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<div id="body8662426"></div>
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<div>
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<ul class="ullinks">
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<li class="ulchildlink"><strong><a href="modelarts_21_0086.html">How Do I Import the .h5 Model of Keras to ModelArts?</a></strong><br>
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</li>
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<li class="ulchildlink"><strong><a href="modelarts_05_0124.html">How Do I Import a Model Downloaded from OBS to ModelArts?</a></strong><br>
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</li>
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</ul>
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<h1 class="topictitle1">Labeling Data</h1>
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<div id="body8662426"><p id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_p18494155294015">Before data labeling, consider how to design labels. The labels must correspond to the distinct characteristics of the detected images and are easy to identify (the detected object in an image is highly distinguished from the background). Each label specifies the expected recognition result of the detected images. After the label design is complete, prepare images based on the designed labels. It is recommended that the number of all images to be detected be greater than 100. If the labels of some images are similar, prepare more images.</p>
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<ul id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_ul2324154416510"><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li73406169536">During labeling, the variance of a class should be as small as possible. That is, the labeled objects of the same class should be as similar as possible. The labeled objects of different classes should be as different as possible.</li><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li113251544155114">The contrast between the labeled objects and the image background should be as stark as possible.</li><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li1032584410515">In object detection labeling, a target object must be entirely contained within a labeling box. If there are multiple objects in an image, do not relabel or miss any objects.</li></ul>
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<div class="section" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_section162734391141"><h4 class="sectiontitle">Labeling Images</h4><ol id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_ol927313916412"><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li92731439046">On the <span class="wintitle" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_wintitle19996175803615"><b>Label Data</b></span> tab page, click the <span class="wintitle" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_wintitle119971258113616"><b>Unlabeled</b></span> tab. All unlabeled images are displayed. Click an image to go to the labeling page.</li><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li162743392419">Left-click and drag the mouse to select the area where the target object is located. In the dialog box that is displayed, select the label color, enter the label name, for example, <span class="parmvalue" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_parmvalue1698558173712"><b>yunbao</b></span>, and press <span class="uicontrol" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_uicontrol16986148163713"><b>Enter</b></span>. After the labeling is complete, the status of the images changes to <span class="parmvalue" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_parmvalue22181833203720"><b>Labeled</b></span>.<div class="p" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_p12994125571812">More descriptions of data labeling are as follows:<ul id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_ul14130556186"><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li1741385511187">You can click the arrow keys in the upper and lower parts of the image, or press the left and right arrow keys on the keyboard to select another image. Then, repeat the preceding operations to label the image. If an image contains more than one object, you can label all the objects.</li><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li84131755181811">You can add multiple labels with different colors for an object detection ExeML project for easy identification. After selecting an object, select a new color and enter a new label name in the dialog box that is displayed to add a new label.</li><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li57507251193">In an ExeML project, object detection supports only rectangular labeling boxes. In the <span class="parmname" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_parmname1028172015276"><b>Data Management</b></span> function, more types of labeling boxes are supported for object detection datasets.</li><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li1974113405201">In the <span class="wintitle" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_wintitle15292122919275"><b>Label Data</b></span> window, you can scroll the mouse to zoom in or zoom out on the image to quickly locate the object.</li></ul>
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<div class="section" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_section162734391141"><h4 class="sectiontitle">Labeling Images</h4><ol id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_ol927313916412"><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li92731439046">On the <span class="wintitle" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_wintitle19996175803615"><b>Label Data</b></span> tab page, click the <span class="wintitle" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_wintitle119971258113616"><b>Unlabeled</b></span> tab. All unlabeled images are displayed. Click an image to go to the labeling page.</li><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li162743392419">Drag the mouse to select the area where the target object is located. In the dialog box that appears, select the label color, enter the label name, and press <span class="uicontrol" id="modelarts_21_0011__uicontrol1043611226195"><b>Enter</b></span>. After the labeling is complete, the status of the images changes to <span class="parmvalue" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_parmvalue22181833203720"><b>Labeled</b></span>.<div class="p" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_p12994125571812">More descriptions of data labeling are as follows:<ul id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_ul14130556186"><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li1741385511187">You can click the arrow keys in the upper and lower parts of the image, or press the left and right arrow keys on the keyboard to select another image. Then, repeat the preceding operations to label the image. If an image contains more than one object, you can label all the objects.</li><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li84131755181811">You can add multiple labels with different colors for an object detection ExeML project for easy identification. After selecting an object, select a new color and enter a new label name in the dialog box that is displayed to add a new label.</li><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li57507251193">In an ExeML project, object detection supports only rectangular labeling boxes. In the <span class="parmname" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_parmname1028172015276"><b>Data Management</b></span> function, more types of labeling boxes are supported for object detection datasets.</li><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li1974113405201">In the <span class="wintitle" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_wintitle15292122919275"><b>Label Data</b></span> window, you can scroll the mouse to zoom in or zoom out on the image to quickly locate the object.</li></ul>
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</div>
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<p id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_p12413115518188"></p>
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</li><li id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_li327433914415">After all images in the image directory are labeled, click <span class="uicontrol" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_uicontrol11132135215273"><b>ExeML</b></span> in the upper left corner. In the dialog box that is displayed, click <span class="uicontrol" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_uicontrol158485563273"><b>OK</b></span> to save the labeling information. The <span class="wintitle" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_wintitle1143118812289"><b>Label Data</b></span> page is displayed. On the <span class="wintitle" id="modelarts_21_0011__en-us_topic_0284258840_en-us_topic_0169446160_wintitle1601836164411"><b>Labeled</b></span> tab page, you can view the labeled images or view the label names and quantity in the right pane.</li></ol>
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<a name="modelarts_21_0015"></a><a name="modelarts_21_0015"></a>
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<h1 class="topictitle1">Preparing Data</h1>
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<div id="body32001227"><p id="modelarts_21_0015__en-us_topic_0000001097054503_p109811029142820">Before using ModelArts to build a predictive analytics model, upload data to OBS. </p>
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<div id="body8662426"><p id="modelarts_21_0015__en-us_topic_0000001097054503_p109811029142820">Before using ModelArts to build a predictive analytics model, upload data to OBS. </p>
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<div class="section" id="modelarts_21_0015__en-us_topic_0000001097054503_section155111779288"><h4 class="sectiontitle">Uploading Data to OBS</h4><p id="modelarts_21_0015__en-us_topic_0000001097054503_p16847114217310">This operation uses the OBS client to upload data. For more information about how to create a bucket and upload files, see "Creating a Bucket" and "Uploading an Object".</p>
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<p id="modelarts_21_0015__en-us_topic_0000001097054503_p496211317215">Perform the following operations to import data to the dataset for model training and building.</p>
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<ol id="modelarts_21_0015__en-us_topic_0000001097054503_ol79623313216"><li id="modelarts_21_0015__en-us_topic_0000001097054503_li4741337144812">Log in to OBS Console and create a bucket. </li><li id="modelarts_21_0015__en-us_topic_0000001097054503_li1790822143610">Upload a file to the OBS bucket. If you have a large amount of data, use OBS Browser+ to upload data or folders. The uploaded data must meet the dataset requirements of the ExeML project.</li></ol>
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</div>
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<div class="section" id="modelarts_21_0015__en-us_topic_0000001097054503_section158944528285"><h4 class="sectiontitle">Requirements on Datasets</h4><ul id="modelarts_21_0015__en-us_topic_0000001097054503_ul7356124135318"><li id="modelarts_21_0015__en-us_topic_0000001097054503_li1435615475311">The name of a file 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, <span class="filepath" id="modelarts_21_0015__en-us_topic_0000001097054503_filepath7246562533"><b>/obs-xxx/data/input.csv</b></span>.</li><li id="modelarts_21_0015__en-us_topic_0000001097054503_li5356124105316">The files are saved in CSV format. Use newline characters (\n) to separate lines and commas (,) to separate columns in the file. The column content cannot contain special characters such as commas (,) and newline characters (\n). The quotation marks are not supported. It is recommended that the column content consist of letters and digits.</li><li id="modelarts_21_0015__en-us_topic_0000001097054503_li14380220175411">Data training<ul id="modelarts_21_0015__en-us_topic_0000001097054503_ul1816917442542"><li id="modelarts_21_0015__en-us_topic_0000001097054503_li1920311397541">The number of training columns is the same. There are at least 100 different data records in total (a feature with different values is considered as different data).</li><li id="modelarts_21_0015__en-us_topic_0000001097054503_li22410625515">The training columns cannot contain timestamp data (such as yy-mm-dd or yyyy-mm-dd).</li><li id="modelarts_21_0015__en-us_topic_0000001097054503_li2781225185914">If a column has only one value, the column is considered invalid. Ensure that there are at least two values in the label column and no data is missing.<div class="note" id="modelarts_21_0015__en-us_topic_0000001097054503_note115155563335"><img src="public_sys-resources/note_3.0-en-us.png"><span class="notetitle"> </span><div class="notebody"><p id="modelarts_21_0015__en-us_topic_0000001097054503_p14515856133317">The label column is the training target specified in a training task. It is the output (prediction item) for the model trained using the dataset.</p>
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<div class="section" id="modelarts_21_0015__en-us_topic_0000001097054503_section158944528285"><h4 class="sectiontitle">Requirements on Datasets</h4><ul id="modelarts_21_0015__en-us_topic_0000001097054503_ul7356124135318"><li id="modelarts_21_0015__en-us_topic_0000001097054503_li1435615475311">The name of a file 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, <span class="filepath" id="modelarts_21_0015__en-us_topic_0000001097054503_filepath7246562533"><b>/obs-xxx/data/input.csv</b></span>.</li><li id="modelarts_21_0015__en-us_topic_0000001097054503_li5356124105316">The files are saved in CSV format. Use newline characters (\n) to separate lines and commas (,) to separate columns in the file. The column content cannot contain special characters such as commas (,) and newline characters (\n). The quotation marks are not supported. It is recommended that the column content consist of letters and digits.</li><li id="modelarts_21_0015__en-us_topic_0000001097054503_li14380220175411">Data training<ul id="modelarts_21_0015__en-us_topic_0000001097054503_ul1816917442542"><li id="modelarts_21_0015__en-us_topic_0000001097054503_li1920311397541">The number of training columns is the same. There are at least 100 different data records in total (a feature with different values is considered as different data).</li><li id="modelarts_21_0015__en-us_topic_0000001097054503_li22410625515">The training columns cannot contain timestamp data (such as yy-mm-dd or yyyy-mm-dd).</li><li id="modelarts_21_0015__en-us_topic_0000001097054503_li2781225185914">If a column has only one value, the column is considered invalid. Ensure that there are at least two values in the label column and no data is missing.<div class="note" id="modelarts_21_0015__en-us_topic_0000001097054503_note115155563335"><img src="public_sys-resources/note_3.0-en-us.png"><span class="notetitle"> </span><div class="notebody"><p id="modelarts_21_0015__en-us_topic_0000001097054503_p14515856133317">The label column is the training target specified in a training task. It is the output (prediction item) for the model trained using the dataset.</p>
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</div></div>
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</li><li id="modelarts_21_0015__en-us_topic_0000001097054503_li8935121835513">In addition to the label column, the dataset must contain at least two valid feature columns. Ensure that there are at least two values in each feature column and that the percentage of missing data must be lower than 10%.</li><li id="modelarts_21_0015__en-us_topic_0000001097054503_li2961175915418">The training data in CSV file cannot contain the table header. Otherwise, the training fails.</li><li id="modelarts_21_0015__en-us_topic_0000001097054503_li14231851145514">Due to the limitation of the feature filtering algorithm, place the label column in the last column of the dataset. Otherwise, the training may fail.</li></ul>
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</li></ul>
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@ -15,59 +15,59 @@
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<ul id="modelarts_21_0015__en-us_topic_0000001097054503_ul15579111555"><li id="modelarts_21_0015__en-us_topic_0000001097054503_li155820125519">The OBS path of the input data must redirect to the data files. The data files must be stored in a folder in an OBS bucket rather than the root directory of the OBS bucket, for example, <span class="filepath" id="modelarts_21_0015__en-us_topic_0000001097054503_filepath12980181557"><b>/obs-xxx/data/input.csv</b></span>.</li><li id="modelarts_21_0015__en-us_topic_0000001097054503_li52741466556">The input data must be in CSV format. The data files do not contain the table header and the number of valid data lines must be greater than 100. The number of columns must be less than 200, and the total data size cannot exceed 100 MB.</li></ul>
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</div>
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<div class="section" id="modelarts_21_0015__en-us_topic_0000001097054503_section207971911713"><h4 class="sectiontitle">Predictive Analytics File Example</h4><div class="p" id="modelarts_21_0015__en-us_topic_0000001097054503_p1936116555618">Take the iris dataset as an example. Predict an iris species based on the lengths and widths of the iris calyx and petal.
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<div class="tablenoborder"><table cellpadding="4" cellspacing="0" summary="" id="modelarts_21_0015__en-us_topic_0000001097054503_table207931411615" frame="border" border="1" rules="all"><caption><b>Table 1 </b>Parameters and meanings of data sources</caption><thead align="left"><tr id="modelarts_21_0015__en-us_topic_0000001097054503_row77919116115"><th align="left" class="cellrowborder" valign="top" width="19.801980198019802%" id="mcps1.3.5.2.1.2.5.1.1"><p id="modelarts_21_0015__en-us_topic_0000001097054503_p137917111111">Parameter</p>
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<div class="tablenoborder"><table cellpadding="4" cellspacing="0" summary="" id="modelarts_21_0015__en-us_topic_0000001097054503_table207931411615" frame="border" border="1" rules="all"><caption><b>Table 1 </b>Parameters and meanings of data sources</caption><thead align="left"><tr id="modelarts_21_0015__en-us_topic_0000001097054503_row77919116115"><th align="left" class="cellrowborder" valign="top" width="23.45%" id="mcps1.3.5.2.1.2.5.1.1"><p id="modelarts_21_0015__en-us_topic_0000001097054503_p137917111111">Parameter</p>
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</th>
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<th align="left" class="cellrowborder" valign="top" width="24.752475247524753%" id="mcps1.3.5.2.1.2.5.1.2"><p id="modelarts_21_0015__en-us_topic_0000001097054503_p127911512011">Meaning</p>
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<th align="left" class="cellrowborder" valign="top" width="25.72%" id="mcps1.3.5.2.1.2.5.1.2"><p id="modelarts_21_0015__en-us_topic_0000001097054503_p127911512011">Meaning</p>
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</th>
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<th align="left" class="cellrowborder" valign="top" width="19.801980198019802%" id="mcps1.3.5.2.1.2.5.1.3"><p id="modelarts_21_0015__en-us_topic_0000001097054503_p9791213114">Type</p>
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<th align="left" class="cellrowborder" valign="top" width="18.790000000000003%" id="mcps1.3.5.2.1.2.5.1.3"><p id="modelarts_21_0015__en-us_topic_0000001097054503_p9791213114">Type</p>
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</th>
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<th align="left" class="cellrowborder" valign="top" width="35.64356435643564%" id="mcps1.3.5.2.1.2.5.1.4"><p id="modelarts_21_0015__en-us_topic_0000001097054503_p137911119112">Description</p>
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<th align="left" class="cellrowborder" valign="top" width="32.04%" id="mcps1.3.5.2.1.2.5.1.4"><p id="modelarts_21_0015__en-us_topic_0000001097054503_p137911119112">Description</p>
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</th>
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</tr>
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</thead>
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<tbody><tr id="modelarts_21_0015__en-us_topic_0000001097054503_row1679212117112"><td class="cellrowborder" valign="top" width="19.801980198019802%" headers="mcps1.3.5.2.1.2.5.1.1 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p177911116113">attr_1</p>
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<tbody><tr id="modelarts_21_0015__en-us_topic_0000001097054503_row1679212117112"><td class="cellrowborder" valign="top" width="23.45%" headers="mcps1.3.5.2.1.2.5.1.1 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p177911116113">attr_1</p>
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</td>
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<td class="cellrowborder" valign="top" width="24.752475247524753%" headers="mcps1.3.5.2.1.2.5.1.2 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p3791914119">Calyx length</p>
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<td class="cellrowborder" valign="top" width="25.72%" headers="mcps1.3.5.2.1.2.5.1.2 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p3791914119">Calyx length</p>
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</td>
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<td class="cellrowborder" valign="top" width="19.801980198019802%" headers="mcps1.3.5.2.1.2.5.1.3 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p27911411919">Double</p>
|
||||
<td class="cellrowborder" valign="top" width="18.790000000000003%" headers="mcps1.3.5.2.1.2.5.1.3 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p27911411919">Double</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="35.64356435643564%" headers="mcps1.3.5.2.1.2.5.1.4 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p167921711510">Length of the target iris calyx</p>
|
||||
<td class="cellrowborder" valign="top" width="32.04%" headers="mcps1.3.5.2.1.2.5.1.4 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p167921711510">Length of the target iris calyx</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr id="modelarts_21_0015__en-us_topic_0000001097054503_row147922116110"><td class="cellrowborder" valign="top" width="19.801980198019802%" headers="mcps1.3.5.2.1.2.5.1.1 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p7792711411">attr_2</p>
|
||||
<tr id="modelarts_21_0015__en-us_topic_0000001097054503_row147922116110"><td class="cellrowborder" valign="top" width="23.45%" headers="mcps1.3.5.2.1.2.5.1.1 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p7792711411">attr_2</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="24.752475247524753%" headers="mcps1.3.5.2.1.2.5.1.2 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p15792712118">Calyx width</p>
|
||||
<td class="cellrowborder" valign="top" width="25.72%" headers="mcps1.3.5.2.1.2.5.1.2 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p15792712118">Calyx width</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="19.801980198019802%" headers="mcps1.3.5.2.1.2.5.1.3 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p1579215112113">Double</p>
|
||||
<td class="cellrowborder" valign="top" width="18.790000000000003%" headers="mcps1.3.5.2.1.2.5.1.3 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p1579215112113">Double</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="35.64356435643564%" headers="mcps1.3.5.2.1.2.5.1.4 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p4792191314">Width of the target calyx</p>
|
||||
<td class="cellrowborder" valign="top" width="32.04%" headers="mcps1.3.5.2.1.2.5.1.4 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p4792191314">Width of the target calyx</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr id="modelarts_21_0015__en-us_topic_0000001097054503_row167921616119"><td class="cellrowborder" valign="top" width="19.801980198019802%" headers="mcps1.3.5.2.1.2.5.1.1 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p177924119111">attr_3</p>
|
||||
<tr id="modelarts_21_0015__en-us_topic_0000001097054503_row167921616119"><td class="cellrowborder" valign="top" width="23.45%" headers="mcps1.3.5.2.1.2.5.1.1 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p177924119111">attr_3</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="24.752475247524753%" headers="mcps1.3.5.2.1.2.5.1.2 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p679216110111">Petal length</p>
|
||||
<td class="cellrowborder" valign="top" width="25.72%" headers="mcps1.3.5.2.1.2.5.1.2 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p679216110111">Petal length</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="19.801980198019802%" headers="mcps1.3.5.2.1.2.5.1.3 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p177921814114">Double</p>
|
||||
<td class="cellrowborder" valign="top" width="18.790000000000003%" headers="mcps1.3.5.2.1.2.5.1.3 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p177921814114">Double</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="35.64356435643564%" headers="mcps1.3.5.2.1.2.5.1.4 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p1779211117119">Length of the target iris petal</p>
|
||||
<td class="cellrowborder" valign="top" width="32.04%" headers="mcps1.3.5.2.1.2.5.1.4 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p1779211117119">Length of the target iris petal</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr id="modelarts_21_0015__en-us_topic_0000001097054503_row157921618114"><td class="cellrowborder" valign="top" width="19.801980198019802%" headers="mcps1.3.5.2.1.2.5.1.1 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p5792101714">attr_4</p>
|
||||
<tr id="modelarts_21_0015__en-us_topic_0000001097054503_row157921618114"><td class="cellrowborder" valign="top" width="23.45%" headers="mcps1.3.5.2.1.2.5.1.1 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p5792101714">attr_4</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="24.752475247524753%" headers="mcps1.3.5.2.1.2.5.1.2 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p117925114113">Petal width</p>
|
||||
<td class="cellrowborder" valign="top" width="25.72%" headers="mcps1.3.5.2.1.2.5.1.2 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p117925114113">Petal width</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="19.801980198019802%" headers="mcps1.3.5.2.1.2.5.1.3 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p979217115117">Double</p>
|
||||
<td class="cellrowborder" valign="top" width="18.790000000000003%" headers="mcps1.3.5.2.1.2.5.1.3 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p979217115117">Double</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="35.64356435643564%" headers="mcps1.3.5.2.1.2.5.1.4 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p17792111716">Width of the target iris petal</p>
|
||||
<td class="cellrowborder" valign="top" width="32.04%" headers="mcps1.3.5.2.1.2.5.1.4 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p17792111716">Width of the target iris petal</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr id="modelarts_21_0015__en-us_topic_0000001097054503_row17921619111"><td class="cellrowborder" valign="top" width="19.801980198019802%" headers="mcps1.3.5.2.1.2.5.1.1 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p6792311817">attr_5</p>
|
||||
<tr id="modelarts_21_0015__en-us_topic_0000001097054503_row17921619111"><td class="cellrowborder" valign="top" width="23.45%" headers="mcps1.3.5.2.1.2.5.1.1 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p6792311817">attr_5</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="24.752475247524753%" headers="mcps1.3.5.2.1.2.5.1.2 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p1079261817">Species</p>
|
||||
<td class="cellrowborder" valign="top" width="25.72%" headers="mcps1.3.5.2.1.2.5.1.2 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p1079261817">Species</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="19.801980198019802%" headers="mcps1.3.5.2.1.2.5.1.3 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p17792131112">String</p>
|
||||
<td class="cellrowborder" valign="top" width="18.790000000000003%" headers="mcps1.3.5.2.1.2.5.1.3 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p17792131112">String</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="35.64356435643564%" headers="mcps1.3.5.2.1.2.5.1.4 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p46361821331">Species of the iris</p>
|
||||
<td class="cellrowborder" valign="top" width="32.04%" headers="mcps1.3.5.2.1.2.5.1.4 "><p id="modelarts_21_0015__en-us_topic_0000001097054503_p46361821331">Species of the iris</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
|
||||
12
docs/modelarts/umn/modelarts_21_0086.html
Normal file
12
docs/modelarts/umn/modelarts_21_0086.html
Normal file
@ -0,0 +1,12 @@
|
||||
<a name="modelarts_21_0086"></a><a name="modelarts_21_0086"></a>
|
||||
|
||||
<h1 class="topictitle1">How Do I Import the .h5 Model of Keras to ModelArts?</h1>
|
||||
<div id="body0000001862425257"><p id="modelarts_21_0086__p23911432162220">ModelArts does not support the import of models in .h5 format. You can convert the models in .h5 format of Keras to the TensorFlow format and then import the models to ModelArts.</p>
|
||||
<p id="modelarts_21_0086__p0391103242212">For details about how to convert the Keras format to the TensorFlow format, see the <a href="https://keras.io/about/" target="_blank" rel="noopener noreferrer">Keras official website</a>.</p>
|
||||
</div>
|
||||
<div>
|
||||
<div class="familylinks">
|
||||
<div class="parentlink"><strong>Parent topic:</strong> <a href="modelarts_05_0016.html">Model Management</a></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@ -60,8 +60,8 @@
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
</li><li id="modelarts_23_0012__en-us_topic_0170889732_li174505684419">In the <strong id="modelarts_23_0012__en-us_topic_0170889732_b3336520112416">Add Label</strong> text box, enter a new label name, select the label color, and click <strong id="modelarts_23_0012__en-us_topic_0170889732_b240743122410">Add</strong>. Alternatively, select an existing label from the drop-down list.<p id="modelarts_23_0012__en-us_topic_0170889732_p7365163717223">Label all objects in an image. Multiple labels can be added to an image. After labeling an image, click an image that has not been labeled in the image list below to label the new image.</p>
|
||||
<div class="fignone" id="modelarts_23_0012__en-us_topic_0170889732_fig13103527192513"><span class="figcap"><b>Figure 1 </b>Adding an object detection label</span><br><span><img id="modelarts_23_0012__en-us_topic_0170889732_image983614226252" src="en-us_image_0000001846058033.png" width="469.49" height="414.484658"></span></div>
|
||||
</li><li id="modelarts_23_0012__en-us_topic_0170889732_li174505684419">In the <strong id="modelarts_23_0012__en-us_topic_0170889732_b3336520112416">Add Label</strong> text box, enter a new label name, select the label color, and click <strong id="modelarts_23_0012__en-us_topic_0170889732_b240743122410">Add</strong>. Alternatively, select an existing label from the drop-down list.<div class="p" id="modelarts_23_0012__en-us_topic_0170889732_p7365163717223">Label all objects in an image. Multiple labels can be added to an image. After labeling an image, click an image that has not been labeled in the image list below to label the new image.<div class="fignone" id="modelarts_23_0012__en-us_topic_0170889732_fig6856193652220"><span class="figcap"><b>Figure 1 </b>Adding an object detection label</span><br><span><img id="modelarts_23_0012__image1209206493" src="en-us_image_0000001862605765.png" height="245.02895900000001" width="469.49"></span></div>
|
||||
</div>
|
||||
</li><li id="modelarts_23_0012__en-us_topic_0170889732_li14243183024617">Click <strong id="modelarts_23_0012__en-us_topic_0170889732_b588784925011">Back to Data Labeling Preview</strong> in the upper left part of the page to view the labeling information. In the dialog box that is displayed, click <strong id="modelarts_23_0012__en-us_topic_0170889732_b72151327519">Yes</strong> to save the labeling settings.<p id="modelarts_23_0012__en-us_topic_0170889732_p84570237228">The selected image is automatically moved to the <strong id="modelarts_23_0012__en-us_topic_0170889732_b143541436185120">Labeled</strong> tab page. On the <strong id="modelarts_23_0012__en-us_topic_0170889732_b73551365511">Unlabeled</strong> and <span class="wintitle" id="modelarts_23_0012__en-us_topic_0170889732_wintitle564284011714"><b>All</b></span> tab pages, the labeling information is updated along with the labeling process, including the added label names and the number of images for each label.</p>
|
||||
</li></ol>
|
||||
|
||||
@ -125,7 +125,7 @@
|
||||
<div class="section" id="modelarts_23_0012__en-us_topic_0170889732_section0534612151819"><h4 class="sectiontitle">Modifying Labeling Information</h4><p id="modelarts_23_0012__en-us_topic_0170889732_p1981864110595">After labeling data, you can modify labeled data on the <strong id="modelarts_23_0012__en-us_topic_0170889732_b1663795315538">Labeled</strong> tab page.</p>
|
||||
<ul id="modelarts_23_0012__en-us_topic_0170889732_en-us_topic_0170889732_ul814710065510"><li id="modelarts_23_0012__en-us_topic_0170889732_en-us_topic_0170889732_li12147807557"><strong id="modelarts_23_0012__en-us_topic_0170889732_b273116552532">Modifying based on images</strong><p id="modelarts_23_0012__en-us_topic_0170889732_en-us_topic_0170889732_p758718288619">On the dataset details page, click the <span class="wintitle" id="modelarts_23_0012__en-us_topic_0170889732_wintitle1758782820618"><b>Labeled</b></span> tab, select the images to be modified, and click the images. The labeling page is displayed. Modify the image information in the label information area on the right.</p>
|
||||
<ul id="modelarts_23_0012__en-us_topic_0170889732_en-us_topic_0170889732_ul14587172810617"><li id="modelarts_23_0012__en-us_topic_0170889732_en-us_topic_0170889732_li2614184251314">Modifying a label: In the <span class="parmname" id="modelarts_23_0012__en-us_topic_0170889732_en-us_topic_0170889732_parmname78621648883"><b>Labeling</b></span> area, click the edit icon, enter the correct label name in the text box, and click the check mark to complete the modification. Alternatively, click a label. In the image labeling area, adjust the position and size of the bounding box. After the adjustment is complete, click another label to save the modification.</li><li id="modelarts_23_0012__en-us_topic_0170889732_en-us_topic_0170889732_li290381217455">Deleting a label: In the <span class="parmname" id="modelarts_23_0012__en-us_topic_0170889732_parmname1642341153716"><b>Labeling</b></span> area, click the deletion icon to delete a label from the image.<p id="modelarts_23_0012__en-us_topic_0170889732_en-us_topic_0170889732_p1550201354514">After deleting the label, click <span class="parmname" id="modelarts_23_0012__en-us_topic_0170889732_parmname2073995219117"><b>Back to Data Labeling Preview</b></span> in the upper left corner of the page to exit the labeling page. In the dialog box that is displayed, save the modification. After all labels of an image are deleted, the image is displayed on the <span class="wintitle" id="modelarts_23_0012__en-us_topic_0170889732_wintitle20261142415314"><b>Unlabeled</b></span> tab page.</p>
|
||||
<div class="fignone" id="modelarts_23_0012__en-us_topic_0170889732_fig9844112133113"><span class="figcap"><b>Figure 2 </b>Editing an object detection label</span><br><span><img id="modelarts_23_0012__en-us_topic_0170889732_image16546455123017" src="en-us_image_0000001799498984.png" width="469.49" height="251.226094"></span></div>
|
||||
<div class="fignone" id="modelarts_23_0012__en-us_topic_0170889732_fig9844112133113"><span class="figcap"><b>Figure 2 </b>Editing an object detection label</span><br><span><img id="modelarts_23_0012__image1790713165118" src="en-us_image_0000001862606185.png" height="186.007416" width="469.49"></span></div>
|
||||
</li></ul>
|
||||
</li></ul>
|
||||
<ul id="modelarts_23_0012__en-us_topic_0170889732_en-us_topic_0170889732_ul141472065510"><li id="modelarts_23_0012__en-us_topic_0170889732_en-us_topic_0170889732_li2014730135520"><strong id="modelarts_23_0012__en-us_topic_0170889732_b5400101165818">Modifying based on labels</strong><p id="modelarts_23_0012__en-us_topic_0170889732_en-us_topic_0170889732_p144965385189">On the dataset details page, click the <strong id="modelarts_23_0012__en-us_topic_0170889732_b1012011214526">Labeled</strong> tab. The information about all labels is displayed on the right.</p>
|
||||
|
||||
@ -15,7 +15,7 @@
|
||||
</li></ol>
|
||||
</div>
|
||||
<div class="section" id="modelarts_23_0062__en-us_topic_0165025306_section1666533761611"><a name="modelarts_23_0062__en-us_topic_0165025306_section1666533761611"></a><a name="en-us_topic_0165025306_section1666533761611"></a><h4 class="sectiontitle">File Prediction</h4><ol id="modelarts_23_0062__en-us_topic_0165025306_ol1422913844319"><li id="modelarts_23_0062__en-us_topic_0165025306_li1122914381439">Log in to the ModelArts management console and choose <strong id="modelarts_23_0062__en-us_topic_0165025306_b2615151815817">Service Deployment</strong> > <strong id="modelarts_23_0062__en-us_topic_0165025306_b36161118589">Real-Time Services</strong>.</li><li id="modelarts_23_0062__en-us_topic_0165025306_li19229193894314">On the <strong id="modelarts_23_0062__en-us_topic_0165025306_b1830532013818">Real-Time Services</strong> page, click the name of the target service. The service details page is displayed. On the <span class="wintitle" id="modelarts_23_0062__en-us_topic_0165025306_wintitle171929332349"><b>Prediction</b></span> tab page, click <span class="uicontrol" id="modelarts_23_0062__en-us_topic_0165025306_uicontrol13195184017341"><b>Upload</b></span> and select a test file. After the file is uploaded successfully, click <strong id="modelarts_23_0062__en-us_topic_0165025306_b14717181843717">Predict</strong> to perform a prediction test.<p id="modelarts_23_0062__en-us_topic_0165025306_p1651110248"></p>
|
||||
<div class="fignone" id="modelarts_23_0062__en-us_topic_0165025306_fig6954201305"><span class="figcap"><b>Figure 3 </b>Image prediction</span><br><span><img id="modelarts_23_0062__en-us_topic_0165025306_image52281815605" src="en-us_image_0000001799338600.png" width="469.49" height="206.7086"></span></div>
|
||||
<div class="fignone" id="modelarts_23_0062__en-us_topic_0165025306_fig6954201305"><span class="figcap"><b>Figure 3 </b>Image prediction</span><br><span><img id="modelarts_23_0062__image19901131221514" src="en-us_image_0000001862682365.png" height="152.025384" width="469.49"></span></div>
|
||||
</li></ol>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@ -39,7 +39,7 @@
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="10.549999999999999%" headers="mcps1.3.2.3.2.5.1.3 "><p id="modelarts_23_0092__en-us_topic_0172466149_p19204171914431">String</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="62.8%" headers="mcps1.3.2.3.2.5.1.4 "><p id="modelarts_23_0092__en-us_topic_0172466149_p5204141974315">Model runtime environment. pyton3.6 is used by default The value of <strong id="modelarts_23_0092__en-us_topic_0172466149_b14218121182017">runtime</strong> depends on the value of <strong id="modelarts_23_0092__en-us_topic_0172466149_b207551025182019">model_type</strong>. If <strong id="modelarts_23_0092__en-us_topic_0172466149_b0193736132020">model_type</strong> is set to <strong id="modelarts_23_0092__en-us_topic_0172466149_b73210167214">Image</strong>, you do not need to set <strong id="modelarts_23_0092__en-us_topic_0172466149_b164342022152118">runtime</strong>. If <strong id="modelarts_23_0092__en-us_topic_0172466149_b1211692512114">model_type</strong> is set to another frequently-used framework, select the engine and development environment. </p>
|
||||
<td class="cellrowborder" valign="top" width="62.8%" headers="mcps1.3.2.3.2.5.1.4 "><p id="modelarts_23_0092__en-us_topic_0172466149_p5204141974315">Model runtime environment. Python 3.6 is used by default The value of <strong id="modelarts_23_0092__en-us_topic_0172466149_b14218121182017">runtime</strong> depends on the value of <strong id="modelarts_23_0092__en-us_topic_0172466149_b207551025182019">model_type</strong>. If <strong id="modelarts_23_0092__en-us_topic_0172466149_b0193736132020">model_type</strong> is set to <strong id="modelarts_23_0092__en-us_topic_0172466149_b73210167214">Image</strong>, you do not need to set <strong id="modelarts_23_0092__en-us_topic_0172466149_b164342022152118">runtime</strong>. If <strong id="modelarts_23_0092__en-us_topic_0172466149_b1211692512114">model_type</strong> is set to another frequently-used framework, select the engine and development environment. </p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr id="modelarts_23_0092__en-us_topic_0172466149_row82045192436"><td class="cellrowborder" valign="top" width="15.67%" headers="mcps1.3.2.3.2.5.1.1 "><p id="modelarts_23_0092__en-us_topic_0172466149_p19204819124318">swr_location</p>
|
||||
|
||||
@ -17,7 +17,7 @@
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="18%" headers="mcps1.3.2.3.2.4.1.2 "><p id="modelarts_23_0100__en-us_topic_0172873542_p715417479575">String array</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="62%" headers="mcps1.3.2.3.2.4.1.3 "><p id="modelarts_23_0100__en-us_topic_0172873542_p115414715573">List of detected objects, for example, <strong id="modelarts_23_0100__en-us_topic_0172873542_b173131141033">["yunbao","cat"]</strong></p>
|
||||
<td class="cellrowborder" valign="top" width="62%" headers="mcps1.3.2.3.2.4.1.3 "><p id="modelarts_23_0100__en-us_topic_0172873542_p115414715573">Types of detected objects, for example, <strong id="modelarts_23_0100__b1663229162019">["bicycle","bus"]</strong></p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr id="modelarts_23_0100__en-us_topic_0172873542_row12154154718574"><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.2.3.2.4.1.1 "><p id="modelarts_23_0100__en-us_topic_0172873542_p215444715720">detection_boxes</p>
|
||||
|
||||
@ -157,7 +157,7 @@
|
||||
</li></ol>
|
||||
<p id="modelarts_23_0233__en-us_topic_0000001133351332_en-us_topic_0000001071986951_p1494462351313"></p>
|
||||
</div>
|
||||
<div class="section" id="modelarts_23_0233__en-us_topic_0000001133351332_en-us_topic_0000001071986951_section1959512404445"><a name="modelarts_23_0233__en-us_topic_0000001133351332_en-us_topic_0000001071986951_section1959512404445"></a><a name="en-us_topic_0000001133351332_en-us_topic_0000001071986951_section1959512404445"></a><h4 class="sectiontitle">Supported Policies</h4><p id="modelarts_23_0233__en-us_topic_0000001133351332_en-us_topic_0000001071986951_p192511553204411">ModelArts supports auto search. Auto search automatically finds the optimal hyperparameters without any code modification. This improves model precision and convergence speed. For details, see <a href="modelarts_23_0302_0.html#modelarts_23_0302_0__en-us_topic_0000001159996229_section54440253422">Setting Hyperparameter Search</a>.</p>
|
||||
<div class="section" id="modelarts_23_0233__en-us_topic_0000001133351332_en-us_topic_0000001071986951_section1959512404445"><a name="modelarts_23_0233__en-us_topic_0000001133351332_en-us_topic_0000001071986951_section1959512404445"></a><a name="en-us_topic_0000001133351332_en-us_topic_0000001071986951_section1959512404445"></a><h4 class="sectiontitle">Supported Policies</h4><p id="modelarts_23_0233__en-us_topic_0000001133351332_en-us_topic_0000001071986951_p192511553204411">ModelArts supports auto search. Auto search automatically finds the optimal hyperparameters without any code modification. This improves model precision and convergence speed. </p>
|
||||
<p id="modelarts_23_0233__en-us_topic_0000001133351332_p990745441112">Auto search supports only the following engines:</p>
|
||||
<ul id="modelarts_23_0233__ul1949219164175"><li id="modelarts_23_0233__li44927169175">mindspore_1.7.0-cann_5.1.0-py_3.7-euler_2.8.3-aarch64</li><li id="modelarts_23_0233__li5492216181711">pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64</li><li id="modelarts_23_0233__li1449281641717">tensorflow_1.15-cann_5.1.0-py_3.7-euler_2.8.3-aarch64</li><li id="modelarts_23_0233__li104928162174">tensorflow_2.1.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64</li></ul>
|
||||
</div>
|
||||
|
||||
@ -5,32 +5,31 @@
|
||||
<p id="modelarts_23_0402__en-us_topic_0000001160408180_p153211758154019">Operation 1: If a training job uses multiple compute nodes, choose a node from the drop-down list box to view its metrics.</p>
|
||||
<p id="modelarts_23_0402__en-us_topic_0000001160408180_p5332191611591">Operation 2: Click <span class="parmname" id="modelarts_23_0402__parmname1116488779412"><b>cpuUsage</b></span>, <span class="parmname" id="modelarts_23_0402__parmname747261483412"><b>gpuMemUsage</b></span>, <span class="parmname" id="modelarts_23_0402__parmname1221657349412"><b>gpuUtil</b></span>, or <span class="parmname" id="modelarts_23_0402__parmname853662599412"><b>memUsage</b></span> to show or hide the usage chart of the parameter.</p>
|
||||
<p id="modelarts_23_0402__en-us_topic_0000001160408180_p342519281511">Operation 3: Hover the cursor on the graph to view the usage at the specific time.</p>
|
||||
<div class="fignone" id="modelarts_23_0402__en-us_topic_0000001160408180_fig12415019442"><span class="figcap"><b>Figure 1 </b>Resource usage</span><br><span><img id="modelarts_23_0402__en-us_topic_0000001160408180_image164112313513" src="en-us_image_0000001846137545.png" width="498.351" height="252.565404"></span></div>
|
||||
|
||||
<div class="tablenoborder"><table cellpadding="4" cellspacing="0" summary="" id="modelarts_23_0402__en-us_topic_0000001160408180_table29911160452" frame="border" border="1" rules="all"><caption><b>Table 1 </b>Parameters</caption><thead align="left"><tr id="modelarts_23_0402__en-us_topic_0000001160408180_row15991516154519"><th align="left" class="cellrowborder" valign="top" width="18.87%" id="mcps1.3.6.2.3.1.1"><p id="modelarts_23_0402__en-us_topic_0000001160408180_p139911316154517">Parameter</p>
|
||||
<div class="tablenoborder"><table cellpadding="4" cellspacing="0" summary="" id="modelarts_23_0402__en-us_topic_0000001160408180_table29911160452" frame="border" border="1" rules="all"><caption><b>Table 1 </b>Parameters</caption><thead align="left"><tr id="modelarts_23_0402__en-us_topic_0000001160408180_row15991516154519"><th align="left" class="cellrowborder" valign="top" width="18.87%" id="mcps1.3.5.2.3.1.1"><p id="modelarts_23_0402__en-us_topic_0000001160408180_p139911316154517">Parameter</p>
|
||||
</th>
|
||||
<th align="left" class="cellrowborder" valign="top" width="81.13%" id="mcps1.3.6.2.3.1.2"><p id="modelarts_23_0402__en-us_topic_0000001160408180_p8991141611457">Description</p>
|
||||
<th align="left" class="cellrowborder" valign="top" width="81.13%" id="mcps1.3.5.2.3.1.2"><p id="modelarts_23_0402__en-us_topic_0000001160408180_p8991141611457">Description</p>
|
||||
</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody><tr id="modelarts_23_0402__en-us_topic_0000001160408180_row1199111616456"><td class="cellrowborder" valign="top" width="18.87%" headers="mcps1.3.6.2.3.1.1 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p4991151624510">cpuUsage</p>
|
||||
<tbody><tr id="modelarts_23_0402__en-us_topic_0000001160408180_row1199111616456"><td class="cellrowborder" valign="top" width="18.87%" headers="mcps1.3.5.2.3.1.1 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p4991151624510">cpuUsage</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="81.13%" headers="mcps1.3.6.2.3.1.2 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p59911916204512">CPU usage</p>
|
||||
<td class="cellrowborder" valign="top" width="81.13%" headers="mcps1.3.5.2.3.1.2 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p59911916204512">CPU usage</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr id="modelarts_23_0402__en-us_topic_0000001160408180_row209911616134519"><td class="cellrowborder" valign="top" width="18.87%" headers="mcps1.3.6.2.3.1.1 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p4991216174513">gpuMemUsage</p>
|
||||
<tr id="modelarts_23_0402__en-us_topic_0000001160408180_row209911616134519"><td class="cellrowborder" valign="top" width="18.87%" headers="mcps1.3.5.2.3.1.1 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p4991216174513">gpuMemUsage</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="81.13%" headers="mcps1.3.6.2.3.1.2 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p109911166452">GPU memory usage</p>
|
||||
<td class="cellrowborder" valign="top" width="81.13%" headers="mcps1.3.5.2.3.1.2 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p109911166452">GPU memory usage</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr id="modelarts_23_0402__en-us_topic_0000001160408180_row1267403516468"><td class="cellrowborder" valign="top" width="18.87%" headers="mcps1.3.6.2.3.1.1 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p067463520461">gpuUtil</p>
|
||||
<tr id="modelarts_23_0402__en-us_topic_0000001160408180_row1267403516468"><td class="cellrowborder" valign="top" width="18.87%" headers="mcps1.3.5.2.3.1.1 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p067463520461">gpuUtil</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="81.13%" headers="mcps1.3.6.2.3.1.2 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p767443574614">GPU usage</p>
|
||||
<td class="cellrowborder" valign="top" width="81.13%" headers="mcps1.3.5.2.3.1.2 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p767443574614">GPU usage</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr id="modelarts_23_0402__en-us_topic_0000001160408180_row12169174014460"><td class="cellrowborder" valign="top" width="18.87%" headers="mcps1.3.6.2.3.1.1 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p8169154015462">memUsage</p>
|
||||
<tr id="modelarts_23_0402__en-us_topic_0000001160408180_row12169174014460"><td class="cellrowborder" valign="top" width="18.87%" headers="mcps1.3.5.2.3.1.1 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p8169154015462">memUsage</p>
|
||||
</td>
|
||||
<td class="cellrowborder" valign="top" width="81.13%" headers="mcps1.3.6.2.3.1.2 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p17169440104618">Memory usage</p>
|
||||
<td class="cellrowborder" valign="top" width="81.13%" headers="mcps1.3.5.2.3.1.2 "><p id="modelarts_23_0402__en-us_topic_0000001160408180_p17169440104618">Memory usage</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
|
||||
@ -14,8 +14,6 @@
|
||||
</li>
|
||||
<li class="ulchildlink"><strong><a href="modelarts_30_0006.html">Starting, Stopping, or Deleting a Notebook Instance</a></strong><br>
|
||||
</li>
|
||||
<li class="ulchildlink"><strong><a href="modelarts_30_0037.html">Changing the Flavor of a Notebook Instance</a></strong><br>
|
||||
</li>
|
||||
<li class="ulchildlink"><strong><a href="modelarts_30_0033.html">Selecting Storage in DevEnviron</a></strong><br>
|
||||
</li>
|
||||
<li class="ulchildlink"><strong><a href="modelarts_30_0040.html">Dynamically Expanding EVS Disk Capacity</a></strong><br>
|
||||
|
||||
@ -98,7 +98,6 @@
|
||||
<td class="cellrowborder" valign="top" width="83.2%" headers="mcps1.3.3.2.3.3.2.3.2.3.1.2 "><p id="modelarts_30_0004__p2084611102574">Set a whitelist after remote SSH is enabled. This parameter is optional.</p>
|
||||
<p id="modelarts_30_0004__p11606181117474">Add the IP addresses for remotely accessing the notebook instance to the whitelist, for example, the IP address of your local PC or the public IP address of the source device. A maximum of five IP addresses can be added and separated by commas (,). If the parameter is left blank, all IP addresses will be allowed for remote SSH access.</p>
|
||||
<p id="modelarts_30_0004__p17297184414539">If your source device and ModelArts are isolated from each other in network, obtain the public IP address of your source device using a mainstream search engine, for example, by entering "IP address lookup", but not by running <strong id="modelarts_30_0004__b49291417133017">ipconfig</strong> or <strong id="modelarts_30_0004__b9720142512307">ifconfigip</strong> locally.</p>
|
||||
<div class="fignone" id="modelarts_30_0004__fig470582510475"><span class="figcap"><b>Figure 4 </b>IP address lookup</span><br><span><img id="modelarts_30_0004__image13776855134512" src="en-us_image_0000001799338188.png" width="383.8380000000001" height="170.594711"></span></div>
|
||||
<p id="modelarts_30_0004__p94152054154815">After a notebook instance is created, you can change the whitelist IP addresses on the notebook instance details page.</p>
|
||||
</td>
|
||||
</tr>
|
||||
@ -107,7 +106,7 @@
|
||||
</div>
|
||||
</li></ol>
|
||||
</li><li id="modelarts_30_0004__li6228121675817">Click <span class="uicontrol" id="modelarts_30_0004__uicontrol135288764215"><b>Next</b></span>.</li><li id="modelarts_30_0004__li722841610589">After confirming the parameter settings, click <span class="uicontrol" id="modelarts_30_0004__uicontrol17724165293319"><b>Submit</b></span>.<p id="modelarts_30_0004__p1916175592616">Switch to the notebook instance list. The notebook instance is being created. It will take several minutes when its status changes to <span class="parmname" id="modelarts_30_0004__parmname66641932284"><b>Running</b></span>. Then, the notebook instance is created.</p>
|
||||
</li><li id="modelarts_30_0004__li19195132320509">In the notebook instance list, click the instance name. On the instance details page that is displayed, view the instance configuration.<div class="fignone" id="modelarts_30_0004__fig63831517914"><span class="figcap"><b>Figure 5 </b>Details about a notebook instance</span><br><span><img id="modelarts_30_0004__image888991112182" src="en-us_image_0000001806157356.png" width="469.49" height="84.18900000000001"></span></div>
|
||||
</li><li id="modelarts_30_0004__li19195132320509">In the notebook instance list, click the instance name. On the instance details page that is displayed, view the instance configuration.<div class="fignone" id="modelarts_30_0004__fig63831517914"><span class="figcap"><b>Figure 4 </b>Details about a notebook instance</span><br><span><img id="modelarts_30_0004__image888991112182" src="en-us_image_0000001806157356.png" width="469.49" height="84.18900000000001"></span></div>
|
||||
<p id="modelarts_30_0004__p1349010711410">To modify the whitelist, click the modification icon on the right.</p>
|
||||
</li></ol>
|
||||
</div>
|
||||
|
||||
@ -4,8 +4,6 @@
|
||||
<div id="body0000001117288610"></div>
|
||||
<div>
|
||||
<ul class="ullinks">
|
||||
<li class="ulchildlink"><strong><a href="modelarts_30_0008.html">Operation Process in JupyterLab</a></strong><br>
|
||||
</li>
|
||||
<li class="ulchildlink"><strong><a href="modelarts_30_0009.html">JupyterLab Overview and Common Operations</a></strong><br>
|
||||
</li>
|
||||
<li class="ulchildlink"><strong><a href="modelarts_30_0041.html">Uploading Files to JupyterLab</a></strong><br>
|
||||
|
||||
@ -1,14 +0,0 @@
|
||||
<a name="modelarts_30_0008"></a><a name="modelarts_30_0008"></a>
|
||||
|
||||
<h1 class="topictitle1">Operation Process in JupyterLab</h1>
|
||||
<div id="body0000001177095671"><p id="modelarts_30_0008__p1784718215219">ModelArts allows you to access notebook instances online using JupyterLab and develop AI models based on the PyTorch, TensorFlow, or MindSpore engines. The following figure shows the operation process.</p>
|
||||
<div class="fignone" id="modelarts_30_0008__fig159616471461"><span class="figcap"><b>Figure 1 </b>Using JupyterLab to develop and debug code online</span><br><span><img id="modelarts_30_0008__image3687550143811" src="en-us_image_0000001799497928.png" width="497.09814" height="97.75500000000001"></span></div>
|
||||
<ol id="modelarts_30_0008__ol159644713466"><li id="modelarts_30_0008__li19471220247">Create a notebook instance.<p id="modelarts_30_0008__p1947114201410"><a name="modelarts_30_0008__li19471220247"></a><a name="li19471220247"></a>On the ModelArts management console, create a notebook instance with a proper AI engine. For details, see <a href="modelarts_30_0004.html">Creating a Notebook Instance</a>.</p>
|
||||
</li><li id="modelarts_30_0008__li78310218166">Use JupyterLab to access the notebook instance. For details, see <a href="modelarts_30_0009.html#modelarts_30_0009__section195461127123320">Accessing JupyterLab</a>.</li><li id="modelarts_30_0008__li8434153053">Upload training data and code files to JupyterLab. For details, see <a href="modelarts_30_0042.html">Uploading Files from a Local Path to JupyterLab</a>.</li><li id="modelarts_30_0008__li143443451">Compile and debug code in JupyterLab. For details, see <a href="modelarts_30_0009.html">JupyterLab Overview and Common Operations</a>.</li><li id="modelarts_30_0008__li797912247176">In JupyterLab, call the ModelArts SDK to create a training job for in-cloud training.</li></ol>
|
||||
</div>
|
||||
<div>
|
||||
<div class="familylinks">
|
||||
<div class="parentlink"><strong>Parent topic:</strong> <a href="modelarts_30_0007.html">Using JupyterLab to Develop Models</a></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@ -3,7 +3,7 @@
|
||||
<h1 class="topictitle1">JupyterLab Overview and Common Operations</h1>
|
||||
<div id="body0000001117128702"><p id="modelarts_30_0009__p8060118">JupyterLab is the next-generation web-based interactive development environment of Jupyter Notebook, enabling you to compile notebooks, operate terminals, edit Markdown text, enable interaction, and view CSV files and images.</p>
|
||||
<p id="modelarts_30_0009__p124774367">JupyterLab is the future mainstream development environment for developers. It has the same components as Jupyter Notebook, but offering more flexibility and powerful functions.</p>
|
||||
<div class="section" id="modelarts_30_0009__section195461127123320"><a name="modelarts_30_0009__section195461127123320"></a><a name="section195461127123320"></a><h4 class="sectiontitle">Accessing JupyterLab</h4><p id="modelarts_30_0009__p22693228814">To access JupyterLab from a running notebook instance, perform the following operations:</p>
|
||||
<div class="section" id="modelarts_30_0009__section195461127123320"><h4 class="sectiontitle">Accessing JupyterLab</h4><p id="modelarts_30_0009__p22693228814">To access JupyterLab from a running notebook instance, perform the following operations:</p>
|
||||
<ol id="modelarts_30_0009__ol18457181874317"><li id="modelarts_30_0009__li12710135154419">Log in to the ModelArts management console. Choose <strong id="modelarts_30_0009__b196401532124112">DevEnviron</strong> > <strong id="modelarts_30_0009__b5646432154110">Notebook</strong> in the navigation pane on the left. The notebook list of the new version is displayed.</li><li id="modelarts_30_0009__li1693155734312">Click <span class="uicontrol" id="modelarts_30_0009__uicontrol125415517443"><b>Open</b></span> in the <strong id="modelarts_30_0009__b15730171317514">Operation</strong> column of a running notebook instance to access JupyterLab.<div class="fignone" id="modelarts_30_0009__fig1896661974915"><span class="figcap"><b>Figure 1 </b>Accessing a notebook instance</span><br><span><img id="modelarts_30_0009__image1742763217271" src="en-us_image_0000001852878165.png" width="469.49" height="44.688"></span></div>
|
||||
</li><li id="modelarts_30_0009__li2180123515451">The <strong id="modelarts_30_0009__b479214913537">Launcher</strong> page is automatically displayed. Perform required operations. For details, see <a href="https://jupyterlab.readthedocs.io/en/stable/" target="_blank" rel="noopener noreferrer">JupyterLab Documentation</a>.<div class="fignone" id="modelarts_30_0009__fig1727316104710"><span class="figcap"><b>Figure 2 </b>JupyterLab homepage</span><br><span><img id="modelarts_30_0009__image9955141280" src="en-us_image_0000001806319384.png" width="469.49" height="343.19320000000005"></span></div>
|
||||
<ul id="modelarts_30_0009__ul18988918808"><li id="modelarts_30_0009__li17988171812011"><strong id="modelarts_30_0009__b1376442918455">Notebook</strong>: Select a kernel for running notebook, for example, TensorFlow or Python.</li><li id="modelarts_30_0009__li298811186017"><strong id="modelarts_30_0009__b105016375457">Console</strong>: Call the terminal for command control.</li><li id="modelarts_30_0009__li298816188012"><strong id="modelarts_30_0009__b1875973918458">Other</strong>: Edit other files.</li></ul>
|
||||
|
||||
@ -1,15 +0,0 @@
|
||||
<a name="modelarts_30_0037"></a><a name="modelarts_30_0037"></a>
|
||||
|
||||
<h1 class="topictitle1">Changing the Flavor of a Notebook Instance</h1>
|
||||
<div id="body0000001185239663"><p id="modelarts_30_0037__p10262847535">ModelArts allows you to flexibly change the node flavor for a notebook instance.</p>
|
||||
<div class="section" id="modelarts_30_0037__section1167243231410"><h4 class="sectiontitle">Constraints</h4><p id="modelarts_30_0037__p74320384145">The target notebook instance is stopped.</p>
|
||||
</div>
|
||||
<div class="section" id="modelarts_30_0037__section103337881516"><h4 class="sectiontitle">Procedure</h4><ol id="modelarts_30_0037__ol1856510531668"><li id="modelarts_30_0037__li12710135154419">Log in to the ModelArts management console and choose <span class="parmname" id="modelarts_30_0037__parmname11201184416361"><b>DevEnviron</b></span> > <span class="parmname" id="modelarts_30_0037__parmname11581615133914"><b>Notebook</b></span> in the navigation pane on the left to switch to the notebook page.</li><li id="modelarts_30_0037__li1864212106717">In the notebook list, click <span><img id="modelarts_30_0037__image624818301753" src="en-us_image_0000001799497708.png"></span> in the <strong id="modelarts_30_0037__b106681438124516">Flavor</strong> column of the target notebook instance and choose the target flavor from the drop-down list.</li></ol>
|
||||
</div>
|
||||
</div>
|
||||
<div>
|
||||
<div class="familylinks">
|
||||
<div class="parentlink"><strong>Parent topic:</strong> <a href="modelarts_30_0003.html">Managing Notebook Instances</a></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@ -3,18 +3,11 @@
|
||||
<h1 class="topictitle1">Uploading a Local File with a Size Ranging from 100 MB to 5 GB to JupyterLab</h1>
|
||||
<div id="body0000001249954641"><p id="modelarts_30_0046__p12562299137">For a file that exceeds 100 MB but does not exceed 5 GB, upload the file to OBS (an object bucket or a parallel file system), and then download the file from OBS to the target notebook instance. After the download is complete, the file is automatically deleted from OBS.</p>
|
||||
<p id="modelarts_30_0046__p856259191316">Upload a local file with a size ranging from 100 MB to 5 GB to JupyterLab through OBS.</p>
|
||||
<div class="fignone" id="modelarts_30_0046__fig1980083419486"><span class="figcap"><b>Figure 1 </b>Uploading a large file through OBS</span><br><span><img id="modelarts_30_0046__image184068515141" src="en-us_image_0000001846057409.gif" height="273.315" width="523.6875"></span></div>
|
||||
<p id="modelarts_30_0046__p1956279181319">To upload a large file through OBS, set an OBS path.</p>
|
||||
<div class="fignone" id="modelarts_30_0046__fig17839124515211"><span class="figcap"><b>Figure 2 </b>OBS path for uploading the large file</span><br><span><img id="modelarts_30_0046__image16562493135" src="en-us_image_0000001846137489.jpg" width="498.351" height="146.185753"></span></div>
|
||||
<div class="note" id="modelarts_30_0046__note79746214541"><img src="public_sys-resources/note_3.0-en-us.png"><span class="notetitle"> </span><div class="notebody"><p id="modelarts_30_0046__p897512213541">Set an OBS path for uploading local files to JupyterLab. After the setting, this path is used by default in follow-up operations. To change the path, click <span><img id="modelarts_30_0046__image179751521135419" src="en-us_image_0000001799498316.png"></span> in the lower left corner of the file upload window.</p>
|
||||
</div></div>
|
||||
<ul id="modelarts_30_0046__ul185621492133"><li id="modelarts_30_0046__li356220951310">Method 1: Enter a valid OBS path in the text box and click <strong id="modelarts_30_0046__b8978451133911">OK</strong>.<div class="fignone" id="modelarts_30_0046__fig13304236165218"><span class="figcap"><b>Figure 3 </b>Setting a valid OBS path</span><br><span><img id="modelarts_30_0046__image9809132285917" src="en-us_image_0000001846057401.png" width="498.351" height="163.408189"></span></div>
|
||||
</li></ul>
|
||||
<ul id="modelarts_30_0046__ul185621492133"><li id="modelarts_30_0046__li356220951310">Method 1: Enter a valid OBS path in the text box and click <strong id="modelarts_30_0046__b8978451133911">OK</strong>.</li></ul>
|
||||
<p id="modelarts_30_0046__p13562159161317"></p>
|
||||
<ul id="modelarts_30_0046__ul9562691131"><li id="modelarts_30_0046__li1856211921319">Method 2: Select an OBS path in <strong id="modelarts_30_0046__b197516140615">OBS File Browser</strong> and click <strong id="modelarts_30_0046__b1826014593402">OK</strong>.<div class="fignone" id="modelarts_30_0046__fig175351678373"><span class="figcap"><b>Figure 4 </b>Setting a path in OBS File Browser</span><br><span><img id="modelarts_30_0046__image2746131625812" src="en-us_image_0000001799338564.png" width="498.351" height="222.840968"></span></div>
|
||||
</li></ul>
|
||||
<ul id="modelarts_30_0046__ul12562169181312"><li id="modelarts_30_0046__li35624911133">Method 3: Use the default path.<div class="fignone" id="modelarts_30_0046__fig12561351113617"><span class="figcap"><b>Figure 5 </b>Using the default path to upload a file</span><br><span><img id="modelarts_30_0046__image1656219111310" src="en-us_image_0000001799338552.jpg" width="498.351" height="137.549398"></span></div>
|
||||
</li></ul>
|
||||
<ul id="modelarts_30_0046__ul9562691131"><li id="modelarts_30_0046__li1856211921319">Method 2: Select an OBS path in <strong id="modelarts_30_0046__b197516140615">OBS File Browser</strong> and click <strong id="modelarts_30_0046__b1826014593402">OK</strong>.</li></ul>
|
||||
<ul id="modelarts_30_0046__ul12562169181312"><li id="modelarts_30_0046__li35624911133">Method 3: Use the default path.</li></ul>
|
||||
<p id="modelarts_30_0046__p14304229164"><strong id="modelarts_30_0046__b103061824115013">Decompressing a package</strong></p>
|
||||
<p id="modelarts_30_0046__p19606172711433">After a large file is uploaded to Notebook JupyterLab as a compressed package, you can decompress the package in Terminal.</p>
|
||||
<pre class="screen" id="modelarts_30_0046__screen2060622784311">unzip xxx.zip # Directly decompress the package in the path where the package is stored.</pre>
|
||||
|
||||
@ -2,9 +2,8 @@
|
||||
|
||||
<h1 class="topictitle1">Uploading a Local File Larger Than 5 GB to JupyterLab</h1>
|
||||
<div id="body0000001250394643"><p id="modelarts_30_0047__p1911113211412">A file exceeding 5 GB cannot be directly uploaded to JupyterLab.</p>
|
||||
<div class="fignone" id="modelarts_30_0047__fig4865185518552"><span class="figcap"><b>Figure 1 </b>Failed to upload a file exceeding 5 GB</span><br><span><img id="modelarts_30_0047__image4683155145519" src="en-us_image_0000001799498180.png" width="511.71750000000003" height="155.256885"></span></div>
|
||||
<p id="modelarts_30_0047__p1699735151410">To upload files exceeding 5 GB, upload them to OBS. Then, call the ModelArts MoXing or SDK API in the target notebook instance to read and write the files in OBS.</p>
|
||||
<div class="fignone" id="modelarts_30_0047__fig15668759124612"><span class="figcap"><b>Figure 2 </b>Uploading and downloading large files in a notebook instance</span><br><span><img id="modelarts_30_0047__image17377140123211" src="en-us_image_0000001846137341.png" width="513.7125" height="229.48684500000002"></span></div>
|
||||
<div class="fignone" id="modelarts_30_0047__fig15668759124612"><span class="figcap"><b>Figure 1 </b>Uploading and downloading large files in a notebook instance</span><br><span><img id="modelarts_30_0047__image17377140123211" src="en-us_image_0000001846137341.png" width="513.7125" height="229.48684500000002"></span></div>
|
||||
<p id="modelarts_30_0047__p49645471197"></p>
|
||||
<p id="modelarts_30_0047__p896319211513">The procedure is as follows:</p>
|
||||
<ol id="modelarts_30_0047__ol118481211035"><li id="modelarts_30_0047__li10891518104917">Upload the file from a local path to OBS. </li><li id="modelarts_30_0047__li1753062313310">Download the file from OBS to the notebook instance by calling the ModelArts SDK or MoXing API.<ul id="modelarts_30_0047__ul09128174510"><li id="modelarts_30_0047__li1091218171353">Method 1: Call the ModelArts SDK interconnected with the OBS API to download a file from OBS.<p id="modelarts_30_0047__p129121217055"><a name="modelarts_30_0047__li1091218171353"></a><a name="li1091218171353"></a>Example code:</p>
|
||||
@ -24,9 +23,9 @@ mox.file.copy_parallel('obs://bucket_name/sub_dir_0', '/home/ma-user/work/sub_di
|
||||
mox.file.copy('obs://bucket_name/obs_file.txt', '/home/ma-user/work/obs_file.txt')</pre>
|
||||
<p id="modelarts_30_0047__p09121417551">If a .zip file is downloaded, run the following command on the terminal to decompress the package:</p>
|
||||
<pre class="screen" id="modelarts_30_0047__screen814112202113">unzip xxx.zip # Directly decompress the package in the path where the package is stored.</pre>
|
||||
<p id="modelarts_30_0047__p366410451062">After the code is executed, open the terminal shown in <a href="#modelarts_30_0047__fig711883121018">Figure 3</a> and run the <strong id="modelarts_30_0047__b179715505511">ls /home/ma-user/work</strong> command to view the file downloaded to the notebook instance. Alternatively, view the downloaded file in the left navigation pane of Jupyter. If the file is not displayed, refresh the page.</p>
|
||||
<div class="fignone" id="modelarts_30_0047__fig711883121018"><a name="modelarts_30_0047__fig711883121018"></a><a name="fig711883121018"></a><span class="figcap"><b>Figure 3 </b>Opening the terminal</span><br><span><img id="modelarts_30_0047__image5462572082" src="en-us_image_0000001846057261.png" width="469.49" height="92.11580000000001"></span></div>
|
||||
<div class="fignone" id="modelarts_30_0047__fig13860172316217"><span class="figcap"><b>Figure 4 </b>File downloaded to a notebook instance</span><br><span><img id="modelarts_30_0047__image932120129111" src="en-us_image_0000001846137357.png"></span></div>
|
||||
<p id="modelarts_30_0047__p366410451062">After the code is executed, open the terminal shown in <a href="#modelarts_30_0047__fig711883121018">Figure 2</a> and run the <strong id="modelarts_30_0047__b179715505511">ls /home/ma-user/work</strong> command to view the file downloaded to the notebook instance. Alternatively, view the downloaded file in the left navigation pane of Jupyter. If the file is not displayed, refresh the page.</p>
|
||||
<div class="fignone" id="modelarts_30_0047__fig711883121018"><a name="modelarts_30_0047__fig711883121018"></a><a name="fig711883121018"></a><span class="figcap"><b>Figure 2 </b>Opening the terminal</span><br><span><img id="modelarts_30_0047__image5462572082" src="en-us_image_0000001846057261.png" width="469.49" height="92.11580000000001"></span></div>
|
||||
<div class="fignone" id="modelarts_30_0047__fig13860172316217"><span class="figcap"><b>Figure 3 </b>File downloaded to a notebook instance</span><br><span><img id="modelarts_30_0047__image932120129111" src="en-us_image_0000001846137357.png"></span></div>
|
||||
</li></ul>
|
||||
</li></ol>
|
||||
<div class="section" id="modelarts_30_0047__section150441310126"><h4 class="sectiontitle">Error Handling</h4><p id="modelarts_30_0047__p1612442873218">If you download a file from OBS to your notebook instance and the system displays error message "Permission denied", perform the following operations for troubleshooting:</p>
|
||||
|
||||
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Reference in New Issue
Block a user