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I built a deep learning framework in Rust from scratch — Part 3: the road to crates.io
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I built a deep learning framework in Rust from scratch — Part 3: the road to crates.io

DEV Community·Pavel·about 1 month ago
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#phase#ai#rust#fullscreen#exit#cargo
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In Part 1 I argued why a graph-based DL framework in pure Rust was a project worth doing. In Part 2 I wrote the GPU backend on wgpu and figured out how to make TransformerBlock train on it. Both posts ended with the same honest admission: the code was fine, but the project wasn't ready for other humans. This post is about closing that gap. Six phases of work, a v0.2.0 → v0.3.1 bump, and a crate that now looks like something you'd actually reach for. Here's the plan I committed to at the start: Phase 1: cleanup and consistency — get to 0 warnings. Phase 2: API reliability — declarative layer API so users don't hand-manage HashMap<String, Shape> . Phase 3: GPU completeness — every CPU op should have a WGSL twin. Phase 5: ecosystem — RoPE done properly, Slice/Concat primitives, CI. Phase 6: pre-release polish — fmt, clippy, docs.rs, CNN example. (Phase 4 — performance — is intentionally deferred to v0.5.…

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