| # External tutorials |
| |
| ## Upstream tutorial |
| |
| - [LLVM - Lighthouse](https://github.com/llvm/lighthouse): "In essence, this |
| project should guide you through using MLIR for your own projects, showing the |
| way, but not forcing you to follow a particular path. Essentially, the role of |
| a lighthouse." |
| |
| ## Third-party tutorials |
| |
| The following lists tutorials and blogs that people have created independently. |
| |
| **Disclaimer**: These tutorials and blogs are maintained by third parties and |
| may therefore be out of sync with the upstream implementation. Please do not |
| report bugs upstream (e.g., on Discourse). |
| |
| - MLIR for beginners — |
| [Blog](https://www.jeremykun.com/2023/08/10/mlir-getting-started/) / |
| [Repository](https://github.com/j2kun/mlir-tutorial): A general introduction |
| to MLIR. Resembles the Toy tutorial. |
| |
| - [mlir-tutor](https://github.com/Groverkss/mlir-tutor): Exercises for learning |
| MLIR. |
| |
| - [MLIR introduction by Stephen Diehl](https://www.stephendiehl.com/tags/mlir/): |
| Focuses on explaining dialects and passes. |
| |
| - [End-to-End MLIR pipeline](https://github.com/DavidGinten/ML-compiler-exercise): |
| Demonstrates how deep learning models can be lowered from an ML framework to |
| executable binaries. |
