Compare commits
11 Commits
v0.1.0-dev
...
v0.1.0-rc0
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ced8f532dd |
@@ -1,5 +1,5 @@
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[bumpversion]
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[bumpversion]
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current_version = 0.1.0-dev0
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current_version = 0.1.0-rc0
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commit = True
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commit = True
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tag = True
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tag = True
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parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?
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parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?
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20
.travis.yml
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20
.travis.yml
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@@ -0,0 +1,20 @@
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dist: bionic
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sudo: false
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language: python
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python: 3.8
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# cache:
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# directories:
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# - $HOME/.prototorch
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install:
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- pip install . --progress-bar off
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- pip install codecov
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- pip install pytest
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script:
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- coverage run -m pytest
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# Push the results to codecov
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after_success:
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- codecov
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# - bash <(curl -s https://codecov.io/bash)
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@@ -1,9 +1,11 @@
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include .bumpversion.cfg
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include .bumpversion.cfg
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include LICENSE
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include LICENSE
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include tox.ini
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include tox.ini
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include *.yml
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recursive-include docs *.bat
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recursive-include docs *.bat
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recursive-include docs *.png
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recursive-include docs *.png
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recursive-include docs *.py
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recursive-include docs *.py
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recursive-include docs *.rst
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recursive-include docs *.rst
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recursive-include docs Makefile
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recursive-include docs Makefile
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recursive-include examples *.py
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recursive-include examples *.py
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recursive-include tests *.py
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18
README.md
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README.md
@@ -3,8 +3,13 @@
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ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in
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ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in
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prototype-based machine learning algorithms.
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prototype-based machine learning algorithms.
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[](https://travis-ci.org/si-cim/prototorch)
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[](https://badge.fury.io/gh/si-cim%2Fprototorch)
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[](https://badge.fury.io/py/prototorch)
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|

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[](https://codecov.io/gh/si-cim/prototorch)
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[](https://codecov.io/gh/si-cim/prototorch)
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[](https://pepy.tech/project/prototorch)
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[](https://github.com/si-cim/prototorch/blob/master/LICENSE)
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## Description
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## Description
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@@ -47,3 +52,16 @@ API, with more algorithms and techniques coming soon. If you would simply like
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to be able to use those algorithms to train large ML models on a GPU, ProtoTorch
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to be able to use those algorithms to train large ML models on a GPU, ProtoTorch
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lets you do this without requiring a black-belt in high-performance Tensor
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lets you do this without requiring a black-belt in high-performance Tensor
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computation.
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computation.
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## Bibtex
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If you would like to cite the package, please use this:
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```bibtex
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@misc{Ravichandran2020,
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author = {Ravichandran, J},
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title = {ProtoTorch},
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year = {2020},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/si-cim/prototorch}}
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}
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3
RELEASE.md
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3
RELEASE.md
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@@ -0,0 +1,3 @@
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# Release 0.1.0-dev0
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Initial public release of ProtoTorch.
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@@ -1 +1 @@
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__version__ = '0.1.0-dev0'
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__version__ = '0.1.0-rc0'
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2
setup.py
2
setup.py
@@ -10,7 +10,7 @@ with open('README.md', 'r') as fh:
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long_description = fh.read()
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long_description = fh.read()
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setup(name='prototorch',
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setup(name='prototorch',
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version='0.1.0-dev0',
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version='0.1.0-rc0',
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description='Highly extensible, GPU-supported '
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description='Highly extensible, GPU-supported '
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'Learning Vector Quantization (LVQ) toolbox '
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'Learning Vector Quantization (LVQ) toolbox '
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'built using PyTorch and its nn API.',
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'built using PyTorch and its nn API.',
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0
tests/__init__.py
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0
tests/__init__.py
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10
tox.ini
10
tox.ini
@@ -4,12 +4,12 @@
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# and then run "tox" from this directory.
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# and then run "tox" from this directory.
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[tox]
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[tox]
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envlist = py36
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envlist = py36,py37,py38
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[testenv]
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[testenv]
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deps =
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deps =
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numpy
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pytest
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unittest-xml-reporting
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coverage
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commands =
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commands =
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python -m xmlrunner -o reports
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pip install -e .
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coverage run -m pytest
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