8 Advanced parallelization - Deep Learning with JAX

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Using easy-to-revise parallelism with xmap() · Compiling and automatically partitioning functions with pjit() · Using tensor sharding to achieve parallelization with XLA · Running code in multi-host configurations
8 Advanced parallelization - Deep Learning with JAX
Writing a Training Loop in JAX and Flax
8 Advanced parallelization - Deep Learning with JAX
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8 Advanced parallelization - Deep Learning with JAX
JAX: accelerated machine learning research via composable function
8 Advanced parallelization - Deep Learning with JAX
7 Parallelizing your computations - Deep Learning with JAX
8 Advanced parallelization - Deep Learning with JAX
GitHub - google/jax: Composable transformations of Python+NumPy
8 Advanced parallelization - Deep Learning with JAX
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8 Advanced parallelization - Deep Learning with JAX
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8 Advanced parallelization - Deep Learning with JAX
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8 Advanced parallelization - Deep Learning with JAX
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8 Advanced parallelization - Deep Learning with JAX
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8 Advanced parallelization - Deep Learning with JAX
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8 Advanced parallelization - Deep Learning with JAX
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8 Advanced parallelization - Deep Learning with JAX
Applying sequence and parallel graph splits on a data-parallel
8 Advanced parallelization - Deep Learning with JAX
Efficiently Scale LLM Training Across a Large GPU Cluster with
8 Advanced parallelization - Deep Learning with JAX
Tutorial 6 (JAX): Transformers and Multi-Head Attention — UvA DL
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