1
0
mirror of https://git.FreeBSD.org/ports.git synced 2025-01-22 08:58:47 +00:00

New port: misc/mmdnn: Tools to inter-operate among different deep learning frameworks

This commit is contained in:
Yuri Victorovich 2019-11-26 22:11:11 +00:00
parent 913bcfeb10
commit 6d5e6fe981
Notes: svn2git 2021-03-31 03:12:20 +00:00
svn path=/head/; revision=518484
4 changed files with 53 additions and 0 deletions

View File

@ -248,6 +248,7 @@
SUBDIR += metromap
SUBDIR += mime-support
SUBDIR += mirmon
SUBDIR += mmdnn
SUBDIR += mmv
SUBDIR += molequeue
SUBDIR += morse

27
misc/mmdnn/Makefile Normal file
View File

@ -0,0 +1,27 @@
# $FreeBSD$
PORTNAME= mmdnn
DISTVERSION= 0.2.5-21
DISTVERSIONSUFFIX= -gc1c748e
CATEGORIES= misc python # machine-learning
MAINTAINER= yuri@FreeBSD.org
COMMENT= Tools to inter-operate among different deep learning frameworks
LICENSE= MIT
LICENSE_FILE= ${WRKSRC}/LICENSE
RUN_DEPENDS= ${PYNUMPY} \
${PYTHON_PKGNAMEPREFIX}pillow>=3.1.0:graphics/py-pillow@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}protobuf>=3.6.0:devel/py-protobuf@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}six>=1.10.0:devel/py-six@${PY_FLAVOR}
USES= python
USE_GITHUB= yes
GH_ACCOUNT= microsoft
GH_PROJECT= MMdnn
USE_PYTHON= distutils noflavors autoplist
NO_ARCH= yes
.include <bsd.port.mk>

3
misc/mmdnn/distinfo Normal file
View File

@ -0,0 +1,3 @@
TIMESTAMP = 1574803092
SHA256 (microsoft-MMdnn-0.2.5-21-gc1c748e_GH0.tar.gz) = a5ba7e8e07b28fdc5323678641c9370b20ec499490668a4376d8ddc2b3fc0fcd
SIZE (microsoft-MMdnn-0.2.5-21-gc1c748e_GH0.tar.gz) = 2265465

22
misc/mmdnn/pkg-descr Normal file
View File

@ -0,0 +1,22 @@
MMdnn is a set of tools to help users inter-operate among different deep
learning frameworks. E.g. model conversion and visualization. Convert models
between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
A comprehensive, cross-framework solution to convert, visualize and diagnose
deep neural network models. The "MM" in MMdnn stands for model management and
"dnn" is an acronym for the deep neural network.
Major features:
* Find model
- We provide a model collection to help you find some popular models
- We provide a model visualizer to display the network architecture more
intuitively
* Conversion
- We implement a universal converter to convert DNN models between
frameworks, which means you can train on one framework and deploy on another
* Retrain
- In the converter, we can generate some training/inference code snippet to
simplify the retrain/evaluate work
* Deployment
WWW: https://github.com/microsoft/MMdnn