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Figure 3 Clicking SHUT DOWN to stop an instance Parent topic: Using a Notebook Instance for AI Development Through JupyterLab
Speech Labeling Model training requires a large amount of labeled data. Therefore, before the model training, label the unlabeled audio files. ModelArts enables you to label audio files in batches by one click. In addition, you can modify the labels of audio files, or remove their
Image Classification Model training uses a large number of labeled images. Therefore, before the model training, add labels to the images that are not labeled. You can add labels to images by manual labeling or auto labeling. In addition, you can modify the labels of images, or remove
Image Segmentation Training a model uses a large number of labeled images. Therefore, label images before the model training. You can label images on the ModelArts management console. Alternatively, modify labels, or delete them and label them again. Before labeling an image in image
Text Triplet Triplet labeling is suitable for scenarios where structured information, such as subjects, predicates, and objects, needs to be labeled in statements. With this function, not only entities in statements, but also relationships between entities can be labeled. Triplet
Object Detection Training a model uses a large number of labeled images. Therefore, label images before the model training. You can add labels to images by manual labeling or auto labeling. In addition, you can modify the labels of images, or remove their labels and label the images
Import Operation After a dataset is created, you can directly synchronize data from the dataset. Alternatively, you can import more data by importing the dataset. Data can be imported from an OBS directory or the manifest file. Prerequisites You have created a dataset. You have stored
Video Labeling Model training requires a large amount of labeled video data. Therefore, before the model training, label the unlabeled video files. ModelArts enables you to label video files. In addition, you can modify the labels of video files, or remove their labels and label the
Text Classification Model training requires a large amount of labeled data. Therefore, before the model training, add labels to the files that are not labeled. In addition, you can modify, delete, and re-label the labeled text. Text classification classifies text content based on
Member Management There is no member in a new team. You need to add members who will participate in a team labeling task. A maximum of 100 members can be added to a team. If there are more than 100 members, add them to different teams for better management. Adding a Member In the
Team Management Team labeling is managed in a unit of teams. To enable team labeling for a dataset, a team must be specified. Multiple members can be added to a team. Background An account can have a maximum of 10 teams. An account must have at least one team to enable team labeling
Sound Classification Model training requires a large amount of labeled data. Therefore, before the model training, label the unlabeled audio files. ModelArts enables you to label audio files in batches by one click. In addition, you can modify the labels of audio files, or remove
In the navigation pane on the left, choose AI Dedicated Resource Pools > Elastic Clusters. On the displayed page, configure the container engine size as needed. For details, see Resizing a Dedicated Resource Pool. If the fault persists, contact technical support.
Data Management (Old Version to Be Terminated) Introduction to Data Management Creating a Dataset (Old Version) Labeling Data Importing Data Exporting Data Modifying a Dataset Publishing a Dataset Deleting a Dataset Managing Dataset Versions Auto Labeling Confirming Hard Examples
Creating a Training Job Create a ModelArts training job based on the SFS Turbo shared file storage. Log in to the ModelArts console. In the navigation pane, choose Training Management > Training Jobs. Click Create Training Job in the upper right corner. On the displayed page, set
Using Notebook Instances Remotely Through PyCharm Connecting to a Notebook Instance Through PyCharm Toolkit Manually Connecting to a Notebook Instance Through PyCharm Uploading Data to a Notebook Instance Through PyCharm Parent topic: Using Notebook for AI Development and Debugging
Uploading Files to JupyterLab Uploading Files from a Local Path to JupyterLab Cloning GitHub Open-Source Repository Files to JupyterLab Uploading OBS Files to JupyterLab Uploading Remote Files to JupyterLab Parent topic: Using a Notebook Instance for AI Development Through JupyterLab
Connecting to a Notebook Instance Through PyCharm Toolkit AI developers use PyCharm to develop algorithms or models. ModelArts provides the PyCharm Toolkit plug-in to help AI developers quickly submit locally developed code to the ModelArts training environment.
Collecting Text Analysis Indicator Statistics The text analysis indicator statistics function is used to query call record statistics after AI inspection is complete.
It can help AI developers detect data problems in advance and effectively prevent algorithm precision deterioration or training failures caused by noisy data. Data cleansing: Data cleansing refers to the process of removing, correcting, or supplementing data.
