提交 83233a9d authored 作者: Frédéric Bastien's avatar Frédéric Bastien 提交者: GitHub

Merge pull request #6374 from abergeron/conda

Add conda recipe
{% set version = "0.10.0beta1" %}
package:
name: theano
version: {{ version }}
source:
git_url: https://github.com/Theano/Theano.git
git_tag: rel-{{ version }}
build:
noarch: python
script: python setup.py install --single-version-externally-managed --record record.txt
requirements:
build:
- m2-filesystem [win]
- m2-git [win]
- git [not win]
- python
- setuptools
- six >=1.9.0
- numpy >=1.9.1
- scipy >=0.14.0
- pygpu >=0.6,<0.7
run:
- python
- mkl-service
- libpython >=2.0 [win]
- m2w64-toolchain [win]
- six >=1.9.0
- numpy >=1.9.1
- scipy >=0.14.0
- pygpu >=0.6,<0.7
test:
requires:
- nose >=1.3.0
- nose-parameterized >=0.5.0
imports:
- theano
- theano.compile
- theano.compile.sandbox
- theano.compile.tests
- theano.gof
- theano.gof.tests
- theano.gpuarray
- theano.gpuarray.tests
- theano.misc
- theano.sandbox
- theano.scalar
- theano.scalar.tests
- theano.sparse
- theano.sparse.tests
- theano.tensor
- theano.tensor.nnet
- theano.tensor.nnet.tests
- theano.tensor.signal
- theano.tensor.signal.tests
- theano.tensor.tests
- theano.tests
about:
home: http://deeplearning.net/software/theano/
license: BSD 3-Clause
license_family: BSD
summary: Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.
description: |
Theano is a Python library that allows you to define, optimize, and
evaluate mathematical expressions involving multi-dimensional arrays
efficiently, featuring tight integration with NumPy, transparent use
of a GPU, efficient symbolic differentiation, speed and stability
optimizations and dynamic C code generation.
dev_url: https://github.com/Theano/Theano
doc_url: http://deeplearning.net/software/theano/
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