Tutorials
January 17, 2024 ยท View on GitHub
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Tutorials
- Running Hello World example in a unified way on Windows, Linux and MacOS
- Testing CM automation language to run image classification on any platform with CPU and GPU
- Reproducing experiments from the IPOL'22 journal article using CM
- MLPerf modularization, automation and reproducibility using the CM automation language:
- Running MLPerf RetinaNet inference benchmark on CPU via CM (Student Cluster Competition'22 tutorial)
- Running MLPerf BERT inference benchmark on CUDA GPU via CM (official submission)
- Running all MLPerf inference benchmarks out-of-the-box for MLPerf inference v3.1 community submission
- Customizing MLPerf inference benchmark and preparing submission
- Measuring power during MLPerf inference benchmarks
- Automating TinyMLPerf benchmark
- Reproducing/replicating Tiny MLPerf benchmark
- Reproducing/replicating MLPerf training benchmark
- Adding common CM interface to reproduce research projects and papers
- Understanding CM concepts
- Adding new CM scripts and automation pipelines/workflows