Show HN: Pyvers – Python library for handling BCB changes and multiple back ends

4 months ago 5

A Python library for dynamic dispatch based on module versions and backends.

What can you do with pyvers?

🔄 Handle breaking changes between different versions of a library without cluttering your code
🔀 Switch seamlessly between different backend implementations (e.g., CPU vs GPU)
✨ Support multiple versions of a dependency in the same codebase without complex if/else logic
🚀 Write version-specific optimizations while maintaining backward compatibility
🧹 Keep your code clean and maintainable while supporting multiple environments

pyvers lets you write version-specific implementations that are automatically selected based on the installed package version or backend. Here's a simple example:

from pyvers import implement_for, register_backend, get_backend, set_backend # Register numpy backend - you could register more than one backend! register_backend(group="numpy", backends={"numpy": "numpy"}) # Function for NumPy < 2.0 (using bool8) @implement_for("numpy", from_version=None, to_version="2.0.0") def create_mask(arr): np = get_backend("numpy") return np.array([x > 0 for x in arr], dtype=np.bool8) # Function for NumPy >= 2.0 (using bool_) @implement_for("numpy", from_version="2.0.0") def create_mask(arr): # noqa: F811 np = get_backend("numpy") return np.array([x > 0 for x in arr], dtype=np.bool_) # Implement a jax version of this register_backend(group="numpy", backends={"jax.numpy": "jax.numpy"}) register_backend(group="jax", backends={"jax": "jax"}) # For version checking @implement_for("jax") # Check jax version instead of jax.numpy def create_mask(arr): # noqa: F811 import jax import jax.numpy as jnp # Import directly for clarity # Use JAX's JIT compilation and vectorization @jax.jit def _create_mask(x): return jnp.greater(x, 0).astype(jnp.bool_) return _create_mask(jnp.asarray(arr)) # The correct implementation is automatically chosen based on your NumPy version result = create_mask([-1, 2, -3, 4]) print("NumPy result:", result) with set_backend("numpy", "jax.numpy"): result = create_mask([-1, 2, -3, 4]) print("JAX result:", result)

Check out the examples folder for more advanced use cases:

Switching between NumPy and JAX.numpy backends
Handling CPU (SciPy) vs GPU (CuPy) implementations
Managing breaking changes in PyTorch 2.0
Supporting both gym and gymnasium APIs

Automatically select the right implementation based on package versions:

@implement_for("torch", from_version="2.0.0") def optimize_model(model): return torch.compile(model) # Only available in PyTorch 2.0+ @implement_for("torch", from_version=None, to_version="2.0.0") def optimize_model(model): # noqa: F811 return model # Fallback for older versions

Easily switch between different implementations:

# Register both backends register_backend(group="numpy", backends={ "numpy": "numpy", "jax.numpy": "jax.numpy" }) # Use context manager to switch backends with set_backend("numpy", "jax.numpy"): result = your_function() # Uses JAX with set_backend("numpy", "numpy"): result = your_function() # Uses NumPy

Backends are imported only when needed, so you can have optional dependencies:

register_backend(group="sparse", backends={ "scipy.sparse": "scipy.sparse", # CPU backend "cupyx.scipy.sparse": "cupyx.scipy.sparse" # GPU backend - does NOT require cupy to be installed! })

Contributions are welcome! Please feel free to submit a Pull Request.