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Accelerated array computing and code transformations for R. Numerical programs operating on arrays can be just-in-time compiled to optimized executables via XLA -- the same compiler that powers JAX an
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開發工具R · PythonGitHub @r-xla上線中營運者未確認
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Package website: release \|
dev
Accelerated array computing and code transformations for R, allowing you
to run numerical programs at the speed of light. The package supports
just-in-time (JIT) compilation for very fast execution and reverse-mode
automatic differentiation. Programs can run on CPU and NVIDIA GPU.
See the installation
guide for more
details, including prebuilt Docker images.
{anvl} makes numerical R code run fast on CPUs and GPUs, and computes
gradients of your functions automatically. It aspires to be for R what
JAX is for Python.檢視完整 README ↗目前無法載入完整 README。請透過原文連結閱讀。 以下為已儲存的摘錄。
Package website: release \|
dev
Accelerated array computing and code transformations for R, allowing you
to run numerical programs at the speed of light. The package supports
just-in-time (JIT) compilation for very fast execution and reverse-mode
automatic differentiation. Programs can run on CPU and NVIDIA GPU.
See the installation
guide for more
details, including prebuilt Docker images.
{anvl} makes numerical R code run fast on CPUs and GPUs, and computes
gradients of your functions automatically. It aspires to be for R what
JAX is for Python.檢視完整 README ↗