (such as yy-mm-dd and yyyy-mm-dd). If a column has only one value, the column is considered invalid and discarded. Ensure that the dataset contains at least two valid columns except the label column. If you select continuous values for a label column, ensure that the column contains only digits and the training data has at least 25 different values. The training data CSV file cannot contain the table header. Otherwise, the training fails.
Requirements for Files Uploaded to OBS

2
umn/source/faqs/exeml/what_are_the_requirements_for_training_data_when_you_create_a_predictive_analytics_project_in_exeml.rst
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@ -10,5 +10,5 @@ Requirements on Datasets
- Data files cannot be stored in the root directory of an OBS bucket.
- The name of files in a dataset consists of letters, digits, hyphens (-), and underscores (_), and the file name extension is CSV.
- The files are saved in CSV format. Use newline characters (\n or LF) to separate lines and commas (,) to separate columns of the file content. The file content cannot contain Chinese characters. The column content cannot contain special characters such as commas (,) and newline characters. The quotation marks are not supported. It is recommended that the column content consist of letters and digits.
- The files are saved in CSV format. Use newline characters (\\n or LF) to separate lines and commas (,) to separate columns of the file content. The file content cannot contain Chinese characters. The column content cannot contain special characters such as commas (,) and newline characters. The quotation marks are not supported. It is recommended that the column content consist of letters and digits.
- The number of columns in the training data must be the same, and the total number of data records must be greater than or equal to 100. The training columns cannot contain data of the timestamp format (such as yy-mm-dd or yyyy-mm-dd). If you select continuous values for a label column, ensure that the column contains only digits and the training data has at least 25 different values. The training data CSV file cannot contain the table header. Otherwise, the training fails.

22
umn/source/faqs/general_issues/which_ai_frameworks_does_modelarts_support.rst
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@ -57,27 +57,27 @@ Supported AI engines and versions when creating training jobs are as follows:
.. table:: **Table 2** AI engines supported by training jobs
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+-------------------------+
| Environment | Supported Chip | System Architecture | System Version | AI Engine and Version | Supported CUDA Version |
+=================+================+=====================+================+===============================+========================+
+=================+================+=====================+================+===============================+=========================+
| TensorFlow | CPU and GPU | x86_64 | Ubuntu 16.04 | TF-1.13.1-python3.6 | CUDA 10.0 |
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+-------------------------+
| | | | | TF-1.8.0-python3.6 | CUDA 9.0 |
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+-------------------------+
| | | | | TF-2.1.0-python3.6 | CUDA 10.1 |
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+-------------------------+
| Caffe | CPU and GPU | x86_64 | Ubuntu 16.04 | Caffe-1.0.0-python2.7 | CUDA 8.0 |
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+-------------------------+
| Spark_MLlib | CPU | x86_64 | Ubuntu 16.04 | Spark-2.3.2-python3.6 | N/A |
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+-------------------------+
| XGBoost-Sklearn | CPU | x86_64 | Ubuntu 16.04 | Scikit_Learn-0.18.1-python3.6 | N/A |
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+-------------------------+
| PyTorch | CPU and GPU | x86_64 | Ubuntu 16.04 | PyTorch-1.3.0-python3.6 | CUDA 10.0 |
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+-------------------------+
| | | | | PyTorch-1.0.0-python3.6 | CUDA 9.0 |
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+-------------------------+
| MXNet | CPU/GPU | x86_64 | Ubuntu16.04 | MXNet-1.2.1-python3.6 | CUDA 9.0 |
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+-------------------------+
Model Inference
---------------

2
umn/source/model_deployment/real-time_services/viewing_service_details.rst
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@ -156,7 +156,7 @@ Customized settings can be used in the following scenarios:
+-----------+-------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Character | Description |
+===========+=============================================================================================================================================================+
| . | Match any single character except **``\n``**. To match any character including **``\n``**, use **(.|\n)**. |
| . | Match any single character except **\\n**. To match any character including **\\n**, use **(.|\\n)**. |
+-----------+-------------------------------------------------------------------------------------------------------------------------------------------------------------+
| \* | Match the subexpression that it follows for zero or multiple times. For example, **zo\*** can match **z** and **zoo**. |
+-----------+-------------------------------------------------------------------------------------------------------------------------------------------------------------+

2
umn/source/model_management/importing_a_model/importing_a_meta_model_from_obs.rst
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@ -144,7 +144,7 @@ Procedure
Follow-Up Procedure
-------------------
- :ref:`Model Deployment <modelarts_23_0058>`: On the **Models** page, click the triangle next to a model name to view all versions of the model. Locate the row that contains the target version, click **Deploy** in the **Operation** column, and select the deployment type configured when importing the model from the drop-down list. On the **Deploy** page, set parameters by referring to\ :ref:`Introduction to Model Deployment <modelarts_23_0058>` .
- :ref:`Model Deployment <modelarts_23_0058>`: On the **Models** page, click the triangle next to a model name to view all versions of the model. Locate the row that contains the target version, click **Deploy** in the **Operation** column, and select the deployment type configured when importing the model from the drop-down list. On the **Deploy** page, set parameters by referring to :ref:`Introduction to Model Deployment <modelarts_23_0058>` .
.. |image1| image:: /_static/images/en-us_image_0000001156920973.png
.. |image2| image:: /_static/images/en-us_image_0000001156920973.png

22
umn/source/training_management/creating_a_training_job/using_frequently-used_frameworks_to_train_models.rst
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@ -31,27 +31,27 @@ ModelArts supports the following AI engines and versions.
.. table:: **Table 1** AI engines supported by training jobs
+-----------------+----------------+---------------------+----------------+-------------------------------+-----------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
| Environment | Supported Chip | System Architecture | System Version | AI Engine and Version | Supported CUDA Version |
+=================+================+=====================+================+===============================+=======================+
+=================+================+=====================+================+===============================+========================+
| TensorFlow | CPU and GPU | x86_64 | Ubuntu 16.04 | TF-1.13.1-python3.6 | CUDA 10.0 |
+-----------------+----------------+---------------------+----------------+-------------------------------+-----------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
| | | | | TF-1.8.0-python3.6 | CUDA 9.0 |
+-----------------+----------------+---------------------+----------------+-------------------------------+-----------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
| | | | | TF-2.1.0-python3.6 | CUDA 10.1 |
+-----------------+----------------+---------------------+----------------+-------------------------------+-----------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
| Caffe | CPU and GPU | x86_64 | Ubuntu 16.04 | Caffe-1.0.0-python2.7 | CUDA 8.0 |
+-----------------+----------------+---------------------+----------------+-------------------------------+-----------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
| Spark_MLlib | CPU | x86_64 | Ubuntu 16.04 | Spark-2.3.2-python3.6 | N/A |
+-----------------+----------------+---------------------+----------------+-------------------------------+-----------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
| XGBoost-Sklearn | CPU | x86_64 | Ubuntu 16.04 | Scikit_Learn-0.18.1-python3.6 | N/A |
+-----------------+----------------+---------------------+----------------+-------------------------------+-----------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
| PyTorch | CPU and GPU | x86_64 | Ubuntu 16.04 | PyTorch-1.3.0-python3.6 | CUDA 10.0 |
+-----------------+----------------+---------------------+----------------+-------------------------------+-----------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
| | | | | PyTorch-1.0.0-python3.6 | CUDA 9.0 |
+-----------------+----------------+---------------------+----------------+-------------------------------+-----------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
| MXNet | CPU/GPU | x86_64 | Ubuntu16.04 | MXNet-1.2.1-python3.6 | CUDA 9.0 |
+-----------------+----------------+---------------------+----------------+-------------------------------+-----------------------+
+-----------------+----------------+---------------------+----------------+-------------------------------+------------------------+
Creating a Training Job
-----------------------