documentation: update framework version links (#4178)

Author: trungleduc

src/sagemaker/chainer/estimator.py

@@ -108,7 +108,7 @@ def __init__(
             framework_version (str): Chainer version you want to use for
                 executing your model training code. Defaults to ``None``. Required unless
                 ``image_uri`` is provided. List of supported versions:
-                https://github.com/aws/sagemaker-python-sdk#chainer-sagemaker-estimators.
+                https://sagemaker.readthedocs.io/en/stable/frameworks/chainer/using_chainer.html#using-chainer-with-the-sagemaker-python-sdk.
             image_uri (str): If specified, the estimator will use this image
                 for training and hosting, instead of selecting the appropriate
                 SageMaker official image based on framework_version and

src/sagemaker/huggingface/model.py

@@ -138,12 +138,10 @@ def __init__(
                 unless ``image_uri`` is provided.
             tensorflow_version (str): TensorFlow version you want to use for
                 executing your inference code. Defaults to ``None``. Required unless
-                ``pytorch_version`` is provided. List of supported versions:
-                https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.
+                ``pytorch_version`` is provided. The current supported version is ``2.4.1``.
             pytorch_version (str): PyTorch version you want to use for
                 executing your inference code. Defaults to ``None``. Required unless
-                ``tensorflow_version`` is provided. List of supported versions:
-                https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.
+                ``tensorflow_version`` is provided. The current supported versions are ``1.7.1`` and ``1.6.0``.
             py_version (str): Python version you want to use for executing your
                 model training code. Defaults to ``None``. Required unless
                 ``image_uri`` is provided.

src/sagemaker/mxnet/estimator.py

@@ -77,7 +77,7 @@ def __init__(
             framework_version (str): MXNet version you want to use for executing
                 your model training code. Defaults to `None`. Required unless
                 ``image_uri`` is provided. List of supported versions.
-                https://github.com/aws/sagemaker-python-sdk#mxnet-sagemaker-estimators.
+                https://aws.amazon.com/releasenotes/available-deep-learning-containers-images/.
             py_version (str): Python version you want to use for executing your
                 model training code. One of 'py2' or 'py3'. Defaults to ``None``. Required
                 unless ``image_uri`` is provided.

src/sagemaker/sklearn/estimator.py

@@ -75,7 +75,7 @@ def __init__(
             framework_version (str): Scikit-learn version you want to use for
                 executing your model training code. Defaults to ``None``. Required
                 unless ``image_uri`` is provided. List of supported versions:
-                https://github.com/aws/sagemaker-python-sdk#sklearn-sagemaker-estimators
+                https://docs.aws.amazon.com/sagemaker/latest/dg/sklearn.html
             py_version (str): Python version you want to use for executing your
                 model training code (default: 'py3'). Currently, 'py3' is the only
                 supported version. If ``None`` is passed in, ``image_uri`` must be

src/sagemaker/tensorflow/estimator.py

@@ -59,7 +59,7 @@ def __init__(
             framework_version (str): TensorFlow version you want to use for executing your model
                 training code. Defaults to ``None``. Required unless ``image_uri`` is provided.
                 List of supported versions:
-                https://github.com/aws/sagemaker-python-sdk#tensorflow-sagemaker-estimators.
+                https://aws.amazon.com/releasenotes/available-deep-learning-containers-images/.
             model_dir (str or PipelineVariable): S3 location where the checkpoint data and models
                 can be exported to during training (default: None). It will be passed in the
                 training script as one of the command line arguments. If not specified,