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‎.gitignore

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*~
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.pytest_cache/
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*.swp
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.docker/
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.docker/
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env/
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.vscode/

‎CHANGELOG.md

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# Changelog
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## v2.19.0 (2020-12-08)
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### Features
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* add tensorflow 1.15.4 and 2.3.1 as valid versions
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* add py36 as valid python version for pytorch 1.6.0
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* auto-select container version for p4d and smdistributed
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* add edge packaging job support
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* Add Clarify Processor, Model Bias, Explainability, and Quality Monitors support. (#494)
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* add model parallelism support
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* add data parallelism support (#454) (#511)
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* support creating and updating profiler in training job (#444) (#526)
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### Bug Fixes and Other Changes
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* bump boto3 and smdebug_rulesconfig versions for reinvent and enable data parallel integ tests
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* run UpdateTrainingJob tests only during allowed secondary status
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* Remove workarounds and apply fixes to Clarify and MM integ tests
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* add p4d to smdataparallel supported instances
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* Mount metadata directory when starting local mode docker container
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* add integ test for profiler
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* Re-enable model monitor integration tests.
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### Documentation Changes
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* add SageMaker distributed libraries documentation
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* update documentation for the new SageMaker Debugger APIs
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* minor updates to doc strings
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## v2.18.0 (2020-12-03)
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### Features
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* all de/serializers support content type
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* warn on 'Stopped' (non-Completed) jobs
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* all predictors support serializer/deserializer overrides
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### Bug Fixes and Other Changes
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* v2 upgrade tool should ignore cell starting with '%'
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* use iterrows to iterate pandas dataframe
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* check for distributions in TF estimator
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### Documentation Changes
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* Update link to Sagemaker PyTorch Docker Containers
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* create artifact restricted to SM context note
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### Testing and Release Infrastructure
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* remove flaky assertion in test_integ_history_server
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* adjust assertion of TensorFlow MNIST test
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## v2.17.0 (2020-12-02)
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### Features

‎VERSION

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2.17.1.dev0
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2.19.1.dev0

‎doc/_static/theme_overrides.css

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/* override table width restrictions */
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.wy-table-responsive table td, .wy-table-responsive table th {
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white-space: normal;
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}
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.wy-table-responsive {
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margin-bottom: 24px;
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max-width: 100%;
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overflow: visible;
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}

‎doc/api/index.rst

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prep_data/feature_store
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training/index
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training/distributed
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inference/index
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utility/index

‎doc/api/training/debugger.rst

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Debugger
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--------
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.. automodule:: sagemaker.debugger
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:members:
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:undoc-members:
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Amazon SageMaker Debugger provides full visibility
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into training jobs of state-of-the-art machine learning models.
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This SageMaker Debugger module provides high-level methods
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to set up Debugger configurations to
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monitor, profile, and debug your training job.
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Configure the Debugger-specific parameters when constructing
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a SageMaker estimator to gain visibility and insights
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into your training job.
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.. currentmodule:: sagemaker.debugger
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.. autoclass:: get_rule_container_image_uri
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:show-inheritance:
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.. autoclass:: get_default_profiler_rule
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:show-inheritance:
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.. class:: sagemaker.debugger.rule_configs
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A helper module to configure the SageMaker Debugger built-in rules with
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the :class:`~sagemaker.debugger.Rule` classmethods and
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and the :class:`~sagemaker.debugger.ProfilerRule` classmethods.
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For a full list of built-in rules, see
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`List of Debugger Built-in Rules
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<https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-rules.html>`_.
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This module is imported from the Debugger client library for rule configuration.
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For more information, see
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`Amazon SageMaker Debugger RulesConfig
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<https://github.com/awslabs/sagemaker-debugger-rulesconfig>`_.
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.. autoclass:: RuleBase
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:show-inheritance:
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.. autoclass:: Rule
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:show-inheritance:
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:inherited-members:
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.. autoclass:: ProfilerRule
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:show-inheritance:
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:inherited-members:
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.. autoclass:: CollectionConfig
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:show-inheritance:
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.. autoclass:: DebuggerHookConfig
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:show-inheritance:
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.. autoclass:: TensorBoardOutputConfig
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:show-inheritance:
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.. autoclass:: ProfilerConfig
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:show-inheritance:
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.. autoclass:: FrameworkProfile
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:show-inheritance:
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.. autoclass:: DetailedProfilingConfig
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:show-inheritance:
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.. autoclass:: DataloaderProfilingConfig
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:show-inheritance:
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.. autoclass:: PythonProfilingConfig
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:show-inheritance:
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.. autoclass:: PythonProfiler
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:show-inheritance:
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.. autoclass:: cProfileTimer
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:show-inheritance:
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.. automodule:: sagemaker.debugger.metrics_config
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:members: StepRange, TimeRange
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:undoc-members:

‎doc/api/training/distributed.rst

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Distributed Training APIs
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-------------------------
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SageMaker distributed training libraries offer both data parallel and model parallel training strategies.
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They combine software and hardware technologies to improve inter-GPU and inter-node communications.
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They extend SageMaker’s training capabilities with built-in options that require only small code changes to your training scripts.
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.. toctree::
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:maxdepth: 3
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smd_data_parallel
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smd_model_parallel

‎doc/api/training/index.rst

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#############
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.. toctree::
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:maxdepth: 1
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:glob:
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:maxdepth: 4
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*
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analytics
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automl
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debugger
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estimators
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algorithm
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tuner
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parameter
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processing

‎doc/api/training/processing.rst

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:members:
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:undoc-members:
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:show-inheritance:
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.. automodule:: sagemaker.clarify
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:members:
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:undoc-members:
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:show-inheritance:

‎doc/api/training/smd_data_parallel.rst

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###################################
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Distributed data parallel
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###################################
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SageMaker distributed data parallel (SDP) extends SageMaker’s training
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capabilities on deep learning models with near-linear scaling efficiency,
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achieving fast time-to-train with minimal code changes.
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- SDP optimizes your training job for AWS network infrastructure and EC2 instance topology.
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- SDP takes advantage of gradient update to communicate between nodes with a custom AllReduce algorithm.
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When training a model on a large amount of data, machine learning practitioners
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will often turn to distributed training to reduce the time to train.
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In some cases, where time is of the essence,
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the business requirement is to finish training as quickly as possible or at
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least within a constrained time period.
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Then, distributed training is scaled to use a cluster of multiple nodes,
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meaning not just multiple GPUs in a computing instance, but multiple instances
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with multiple GPUs. As the cluster size increases, so does the significant drop
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in performance. This drop in performance is primarily caused the communications
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overhead between nodes in a cluster.
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.. rubric:: Customize your training script
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To customize your own training script, you will need the following:
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.. raw:: html
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<div data-section-style="5" style="">
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- You must provide TensorFlow / PyTorch training scripts that are
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adapted to use SDP.
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- Your input data must be in an S3 bucket or in FSx in the AWS region
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that you will use to launch your training job. If you use the Jupyter
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notebooks provided, create a SageMaker notebook instance in the same
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region as the bucket that contains your input data. For more
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information about storing your training data, refer to
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the `SageMaker Python SDK data
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inputs <https://sagemaker.readthedocs.io/en/stable/overview.html#use-file-systems-as-training-inputs>`__ documentation.
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.. raw:: html
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</div>
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Use the API guides for each framework to see
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examples of training scripts that can be used to convert your training scripts.
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Then, use one of the example notebooks as your template to launch a training job.
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You’ll need to swap your training script with the one that came with the
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notebook and modify any input functions as necessary.
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Once you have launched a training job, you can monitor it using CloudWatch.
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Then you can see how to deploy your trained model to an endpoint by
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following one of the example notebooks for deploying a model. Finally,
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you can follow an example notebook to test inference on your deployed
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model.
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.. toctree::
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:maxdepth: 2
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smd_data_parallel_pytorch
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smd_data_parallel_tensorflow

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