framework version updates

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)|
 

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

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.
 
 ---
 

docs/tensorflow.md

@@ -12,17 +12,14 @@
 
 ## Amazon SageMaker Debugger Support for TensorFlow<a name="support"></a>
 
-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`