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change: add MXNet 1.4.1 support (aws#886)
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‎README.rst

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@@ -173,9 +173,9 @@ MXNet SageMaker Estimators
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By using MXNet SageMaker Estimators, you can train and host MXNet models on Amazon SageMaker.
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Supported versions of MXNet: ``0.12.1``, ``1.0.0``, ``1.1.0``, ``1.2.1``, ``1.3.0``, ``1.4.0``.
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Supported versions of MXNet: ``0.12.1``, ``1.0.0``, ``1.1.0``, ``1.2.1``, ``1.3.0``, ``1.4.0``, ``1.4.1``.
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Supported versions of MXNet for Elastic Inference: ``1.3.0``, ``1.4.0``.
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Supported versions of MXNet for Elastic Inference: ``1.3.0``, ``1.4.0``, ``1.4.1``.
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We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
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‎doc/using_mxnet.rst

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With the SageMaker Python SDK, you can train and host MXNet models on Amazon SageMaker.
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Supported versions of MXNet: ``1.4.0``, ``1.3.0``, ``1.2.1``, ``1.1.0``, ``1.0.0``, ``0.12.1``.
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Supported versions of MXNet: ``0.12.1``, ``1.0.0``, ``1.1.0``, ``1.2.1``, ``1.3.0``, ``1.4.0``, ``1.4.1``.
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Supported versions of MXNet for Elastic Inference: ``1.4.0``, ``1.3.0``.
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Supported versions of MXNet for Elastic Inference: ``1.3.0``, ``1.4.0``, ``1.4.1``.
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Training with MXNet
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-------------------
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The Docker images have the following dependencies installed:
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+
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| Dependencies | MXNet 0.12.1 | MXNet 1.0.0 | MXNet 1.1.0 | MXNet 1.2.1 | MXNet 1.3.0 | MXNet 1.4.0 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+
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| Python | 2.7 or 3.5 | 2.7 or 3.5| 2.7 or 3.5| 2.7 or 3.5| 2.7 or 3.5| 2.7 or 3.6|
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+
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| CUDA (GPU image only) | 9.0 | 9.0 | 9.0 | 9.0 | 9.0 | 9.2 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+
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| numpy | 1.13.3 | 1.13.3 | 1.13.3 | 1.14.5 | 1.14.6 | 1.16.3 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+
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| onnx | N/A | N/A | N/A | 1.2.1 | 1.2.1 | 1.4.1 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+
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| keras-mxnet | N/A | N/A | N/A | N/A | 2.2.2 | 2.2.4.1 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+
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| Dependencies | MXNet 0.12.1 | MXNet 1.0.0 | MXNet 1.1.0 | MXNet 1.2.1 | MXNet 1.3.0 | MXNet 1.4.0 | MXNet 1.4.1 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+
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| Python | 2.7 or 3.5 | 2.7 or 3.5| 2.7 or 3.5| 2.7 or 3.5| 2.7 or 3.5| 2.7 or 3.6| 2.7 or 3.6|
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+
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| CUDA (GPU image only) | 9.0 | 9.0 | 9.0 | 9.0 | 9.0 | 9.2 | 10.0 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+
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| numpy | 1.13.3 | 1.13.3 | 1.13.3 | 1.14.5 | 1.14.6 | 1.16.3 | 1.14.5 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+
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| onnx | N/A | N/A | N/A | 1.2.1 | 1.2.1 | 1.4.1 | 1.4.1 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+
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| keras-mxnet | N/A | N/A | N/A | N/A | 2.2.2 | 2.2.4.1 | 2.2.4.1 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+
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The Docker images extend Ubuntu 16.04.
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You can select version of MXNet by passing a ``framework_version`` keyword arg to the MXNet Estimator constructor. Currently supported versions are listed in the above table. You can also set ``framework_version`` to only specify major and minor version, e.g ``1.2``, which will cause your training script to be run on the latest supported patch version of that minor version, which in this example would be 1.2.1.
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You can select version of MXNet by passing a ``framework_version`` keyword arg to the MXNet Estimator constructor. Currently supported versions are listed in the above table. You can also set ``framework_version`` to only specify major and minor version, e.g ``1.4``, which will cause your training script to be run on the latest supported patch version of that minor version, which in this example would be 1.4.1.
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Alternatively, you can build your own image by following the instructions in the SageMaker MXNet containers repository, and passing ``image_name`` to the MXNet Estimator constructor.
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You can visit the SageMaker MXNet container repositories here:

‎src/sagemaker/mxnet/README.rst

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With the SageMaker Python SDK, you can train and host MXNet models on Amazon SageMaker.
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Supported versions of MXNet: ``1.4.0``, ``1.3.0``, ``1.2.1``, ``1.1.0``, ``1.0.0``, ``0.12.1``.
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Supported versions of MXNet: ``0.12.1``, ``1.0.0``, ``1.1.0``, ``1.2.1``, ``1.3.0``, ``1.4.0``, ``1.4.1``.
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Supported versions of MXNet for Elastic Inference: ``1.4.0``, ``1.3.0``.
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Supported versions of MXNet for Elastic Inference: ``1.3.0``, ``1.4.0``, ``1.4.1``.
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For information about using MXNet with the SageMaker Python SDK, see https://sagemaker.readthedocs.io/en/stable/using_mxnet.html.
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The Docker images have the following dependencies installed:
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+
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| Dependencies | MXNet 0.12.1 | MXNet 1.0.0 | MXNet 1.1.0 | MXNet 1.2.1 | MXNet 1.3.0 | MXNet 1.4.0 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+
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| Python | 2.7 or 3.5 | 2.7 or 3.5| 2.7 or 3.5| 2.7 or 3.5| 2.7 or 3.5| 2.7 or 3.6|
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+
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| CUDA (GPU image only) | 9.0 | 9.0 | 9.0 | 9.0 | 9.0 | 9.2 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+
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| numpy | 1.13.3 | 1.13.3 | 1.13.3 | 1.14.5 | 1.14.6 | 1.16.3 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+
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| onnx | N/A | N/A | N/A | 1.2.1 | 1.2.1 | 1.4.1 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+
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| keras-mxnet | N/A | N/A | N/A | N/A | 2.2.2 | 2.2.4.1 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+
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| Dependencies | MXNet 0.12.1 | MXNet 1.0.0 | MXNet 1.1.0 | MXNet 1.2.1 | MXNet 1.3.0 | MXNet 1.4.0 | MXNet 1.4.1 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+
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| Python | 2.7 or 3.5 | 2.7 or 3.5| 2.7 or 3.5| 2.7 or 3.5| 2.7 or 3.5| 2.7 or 3.6| 2.7 or 3.6|
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+
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| CUDA (GPU image only) | 9.0 | 9.0 | 9.0 | 9.0 | 9.0 | 9.2 | 10.0 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+
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| numpy | 1.13.3 | 1.13.3 | 1.13.3 | 1.14.5 | 1.14.6 | 1.16.3 | 1.14.5 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+
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| onnx | N/A | N/A | N/A | 1.2.1 | 1.2.1 | 1.4.1 | 1.4.1 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+
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| keras-mxnet | N/A | N/A | N/A | N/A | 2.2.2 | 2.2.4.1 | 2.2.4.1 |
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+-------------------------+--------------+-------------+-------------+-------------+-------------+-------------+-------------+
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The Docker images extend Ubuntu 16.04.
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You can select version of MXNet by passing a ``framework_version`` keyword arg to the MXNet Estimator constructor. Currently supported versions are listed in the above table. You can also set ``framework_version`` to only specify major and minor version, e.g ``1.2``, which will cause your training script to be run on the latest supported patch version of that minor version, which in this example would be 1.2.1.
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You can select version of MXNet by passing a ``framework_version`` keyword arg to the MXNet Estimator constructor. Currently supported versions are listed in the above table. You can also set ``framework_version`` to only specify major and minor version, e.g ``1.4``, which will cause your training script to be run on the latest supported patch version of that minor version, which in this example would be 1.4.1.
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Alternatively, you can build your own image by following the instructions in the SageMaker MXNet containers repository, and passing ``image_name`` to the MXNet Estimator constructor.
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You can visit the SageMaker MXNet container repositories here:

‎src/sagemaker/mxnet/estimator.py

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__framework_name__ = "mxnet"
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_LOWEST_SCRIPT_MODE_VERSION = ["1", "3"]
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LATEST_VERSION = "1.4"
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LATEST_VERSION = "1.4.1"
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"""The latest version of MXNet included in the SageMaker pre-built Docker images."""
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def __init__(

‎tests/conftest.py

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"1.4",
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"1.4.1",
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],
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)
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def mxnet_version(request):

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