Hf Cloud Python Env Setup
Hugging FaceOfficialPublished by Hugging Face
- Skill
- Workflow
- Cloud and DevOps
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a robust, repeatable workflow for setting up isolated Python environments specifically tailored for SageMaker and AWS automation. By enforcing strict isolation from system Python and utilizing current SDK versions, it eliminates common deployment failures caused by dependency conflicts, stale libraries, or incompatible Python versions. The workflow supports both automated script-based setup and manual configuration, ensuring consistent execution across macOS, Linux, and Windows.
What it does
- Creates isolated virtual environments using Python 3.10-3.12.
- Automates the installation of current boto3 and AWS CLI packages.
- Provides idempotent scripts for environment initialization and verification.
- Offers guidance on managing dependencies for SageMaker-specific projects.
- Standardizes the invocation of Python interpreters to avoid path-related errors.
- Includes diagnostic tools to verify installed package versions.
When to use it
- Before starting any SageMaker deployment, training job, or AWS automation task.
- When encountering 'module not found' or dependency resolution errors in existing projects.
- When preparing a local development environment to interact with AWS APIs.
- When upgrading or troubleshooting SageMaker SDK or boto3 integrations.
Advantages
- Prevents pollution of system-level Python installations.
- Reduces deployment failures by ensuring up-to-date API surfaces.
- Ensures cross-platform consistency for deployment scripts.
- Simplifies dependency management by avoiding unnecessary version pinning.
- Provides clear, explicit paths for interpreter invocation.
Usage examples
- “Set up the environment for my new SageMaker project.”
- The agent executes the setup script to create a .venv directory with Python 3.12 and installs the required AWS SDKs.
- “Check if my boto3 version is current.”
- The agent runs the version check script within the isolated environment to report the installed boto3 and botocore versions.
- “I am getting a dependency error when running my deployment script.”
- The agent recreates the isolated environment using the standard setup workflow to resolve potential conflicts and ensure a clean state.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/huggingface/hf-cloud-python-env-setup/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip hf-cloud-python-env-setup.zip -d .agents/skills
Documentation by Hugging Face
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.
Two specific SDK cases that still need care:
Generative rerankers: the SDK routes the
text-rankingtask to TEI unconditionally, which is wrong for causal-LM rerankers like Qwen3-Reranker — those need vLLM (seehf-cloud-serving-image-selection). Pass the container explicitly for these models.SSO assumed-role credentials: v3 has had credential-resolution regressions in
ModelTrainer/FrameworkProcessorunder SSO profiles. If SDK calls fail with credential errors whileaws sts get-caller-identitysucceeds in the same shell, suspect this rather than your AWS config.
If you use the SDK, install it into the isolated env like everything else (.venv/bin/python -m pip install sagemaker). The bundled scripts don't require it.
How to set up
The fastest path is the bundled script — it's Python, so it runs the same on Windows, macOS, and Linux:
python3 scripts/setup_env.py # macOS / Linux
python scripts/setup_env.py # Windows (PowerShell / cmd)This script detects uv and uses it if available (faster), falls back to the stdlib venv module, creates .venv/ with Python 3.12 (override: python3 setup_env.py .venv 3.11), refuses unsupported Python versions, installs from the bundled requirements.txt, and is idempotent. It also prints the correct interpreter path for the host OS (see below).
Manual equivalent:
# Preferred: uv
uv venv --python 3.12 .venv
uv pip install --python .venv/bin/python --upgrade boto3 awscli # Windows: .venv\Scripts\python.exe
# Fallback: stdlib venv
python3.12 -m venv .venv
.venv/bin/python -m pip install --upgrade pip boto3 awscliAfter setup, invoke the env's Python explicitly rather than activating the venv. The interpreter path differs by platform:
.venv/bin/python deploy.py # macOS / Linux
.venv\Scripts\python.exe deploy.py # WindowsThis works the same in scripts, interactive shells, and agent tool calls. The rest of this skill writes .venv/bin/python for brevity — on Windows substitute .venv\Scripts\python.exe.
Verifying
.venv/bin/python scripts/check_versions.pyPrints versions of boto3, botocore, awscli. Uses importlib.metadata.version() so it works on every package, including ones without __version__. Pass arbitrary names: ... check_versions.py transformers huggingface_hub.
Deployment-specific extras
Default requirements.txt covers SageMaker orchestration. Some deployments need extras (huggingface_hub for model inspection, transformers for tokenizer validation). Add these to a deployment-specific requirements file in the project, install with the env's Python, don't pin unless there's a reason.
Common pitfalls
Mysterious pip install resolution errors
Almost always Python 3.13+ trying to install packages without wheels yet, or installing into a polluted system Python. Recreate at 3.12: delete .venv and re-run python3 setup_env.py .venv 3.12 (the script recreates the env when the version doesn't match, so you can also just re-run it).
pip install succeeded but the script says "module not found"
You installed into a different interpreter than the one running the script. Always invoke Python explicitly: .venv/bin/python -m pip install ... and .venv/bin/python deploy.py.
Inline python -c "..." one-liners fail in PowerShell
PowerShell's quoting rules mangle nested/escaped quotes in inline Python. Don't debug the quoting — write the snippet to a small .py file and run that. (All bundled helpers are files for exactly this reason.)
boto3 call fails with "unknown parameter"
Your boto3 is older than the API surface. Upgrade with .venv/bin/python -m pip install --upgrade boto3. Don't downgrade the script to match an old version.
sagemaker (the SDK) installed but the bundled scripts fail
The bundled scripts don't use the SDK — they only need boto3/awscli from requirements.txt. Installing sagemaker alongside is harmless, but it doesn't replace the requirements install.
Files
- ATTRIBUTION.mdAdded by AutoGineer894 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md6.6 KiB
- requirements.txt796 B
- scripts/check_versions.pyScript740 B
- scripts/setup_env.pyScript4.9 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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Needs: Needs Python
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Attribution and licence
- Published by
- Hugging Face
- Publisher
- Hugging Face (Official)
- Source
- huggingface/skills · skills/hf-cloud-python-env-setup · commit 25910b3
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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