Visualize Amazon SageMaker Debugger output tensors in TensorBoard

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
   )
   estimator.fit()

   The estimator.fit() 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 AWS 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