Get Started#
This guide provides installation instructions for the SageMaker HyperPod CLI and SDK.
System Requirements#
Supported Platforms#
Linux
macOS
Note
Windows is not supported at this time.
Supported ML Frameworks for Training#
PyTorch (version ≥ 1.10)
Supported Python Versions#
3.9 and above
Prerequisites#
For Training#
SageMaker HyperPod CLI currently supports HyperPodPytorchJob training workloads.
To run these jobs, install the SageMaker Training Operator.
For Inference#
The CLI supports creating inference endpoints using JumpStart models or custom models. To enable this, install the SageMaker Inference Operator.
Installation Options#
Install from PyPI#
It’s recommended to install the SageMaker HyperPod CLI and SDK in a Python virtual environment to avoid conflicts with other packages:
# Create a virtual environment
python -m venv {venv-name}
# Activate the virtual environment
source {venv-name}/bin/activate
Note
Remember to activate your virtual environment (source {venv-name}/bin/activate) each time you want to use the HyperPod CLI and SDK if you chose the virtual environment installation method.
You can install the SageMaker HyperPod CLI and SDK directly using pip:
# Install from PyPI
pip install sagemaker-hyperpod
To verify that the installation was successful, run:
# Verify CLI installation
hyp --help