Use the PyTorch framework estimators in the SageMaker Python SDK - Amazon SageMaker

Use the PyTorch framework estimators in the SageMaker Python SDK

You can launch distributed training by adding the distribution argument to the SageMaker framework estimators, PyTorch or TensorFlow. For more details, choose one of the frameworks supported by the SageMaker distributed data parallelism (SMDDP) library from the following selections.

PyTorch

The following launcher options are available for launching PyTorch distributed training.

  • pytorchddp – This option runs mpirun and sets up environment variables needed for running PyTorch distributed training on SageMaker. To use this option, pass the following dictionary to the distribution parameter.

    { "pytorchddp": { "enabled": True } }

  • torch_distributed – This option runs torchrun and sets up environment variables needed for running PyTorch distributed training on SageMaker. To use this option, pass the following dictionary to the distribution parameter.

    { "torch_distributed": { "enabled": True } }

  • smdistributed – This option also runs mpirun but with smddprun that sets up environment variables needed for running PyTorch distributed training on SageMaker.

    { "smdistributed": { "dataparallel": { "enabled": True } } }

If you chose to replace NCCL AllGather to SMDDP AllGather, you can use all three options. Choose one option that fits with your use case.

If you chose to replace NCCL AllReduce with SMDDP AllReduce, you should choose one of the mpirun-based options: smdistributed or pytorchddp. You can also add additional MPI options as follows.

{ 
    "pytorchddp": {
        "enabled": True, 
        "custom_mpi_options": "-verbose -x NCCL_DEBUG=VERSION"
    }
}
{ 
    "smdistributed": { 
        "dataparallel": {
            "enabled": True, 
            "custom_mpi_options": "-verbose -x NCCL_DEBUG=VERSION"
        }
    }
}

The following code sample shows the basic structure of a PyTorch estimator with distributed training options.

from sagemaker.pytorch import PyTorch

pt_estimator = PyTorch(
    base_job_name="training_job_name_prefix",
    source_dir="subdirectory-to-your-code",
    entry_point="adapted-training-script.py",
    role="SageMakerRole",
    py_version="py310",
    framework_version="2.0.1",

    # For running a multi-node distributed training job, specify a value greater than 1
    # Example: 2,3,4,..8
    instance_count=2,

    # Instance types supported by the SageMaker data parallel library: 
    # ml.p4d.24xlarge, ml.p4de.24xlarge
    instance_type="ml.p4d.24xlarge",

    # Activate distributed training with SMDDP
    distribution={ "pytorchddp": { "enabled": True } }  # mpirun, activates SMDDP AllReduce OR AllGather
    # distribution={ "torch_distributed": { "enabled": True } }  # torchrun, activates SMDDP AllGather
    # distribution={ "smdistributed": { "dataparallel": { "enabled": True } } }  # mpirun, activates SMDDP AllReduce OR AllGather
)

pt_estimator.fit("s3://bucket/path/to/training/data")
Note

PyTorch Lightning and its utility libraries such as Lightning Bolts are not preinstalled in the SageMaker PyTorch DLCs. Create the following requirements.txt file and save in the source directory where you save the training script.

# requirements.txt
pytorch-lightning
lightning-bolts

For example, the tree-structured directory should look like the following.

├── pytorch_training_launcher_jupyter_notebook.ipynb
└── sub-folder-for-your-code
    ├──  adapted-training-script.py
    └──  requirements.txt

For more information about specifying the source directory to place the requirements.txt file along with your training script and a job submission, see Using third-party libraries in the Amazon SageMaker Python SDK documentation.

Considerations for activating SMDDP collective operations and using the right distributed training launcher options

  • SMDDP AllReduce and SMDDP AllGather are not mutually compatible at present.

  • SMDDP AllReduce is activated by default when using smdistributed or pytorchddp, which are mpirun-based launchers, and NCCL AllGather is used.

  • SMDDP AllGather is activated by default when using torch_distributed launcher, and AllReduce falls back to NCCL.

  • SMDDP AllGather can also be activated when using the mpirun-based launchers with an additional environment variable set as follows.

    export SMDATAPARALLEL_OPTIMIZE_SDP=true
TensorFlow
Important

The SMDDP library discontinued support for TensorFlow and is no longer available in DLCs for TensorFlow later than v2.11.0. To find previous TensorFlow DLCs with the SMDDP library installed, see TensorFlow (deprecated).

from sagemaker.tensorflow import TensorFlow

tf_estimator = TensorFlow(
    base_job_name = "training_job_name_prefix",
    entry_point="adapted-training-script.py",
    role="SageMakerRole",
    framework_version="2.11.0",
    py_version="py38",

    # For running a multi-node distributed training job, specify a value greater than 1
    # Example: 2,3,4,..8
    instance_count=2,

    # Instance types supported by the SageMaker data parallel library: 
    # ml.p4d.24xlarge, ml.p3dn.24xlarge, and ml.p3.16xlarge
    instance_type="ml.p3.16xlarge",

    # Training using the SageMaker data parallel distributed training strategy
    distribution={ "smdistributed": { "dataparallel": { "enabled": True } } }
)

tf_estimator.fit("s3://bucket/path/to/training/data")