tch-rs

What it is

tch-rs provides Rust developers with access to the powerful deep learning capabilities of PyTorch through its C++ API, enabling high-performance machine learning applications.

tch-rs uniquely offers Rust developers seamless access to PyTorch's powerful capabilities, making it ideal for high-performance ML applications in a Rust environment.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Steep learning curve for non-Python developers

   tch-rs leverages PyTorch's C++ API, which might be unfamiliar to Rust developers used to idiomatic Rust patterns.

2. 02

   Frequent breaking changes between versions

   The library has undergone significant refactoring in minor version updates, leading to the need for substantial code modifications by users.

3. 03

   Limited community and support

   As an open-source project with a niche user base, tch-rs may have slower response times for issues and feature requests compared to larger projects.

4. 04

   Complex setup process

   Setting up the development environment requires configuring both Rust and PyTorch C++ API dependencies, which can be error-prone and time-consuming.

What it does well

• Direct access to PyTorch's C++ API from Rust

• High-performance machine learning capabilities

• Cross-platform support (Linux, macOS, Windows)

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.