HF Cloud Python Setup
Creates an isolated Python environment with current boto3 for SageMaker and AWS work.
Install
npx skills add huggingface/skills --skill hf-cloud-python-env-setup -g -a claude-code -y- Repository
- huggingface/skills11.2k
- Category
- DevOps
- Updated
- 5d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 1,514 tokens
- Runs scripts
- Yes, review before use
What it does
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.
Python Environment Setup for SageMaker
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.
Core rules
- Never use the system Python. Always work inside an isolated environment.
- 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.
- Install the latest of each package. Don't defensively pin
boto3orawscli. Newer ones have current API surfaces and security fixes. Only pin if the user explicitly requires a specific version. - Check installed versions correctly. Use
importlib.metadata.version("package-name"), nevermodule.__version__. The latter is inconsistent across packages. - The bundled scripts use
boto3directly. The SageMaker Python SDK is a valid alternative, see "boto3 vs the SageMaker SDK" below.
boto3 vs the SageMaker SDK
The bundled deploy scripts (deploy.py, deploy_async.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 design, each skill produces a concrete value (region, role ARN, image URI) that the next one consumes, and boto3 is the stable underlying API client.
The SageMaker Python SDK (v3) is fine to use when the user prefers it or their project already does. Since PR #5960 (June 2026), ModelBuilder auto-routes HuggingFace models to the current containers (text-generation → HuggingFace vLLM, multimodal → vLLM-Omni, embeddings → TEI). Don't avoid the SDK over stale-image or wrong-container concerns, that routing is fixed.
