dGinten
b87fe380d7
Add a new page to the MLIR documentation that links to the upstream Lighthouse project as well as additional third-party tutorials. The goal is to make it easier for newcomers to discover MLIR learning resources beyond the Toy tutorial. The underlying discussion/RFC can be found [here](https://discourse.llvm.org/t/rfc-tutorial-a-beginner-friendly-end-to-end-mlir-compiler-pipeline/89788).
32 lines
1.2 KiB
Markdown
32 lines
1.2 KiB
Markdown
# External tutorials
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## Upstream tutorial
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- [LLVM - Lighthouse](https://github.com/llvm/lighthouse): "In essence, this
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project should guide you through using MLIR for your own projects, showing the
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way, but not forcing you to follow a particular path. Essentially, the role of
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a lighthouse."
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## Third-party tutorials
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The following lists tutorials and blogs that people have created independently.
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**Disclaimer**: These tutorials and blogs are maintained by third parties and
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may therefore be out of sync with the upstream implementation. Please do not
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report bugs upstream (e.g., on Discourse).
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- MLIR for beginners —
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[Blog](https://www.jeremykun.com/2023/08/10/mlir-getting-started/) /
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[Repository](https://github.com/j2kun/mlir-tutorial): A general introduction
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to MLIR. Resembles the Toy tutorial.
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- [mlir-tutor](https://github.com/Groverkss/mlir-tutor): Exercises for learning
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MLIR.
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- [MLIR introduction by Stephen Diehl](https://www.stephendiehl.com/tags/mlir/):
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Focuses on explaining dialects and passes.
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- [End-to-End MLIR pipeline](https://github.com/DavidGinten/ML-compiler-exercise):
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Demonstrates how deep learning models can be lowered from an ML framework to
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executable binaries.
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