Update information about saving models in the MXNet README #616
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laurenyu
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doc/using_mxnet.rst
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| The training script is very similar to a training script you might run outside of SageMaker, but you can access useful properties about the training environment through various environment variables, including the following: | ||
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| * ``SM_MODEL_DIR``: A string that represents the path where the training job should write the model artifacts to. |
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May be we should explain this is just '/opt/ml/model' and point to the BYO document?
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I'm going to punt on this and let Eric decide what should point where with these kinds of things
doc/using_mxnet.rst
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| * ``SM_MODEL_DIR``: A string that represents the path where the training job should write the model artifacts to. | ||
| After training, artifacts in this directory are uploaded to S3 for model hosting. | ||
| * ``SM_NUM_GPUS``: An integer representing the number of GPUs available to the host. | ||
| * ``SM_OUTPUT_DATA_DIR``: A string that represents the path to the directory to write output artifacts to. |
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I think this is not true. I just tested this actually. Things stored in '/opt/ml/output' doesn't get uploaded. Only reference I can find about this directory is if you put anything in '/opt/ml/output/failure' will show up in the DescribeTrainingJob call response.
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but our integ tests checking for a success file in the output dir do pass?
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| When running your training script on SageMaker, it will have access to some pre-installed third-party libraries including ``mxnet``, ``numpy``, ``onnx``, and ``keras-mxnet``. For more information on the runtime environment, including specific package versions, see `SageMaker MXNet Containers <#sagemaker-mxnet-containers>`__. | ||
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| If there are other packages you want to use with your script, you can include a `requirements.txt <https://pip.pypa.io/en/stable/user_guide/#requirements-files>`__ file in the same directory as your training script to install other dependencies at runtime. |
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Does the requirement.txt thing still work?
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yes. it's disabled only for TF script mode.
doc/using_mxnet.rst
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| mxnet_estimator = MXNet('train.py', | ||
| train_instance_type='ml.p2.xlarge', | ||
| train_instance_count=1, | ||
| framework_version='1.2.1') |
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I would add the hyperparamters in the example training script argparse part here.
| SageMaker. For convenience, accepts other types besides str, but | ||
| str() will be called on keys and values to convert them before | ||
| training. | ||
| - ``py_version`` Python version you want to use for executing your |
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Add the options are 'py2' and 'py3'?
| - ``train_volume_size`` Size in GB of the EBS volume to use for storing | ||
| input data during training. Must be large enough to store training | ||
| data if input_mode='File' is used (which is the default). | ||
| - ``train_max_run`` Timeout in seconds for training, after which Amazon |
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This is such a bad name for what it is -_-
doc/using_mxnet.rst
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| s3 location to a directory in the Docker container. 'Pipe' - Amazon | ||
| SageMaker streams data directly from s3 to the container via a Unix | ||
| named pipe. | ||
| - ``output_path`` s3 location where you want the training result (model |
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We can have a local file system location for this in local mode. Right?
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I think so. this is inaccurate across all of our docs, then. tweaked the wording here
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...and some other tweaks. The weirdness around how I formatted the ToC title is because GH doesn't seem to render the ToC directive in a nice way (i.e. the title is just normal paragraph text).
Update: things got a little messy as I was trying to merge in master. So the Sphinx file should be basically the same as the README. The reason it looks like the entire file was changed is because the Sphinx file had Windows-style line endings (CRLF).
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