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fb16dfecae
Commit b7f05445c0 has added WWW entries to port Makefiles based on
WWW: lines in pkg-descr files.
This commit removes the WWW: lines of moved-over URLs from these
pkg-descr files.
Approved by: portmgr (tcberner)
25 lines
1.4 KiB
Plaintext
25 lines
1.4 KiB
Plaintext
JAX is Autograd and XLA, brought together for high-performance machine learning
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research.
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With its updated version of Autograd, JAX can automatically differentiate native
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Python and NumPy functions. It can differentiate through loops, branches,
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recursion, and closures, and it can take derivatives of derivatives of
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derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation)
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via grad as well as forward-mode differentiation, and the two can be composed
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arbitrarily to any order.
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What's new is that JAX uses XLA to compile and run your NumPy programs on GPUs
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and TPUs. Compilation happens under the hood by default, with library calls
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getting just-in-time compiled and executed. But JAX also lets you just-in-time
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compile your own Python functions into XLA-optimized kernels using a
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one-function API, jit. Compilation and automatic differentiation can be composed
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arbitrarily, so you can express sophisticated algorithms and get maximal
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performance without leaving Python. You can even program multiple GPUs or TPU
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cores at once using pmap, and differentiate through the whole thing.
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Dig a little deeper, and you'll see that JAX is really an extensible system for
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composable function transformations. Both grad and jit are instances of such
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transformations. Others are vmap for automatic vectorization and pmap for
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single-program multiple-data (SPMD) parallel programming of multiple
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accelerators, with more to come.
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