einx is a Python library that provides a universal interface to formulate tensor operations in frameworks such as Numpy, PyTorch, Jax, Tensorflow, and MLX, using an Einstein-inspired notation. It offers a streamlined approach to complex tensor manipulations, often by compiling operations to backend-specific function calls, which helps minimize overhead. The current version is 0.4.3, with frequent minor releases addressing fixes and adding support for new backends.
pip install einxVerified import paths — ran on the pinned version, not inferred.
This example demonstrates basic tensor operations using `einx` with a NumPy array. `einx` automatically detects and uses an available backend (e.g., NumPy, PyTorch, JAX) for the tensor operations. The string notation defines how axes are manipulated.
Ensure your desired tensor backend (e.g., `pip install torch`) is installed. For PyTorch, use `pip install einx[torch]`.
Review any code that traces or compiles `einx` operations and ensure that any necessary backend functions are explicitly imported or accessed via the backend's module rather than assuming `einx`'s presence in the compiled context.
Adjust static type checking configurations or explicit type hints to account for `einx`'s more flexible `typing.Any` annotations for tensor parameters.
Consult the updated documentation and tutorials (especially the 'How does the notation work?' section) to understand the full vectorization analogy and ensure your `einx` expressions align with this paradigm.
Install the 'einx' library using pip: 'pip install einx'.
Verify the correct import statement by consulting the 'einx' documentation for the desired component.
Check the 'einx' documentation to confirm the existence and correct usage of 'X'.
Review the function's signature in the 'einx' documentation and provide all necessary arguments.
Ensure that the value provided for 'arg' meets the expected criteria as specified in the 'einx' documentation.