gloo VS ignite

These are two separate libraries that do very different things but share the same name. They are also written in two separate languages. That is a sizable gap between them, and reusing names happens often with libraries. Gloo (rust-wasm, this post) is also not new. Though, relative to Gloo (Go, solo-io), it is newer. But, there is also a Github repo even older than Gloo (solo-io): https://github.com/facebookincubator/gloo. As well, even if these were for some odd reason all about wasm, none of them are actually that popular. solo-io Gloo has the most stars (though that isn't the best metric of popularity, since it is relative to the community that actually uses it), but 3k simply isn't that much. There is certainly a good argument to look down on libraries that reuse popular library names, but this isn't really the case here. Both started not too long after each other (solo-io would not have most of the stars it currently has when Gloo-Rust started), are in separate languages (thus separate communities), and do very separate things.

backends from native torch distributed configuration: nccl, gloo, mpi.

Along with the PyTorch-Ignite 0.4.5 release, we are excited to announce the new release of the web application for generating PyTorch-Ignite's training pipelines. This blog post is an overview of the key features and updates of the Code Generator v0.2.0 project release.

PyTorch-Ignite's ignite.distributed (idist) submodule introduced in version v0.4.0 (July 2020) quickly turns single-process code into its data distributed version.

More details about distributed helpers provided by PyTorch-Ignite can be found in the documentation. A complete example of training on CIFAR10 can be found here.