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Update sagemaker.md #250

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NihalHarish May 15, 2020
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10 changes: 5 additions & 5 deletions docs/sagemaker.md
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Expand Up @@ -27,9 +27,9 @@ Here's a list of frameworks and versions which support this experience.

| Framework | Version |
| --- | --- |
| [TensorFlow](tensorflow.md) | 1.15 |
| [TensorFlow](tensorflow.md) | 1.15, 2.1 |
| [MXNet](mxnet.md) | 1.6 |
| [PyTorch](pytorch.md) | 1.3 |
| [PyTorch](pytorch.md) | 1.4, 1.5 |
| [XGBoost](xgboost.md) | >=0.90-2 [As Built-in algorithm](xgboost.md#use-xgboost-as-a-built-in-algorithm)|

More details for the deep learning frameworks on which containers these are can be found here: [SageMaker Framework Containers](https://docs.aws.amazon.com/sagemaker/latest/dg/pre-built-containers-frameworks-deep-learning.html) and [AWS Deep Learning Containers](https://aws.amazon.com/machine-learning/containers/). You do not have to specify any training container image if you want to use them on SageMaker. You only need to specify the version above to use these containers.
Expand All @@ -40,11 +40,11 @@ This library `smdebug` itself supports versions other than the ones listed above

| Framework | Versions |
| --- | --- |
| [TensorFlow](tensorflow.md) | 1.13, 1.14, 1.15 |
| [TensorFlow](tensorflow.md) | 1.13, 1.14, 1.15, 2.1, 2.2 |
| Keras (with TensorFlow backend) | 2.3 |
| [MXNet](mxnet.md) | 1.4, 1.5, 1.6 |
| [PyTorch](pytorch.md) | 1.2, 1.3 |
| [XGBoost](xgboost.md) | [As Framework](xgboost.md#use-xgboost-as-a-framework) |
| [PyTorch](pytorch.md) | 1.2, 1.3, 1.4, 1.5 |
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How about zero script change table above?

| [XGBoost](xgboost.md) | 0.90-2, 1.0-1 |

#### Setting up SageMaker Debugger with your script on your container

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