8 Advanced parallelization - Deep Learning with JAX
Por um escritor misterioso
Last updated 08 julho 2024
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
Energies, Free Full-Text
Convolution hierarchical deep-learning neural network (C-HiDeNN
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Scaling Language Model Training to a Trillion Parameters Using
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Training Deep Networks with Data Parallelism in Jax
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