Principle:Allenai Open instruct Project Dependency Management
| Knowledge Sources | |
|---|---|
| Domains | Build_System, Dependencies |
| Last Updated | 2026-02-07 02:00 GMT |
Overview
Principle of maintaining a single source of truth for project dependencies, build configuration, and development tooling to ensure reproducible builds and consistent development environments.
Description
Modern Python ML projects require careful dependency management due to the complex interactions between GPU-accelerated libraries (PyTorch, vLLM, Flash Attention), distributed computing frameworks (Ray, DeepSpeed), and their CUDA dependencies. The pyproject.toml standard (PEP 621) provides a declarative format for specifying all project metadata, dependencies with version constraints, platform-specific package sources, and tool configurations in a single file. This approach ensures that all developers and CI systems use identical dependency versions, linting rules, and test configurations.
Usage
Apply this principle as the foundation for any Python ML project. The pyproject.toml should be the first file consulted when setting up a development environment, understanding project dependencies, or configuring development tools.
Theoretical Basis
Dependency resolution follows semantic versioning constraints:
# Abstract dependency resolution
for package in declared_dependencies:
version = resolve(
package.name,
constraints=package.version_spec, # e.g., ">=2.9.0,<2.10"
platform=current_platform, # e.g., linux x86_64
index=platform_specific_index # e.g., PyTorch CUDA index
)
install(package.name, version)
Key constraints in ML projects:
- CUDA compatibility: PyTorch version must match CUDA toolkit
- Framework interop: vLLM, DeepSpeed, and Flash Attention have strict PyTorch version requirements
- Platform restriction: GPU-accelerated packages only available for Linux x86_64