Warning
In favor of Amazon SageMaker Profiler, SageMaker AI Debugger deprecates the framework profiling feature starting from TensorFlow 2.11 and PyTorch 2.0. You can still use the feature in the previous versions of the frameworks and SDKs as follows.
-
SageMaker Python SDK <= v2.130.0
-
PyTorch >= v1.6.0, < v2.0
-
TensorFlow >= v2.3.1, < v2.11
See also March 16, 2023.
To enable Debugger framework profiling, configure the
framework_profile_params parameter when you construct an estimator.
Debugger framework profiling collects framework metrics, such as data from initialization
stage, data loader processes, Python operators of deep learning frameworks and training
scripts, detailed profiling within and between steps, with cProfile or Pyinstrument
options. Using the FrameworkProfile class, you can configure custom
framework profiling options.
Note
Before getting started with Debugger framework profiling, verify that the framework used to build your model is supported by Debugger for framework profiling. For more information, see Supported frameworks and algorithms.
Debugger saves the framework metrics in a default S3 bucket. The format of the
default S3 bucket URI is
s3://sagemaker-<region>-<12digit_account_id>/<training-job-name>/profiler-output/.
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