currytang/amadeus/create-env

Create Environment

Initialize and configure the project environment based on the selected package manager and project type.

Supported Managers

pixi (recommended for ML / data science)

• Conda-compatible, reproducible lock files, multi-language support
• Install: curl -fsSL https://pixi.sh/install.sh | bash && export PATH="$HOME/.pixi/bin:$PATH"

uv (recommended for pure Python)

• Extremely fast pip/PyPI package manager, PEP 517/518 compliant
• Install: curl -LsSf https://astral.sh/uv/install.sh | sh && source "$HOME/.cargo/env"

Workflow

1. Check whether the package manager is installed; install if missing
2. Check for an existing config file (pixi.toml, pyproject.toml, requirements.txt)
3. Initialize if no config exists: pixi init or uv init --python 3.11
4. Install packages based on project type (see table below)
5. Verify key imports succeed and lock file is present

Package Sets by Project Type

pixi Commands

# Ensure pixi is installed
command -v pixi || (curl -fsSL https://pixi.sh/install.sh | bash && export PATH="$HOME/.pixi/bin:$PATH")
 
# Initialize (skip if pixi.toml exists)
test -f pixi.toml || pixi init
 
# Deep learning (GPU — adjust cuda version as needed)
pixi add python pytorch torchvision torchaudio -c pytorch -c conda-forge
 
# Data science
pixi add python numpy pandas scikit-learn matplotlib seaborn jupyterlab -c conda-forge
 
# NLP
pixi add python transformers datasets tokenizers accelerate -c conda-forge
 
# Verify
pixi run python -c "import torch; print('torch', torch.__version__)"

uv Commands

# Ensure uv is installed
command -v uv || (curl -LsSf https://astral.sh/uv/install.sh | sh && source "$HOME/.cargo/env")
 
# Initialize (skip if pyproject.toml exists)
test -f pyproject.toml || uv init --python 3.11
 
# Deep learning
uv add torch torchvision torchaudio
 
# Data science
uv add numpy pandas scikit-learn matplotlib seaborn jupyterlab ipykernel
 
# NLP
uv add transformers datasets tokenizers accelerate
 
# Verify
uv run python -c "import torch; print('torch', torch.__version__)"

From Repo URL

If a repository URL was provided during jumpstart:

1. Clone minimally: git clone --depth 1 <url> _repo_tmp
2. Copy dependency files: cp _repo_tmp/requirements*.txt . 2>/dev/null || true
3. Install copied requirements: pixi add $(grep -v '^#' requirements.txt | head -50 | tr '\n' ' ') -c conda-forge
4. Clean up: rm -rf _repo_tmp

Verification Targets

After setup, confirm:

• pixi run python --version or uv run python --version succeeds
• Key package imports for the project type succeed
• Lock file exists: pixi.lock (pixi) or uv.lock (uv)

Error Handling

• If pixi/uv install fails: try pip install as fallback, note the degraded state
• If package not found in conda channel: add -c conda-forge or try uv instead
• If CUDA packages fail: install CPU-only variant first, note GPU variant for later