diff --git a/README.md b/README.md index c2cfd7d58..4a1775b19 100644 --- a/README.md +++ b/README.md @@ -64,11 +64,11 @@ The following frameworks are available AWS Deep Learning Containers with the dee | Framework | Version | | --- | --- | | [TensorFlow](docs/tensorflow.md) | 1.15, 2.1.0, 2.2.0, 2.3.0 | -| [MXNet](docs/mxnet.md) | 1.6 | +| [MXNet](docs/mxnet.md) | 1.6, 1.7 | | [PyTorch](docs/pytorch.md) | 1.4, 1.5, 1.6 | | [XGBoost](docs/xgboost.md) | 0.90-2, 1.0-1 ([As a built-in algorithm](docs/xgboost.md#use-xgboost-as-a-built-in-algorithm))| -**Note**: Debugger with zero script change is partially available for TensorFlow v2.1.0 and v2.3.0. The `inputs`, `outputs`, `gradients`, and `layers` built-in collections are currently not available for these TensorFlow versions. +**Note**: Debugger with zero script change is partially available for TensorFlow v2.1.0. The `inputs`, `outputs`, `gradients`, and `layers` built-in collections are currently not available for these TensorFlow versions. ### AWS training containers with script mode @@ -78,7 +78,7 @@ The `smdebug` library supports frameworks other than the ones listed above while | --- | --- | | [TensorFlow](docs/tensorflow.md) | 1.13, 1.14, 1.15, 2.1.0, 2.2.0, 2.3.0 | | Keras (with TensorFlow backend) | 2.3 | -| [MXNet](docs/mxnet.md) | 1.4, 1.5, 1.6 | +| [MXNet](docs/mxnet.md) | 1.4, 1.5, 1.6, 1.7 | | [PyTorch](docs/pytorch.md) | 1.2, 1.3, 1.4, 1.5, 1.6 | | [XGBoost](docs/xgboost.md) | 0.90-2, 1.0-1 (As a framework)| diff --git a/docs/mxnet.md b/docs/mxnet.md index e54d50055..d418647d2 100644 --- a/docs/mxnet.md +++ b/docs/mxnet.md @@ -10,8 +10,8 @@ ## Support -- Zero Script Change experience where you need no modifications to your training script is supported in the official [SageMaker Framework Container for MXNet 1.6](https://docs.aws.amazon.com/sagemaker/latest/dg/pre-built-containers-frameworks-deep-learning.html), or the [AWS Deep Learning Container for MXNet 1.6](https://aws.amazon.com/machine-learning/containers/). -- This library itself supports the following versions when you use our API which requires a few minimal changes to your training script: MXNet 1.4, 1.5, 1.6. +- Zero Script Change experience where you need no modifications to your training script is supported in the official [AWS Deep Learning Container for MXNet](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#general-framework-containers). +- This library itself supports the following versions when you use our API which requires a few minimal changes to your training script: MXNet 1.4, 1.5, 1.6, and 1.7. - Only Gluon models are supported - When the Gluon model is hybridized, inputs and outputs of intermediate layers can not be saved - Parameter server based distributed training is not yet supported diff --git a/docs/pytorch.md b/docs/pytorch.md index 5a9f380df..f312d19e1 100644 --- a/docs/pytorch.md +++ b/docs/pytorch.md @@ -9,9 +9,9 @@ ## Support ### Versions -- Zero Script Change experience where you need no modifications to your training script is supported in the official [SageMaker Framework Container for PyTorch 1.3](https://docs.aws.amazon.com/sagemaker/latest/dg/pre-built-containers-frameworks-deep-learning.html), or the [AWS Deep Learning Container for PyTorch 1.3](https://aws.amazon.com/machine-learning/containers/). +- Zero Script Change experience where you need no modifications to your training script is supported in the official [AWS Deep Learning Container for PyTorch](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#general-framework-containers). -- The library itself supports the following versions when using changes to the training script: PyTorch 1.2, 1.3. +- The library itself supports the following versions when using changes to the training script: PyTorch 1.2, 1.3, 1.4, 1.5, and 1.6. --- diff --git a/docs/tensorflow.md b/docs/tensorflow.md index 2f50a9fc2..590630b77 100644 --- a/docs/tensorflow.md +++ b/docs/tensorflow.md @@ -12,17 +12,14 @@ ## Amazon SageMaker Debugger Support for TensorFlow -Amazon SageMaker Debugger python SDK and its client library `smdebug` now fully support TensorFlow 2.2 with the latest version release. +Amazon SageMaker Debugger python SDK and its client library `smdebug` now fully support TensorFlow 2.3 with the latest version release. -- [Amazon SageMaker Python SDK PyPI](https://pypi.org/project/sagemaker/) -- [The latest smdebug PyPI release](https://pypi.org/project/smdebug/) - -Using Debugger, you can access tensors of any kind for TensorFlow models, from the Keras model zoo to your own custom model, and save them using Debugger built-in or custom tensor collections. You can run your training script on [the official AWS Deep Learning Containers](https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-container.html) where Debugger can automatically capture tensors from your training job. It doesn't matter whether your TensorFlow models use Keras API or pure TensorFlow API (in eager mode or non-eager mode), you can directly run them on the AWS Deep Learning Containers. +Using Debugger, you can access tensors of any kind for TensorFlow models, from the Keras model zoo to your own custom model, and save them using Debugger built-in or custom tensor collections. You can run your training script on [the official AWS Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#general-framework-containers) where Debugger can automatically capture tensors from your training job. It doesn't matter whether your TensorFlow models use Keras API or pure TensorFlow API (in eager mode or non-eager mode), you can directly run them on the AWS Deep Learning Containers. Debugger and its client library `smdebug` support debugging your training job on other AWS training containers and custom containers. In this case, a hook registration process is required to manually add the hook features to your training script. For a full list of AWS TensorFlow containers to use Debugger, see [SageMaker containers to use Debugger with script mode](https://docs.aws.amazon.com/sagemaker/latest/dg/train-debugger.html#debugger-supported-aws-containers). For a complete guide for using custom containers, see [Use Debugger in Custom Training Containers](https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-bring-your-own-container.html). ### New Features supported by Debugger -- The latest TensorFlow version fully covered by Debugger is 2.2.0 +- The latest TensorFlow version fully covered by Debugger is 2.3.0 - Debug training jobs with the TensorFlow framework or Keras TensorFlow - Debug training jobs with the TensorFlow eager or non-eager mode - New built-in tensor collections: `inputs`, `outputs`, `layers`, `gradients`