| # 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. |