hf-cloud-python-env-setup
Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.
Target: Software development & testing. Automatically collected · unverified.
Excerpt from the author’s documentation
Extracted from a fixed version of SKILL.md in its original language. This is the author’s documentation; installation, compatibility and runtime results have not been verified by this site.
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Topics in the source
- Python Environment Setup for SageMaker
- Core rules
- boto3 vs the SageMaker SDK
- How to set up
- Verifying
- Deployment-specific extras
- Common pitfalls
Most SageMaker deployment failures that look like AWS problems are actually Python environment problems: wrong Python version, broken dependency resolution, stale SDK that doesn't know about a current API. This skill makes env setup boring and correct. 1. Never use the system Python. Always work inside an isolated environment. 2. Pin the Python version, not the package versions. Use 3.10, 3.11, or 3.12. Avoid 3.13+ — ML libraries lag on wheel availability and dependency resolution breaks in confusing ways. 3. Install the latest of each package. Don't defensively pin boto3 or awscli. Newer ones have current API surfaces and security fixes. Only pin if the user explicitly requires a specific version. 4. Check installed versions correctly. Use importlib.metadata.version("package-name"), never module.version. The latter is inconsistent across packages. 5. The bundled scripts use boto3 directly. The SageMaker Python SDK is a valid alternative — see "boto3 vs the SageMaker SDK" below. The bundled deploy scripts (deploy.py, deployasync.py, teardown.py) use boto3 directly and read image URIs from AWS's published Deep Learning Containers catalog. That fits this workflow's explicit-stages de
This is an excerpt. Refer to the complete source and its current requirements.
- Source repository
- huggingface/skills
- Source file
- skills/hf-cloud-python-env-setup/SKILL.md
- Fixed revision
- 25910b3d0ba31a2ba102f901cdc9e008c278a69c
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This record was collected automatically. Its documentation, suitability and runtime behavior have not been manually verified.
Author: huggingface/skills · original repository metadata. Documentation review: Not reviewed. Last source check: 2026-11-09T16:01:02.677Z.
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