tiny-ml VS crates.io

Im working on a toy project of mine, a simple tiny crate for machine learning (mainly because i have nothing better to do): https://github.com/MilimTheTrueOne/tiny-ml

Rust has a rich ecosystem of frameworks and libraries that let you read, parse, and manipulate text files, interact with cloud services and databases, and perform any other job that your project's development workflow may require. And because of its strong typing and tight memory management, you are much less likely to write programs that behave unexpectedly in production.

All the code discussed in this article can be accessed through this repository. For integration with existing projects consider using keyword_extraction crate available on crates.io.

So, be sure to double-check your critical libraries and be sure their alternatives exist in the Rust ecosystem. There’s a good chance the crates you need are available in Rust's crates.io repository.

The previous section was very simple, this section is also very simple but introduces us to cargo which is Rust's package manager, as a JS dev my mind goes straight to NPM.

Now, there has to be a place where all these packages come from. Similar to npmjs registry, where all node packages are registered, stored and retrieved, Rust also has something called crates.io where many helpful packages and dependencies are registered.

Before proceeding, let's check https://crates.io/, the official Rust package registry.

The most thrilling aspect has been the joy of writing the backend. It's like every struct, enum, and method in Rust forms this interconnected Multiverse of code , which you can see in crates.io which is best Documentation experience I Ever Had.

5. Crates.io

When using crates from eg. crates.io, and also things like std and core

Vendoring your packages was very tedious to even remotely get to work with Cargo. I spent a very long time getting Cargo to work together with cargo-local-registry. We vendor crates from crates.io and a custom internal registry.