Distributed Machine Learning

See this for the different paradigms: https://colossalai.org/docs/concepts/paradigms_of_parallelism/

These seem like a great set of walkthroughs: https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/scaling/JAX/single_gpu_techniques.html

Resources

• Data-Parallel Distributed Training of Deep Learning Models
• Pipeline-Parallelism: Distributed Training via Model Partitioning

Really good blog from JAX

• https://jax-ml.github.io/scaling-book/training/#tensor-parallelism
  • First cited by this https://fleetwood.dev/posts/domain-specific-architectures, which I mention in AI Inference

There are 2 ways to parallelize: https://docs.oneflow.org/en/v0.4.0/extended_topics/model_mixed_parallel.html