Access a collection of guides and tools for training and fine-tuning machine learning models.
From the README
- allreducebench.py - a much easier way to benchmark network throughput than nccl-tests.
- torch-distributed-gpu-test.py - a tool to quickly test your inter-node connectivity
- mamf-finder.py - what is the actual TFLOPS measurement you can get from your accelerator.
Excerpts from the project README on GitHub. Copyright and licensing remain with the respective authors.
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ml-engineering has 18.8k stars on GitHub. It has been forked 1.2k times. ml-engineering is written mainly in Python. It has been in active development since 2020. ml-engineering is available under the CC-BY-SA-4.0 license. Its main topics are ai, debugging, gpus, inference.
What is ml-engineering?
Machine Learning Engineering Open Book
Is ml-engineering open source?
ml-engineering is an open-source project. It is released under the CC-BY-SA-4.0 license.
Is ml-engineering free?
Yes. ml-engineering is free and open source — you can use, modify and self-host it.
What license does ml-engineering use?
ml-engineering is available under the CC-BY-SA-4.0 license.
What language is ml-engineering written in?
ml-engineering is written mainly in Python.
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