hf-cloud-python-env-setup

name

hf-cloud-python-env-setup

description

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

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.

boto3 vs the SageMaker SDK

The bundled deploy scripts (deploy.py, deploy_async.py, ) use directly and read image URIs from . 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 is the stable underlying API client.