Conda Environments and Makefile Reference¶

This page documents the conda environment files and Makefile targets used in the developer install path.

Environment files¶

File	Env name	Platform	What conda provides
`environment.yml`	`hydra-suite`	All	Python, NumPy, SciPy, PySide6, Qt6, OpenCV, Numba
`environment-mps.yml`	`hydra-suite-mps`	macOS M1-M4	Same as CPU
`environment-cuda.yml`	`hydra-suite-cuda`	Linux/Windows (NVIDIA)	Same + CUDA 12 runtime libs (cublas, cudnn, cufft)
`environment-rocm.yml`	`hydra-suite-rocm`	Linux (AMD)	Same as CPU (ROCm is system-level)

The conda environments provide system libraries only (Qt, OpenGL, CUDA runtime). Python packages are installed separately via make install-*, which runs uv pip install -r requirements-*.txt.

Requirements files¶

File	Inherits	Adds (beyond pyproject.toml)
`requirements.txt`	`-e .`	`torch`, `torchvision` (CPU)
`requirements-mps.txt`	`-e .`	`torch`, `torchvision`, `torchaudio`, `onnxruntime`
`requirements-cuda.txt`	`-e .`	`torch`, `torchvision`, `torchaudio`, `tensorrt`, `onnxruntime-gpu`
`requirements-cuda12.txt`	`-r requirements-cuda.txt`	`--extra-index-url .../cu128`, `tensorrt-cu12-*`, `cupy-cuda12x`
`requirements-cuda13.txt`	`-r requirements-cuda.txt`	`--extra-index-url .../cu130`, `tensorrt-cu13-*`, `cupy-cuda13x`
`requirements-rocm.txt`	`-e .`	`torch` (ROCm), `cupy-rocm-6-0`, `onnxruntime`
`requirements-dev.txt`	(standalone)	pytest, black, flake8, mypy, build, twine, etc.

All requirements files include -e . which installs the package in editable mode, pulling base dependencies from pyproject.toml. This means dependencies are declared once — in pyproject.toml — and requirements files only add what pyproject.toml cannot express (torch index URLs, GPU-specific wheels).

ONNX Runtime and CUDA compatibility¶

onnxruntime-gpu==1.24.1 links against CUDA 12 user-space libraries (libcublasLt.so.12, libcudart.so.12). This is handled differently by each install path:

pip path: PyTorch's CUDA wheel installs nvidia-cublas-cu12, nvidia-cudnn-cu12, etc. as pip dependencies and preloads them via ctypes at import time. ONNX Runtime finds them in the same process.
conda path: environment-cuda.yml installs CUDA 12 runtime libs via conda packages. make install-cuda writes LD_LIBRARY_PATH activation hooks.

Both approaches work on CUDA 13 systems — CUDA 12 user-space libs coexist with a CUDA 13 driver.

Makefile targets¶

Setup (creates conda environment)¶

make setup            # CPU
make setup-mps        # Apple Silicon
make setup-cuda       # NVIDIA CUDA
make setup-rocm       # AMD ROCm

Install (pip packages into activated environment)¶

make install                      # CPU
make install-mps                  # Apple Silicon
make install-cuda CUDA_MAJOR=13  # NVIDIA CUDA 13
make install-cuda CUDA_MAJOR=12  # NVIDIA CUDA 12
make install-rocm                 # AMD ROCm
make install-dev                  # Dev tools (formatting, linting, testing, publishing)

Update (refresh both conda and pip)¶

make env-update                      # CPU
make env-update-mps                  # Apple Silicon
make env-update-cuda CUDA_MAJOR=13  # NVIDIA CUDA
make env-update-rocm                 # AMD ROCm

Remove¶

make env-remove         # CPU
make env-remove-mps     # Apple Silicon
make env-remove-cuda    # NVIDIA CUDA
make env-remove-rocm    # AMD ROCm

Other useful targets¶

make help              # Full command catalog
make pytest            # Run tests
make format            # Format code (autopep8 → black → isort)
make lint              # Lint at moderate severity
make build             # Build wheel and sdist for PyPI
make publish-test      # Upload to Test PyPI
make publish           # Upload to real PyPI