# HF Cloud Python Setup

Creates an isolated Python environment with current boto3 for SageMaker and AWS work.

- Repository: https://github.com/huggingface/skills (11249 stars)
- Category: DevOps
- License: Apache-2.0
- Last updated: 2026-10-30
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add huggingface/skills --skill hf-cloud-python-env-setup -g -a claude-code -y`
Codex: `npx skills add huggingface/skills --skill hf-cloud-python-env-setup -g -a codex -y`
Cursor: `npx skills add huggingface/skills --skill hf-cloud-python-env-setup -g -a cursor -y`
Gemini CLI: `npx skills add huggingface/skills --skill hf-cloud-python-env-setup -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add huggingface/skills`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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`, `teardown.py`) use `boto3` directly and read image URIs from [AWS's published Deep Learning Containers catalog](https://aws.github.io/deep-learning-containers/reference/available_images/). 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](https://github.com/aws/sagemaker-python-sdk/pull/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.

The rest: https://github.com/huggingface/skills/blob/HEAD/skills/hf-cloud-python-env-setup/SKILL.md