Using SageMaker SDK simplifies the creation of baseline metrics and scheduling model monitor. For the algorithms we use in this post (such as Wasserstein distance and Kolmogorov–Smirnov test), the container that we build needs access to both the training dataset a...
If the publish_cloudwatch_metrics value is Enabled in the Environment map in the /opt/ml/processing/processingjobconfig.json file, the container code emits Amazon CloudWatch metrics in this location: /opt/ml/output/metrics/cloudwatch. The schema for this file is closely based on the CloudWatch...
trainer.save_model()# Get rid of unused checkpoints inside the container to limit the model.tar.gz sizeos.system(f"rm -rf{args.model_dir}/checkpoint-*/") The full code is available onGitHub. Deploy the trained Hugging Face model to SageMaker...
新增下列引數來指定 PyTorch 和其他套件的版本。此外,請指出 SageMaker AI 資料平行程式庫和其他軟體的 Amazon S3 儲存貯體路徑,以使用 AWS 資源,例如 Amazon S3 外掛程式。 適用於 的 AWS Deep Learning Container 官方 Dockerfiles PyTorch 若要尋找 URLsSMDATAPARALLEL_BINARY引數,請參閱 的查詢資料表支援的架構...
Containerize the model in a Docker container. Add the container to ECR. Amazon SageMaker will retrieve this image for training and inference during the active learning workflow. The final code cell in the notebook prints your Docker image’s ECR ID. You can use ...
After we finish fine-tuning our model with our news data, we can test it by deploying a SageMaker endpoint for each. Again, we rely on the pre-built AWS DLC for Hugging Face, but this time theHuggingFaceModelpoints towards the inference container images: ...
Après avoir créé le Dockerfile, consultezAdapting Your Own Training Containerpour savoir comment créer le conteneur Docker, l'héberger sur Amazon ECR et exécuter une tâche de formation à l'aide de Python. SageMaker SDK L'exemple de code suivant montre un Dockerfile complet après avoir...
在此debugger_custom_container_test_folder範例中,開啟您的 SageMaker AI JupyterLab 並建立新的資料夾,以儲存您的訓練指令碼和 Dockerfile。下列程式碼範例是一個包含基本 Docker 建置命令的 Dockerfile。將下列程式碼貼入 Dockerfile 文字檔案並儲存。將訓練指令碼上傳至相同的資料夾。 # Specify a docker base im...
( image_uri=byoc_image_uri, entry_point="./debugger_custom_container_test_folder/your-training-script.py" role=sagemaker.get_execution_role(), base_job_name='debugger-custom-container-test', instance_count=1, instance_type='ml.p3.2xlarge', # Debugger-specific parameters profiler_config=...
The SageMaker Python SDK provides open-source APIs and containers to train and deploy models on SageMaker, using several different ML and deep learning frameworks. The Hugging Face integration with SageMaker allows you to build Hugging Face models at scale on your own domain-spec...