Installation & Compatibility

Installation

We recommend installing scio from PyPI:

# Coming soon...

If you wish to install from source or wheels manually, you can download release assets directly on the GitHub repository, or clone its current state with:

git clone https://github.com/eliegoudout/scio.git

OS Compatibility

The library is available and functional on Ubuntu, Windows and MacOS (minus faiss-related features ─ feel free to show your interest here).

Framework Compatiblity

Although confidence scores are not a particularity of Neural Networks, scio is precisely developed to handle such models. More precisely, only PyTorch models (inheriting from torch.nn.Module) are currently supported.

GPU Compatibility

Our library is fully compatible and tested with CUDA devices for native use of GPU acceleration! Note however that features using faiss currently use CPU-bound indexes, introducing a potential GPU > CPU > GPU bottleneck. This may be improved in future versions ─ feel free to show your interest by opening a related issue.

Supported Data Types

The package is fully compatible and tested with torch.half (float16), torch.float (float32) and torch.double (float64) data types. However, we discourage the use of gradient-based algorithms with torch.half data, as they can easily generate irrelevant results due to potential nan values. Finally, note that faiss-related operations temporarily convert to 32 bits data, regardless of the input type.

There is currently no official support for torch.bfloat16 even though many features may be compatible. Feel free to show your interest by opening a related issue.