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Install

Install this package:

emerge -a dev-python/autograd

If the package is masked, you can unmask it using the autounmask tool or standard emerge options:

autounmask dev-python/autograd

Or alternatively:

emerge --autounmask-write -a dev-python/autograd

Package Information

Description:

Autograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily. The main intended application of Autograd is gradient-based optimization.

Homepage:

https://github.com/HIPS/autograd

License:

MIT

Versions

Metadata

Description

• Autograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily. The main intended application of Autograd is gradient-based optimization.

Maintainers

• Gentoo Science Project (sci@gentoo.org) - Type: project
• Alexander Puck Neuwirth (apn-pucky@gentoo.org) - Type: person

Upstream

• Remote IDs:
  • pypi: autograd
  • github: HIPS/autograd

<pkgmetadata>
	<maintainer type="project">
		<email>sci@gentoo.org</email>
		<name>Gentoo Science Project</name>
	</maintainer>
	<maintainer type="person">
		<email>apn-pucky@gentoo.org</email>
		<name>Alexander Puck Neuwirth</name>
	</maintainer>
	<longdescription lang="en">
	Autograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily. The main intended application of Autograd is gradient-based optimization. 
	</longdescription>
	<upstream>
		<remote-id type="pypi">autograd</remote-id>
		<remote-id type="github">HIPS/autograd</remote-id>
	</upstream>
</pkgmetadata>

Lint Warnings

• [Error] Missing md5-cache for ebuild autograd-9999
• [Error] Missing md5-cache for ebuild autograd-1.8.0

Manifest