Metadata-Version: 1.1
Name: sagemaker_containers
Version: 2.3.5
Summary: Open source library for creating containers to run on Amazon SageMaker.
Home-page: https://github.com/aws/sagemaker-containers/
Author: Amazon Web Services
Author-email: UNKNOWN
License: Apache License 2.0
Description: # SageMaker Containers
        
        SageMaker Containers contains common functionality necessary to create a container compatible with SageMaker. It can be simply used by any container by just installing the module:
        
        ```bash
        pip install sagemaker-containers
        ```
        
        SageMaker Containers gives you tools to create SageMaker-compatible containers, and has additional tools for letting you create Frameworks (SageMaker-compatible containers that can run arbitrary python scripts).
        
        [SageMaker Chainer Container](https://github.com/aws/sagemaker-chainer-container) uses sagemaker-containers.
        
        [SageMaker TensorFlow Container](https://github.com/aws/sagemaker-tensorflow-container) and [SageMaker MXNet Container](https://github.com/aws/sagemaker-mxnet-container) will be ported to use it as well in the future. 
        
        ## Getting Started -  Executing User Scripts on Amazon SageMaker
        
        The objective of this tutorial is to explain how a script is executed inside any SageMaker-compatible container using **SageMaker Containers**.
        
        ### Creating the training job
        
        A SageMaker training job created using the [SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk#sagemaker-python-sdk-overview) [```Chainer```](https://github.com/aws/sagemaker-python-sdk#chainer-sagemaker-estimators), [```TensorFlow```](https://github.com/aws/sagemaker-python-sdk#tensorflow-sagemaker-estimators) and [```MXNet```](https://github.com/aws/sagemaker-python-sdk#mxnet-sagemaker-estimators) takes an user script containing the model to be trained, the Hyperparameters required by the script, and information about the input data. For example:
        
        ```python
        from sagemaker.chainer import Chainer
        
        # for complete list of parameters, see 
        # https://github.com/aws/sagemaker-python-sdk#sagemaker-python-sdk-overview
        estimator = Chainer(entry_point='user-script.py', 
                            hyperparameters={'batch-size':256, 
                                             'learning-rate':0.0001, 
                                             'communicator':'pure_nccl'},
                            ...) 
        
        # starts the training job with an input data channel named training pointing to 
        # s3://bucket/path/to/training/data
        # for more information about data channels, see
        # https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo.html#your-algorithms-training-algo-running-container-inputdataconfig
        chainer_estimator.fit({'training': 's3://bucket/path/to/training/data', 'testing': 's3://bucket/path/to/testing/data')
        ```
        
        ### How a script is executed inside the container
        
        When the container starts for training, **SageMaker Containers** installs the user script as a Python module. The module name matches the script name. In the case above, **user-script.py** is transformed in a Python module named **user-script**.
        
        After that, the Python interpreter executes the user module, passing ```hyperparameters``` as script arguments. The example above will be executed by **SageMaker Containers** as follow:
        
        ```bash
        python -m user-script --batch-size 256 --learning_rate 0.0001 --communicator pure_nccl
        ```
        
        A user provide script consumes the hyperparameters using any argument parsing library, for example:
        
        ```python
        if __name__ == '__main__':
          parser = argparse.ArgumentParser()
        
          parser.add_argument('--learning-rate', type=int, default=1)
          parser.add_argument('--batch-size', type=int, default=64)
          parser.add_argument('--communicator', type=str)
          parser.add_argument('--frequency', type=int, default=20)
        
          args = parser.parse_args()
          ...
        ```
        
        ### Reading additional information from the container
        
        Very often, a user script needs additional information from the container that is not available in ```hyperparameters```.
        SageMaker Containers writes this information as **environment variables** that are available inside the script.
        
        For example, the example above can read information about the **training** channel provided in the training job request:
        
        ```python
        if __name__ == '__main__':
          parser = argparse.ArgumentParser()
        
          ...
          
          # reads input channels training and testing from the environment variables
          parser.add_argument('--training', type=str, default=os.environ['SM_CHANNEL_TRAINING'])
          parser.add_argument('--testing', type=str, default=os.environ['SM_CHANNEL_TESTING'])
        
          args = parser.parse_args()
          ...
        ```
        ### List of provided environment variables by SageMaker Containers
        
        The list of the environment variables is logged and available in cloudwatch logs. From the example above:
        ```bash
        SM_NUM_GPUS=1
        SM_NUM_CPUS=4
        SM_NETWORK_INTERFACE_NAME=ethwe
        
        SM_CURRENT_HOST=algo-1
        SM_HOSTS=["algo-1","algo-2"]
        SM_LOG_LEVEL=20
        
        SM_USER_ARGS=["--batch-size","256","--learning-rate","0.0001","--communicator","pure_nccl"]
        
        SM_HP_LEARNING_RATE=0.0001
        SM_HP_BATCH-SIZE=10000
        
        SM_HPS={"batch-size": '256', "learning-rate": "0.0001","communicator": "pure_nccl"}
        
        SM_CHANNELS=["testing","training"]
        SM_CHANNEL_TRAINING=/opt/ml/input/data/training
        SM_CHANNEL_TESTING=/opt/ml/input/data/test
        
        SM_MODULE_NAME=user_script
        SM_MODULE_DIR=s3://sagemaker-{aws-region}-{aws-id}/{training-job-name}/source/sourcedir.tar.gz
        
        SM_INPUT_DIR=/opt/ml/input
        SM_INPUT_CONFIG_DIR=/opt/ml/input/config
        SM_OUTPUT_DIR=/opt/ml/output
        SM_OUTPUT_DATA_DIR=/opt/ml/output/data/algo-1
        SM_MODEL_DIR=/opt/ml/model
        
        SM_RESOURCE_CONFIG=
        {
            "current_host": "algo-1",
            "hosts": [
                "algo-1",
                "algo-2"
            ]
        }
        
        SM_INPUT_DATA_CONFIG=
        {
            "test": {
                "RecordWrapperType": "None",
                "S3DistributionType": "FullyReplicated",
                "TrainingInputMode": "File"
            },
            "train": {
                "RecordWrapperType": "None",
                "S3DistributionType": "FullyReplicated",
                "TrainingInputMode": "File"
            }
        }
        
        
        SM_FRAMEWORK_MODULE=sagemaker_chainer_container.training:main
        
        SM_TRAINING_ENV=
        {
            "channel_input_dirs": {
                "test": "/opt/ml/input/data/testing",
                "train": "/opt/ml/input/data/training"
            },
            "current_host": "algo-1",
            "framework_module": "sagemaker_chainer_container.training:main",
            "hosts": [
                "algo-1",
                "algo-2"
            ],
            "hyperparameters": {
                "batch-size": 10000,
                "epochs": 1
            },
            "input_config_dir": "/opt/ml/input/config",
            "input_data_config": {
                "test": {
                    "RecordWrapperType": "None",
                    "S3DistributionType": "FullyReplicated",
                    "TrainingInputMode": "File"
                },
                "train": {
                    "RecordWrapperType": "None",
                    "S3DistributionType": "FullyReplicated",
                    "TrainingInputMode": "File"
                }
            },
            "input_dir": "/opt/ml/input",
            "job_name": "preprod-chainer-2018-05-31-06-27-15-511",
            "log_level": 20,
            "model_dir": "/opt/ml/model",
            "module_dir": "s3://sagemaker-{aws-region}-{aws-id}/{training-job-name}/source/sourcedir.tar.gz",
            "module_name": "user_script",
            "network_interface_name": "ethwe",
            "num_cpus": 4,
            "num_gpus": 1,
            "output_data_dir": "/opt/ml/output/data/algo-1",
            "output_dir": "/opt/ml/output",
            "resource_config": {
                "current_host": "algo-1",
                "hosts": [
                    "algo-1",
                    "algo-2"
                ]
            }
        }
        ```
        ## IMPORTANT ENVIRONMENT VARIABLES
        These environment variables are those that you're likely to use when writing a user script. A full list of environment variables is given below.
        ### SM_MODEL_DIR
        ```json
        SM_MODEL_DIR=/opt/ml/model
        ```
        When the training job finishes, the container will be **deleted** including its file system expect for **/opt/ml/model** and **/opt/ml/output**. Use **/opt/ml/model** to save the model checkpoints. These checkpoints will be uploaded to the default S3 bucket. Usage example:
        ```python
        # using it in argparse
        parser.add_argument('model_dir', type=str, default=os.environ['SM_MODEL_DIR'])
        
        # using it as variable
        model_dir = os.environ['SM_MODEL_DIR']
        
        # saving checkpoints to model dir in chainer
        serializers.save_npz(os.path.join(os.environ['SM_MODEL_DIR'], 'model.npz'), model)
        ```
        
        For more information, see: [How Amazon SageMaker Processes Training Output](https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo.html#your-algorithms-training-algo-envvariables).
        
        ### SM_CHANNELS
        ```bash
        SM_CHANNELS='["testing","training"]'
        ```
        Contains the list of input data channels in the container.
        
        When you run training, you can partition your training data into different logical "channels".
        Depending on your problem, some common channel ideas are: "training", "testing", "evaluation" or "images'and "labels".
        
        ```SM_CHANNELS``` includes the name of the available channels in the container as a JSON encoded list. Usage example:
        
        ```python
        import json
        
        # using it in argparse
        parser.add_argument('channel_names', type=int, default=json.loads(os.environ['SM_CHANNELS'])))
        
        # using it as variable
        channel_names = json.loads(os.environ['SM_CHANNELS']))
        ```
        
        ### SM_CHANNEL_```{channel_name}```
        ```bash
        SM_CHANNEL_TRAINING='/opt/ml/input/data/training'
        SM_CHANNEL_TESTING='/opt/ml/input/data/testing'
        ```
        Contains the directory where the channel named ```channel_name``` is located in the container. Usage examples:
        
        ```python
        import json
        
        parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAINING'])
        parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TESTING'])
        
            
        args = parser.parse_args()
        
        train_file = np.load(os.path.join(args.train, 'train.npz'))
        test_file = np.load(os.path.join(args.test, 'test.npz'))
        ```
        
        ### SM_HPS
        ```bash
        SM_HPS='{"batch-size": "256", "learning-rate": "0.0001","communicator": "pure_nccl"}'
        ```
        Contains a JSON encoded dictionary with the user provided hyperparameters. Example usage:
        
        ```python
        import json
        
        hyperparameters = json.loads(os.environ['SM_HPS']))
        # {"batch-size": 256, "learning-rate": 0.0001, "communicator": "pure_nccl"}
        ```
        ### SM_HP_```{hyperparameter_name}```
        ```bash
        SM_HP_LEARNING-RATE=0.0001
        SM_HP_BATCH-SIZE=10000
        SM_HP_COMMUNICATOR=pure_nccl
        ```
        Contains value of the hyperparameter named ```hyperparameter_name```. Usage examples:
        
        ```python
        learning_rate = float(os.environ['SM_HP_LEARNING-RATE'])
        batch_size = int(os.environ['SM_HP_BATCH-SIZE'])
        comminicator = os.environ['SM_HP_COMMUNICATOR']
        ```
        #### SM_CURRENT_HOST
        ```json
        SM_CURRENT_HOST=algo-1
        ```
        The name of the current container on the container network. Usage example:
        
        ```python
        # using it in argparse
        parser.add_argument('current_host', type=str, default=os.environ['SM_CURRENT_HOST'])
        
        # using it as variable
        current_host = os.environ['SM_CURRENT_HOST']
        ```
        
        #### SM_HOSTS
        ```json
        SM_HOSTS='["algo-1","algo-2"]'
        ```
        JSON encoded list containing all the hosts . Usage example:
        
        ```python
        import json
        
        # using it in argparse
        parser.add_argument('hosts', type=nargs, default=json.loads(os.environ['SM_HOSTS']))
        
        # using it as variable
        hosts = json.loads(os.environ['SM_HOSTS'])
        ```
        
        #### SM_NUM_GPUS
        ```json
        SM_NUM_GPUS=1
        ```
        The number of gpus available in the current container. Usage example:
        
        ```python
        # using it in argparse
        parser.add_argument('num_gpus', type=int, default=os.environ['SM_NUM_GPUS'])
        
        # using it as variable
        num_gpus = int(os.environ['SM_NUM_GPUS'])
        ```
        ## Environment Variables full specification:
        #### SM_NUM_CPUS
        ```json
        SM_NUM_CPUS=32
        ```
        The number of cpus available in the current container. Usage example:
        ```python
        # using it in argparse
        parser.add_argument('num_cpus', type=int, default=os.environ['SM_NUM_CPUS'])
        
        # using it as variable
        num_cpus = int(os.environ['SM_NUM_CPUS'])
        ```
        
        
        #### SM_LOG_LEVEL
        ```json
        SM_LOG_LEVEL=20
        ```
        
        The current log level in the container. Usage example:
        ```python
        import logging
        
        logger = logging.getLogger(__name__)
        
        logger.setLevel(int(os.environ.get('SM_LOG_LEVEL', logging.INFO)))
        ```
        
        ### SM_NETWORK_INTERFACE_NAME
        ```json
        SM_NETWORK_INTERFACE_NAME=ethwe
        ```
        Name of the network interface, useful for distributed training. Usage example:
        ```python
        # using it in argparse
        parser.add_argument('network_interface', type=str, default=os.environ['SM_NETWORK_INTERFACE_NAME'])
        
        # using it as variable
        network_interface = os.environ['SM_NETWORK_INTERFACE_NAME']
        ```
        
        ### SM_USER_ARGS
        ```json
        SM_USER_ARGS='["--batch-size","256","--learning_rate","0.0001","--communicator","pure_nccl"]'
        ```
        
        JSON encoded list with the script arguments provided for training.
        
        ### SM_INPUT_DIR
        ```json
        SM_INPUT_DIR=/opt/ml/input/
        ```
        The path of the input directory, e.g. ```/opt/ml/input/```
        The input_dir, e.g. ```/opt/ml/input/```, is the directory where SageMaker saves input data and configuration files before and during training.
        
        ### SM_INPUT_CONFIG_DIR
        ```json
        SM_INPUT_DIR=/opt/ml/input/config
        ```
        The path of the input directory, e.g. ```/opt/ml/input/config/```. The directory where standard SageMaker configuration files are located, e.g. ```/opt/ml/input/config/```.
        
        SageMaker training creates the following files in this folder when training starts:
        - `hyperparameters.json`: Amazon SageMaker makes the hyperparameters in a CreateTrainingJob request available in this file.
        - `inputdataconfig.json`: You specify data channel information in the InputDataConfig parameter in a CreateTrainingJob request. Amazon SageMaker makes this information available in this file.
        - `resourceconfig.json`: name of the current host and all host containers in the training.
        
        More information about this files can be find here:
            https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo.html
        
        ### SM_OUTPUT_DATA_DIR
        ```json
        SM_OUTPUT_DATA_DIR=/opt/ml/output/data/algo-1
        ```
        The dir to write non-model training artifacts (e.g. evaluation results) which will be retained by SageMaker, e.g. ```/opt/ml/output/data```. 
        
        As your algorithm runs in a container, it generates output including the status of the training job and model and output artifacts. Your algorithm should write this information to the this directory.
        
        ### SM_RESOURCE_CONFIG
        ```json
        SM_RESOURCE_CONFIG='{"current_host":"algo-1","hosts":["algo-1","algo-2"]}'
        ```
        The contents from ```/opt/ml/input/config/resourceconfig.json```. It has the following keys:
        - current_host: The name of the current container on the container network.
            For example, ```'algo-1'```.
        -  hosts: The list of names of all containers on the container network,
            sorted lexicographically. For example, `['algo-1', 'algo-2', 'algo-3']`
            for a three-node cluster.
        
        For more information about resourceconfig.json:
        https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo.html#your-algorithms-training-algo-running-container-dist-training
        
        ### SM_INPUT_DATA_CONFIG
        ```json
        SM_INPUT_DATA_CONFIG='{
            "testing": {
                "RecordWrapperType": "None",
                "S3DistributionType": "FullyReplicated",
                "TrainingInputMode": "File"
            },
            "training": {
                "RecordWrapperType": "None",
                "S3DistributionType": "FullyReplicated",
                "TrainingInputMode": "File"
            }
        }'
        ```
        Input data configuration from ```/opt/ml/input/config/inputdataconfig.json```.
        
        For more information about inpudataconfig.json:
          https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo.html#your-algorithms-training-algo-running-container-dist-training
        
        ### SM_TRAINING_ENV
        ```python
        SM_TRAINING_ENV='
        {
            "channel_input_dirs": {
                "test": "/opt/ml/input/data/testing",
                "train": "/opt/ml/input/data/training"
            },
            "current_host": "algo-1",
            "framework_module": "sagemaker_chainer_container.training:main",
            "hosts": [
                "algo-1",
                "algo-2"
            ],
            "hyperparameters": {
                "batch-size": 10000,
                "epochs": 1
            },
            "input_config_dir": "/opt/ml/input/config",
            "input_data_config": {
                "test": {
                    "RecordWrapperType": "None",
                    "S3DistributionType": "FullyReplicated",
                    "TrainingInputMode": "File"
                },
                "train": {
                    "RecordWrapperType": "None",
                    "S3DistributionType": "FullyReplicated",
                    "TrainingInputMode": "File"
                }
            },
            "input_dir": "/opt/ml/input",
            "job_name": "preprod-chainer-2018-05-31-06-27-15-511",
            "log_level": 20,
            "model_dir": "/opt/ml/model",
            "module_dir": "s3://sagemaker-{aws-region}-{aws-id}/{training-job-name}/source/sourcedir.tar.gz",
            "module_name": "user_script",
            "network_interface_name": "ethwe",
            "num_cpus": 4,
            "num_gpus": 1,
            "output_data_dir": "/opt/ml/output/data/algo-1",
            "output_dir": "/opt/ml/output",
            "resource_config": {
                "current_host": "algo-1",
                "hosts": [
                    "algo-1",
                    "algo-2"
                ]
            }
        }'
        ```
        Provides the entire training information as a JSON encoded dictionary.
        ## License
        
        This library is licensed under the Apache 2.0 License. 
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
