Visualize Amazon SageMaker Debugger Output Tensors in TensorBoard - Amazon SageMaker
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Visualize Amazon SageMaker Debugger Output Tensors in TensorBoard


This page is deprecated in favor of Amazon SageMaker with TensoBoard, which provides a comprehensive TensorBoard experience integrated with SageMaker Training and the access control functionalities of SageMaker Domain. To learn more, see Use TensorBoard to Debug and Analyze Training Jobs in Amazon SageMaker.

Use SageMaker Debugger to create output tensor files that are compatible with TensorBoard. Load the files to visualize in TensorBoard and analyze your SageMaker training jobs. Debugger automatically generates output tensor files that are compatible with TensorBoard. For any hook configuration you customize for saving output tensors, Debugger has the flexibility to create scalar summaries, distributions, and histograms that you can import to TensorBoard.

            An architecture diagram of the Debugger output tensor saving mechanism.

You can enable this by passing DebuggerHookConfig and TensorBoardOutputConfig objects to an estimator.

The following procedure explains how to save scalars, weights, and biases as full tensors, histograms, and distributions that can be visualized with TensorBoard. Debugger saves them to the training container's local path (the default path is /opt/ml/output/tensors) and syncs to the Amazon S3 locations passed through the Debugger output configuration objects.

To save TensorBoard compatible output tensor files using Debugger
  1. Set up a tensorboard_output_config configuration object to save TensorBoard output using the Debugger TensorBoardOutputConfig class. For the s3_output_path parameter, specify the default S3 bucket of the current SageMaker session or a preferred S3 bucket. This example does not add the container_local_output_path parameter; instead, it is set to the default local path /opt/ml/output/tensors.

    import sagemaker from sagemaker.debugger import TensorBoardOutputConfig bucket = sagemaker.Session().default_bucket() tensorboard_output_config = TensorBoardOutputConfig( s3_output_path='s3://{}'.format(bucket) )

    For additional information, see the Debugger TensorBoardOutputConfig API in the Amazon SageMaker Python SDK.

  2. Configure the Debugger hook and customize the hook parameter values. For example, the following code configures a Debugger hook to save all scalar outputs every 100 steps in training phases and 10 steps in validation phases, the weights parameters every 500 steps (the default save_interval value for saving tensor collections is 500), and the bias parameters every 10 global steps until the global step reaches 500.

    from sagemaker.debugger import CollectionConfig, DebuggerHookConfig hook_config = DebuggerHookConfig( hook_parameters={ "train.save_interval": "100", "eval.save_interval": "10" }, collection_configs=[ CollectionConfig("weights"), CollectionConfig( name="biases", parameters={ "save_interval": "10", "end_step": "500", "save_histogram": "True" } ), ] )

    For more information about the Debugger configuration APIs, see the Debugger CollectionConfig and DebuggerHookConfig APIs in the Amazon SageMaker Python SDK.

  3. Construct a SageMaker estimator with the Debugger parameters passing the configuration objects. The following example template shows how to create a generic SageMaker estimator. You can replace estimator and Estimator with other SageMaker frameworks' estimator parent classes and estimator classes. Available SageMaker framework estimators for this functionality are TensorFlow, PyTorch, and MXNet.

    from sagemaker.estimator import Estimator estimator = Estimator( ... # Debugger parameters debugger_hook_config=hook_config, tensorboard_output_config=tensorboard_output_config )

    The method starts a training job, and Debugger writes the output tensor files in real time to the Debugger S3 output path and to the TensorBoard S3 output path. To retrieve the output paths, use the following estimator methods:

    • For the Debugger S3 output path, use estimator.latest_job_debugger_artifacts_path().

    • For the TensorBoard S3 output path, use estimator.latest_job_tensorboard_artifacts_path().

  4. After the training has completed, check the names of saved output tensors:

    from smdebug.trials import create_trial trial = create_trial(estimator.latest_job_debugger_artifacts_path()) trial.tensor_names()
  5. Check the TensorBoard output data in Amazon S3:

    tensorboard_output_path=estimator.latest_job_tensorboard_artifacts_path() print(tensorboard_output_path) !aws s3 ls {tensorboard_output_path}/
  6. Download the TensorBoard output data to your notebook instance. For example, the following Amazon CLI command downloads the TensorBoard files to /logs/fit under the current working directory of your notebook instance.

    !aws s3 cp --recursive {tensorboard_output_path} ./logs/fit
  7. Compress the file directory to a TAR file to download to your local machine.

    !tar -cf logs.tar logs
  8. Download and extract the Tensorboard TAR file to a directory on your device, launch a Jupyter notebook server, open a new notebook, and run the TensorBoard app.

    !tar -xf logs.tar %load_ext tensorboard %tensorboard --logdir logs/fit

The following animated screenshot illustrates steps 5 through 8. It demonstrates how to download the Debugger TensorBoard TAR file and load the file in a Jupyter notebook on your local device.

            An animated screenshot showing how to download and load the Debugger TensorBoard
                file on your local machine.