Merge branch 'master' into qiwye/asgd-dev
This commit is contained in:
unknown
Коммит
a62037dbc7
4
CNTK.sln
4
CNTK.sln
|
|
@ -2320,8 +2320,8 @@ Global
|
|||
{5D29C76D-648A-456F-920D-48230F2FB3C8}.Release_NoOpt|Any CPU.ActiveCfg = Release_CpuOnly|x64
|
||||
{5D29C76D-648A-456F-920D-48230F2FB3C8}.Release_NoOpt|Mixed Platforms.ActiveCfg = Release_CpuOnly|x64
|
||||
{5D29C76D-648A-456F-920D-48230F2FB3C8}.Release_NoOpt|Mixed Platforms.Build.0 = Release_CpuOnly|x64
|
||||
{5D29C76D-648A-456F-920D-48230F2FB3C8}.Release_NoOpt|x64.ActiveCfg = Release_CpuOnly|x64
|
||||
{5D29C76D-648A-456F-920D-48230F2FB3C8}.Release_NoOpt|x64.Build.0 = Release_CpuOnly|x64
|
||||
{5D29C76D-648A-456F-920D-48230F2FB3C8}.Release_NoOpt|x64.ActiveCfg = Release_NoOpt|x64
|
||||
{5D29C76D-648A-456F-920D-48230F2FB3C8}.Release_NoOpt|x64.Build.0 = Release_NoOpt|x64
|
||||
{5D29C76D-648A-456F-920D-48230F2FB3C8}.Release|Any CPU.ActiveCfg = Release|x64
|
||||
{5D29C76D-648A-456F-920D-48230F2FB3C8}.Release|Mixed Platforms.ActiveCfg = Release|x64
|
||||
{5D29C76D-648A-456F-920D-48230F2FB3C8}.Release|Mixed Platforms.Build.0 = Release|x64
|
||||
|
|
|
|||
2
Makefile
2
Makefile
|
|
@ -1176,7 +1176,7 @@ python: $(ALL_LIBS)
|
|||
declare -A py_paths; \
|
||||
py_paths[34]=$(PYTHON34_PATH); \
|
||||
py_paths[35]=$(PYTHON35_PATH); \
|
||||
export LD_LIBRARY_PATH=$$LD_LIBRARY_PATH:$$(echo $(LIBPATH) $(KALDI_LIBPATH) | tr " " :); \
|
||||
export LD_LIBRARY_PATH=$$LD_LIBRARY_PATH:$$(echo $(GDK_NVML_LIB_PATH) $(LIBPATH) $(KALDI_LIBPATH) | tr " " :); \
|
||||
ldd $(LIBDIR)/* | grep "not found" && false; \
|
||||
export CNTK_EXTRA_LIBRARIES=$$(ldd $(LIBDIR)/* | grep "^\s.*=> " | cut -d ">" -f 2- --only-delimited | cut -d "(" -f 1 --only-delimited | sort -u | grep -Ff <(echo $(EXTRA_LIBS_BASENAMES) | xargs -n1)); \
|
||||
test -x $(SWIG_PATH); \
|
||||
|
|
|
|||
|
|
@ -5,11 +5,13 @@ dependencies:
|
|||
- jupyter=1.0.0=py34_3
|
||||
- matplotlib=1.5.3=np111py34_0
|
||||
- numpy=1.11.2=py34_0
|
||||
- pandas=0.19.1=np111py34_0
|
||||
- pillow=3.4.2=py34_0
|
||||
- pip=8.1.2=py34_0
|
||||
- python=3.4.4=5
|
||||
- pyyaml=3.12=py34_0
|
||||
- scipy=0.18.1=np111py34_0
|
||||
- seaborn=0.7.1=py34_0
|
||||
- setuptools=27.2.0=py34_0
|
||||
- six=1.10.0=py34_0
|
||||
- wheel=0.29.0=py34_0
|
||||
|
|
@ -18,3 +20,4 @@ dependencies:
|
|||
- sphinx==1.4.8
|
||||
- sphinx-rtd-theme==0.1.9
|
||||
- twine==1.8.1
|
||||
- gym==0.5.2
|
||||
|
|
|
|||
|
|
@ -3,11 +3,13 @@ dependencies:
|
|||
- jupyter=1.0.0=py34_3
|
||||
- matplotlib=1.5.3=np111py34_0
|
||||
- numpy=1.11.2=py34_0
|
||||
- pandas=0.19.1=np111py34_0
|
||||
- pillow=3.4.2=py34_0
|
||||
- pip=8.1.2=py34_0
|
||||
- python=3.4.4=5
|
||||
- pyyaml=3.12=py34_0
|
||||
- scipy=0.18.1=np111py34_0
|
||||
- seaborn=0.7.1=py34_0
|
||||
- setuptools=27.2.0=py34_1
|
||||
- six=1.10.0=py34_0
|
||||
- wheel=0.29.0=py34_0
|
||||
|
|
@ -16,3 +18,4 @@ dependencies:
|
|||
- sphinx==1.4.8
|
||||
- sphinx-rtd-theme==0.1.9
|
||||
- twine==1.8.1
|
||||
- gym==0.5.2
|
||||
|
|
|
|||
|
|
@ -824,7 +824,20 @@ namespace CNTK
|
|||
auto autoPadding = AsVector<bool>(functionConfig[PrimitiveFunction::AttributeNameAutoPadding].Value<std::vector<DictionaryValue>>());
|
||||
NDShape outputMapCount = { 1 };
|
||||
std::vector<bool> sharing = { true };
|
||||
outputShape = ConvolutionOpOutputShape(op, inputs[0].Shape(), poolingWindowsShape, outputMapCount, strides, sharing, autoPadding, lowerPad, upperPad, false, inferDimensions);
|
||||
auto inputShape = inputs[0].Shape();
|
||||
|
||||
// In case of pooling if the kernel shape is unknown, then treat it as global pooling.
|
||||
if (poolingWindowsShape == NDShape::Unknown)
|
||||
{
|
||||
if ((std::find(autoPadding.begin(), autoPadding.end(), true) != autoPadding.end()) ||
|
||||
(lowerPad.TotalSize() > 0) || (upperPad.TotalSize() > 0))
|
||||
RuntimeError("Padding isn't allowed for Unknown shape!");
|
||||
|
||||
poolingWindowsShape = inputShape.SubShape(0, inputShape.Rank()-1);
|
||||
functionConfig[PrimitiveFunction::AttributeNamePoolingWindowShape] = poolingWindowsShape;
|
||||
}
|
||||
|
||||
outputShape = ConvolutionOpOutputShape(op, inputShape, poolingWindowsShape, outputMapCount, strides, sharing, autoPadding, lowerPad, upperPad, false, inferDimensions);
|
||||
break;
|
||||
}
|
||||
case PrimitiveOpType::SumAll:
|
||||
|
|
@ -863,9 +876,9 @@ namespace CNTK
|
|||
case PrimitiveOpType::Times:
|
||||
{
|
||||
assert(inputs.size() == 2);
|
||||
auto outputRank = functionConfig[PrimitiveFunction::AttributeNameOutputRank].Value<size_t>();
|
||||
auto inferInputRankToMap = functionConfig[PrimitiveFunction::AttributeNameInferInputRankToMap].Value<int>();
|
||||
outputShape = TimesOpOutputShape(inputs[0], inputs[1], outputRank, inferInputRankToMap, inferDimensions);
|
||||
auto outputRank = functionConfig[PrimitiveFunction::AttributeNameOutputRank].Value<size_t>();
|
||||
auto inferInputRankToMap = functionConfig[PrimitiveFunction::AttributeNameInferInputRankToMap].Value<int>();
|
||||
outputShape = TimesOpOutputShape(inputs[0], inputs[1], outputRank, inferInputRankToMap, inferDimensions);
|
||||
break;
|
||||
}
|
||||
case PrimitiveOpType::TransposeTimes:
|
||||
|
|
|
|||
|
|
@ -551,6 +551,15 @@ namespace CNTK
|
|||
{
|
||||
if (inferDimensions)
|
||||
{
|
||||
size_t inputRank = operandShape.Rank();
|
||||
|
||||
// Unknown kernel shape valid only for pooling, however, the shape should have expanded before
|
||||
// this call.
|
||||
if (kernelShape == NDShape::Unknown)
|
||||
{
|
||||
RuntimeError("Kernel shape can't be Unknown!");
|
||||
}
|
||||
|
||||
// infer reduction dimensions if not given
|
||||
// If kernel has a lower rank than the input then the remaining dimensions are to be reduced over.
|
||||
size_t filterRank = kernelShape.Rank();
|
||||
|
|
@ -565,7 +574,6 @@ namespace CNTK
|
|||
kernelShape = kernelShape.SubShape(0, filterRank);
|
||||
}
|
||||
|
||||
size_t inputRank = operandShape.Rank();
|
||||
NDShape fromShape;
|
||||
if (op == PrimitiveOpType::Convolution)
|
||||
fromShape = operandShape;
|
||||
|
|
|
|||
|
|
@ -321,6 +321,15 @@ public:
|
|||
ComputationNetworkOwnedNodeState::CopyTo(*node);
|
||||
TimeStamp::CopyTo(*node);
|
||||
}
|
||||
|
||||
if (flags & CopyNodeFlags::copyNodeAll)
|
||||
{
|
||||
for (auto tag : this->GetTags())
|
||||
{
|
||||
node->SetTag(tag);
|
||||
}
|
||||
}
|
||||
|
||||
node->ClearConfigMemberCache();
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -5,6 +5,10 @@
|
|||
<Configuration>Debug</Configuration>
|
||||
<Platform>x64</Platform>
|
||||
</ProjectConfiguration>
|
||||
<ProjectConfiguration Include="Release_NoOpt|x64">
|
||||
<Configuration>Release_NoOpt</Configuration>
|
||||
<Platform>x64</Platform>
|
||||
</ProjectConfiguration>
|
||||
<ProjectConfiguration Include="Release|x64">
|
||||
<Configuration>Release</Configuration>
|
||||
<Platform>x64</Platform>
|
||||
|
|
|
|||
|
|
@ -52,7 +52,6 @@
|
|||
<TreatWarningsAsErrors>true</TreatWarningsAsErrors>
|
||||
</PropertyGroup>
|
||||
<PropertyGroup Condition="'$(Configuration)|$(Platform)' == 'Release_NoOpt|x64'">
|
||||
<OutputPath>bin\x64\Release_NoOpt\</OutputPath>
|
||||
<DefineConstants>TRACE</DefineConstants>
|
||||
<Optimize>true</Optimize>
|
||||
<TreatWarningsAsErrors>true</TreatWarningsAsErrors>
|
||||
|
|
@ -60,7 +59,6 @@
|
|||
<PlatformTarget>x64</PlatformTarget>
|
||||
<ErrorReport>prompt</ErrorReport>
|
||||
<CodeAnalysisRuleSet>MinimumRecommendedRules.ruleset</CodeAnalysisRuleSet>
|
||||
<Prefer32Bit>true</Prefer32Bit>
|
||||
</PropertyGroup>
|
||||
<ItemGroup>
|
||||
<Reference Include="System" />
|
||||
|
|
|
|||
|
|
@ -21,10 +21,6 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
|
|||
libswscale-dev \
|
||||
libtiff-dev \
|
||||
pkg-config \
|
||||
python-dev \
|
||||
python-numpy \
|
||||
python-pip \
|
||||
python-yaml \
|
||||
wget \
|
||||
zlib1g-dev \
|
||||
# Protobuf
|
||||
|
|
@ -32,12 +28,15 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
|
|||
curl \
|
||||
unzip \
|
||||
# For Kaldi
|
||||
python-dev \
|
||||
automake \
|
||||
libtool \
|
||||
autoconf \
|
||||
subversion \
|
||||
# For Kaldi's dependencies
|
||||
libapr1 libaprutil1 libltdl-dev libltdl7 libserf-1-1 libsigsegv2 libsvn1 m4 && \
|
||||
libapr1 libaprutil1 libltdl-dev libltdl7 libserf-1-1 libsigsegv2 libsvn1 m4 \
|
||||
# For SWIG
|
||||
libpcre++-dev && \
|
||||
rm -rf /var/lib/apt/lists/*
|
||||
|
||||
RUN OPENMPI_VERSION=1.10.3 && \
|
||||
|
|
@ -102,19 +101,6 @@ RUN mkdir /usr/local/CNTKCustomMKL && \
|
|||
wget --no-verbose -O - https://www.cntk.ai/mkl/CNTKCustomMKL-Linux-2.tgz | \
|
||||
tar -xzf - -C /usr/local/CNTKCustomMKL
|
||||
|
||||
RUN pip install pytest sphinx-rtd-theme Pillow
|
||||
|
||||
ENV BLAS=/usr/local/openblas/lib/libopenblas.so
|
||||
ENV LAPACK=/usr/local/openblas/lib/libopenblas.so
|
||||
|
||||
RUN SCIPY_VERSION=0.18.0 && \
|
||||
wget -q -O - https://github.com/scipy/scipy/releases/download/v${SCIPY_VERSION}/scipy-${SCIPY_VERSION}.tar.gz | tar -zxf - && \
|
||||
cd scipy-${SCIPY_VERSION} && \
|
||||
python setup.py build && \
|
||||
python setup.py install && \
|
||||
cd .. && \
|
||||
rm -rf scipy-${SCIPY_VERSION}
|
||||
|
||||
# Install Kaldi
|
||||
ENV KALDI_VERSION=c024e8aa
|
||||
ENV KALDI_PATH /usr/local/kaldi-$KALDI_VERSION
|
||||
|
|
@ -137,17 +123,41 @@ RUN mv /bin/sh /bin/sh.orig && \
|
|||
for dir in $KALDI_PATH/src/*bin; do make -C $dir clean; done && \
|
||||
mv -f /bin/sh.orig /bin/sh
|
||||
|
||||
## PYTHON
|
||||
|
||||
# Swig
|
||||
RUN cd /root && \
|
||||
wget -q http://prdownloads.sourceforge.net/swig/swig-3.0.10.tar.gz -O - | tar xvfz - && \
|
||||
cd swig-3.0.10 && \
|
||||
./configure --without-java --without-perl5 && \
|
||||
make -j $(nproc) && \
|
||||
make install
|
||||
|
||||
# Anaconda
|
||||
RUN wget -q https://repo.continuum.io/archive/Anaconda3-4.2.0-Linux-x86_64.sh && \
|
||||
bash Anaconda3-4.2.0-Linux-x86_64.sh -b && \
|
||||
rm Anaconda3-4.2.0-Linux-x86_64.sh
|
||||
|
||||
RUN wget -q https://raw.githubusercontent.com/Microsoft/CNTK/master/Scripts/linux/conda-linux-cntk-py34-environment.yml -O /tmp/conda-linux-cntk-py34-environment.yml && \
|
||||
/root/anaconda3/bin/conda env create -p /root/anaconda3/envs/cntk-py34/ --file /tmp/conda-linux-cntk-py34-environment.yml
|
||||
|
||||
ENV PATH /root/anaconda3/envs/cntk-py34/bin:$PATH
|
||||
|
||||
WORKDIR /cntk
|
||||
|
||||
# Build CNTK
|
||||
RUN git clone --depth=1 -b master https://github.com/Microsoft/CNTK.git . && \
|
||||
CONFIGURE_OPTS="\
|
||||
--with-kaldi=${KALDI_PATH} \
|
||||
--with-py34-path=/root/anaconda3/envs/cntk-py34" && \
|
||||
mkdir -p build/cpu/release && \
|
||||
cd build/cpu/release && \
|
||||
../../../configure --with-openblas=/usr/local/openblas --with-kaldi=${KALDI_PATH} && \
|
||||
../../../configure $CONFIGURE_OPTS --with-openblas=/usr/local/openblas && \
|
||||
make -j"$(nproc)" all && \
|
||||
cd ../../.. && \
|
||||
mkdir -p build-mkl/cpu/release && \
|
||||
cd build-mkl/cpu/release && \
|
||||
../../../configure --with-mkl=/usr/local/CNTKCustomMKL --with-kaldi=${KALDI_PATH} && \
|
||||
../../../configure $CONFIGURE_OPTS --with-mkl=/usr/local/CNTKCustomMKL && \
|
||||
make -j"$(nproc)" all
|
||||
|
||||
RUN cd Examples/Image/DataSets/CIFAR-10 && \
|
||||
|
|
@ -158,4 +168,4 @@ RUN cd Examples/Image/DataSets/MNIST && \
|
|||
python install_mnist.py && \
|
||||
cd ../../../..
|
||||
|
||||
ENV PATH=/cntk/build/cpu/release/bin:$PATH
|
||||
ENV PATH=/cntk/build/cpu/release/bin:$PATH PYTHONPATH=/cntk/bindings/python LD_LIBRARY_PATH=/cntk/bindings/python/cntk/libs:$LD_LIBRARY_PATH
|
||||
|
|
|
|||
|
|
@ -0,0 +1,196 @@
|
|||
FROM nvidia/cuda:7.5-cudnn5-devel
|
||||
|
||||
ARG ENABLE_1BIT_SGD
|
||||
|
||||
RUN test "$ENABLE_1BIT_SGD" = "true" || ( echo "\033[0;31m\
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\
|
||||
You are attempting to build an image of CNTK with 1-bit Stochastic Gradient\n\
|
||||
Descent (1bit-SGD) enabled. Before proceeding further please ensure you \n\
|
||||
understand the license difference between CNTK and 1bit-SGD. See \n\
|
||||
https://github.com/Microsoft/CNTK/wiki/Enabling-1bit-SGD#license-difference-between-cntk-and-1bit-sgd\n\
|
||||
\n\
|
||||
To build the image pass the following parameter to Docker:\n\
|
||||
\n\
|
||||
--build-arg ENABLE_1BIT_SGD=true\n\
|
||||
\n\
|
||||
Example:\n\
|
||||
\n\
|
||||
docker build -t cntk --build-arg ENABLE_1BIT_SGD=true ./CNTK-GPU-1bit-Image\n\
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\
|
||||
\n\033[0m"; exit 1)
|
||||
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
autotools-dev \
|
||||
build-essential \
|
||||
cmake \
|
||||
git \
|
||||
gfortran-multilib \
|
||||
libavcodec-dev \
|
||||
libavformat-dev \
|
||||
libjasper-dev \
|
||||
libjpeg-dev \
|
||||
libpng-dev \
|
||||
liblapacke-dev \
|
||||
libswscale-dev \
|
||||
libtiff-dev \
|
||||
pkg-config \
|
||||
wget \
|
||||
zlib1g-dev \
|
||||
# Protobuf
|
||||
ca-certificates \
|
||||
curl \
|
||||
unzip \
|
||||
# For Kaldi
|
||||
python-dev \
|
||||
automake \
|
||||
libtool \
|
||||
autoconf \
|
||||
subversion \
|
||||
# For Kaldi's dependencies
|
||||
libapr1 libaprutil1 libltdl-dev libltdl7 libserf-1-1 libsigsegv2 libsvn1 m4 \
|
||||
# For SWIG
|
||||
libpcre++-dev && \
|
||||
rm -rf /var/lib/apt/lists/*
|
||||
|
||||
RUN OPENMPI_VERSION=1.10.3 && \
|
||||
wget -q -O - https://www.open-mpi.org/software/ompi/v1.10/downloads/openmpi-${OPENMPI_VERSION}.tar.gz | tar -xzf - && \
|
||||
cd openmpi-${OPENMPI_VERSION} && \
|
||||
./configure --prefix=/usr/local/mpi && \
|
||||
make -j"$(nproc)" install && \
|
||||
rm -rf /openmpi-${OPENMPI_VERSION}
|
||||
|
||||
ENV PATH /usr/local/mpi/bin:$PATH
|
||||
ENV LD_LIBRARY_PATH /usr/local/mpi/lib:$LD_LIBRARY_PATH
|
||||
|
||||
RUN LIBZIP_VERSION=1.1.2 && \
|
||||
wget -q -O - http://nih.at/libzip/libzip-${LIBZIP_VERSION}.tar.gz | tar -xzf - && \
|
||||
cd libzip-${LIBZIP_VERSION} && \
|
||||
./configure && \
|
||||
make -j"$(nproc)" install && \
|
||||
rm -rf /libzip-${LIBZIP_VERSION}
|
||||
|
||||
ENV LD_LIBRARY_PATH /usr/local/lib:$LD_LIBRARY_PATH
|
||||
|
||||
RUN wget -q -O - https://github.com/NVlabs/cub/archive/1.4.1.tar.gz | tar -C /usr/local -xzf -
|
||||
|
||||
RUN OPENCV_VERSION=3.1.0 && \
|
||||
wget -q -O - https://github.com/Itseez/opencv/archive/${OPENCV_VERSION}.tar.gz | tar -xzf - && \
|
||||
cd opencv-${OPENCV_VERSION} && \
|
||||
cmake -DCMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=/usr/local/opencv-${OPENCV_VERSION} . && \
|
||||
make -j"$(nproc)" install && \
|
||||
rm -rf /opencv-${OPENCV_VERSION}
|
||||
|
||||
RUN OPENBLAS_VERSION=0.2.18 && \
|
||||
wget -q -O - https://github.com/xianyi/OpenBLAS/archive/v${OPENBLAS_VERSION}.tar.gz | tar -xzf - && \
|
||||
cd OpenBLAS-${OPENBLAS_VERSION} && \
|
||||
make -j"$(nproc)" USE_OPENMP=1 | tee make.log && \
|
||||
grep -qF 'OpenBLAS build complete. (BLAS CBLAS LAPACK LAPACKE)' make.log && \
|
||||
grep -qF 'Use OpenMP in the multithreading.' make.log && \
|
||||
make PREFIX=/usr/local/openblas install && \
|
||||
rm -rf /OpenBLAS-${OPENBLAS_VERSION}
|
||||
|
||||
ENV LD_LIBRARY_PATH /usr/local/openblas/lib:$LD_LIBRARY_PATH
|
||||
|
||||
# Install Boost
|
||||
RUN BOOST_VERSION=1_60_0 && \
|
||||
BOOST_DOTTED_VERSION=$(echo $BOOST_VERSION | tr _ .) && \
|
||||
wget -q -O - https://sourceforge.net/projects/boost/files/boost/${BOOST_DOTTED_VERSION}/boost_${BOOST_VERSION}.tar.gz/download | tar -xzf - && \
|
||||
cd boost_${BOOST_VERSION} && \
|
||||
./bootstrap.sh --prefix=/usr/local/boost-${BOOST_DOTTED_VERSION} --with-libraries=filesystem,system,test && \
|
||||
./b2 -d0 -j"$(nproc)" install && \
|
||||
rm -rf /boost_${BOOST_VERSION}
|
||||
|
||||
# Install Protobuf
|
||||
RUN PROTOBUF_VERSION=3.1.0 \
|
||||
PROTOBUF_STRING=protobuf-$PROTOBUF_VERSION && \
|
||||
wget -O - --no-verbose https://github.com/google/protobuf/archive/v${PROTOBUF_VERSION}.tar.gz | tar -xzf - && \
|
||||
cd $PROTOBUF_STRING && \
|
||||
./autogen.sh && \
|
||||
./configure CFLAGS=-fPIC CXXFLAGS=-fPIC --disable-shared --prefix=/usr/local/$PROTOBUF_STRING && \
|
||||
make -j $(nproc) install && \
|
||||
cd .. && \
|
||||
rm -rf $PROTOBUF_STRING
|
||||
|
||||
# Install CNTK custom MKL, version 2
|
||||
RUN mkdir /usr/local/CNTKCustomMKL && \
|
||||
wget --no-verbose -O - https://www.cntk.ai/mkl/CNTKCustomMKL-Linux-2.tgz | \
|
||||
tar -xzf - -C /usr/local/CNTKCustomMKL
|
||||
|
||||
RUN mkdir -p /usr/local/cudnn/cuda/include && \
|
||||
ln -s /usr/include/cudnn.h /usr/local/cudnn/cuda/include/cudnn.h && \
|
||||
mkdir -p /usr/local/cudnn/cuda/lib64 && \
|
||||
ln -s /etc/alternatives/libcudnn_so /usr/local/cudnn/cuda/lib64/libcudnn.so
|
||||
|
||||
# Install Kaldi
|
||||
ENV KALDI_VERSION=c024e8aa
|
||||
ENV KALDI_PATH /usr/local/kaldi-$KALDI_VERSION
|
||||
|
||||
RUN mv /bin/sh /bin/sh.orig && \
|
||||
ln -s -f /bin/bash /bin/sh && \
|
||||
mkdir $KALDI_PATH && \
|
||||
wget --no-verbose -O - https://github.com/kaldi-asr/kaldi/archive/$KALDI_VERSION.tar.gz | tar -xzf - --strip-components=1 -C $KALDI_PATH && \
|
||||
cd $KALDI_PATH && \
|
||||
cd tools && \
|
||||
perl -pi -e 's/^# (OPENFST_VERSION = 1.4.1)$/\1/' Makefile && \
|
||||
./extras/check_dependencies.sh && \
|
||||
make -j $(nproc) all && \
|
||||
cd ../src && \
|
||||
./configure --openblas-root=/usr/local/openblas --shared && \
|
||||
make -j $(nproc) depend && \
|
||||
make -j $(nproc) all && \
|
||||
# Remove some unneeded stuff in $KALDI_PATH to reduce size
|
||||
find $KALDI_PATH -name '*.o' -print0 | xargs -0 rm && \
|
||||
for dir in $KALDI_PATH/src/*bin; do make -C $dir clean; done && \
|
||||
mv -f /bin/sh.orig /bin/sh
|
||||
|
||||
## PYTHON
|
||||
|
||||
# Swig
|
||||
RUN cd /root && \
|
||||
wget -q http://prdownloads.sourceforge.net/swig/swig-3.0.10.tar.gz -O - | tar xvfz - && \
|
||||
cd swig-3.0.10 && \
|
||||
./configure --without-java --without-perl5 && \
|
||||
make -j $(nproc) && \
|
||||
make install
|
||||
|
||||
# Anaconda
|
||||
RUN wget -q https://repo.continuum.io/archive/Anaconda3-4.2.0-Linux-x86_64.sh && \
|
||||
bash Anaconda3-4.2.0-Linux-x86_64.sh -b && \
|
||||
rm Anaconda3-4.2.0-Linux-x86_64.sh
|
||||
|
||||
RUN wget -q https://raw.githubusercontent.com/Microsoft/CNTK/master/Scripts/linux/conda-linux-cntk-py34-environment.yml -O /tmp/conda-linux-cntk-py34-environment.yml && \
|
||||
/root/anaconda3/bin/conda env create -p /root/anaconda3/envs/cntk-py34/ --file /tmp/conda-linux-cntk-py34-environment.yml
|
||||
|
||||
ENV PATH /root/anaconda3/envs/cntk-py34/bin:$PATH
|
||||
|
||||
WORKDIR /cntk
|
||||
|
||||
# Build CNTK
|
||||
RUN git clone --depth=1 --recursive -b master https://github.com/Microsoft/CNTK.git . && \
|
||||
CONFIGURE_OPTS="\
|
||||
--1bitsgd=yes \
|
||||
--with-cuda=/usr/local/cuda \
|
||||
--with-gdk-include=/usr/local/cuda/include \
|
||||
--with-gdk-nvml-lib=/usr/local/cuda/lib64/stubs \
|
||||
--with-kaldi=${KALDI_PATH} \
|
||||
--with-py34-path=/root/anaconda3/envs/cntk-py34 \
|
||||
--with-cudnn=/usr/local/cudnn" && \
|
||||
mkdir -p build/gpu/release && \
|
||||
cd build/gpu/release && \
|
||||
../../../configure $CONFIGURE_OPTS --with-openblas=/usr/local/openblas && \
|
||||
make -j"$(nproc)" all && \
|
||||
cd ../../.. && \
|
||||
mkdir -p build-mkl/gpu/release && \
|
||||
cd build-mkl/gpu/release && \
|
||||
../../../configure $CONFIGURE_OPTS --with-mkl=/usr/local/CNTKCustomMKL && \
|
||||
make -j"$(nproc)" all
|
||||
|
||||
RUN cd Examples/Image/DataSets/CIFAR-10 && \
|
||||
python install_cifar10.py && \
|
||||
cd ../../../..
|
||||
|
||||
RUN cd Examples/Image/DataSets/MNIST && \
|
||||
python install_mnist.py && \
|
||||
cd ../../../..
|
||||
|
||||
ENV PATH=/cntk/build/gpu/release/bin:$PATH PYTHONPATH=/cntk/bindings/python LD_LIBRARY_PATH=/cntk/bindings/python/cntk/libs:$LD_LIBRARY_PATH
|
||||
|
|
@ -15,10 +15,6 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
|
|||
libswscale-dev \
|
||||
libtiff-dev \
|
||||
pkg-config \
|
||||
python-dev \
|
||||
python-numpy \
|
||||
python-pip \
|
||||
python-yaml \
|
||||
wget \
|
||||
zlib1g-dev \
|
||||
# Protobuf
|
||||
|
|
@ -26,12 +22,15 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
|
|||
curl \
|
||||
unzip \
|
||||
# For Kaldi
|
||||
python-dev \
|
||||
automake \
|
||||
libtool \
|
||||
autoconf \
|
||||
subversion \
|
||||
# For Kaldi's dependencies
|
||||
libapr1 libaprutil1 libltdl-dev libltdl7 libserf-1-1 libsigsegv2 libsvn1 m4 && \
|
||||
libapr1 libaprutil1 libltdl-dev libltdl7 libserf-1-1 libsigsegv2 libsvn1 m4 \
|
||||
# For SWIG
|
||||
libpcre++-dev && \
|
||||
rm -rf /var/lib/apt/lists/*
|
||||
|
||||
RUN OPENMPI_VERSION=1.10.3 && \
|
||||
|
|
@ -73,6 +72,15 @@ RUN OPENBLAS_VERSION=0.2.18 && \
|
|||
|
||||
ENV LD_LIBRARY_PATH /usr/local/openblas/lib:$LD_LIBRARY_PATH
|
||||
|
||||
# Install Boost
|
||||
RUN BOOST_VERSION=1_60_0 && \
|
||||
BOOST_DOTTED_VERSION=$(echo $BOOST_VERSION | tr _ .) && \
|
||||
wget -q -O - https://sourceforge.net/projects/boost/files/boost/${BOOST_DOTTED_VERSION}/boost_${BOOST_VERSION}.tar.gz/download | tar -xzf - && \
|
||||
cd boost_${BOOST_VERSION} && \
|
||||
./bootstrap.sh --prefix=/usr/local/boost-${BOOST_DOTTED_VERSION} --with-libraries=filesystem,system,test && \
|
||||
./b2 -d0 -j"$(nproc)" install && \
|
||||
rm -rf /boost_${BOOST_VERSION}
|
||||
|
||||
# Install Protobuf
|
||||
RUN PROTOBUF_VERSION=3.1.0 \
|
||||
PROTOBUF_STRING=protobuf-$PROTOBUF_VERSION && \
|
||||
|
|
@ -84,15 +92,6 @@ RUN PROTOBUF_VERSION=3.1.0 \
|
|||
cd .. && \
|
||||
rm -rf $PROTOBUF_STRING
|
||||
|
||||
# Install Boost
|
||||
RUN BOOST_VERSION=1_60_0 && \
|
||||
BOOST_DOTTED_VERSION=$(echo $BOOST_VERSION | tr _ .) && \
|
||||
wget -q -O - https://sourceforge.net/projects/boost/files/boost/${BOOST_DOTTED_VERSION}/boost_${BOOST_VERSION}.tar.gz/download | tar -xzf - && \
|
||||
cd boost_${BOOST_VERSION} && \
|
||||
./bootstrap.sh --prefix=/usr/local/boost-${BOOST_DOTTED_VERSION} --with-libraries=filesystem,system,test && \
|
||||
./b2 -d0 -j"$(nproc)" install && \
|
||||
rm -rf /boost_${BOOST_VERSION}
|
||||
|
||||
# Install CNTK custom MKL, version 2
|
||||
RUN mkdir /usr/local/CNTKCustomMKL && \
|
||||
wget --no-verbose -O - https://www.cntk.ai/mkl/CNTKCustomMKL-Linux-2.tgz | \
|
||||
|
|
@ -103,20 +102,6 @@ RUN mkdir -p /usr/local/cudnn/cuda/include && \
|
|||
mkdir -p /usr/local/cudnn/cuda/lib64 && \
|
||||
ln -s /etc/alternatives/libcudnn_so /usr/local/cudnn/cuda/lib64/libcudnn.so
|
||||
|
||||
RUN pip install pytest sphinx-rtd-theme Pillow
|
||||
|
||||
ENV BLAS=/usr/local/openblas/lib/libopenblas.so
|
||||
ENV LAPACK=/usr/local/openblas/lib/libopenblas.so
|
||||
|
||||
|
||||
RUN SCIPY_VERSION=0.18.0 && \
|
||||
wget -q -O - https://github.com/scipy/scipy/releases/download/v${SCIPY_VERSION}/scipy-${SCIPY_VERSION}.tar.gz | tar -zxf - && \
|
||||
cd scipy-${SCIPY_VERSION} && \
|
||||
python setup.py build && \
|
||||
python setup.py install && \
|
||||
cd .. && \
|
||||
rm -rf scipy-${SCIPY_VERSION}
|
||||
|
||||
# Install Kaldi
|
||||
ENV KALDI_VERSION=c024e8aa
|
||||
ENV KALDI_PATH /usr/local/kaldi-$KALDI_VERSION
|
||||
|
|
@ -139,14 +124,36 @@ RUN mv /bin/sh /bin/sh.orig && \
|
|||
for dir in $KALDI_PATH/src/*bin; do make -C $dir clean; done && \
|
||||
mv -f /bin/sh.orig /bin/sh
|
||||
|
||||
## PYTHON
|
||||
|
||||
# Swig
|
||||
RUN cd /root && \
|
||||
wget -q http://prdownloads.sourceforge.net/swig/swig-3.0.10.tar.gz -O - | tar xvfz - && \
|
||||
cd swig-3.0.10 && \
|
||||
./configure --without-java --without-perl5 && \
|
||||
make -j $(nproc) && \
|
||||
make install
|
||||
|
||||
# Anaconda
|
||||
RUN wget -q https://repo.continuum.io/archive/Anaconda3-4.2.0-Linux-x86_64.sh && \
|
||||
bash Anaconda3-4.2.0-Linux-x86_64.sh -b && \
|
||||
rm Anaconda3-4.2.0-Linux-x86_64.sh
|
||||
|
||||
RUN wget -q https://raw.githubusercontent.com/Microsoft/CNTK/master/Scripts/linux/conda-linux-cntk-py34-environment.yml -O /tmp/conda-linux-cntk-py34-environment.yml && \
|
||||
/root/anaconda3/bin/conda env create -p /root/anaconda3/envs/cntk-py34/ --file /tmp/conda-linux-cntk-py34-environment.yml
|
||||
|
||||
ENV PATH /root/anaconda3/envs/cntk-py34/bin:$PATH
|
||||
|
||||
WORKDIR /cntk
|
||||
|
||||
# Build CNTK
|
||||
RUN git clone --depth=1 -b master https://github.com/Microsoft/CNTK.git . && \
|
||||
CONFIGURE_OPTS="\
|
||||
--with-cuda=/usr/local/cuda \
|
||||
--with-gdk-include=/usr/local/cuda/include \
|
||||
--with-gdk-nvml-lib=/usr/local/cuda/lib64/stubs \
|
||||
--with-kaldi=${KALDI_PATH} \
|
||||
--with-py34-path=/root/anaconda3/envs/cntk-py34 \
|
||||
--with-cudnn=/usr/local/cudnn" && \
|
||||
mkdir -p build/gpu/release && \
|
||||
cd build/gpu/release && \
|
||||
|
|
@ -166,4 +173,4 @@ RUN cd Examples/Image/DataSets/MNIST && \
|
|||
python install_mnist.py && \
|
||||
cd ../../../..
|
||||
|
||||
ENV PATH=/cntk/build/gpu/release/bin:/usr/local/mpi/bin:$PATH
|
||||
ENV PATH=/cntk/build/gpu/release/bin:$PATH PYTHONPATH=/cntk/bindings/python LD_LIBRARY_PATH=/cntk/bindings/python/cntk/libs:$LD_LIBRARY_PATH
|
||||
|
|
|
|||
|
|
@ -0,0 +1,8 @@
|
|||
# CNTK Dockerfiles
|
||||
|
||||
The Dockerfiles provided here can be used to build CNTK on machines with only CPU, or with GPU with or without 1bit-SGD.
|
||||
If you plan to use the 1bit-SGD version, please make sure you understand the
|
||||
[license difference between CNTK and 1bit-SGD](https://github.com/Microsoft/CNTK/wiki/Enabling-1bit-SGD).
|
||||
|
||||
See also [this page](https://github.com/Microsoft/CNTK/wiki/CNTK-Docker-Containers)
|
||||
that provides general instructions on getting things working with Docker.
|
||||
|
|
@ -1,4 +1,5 @@
|
|||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
# Licensed under the MIT license. See LICENSE.md file in the project root
|
||||
# for full license information.
|
||||
# ==============================================================================
|
||||
|
|
@ -13,7 +14,6 @@ def dfs_walk(node, visitor):
|
|||
node (graph node): the node to start the journey from
|
||||
visitor (Python function or lambda): function that takes a node as
|
||||
argument and returns ``True`` if that node should be returned.
|
||||
|
||||
Returns:
|
||||
List of nodes, for which ``visitor`` was ``True``
|
||||
'''
|
||||
|
|
@ -48,13 +48,112 @@ def find_nodes_by_name(node, node_name):
|
|||
'''
|
||||
Finds nodes in the graph starting from `node` and doing a depth-first
|
||||
search.
|
||||
|
||||
Args:
|
||||
node (graph node): the node to start the journey from
|
||||
node_name (`str`): name for which we are search nodes
|
||||
|
||||
Returns:
|
||||
List of nodes having the specified name
|
||||
'''
|
||||
return dfs_walk(node, lambda x: x.name == node_name)
|
||||
|
||||
def output_function_graph(node,dot_file_path=None,png_file_path=None):
|
||||
'''
|
||||
Walks through every node of the graph starting at ``node``,
|
||||
creates a network graph, and saves it as a string. If dot_file_name or
|
||||
png_file_name specified corresponding files will be saved.
|
||||
|
||||
|
||||
Requirements for DOT output: pydot_ng
|
||||
Requirements for PNG output: pydot_ng and graphviz
|
||||
|
||||
Args:
|
||||
node (graph node): the node to start the journey from
|
||||
dot_file_path (`str`, optional): DOT file path
|
||||
png_file_path (`str`, optional): PNG file path
|
||||
|
||||
Returns:
|
||||
`str` containing all nodes and edges
|
||||
'''
|
||||
|
||||
dot = (dot_file_path != None)
|
||||
png = (png_file_path != None)
|
||||
|
||||
if (dot or png):
|
||||
|
||||
try:
|
||||
import pydot_ng as pydot
|
||||
except ImportError:
|
||||
raise ImportError("PNG and DOT format requires pydot_ng package. Unable to import pydot_ng.")
|
||||
|
||||
# initialize a dot object to store vertices and edges
|
||||
dot_object = pydot.Dot(graph_name="network_graph",rankdir='TB')
|
||||
dot_object.set_node_defaults(shape='rectangle', fixedsize='false',
|
||||
height=.85, width=.85, fontsize=12)
|
||||
dot_object.set_edge_defaults(fontsize=10)
|
||||
|
||||
# string to store model
|
||||
model = ''
|
||||
|
||||
# walk every node of the graph iteratively
|
||||
visitor = lambda x: True
|
||||
stack = [node]
|
||||
accum = []
|
||||
visited = set()
|
||||
|
||||
while stack:
|
||||
node = stack.pop()
|
||||
|
||||
if node in visited:
|
||||
continue
|
||||
|
||||
try:
|
||||
# Function node
|
||||
node = node.root_function
|
||||
stack.extend(node.inputs)
|
||||
|
||||
# add current node
|
||||
model += node.op_name + '('
|
||||
if (dot or png):
|
||||
cur_node = pydot.Node(node.op_name+' '+node.uid,label=node.op_name,shape='circle',
|
||||
fixedsize='true', height=1, width=1)
|
||||
dot_object.add_node(cur_node)
|
||||
|
||||
# add node's inputs
|
||||
for i in range(len(node.inputs)):
|
||||
child = node.inputs[i]
|
||||
|
||||
model += child.uid
|
||||
if (i != len(node.inputs) - 1):
|
||||
model += ", "
|
||||
|
||||
if (dot or png):
|
||||
child_node = pydot.Node(child.uid)
|
||||
dot_object.add_node(child_node)
|
||||
dot_object.add_edge(pydot.Edge(child_node, cur_node,label=str(child.shape)))
|
||||
|
||||
# ad node's output
|
||||
model += ") -> " + node.outputs[0].uid +'\n'
|
||||
|
||||
if (dot or png):
|
||||
out_node = pydot.Node(node.outputs[0].uid)
|
||||
dot_object.add_node(out_node)
|
||||
dot_object.add_edge(pydot.Edge(cur_node,out_node,label=str(node.outputs[0].shape)))
|
||||
|
||||
except AttributeError:
|
||||
# OutputVariable node
|
||||
try:
|
||||
if node.is_output:
|
||||
stack.append(node.owner)
|
||||
except AttributeError:
|
||||
pass
|
||||
|
||||
if visitor(node):
|
||||
accum.append(node)
|
||||
|
||||
if (png):
|
||||
dot_object.write_png(png_file_path, prog='dot')
|
||||
if (dot):
|
||||
dot_object.write_raw(dot_file_path)
|
||||
|
||||
# return lines in reversed order
|
||||
return "\n".join(model.split("\n")[::-1])
|
||||
|
|
|
|||
|
|
@ -171,17 +171,17 @@ def Convolution(filter_shape, # e.g. (3,3)
|
|||
apply_x = apply_x >> activation
|
||||
return Block(apply_x, 'Convolution', Record(W=W, b=b))
|
||||
|
||||
# MaxPooling, AveragePooling -- create a max- or average-pooling layer
|
||||
# TODO: do we need MaxPooling and AveragePooling?
|
||||
# TODO: This is not really a layer as it does not hold learnable parameters. So:
|
||||
# - keep it in layer format, since users may think about it this way?
|
||||
# - turn it into a function (lower-case)? Then how would it work inside Sequential() (we'd need partial application)?
|
||||
from cntk.cntk_py import PoolingType_Max, PoolingType_Average
|
||||
# Create a Pooling layer with one of following types:
|
||||
#
|
||||
# MaxPooling and GlobalMaxPooling
|
||||
# AveragePooling and GlobalAveragePooling
|
||||
#
|
||||
# Setting the filter_shape to None, mean global pooling.
|
||||
from cntk.cntk_py import PoolingType_Max, PoolingType_Average, NDShape
|
||||
def Pooling(op, # PoolingType_Max or _Average
|
||||
filter_shape, # e.g. (3,3)
|
||||
strides=1,
|
||||
pad=False):
|
||||
#UntestedBranchError("Pooling")
|
||||
x = Placeholder(name='pooling_arg')
|
||||
apply_x = pooling (x, op, filter_shape, strides=_as_tuple(strides), auto_padding=_as_tuple(pad))
|
||||
|
||||
|
|
@ -193,16 +193,26 @@ def Pooling(op, # PoolingType_Max or _Average
|
|||
raise ValueError('Pooling: op must be PoolingType_Max or PoolingType_average')
|
||||
return Block(apply_x, op_name)
|
||||
|
||||
# MaxPooling
|
||||
def MaxPooling(filter_shape, # e.g. (3,3)
|
||||
strides=1,
|
||||
pad=False):
|
||||
return Pooling(PoolingType_Max, filter_shape, strides=strides, pad=pad)
|
||||
|
||||
# AveragePooling
|
||||
def AveragePooling(filter_shape, # e.g. (3,3)
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strides=1,
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pad=False):
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return Pooling(PoolingType_Average, filter_shape, strides=strides, pad=pad)
|
||||
|
||||
# GlobalMaxPooling
|
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def GlobalMaxPooling():
|
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return Pooling(PoolingType_Max, NDShape.unknown.dimensions(), pad=False)
|
||||
|
||||
# GlobalAveragePooling
|
||||
def GlobalAveragePooling():
|
||||
return Pooling(PoolingType_Average, NDShape.unknown.dimensions(), pad=False)
|
||||
|
||||
# Recurrence() -- run a block recurrently over a time sequence
|
||||
def Recurrence(over, go_backwards=False, initial_state=initial_state_default_or_None):
|
||||
# helper to compute previous value
|
||||
|
|
|
|||
|
|
@ -41,6 +41,18 @@ def _graph_dict():
|
|||
d['root'] = d['first']
|
||||
|
||||
return d
|
||||
|
||||
def _simple_dict():
|
||||
d = {}
|
||||
|
||||
d['i1'] = input_variable(shape=(2,3), name='i1')
|
||||
d['i2'] = input_variable(shape=(2,3), name='i2')
|
||||
d['p1'] = parameter(shape=(3,2), name='p1')
|
||||
d['op1'] = plus(d['i1'], d['i2'], name='op1')
|
||||
d['op2'] = times(d['op1'], d['p1'], name='op2')
|
||||
d['root'] = d['op2']
|
||||
|
||||
return d
|
||||
|
||||
|
||||
def test_find_nodes():
|
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|
|
@ -57,3 +69,16 @@ def test_find_nodes():
|
|||
|
||||
none = find_nodes_by_name(d['root'], 'none')
|
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assert none == []
|
||||
|
||||
|
||||
def test_output_funtion_graph():
|
||||
d = _simple_dict()
|
||||
|
||||
m = output_function_graph(d['root'])
|
||||
p = "\nPlus"
|
||||
t = "\nTimes"
|
||||
|
||||
assert len(m) != 0
|
||||
assert p in m
|
||||
assert t in m
|
||||
assert m.find(p) < m.find(t)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# Copyright (c) Microsoft. All rights reserved.
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
# Licensed under the MIT license. See LICENSE.md file in the project root
|
||||
# for full license information.
|
||||
# ==============================================================================
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ import math
|
|||
import numpy as np
|
||||
|
||||
from cntk.blocks import default_options
|
||||
from cntk.layers import Convolution, MaxPooling, AveragePooling, Dropout, BatchNormalization, Dense
|
||||
from cntk.layers import Convolution, AveragePooling, GlobalAveragePooling, Dropout, BatchNormalization, Dense
|
||||
from cntk.models import Sequential, LayerStack
|
||||
from cntk.utils import *
|
||||
from cntk.io import MinibatchSource, ImageDeserializer, StreamDef, StreamDefs
|
||||
|
|
@ -136,8 +136,7 @@ def create_resnet_model(input, num_classes):
|
|||
r3_1 = resnet_basic_inc(r2_2, 64)
|
||||
r3_2 = resnet_basic_stack(r3_1, 64, 2)
|
||||
|
||||
# Global average pooling
|
||||
pool = AveragePooling(filter_shape=(8,8), strides=(1,1))(r3_2)
|
||||
pool = GlobalAveragePooling()(r3_2)
|
||||
net = Dense(num_classes, init=he_normal(), activation=None)(pool)
|
||||
|
||||
return net
|
||||
|
|
|
|||
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