8 Advanced parallelization - Deep Learning with JAX
Por um escritor misterioso
Last updated 11 junho 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
Introducing Neuropod, Uber ATG's Open Source Deep Learning
Using Cloud TPU Multislice to scale AI workloads
JAX: accelerated machine learning research via composable function
GitHub - google/jax: Composable transformations of Python+NumPy
Running a deep learning workload with JAX on multinode multi-GPU
Efficiently Scale LLM Training Across a Large GPU Cluster with
Efficiently Scale LLM Training Across a Large GPU Cluster with
MCA, Free Full-Text
Convolution hierarchical deep-learning neural network (C-HiDeNN
Model Parallelism
20 Best Parallel Computing Books of All Time - BookAuthority
Why You Should (or Shouldn't) be Using Google's JAX in 2023
The State of Machine Learning Frameworks in 2019
Differentiable sampling of molecular geometries with uncertainty
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