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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,49 +1,49 @@ | ||
| # Building Caffe using standalone Dockerfile | ||
|
|
||
| The `standalone` subfolder contains docker files for generating both CPU and GPU executable images for Caffe. The images can be built using make, or by running: | ||
|
|
||
| ``` | ||
| docker build -t caffe:cpu standalone/cpu-ubuntu | ||
| ``` | ||
| for example. (Here `ubuntu` can be substituted for `centos`, `gpu` can be substituted for `cpu`, but to keep the readme simple, only the `cpu` case will be discussed in detail). | ||
|
|
||
| Note that the GPU standalone requires a CUDA 7.5 capable driver to be installed on the system and [nvidia-docker] for running the Docker containers. Here it is generally sufficient to use `nvidia-docker` instead of `docker` in any of the commands mentioned. | ||
|
|
||
| # Running Caffe using the docker image | ||
|
|
||
| In order to test the Caffe image, run: | ||
| ``` | ||
| docker run -ti caffe:cpu caffe --version | ||
| ``` | ||
| which should show a message like: | ||
| ``` | ||
| caffe version 1.0.0-rc3 | ||
| ``` | ||
|
|
||
| One can also build and run the Caffe tests in the image using: | ||
| ``` | ||
| docker run -ti caffe:cpu bash -c "cd /opt/caffe/build; make runtest" | ||
| ``` | ||
|
|
||
| In order to get the most out of the caffe image, some more advanced `docker run` options could be used. For example, running: | ||
| ``` | ||
| docker run -ti caffe:cpu caffe time -model /opt/caffe/models/bvlc_alexnet/deploy.prototxt -engine MKLDNN | ||
| ``` | ||
| will measure the performance of AlexNet. You can also run caffe train as well. Note that docker runs all commands as root by default, and thus any output files (e.g. snapshots) generated will be owned by the root user. In order to ensure that the current user is used instead, the following command can be used: | ||
| ``` | ||
| docker run -ti --volume=$(pwd):/workspace -u $(id -u):$(id -g) caffe:cpu caffe train --solver=/opt/caffe/models/bvlc_alexnet/solver.prototxt -engine MKLDNN | ||
| ``` | ||
| where the `-u` Docker command line option runs the commands in the container as the specified user, and the shell command `id` is used to determine the user and group ID of the current user. Note that the Caffe docker images have `/workspace` defined as the default working directory. This can be overridden using the `--workdir=` Docker command line option. Note that you need to prepare dataset before training. | ||
|
|
||
| # Other use-cases | ||
|
|
||
| Although running the `caffe` command in the docker containers as described above serves many purposes, the container can also be used for more interactive use cases. For example, specifying `bash` as the command instead of `caffe` yields a shell that can be used for interactive tasks. (Since the caffe build requirements are included in the container, this can also be used to build and run local versions of caffe). | ||
|
|
||
| Another use case is to run python scripts that depend on `caffe`'s Python modules. Using the `python` command instead of `bash` or `caffe` will allow this, and an interactive interpreter can be started by running: | ||
| ``` | ||
| docker run -ti caffe:cpu python | ||
| ``` | ||
| (`ipython` is also available in the container). | ||
|
|
||
| Since the `caffe/python` folder is also added to the path, the utility executable scripts defined there can also be used as executables. This includes `draw_net.py`, `classify.py`, and `detect.py` | ||
|
|
||
| # Building Caffe using standalone Dockerfile | ||
|
|
||
| The `standalone` subfolder contains docker files for generating both CPU and GPU executable images for Caffe. The images can be built using make, or by running: | ||
|
|
||
| ``` | ||
| docker build -t caffe:cpu standalone/cpu-ubuntu | ||
| ``` | ||
| for example. (Here `ubuntu` can be substituted for `centos`, `gpu` can be substituted for `cpu`, but to keep the readme simple, only the `cpu` case will be discussed in detail). | ||
|
|
||
| Note that the GPU standalone requires a CUDA 7.5 capable driver to be installed on the system and [nvidia-docker] for running the Docker containers. Here it is generally sufficient to use `nvidia-docker` instead of `docker` in any of the commands mentioned. | ||
|
|
||
| # Running Caffe using the docker image | ||
|
|
||
| In order to test the Caffe image, run: | ||
| ``` | ||
| docker run -ti caffe:cpu caffe --version | ||
| ``` | ||
| which should show a message like: | ||
| ``` | ||
| caffe version 1.0.0-rc3 | ||
| ``` | ||
|
|
||
| One can also build and run the Caffe tests in the image using: | ||
| ``` | ||
| docker run -ti caffe:cpu bash -c "cd /opt/caffe/build; make runtest" | ||
| ``` | ||
|
|
||
| In order to get the most out of the caffe image, some more advanced `docker run` options could be used. For example, running: | ||
| ``` | ||
| docker run -ti caffe:cpu caffe time -model /opt/caffe/models/bvlc_alexnet/deploy.prototxt -engine MKLDNN | ||
| ``` | ||
| will measure the performance of AlexNet. You can also run caffe train as well. Note that docker runs all commands as root by default, and thus any output files (e.g. snapshots) generated will be owned by the root user. In order to ensure that the current user is used instead, the following command can be used: | ||
| ``` | ||
| docker run -ti --volume=$(pwd):/workspace -u $(id -u):$(id -g) caffe:cpu caffe train --solver=/opt/caffe/models/bvlc_alexnet/solver.prototxt -engine MKLDNN | ||
| ``` | ||
| where the `-u` Docker command line option runs the commands in the container as the specified user, and the shell command `id` is used to determine the user and group ID of the current user. Note that the Caffe docker images have `/workspace` defined as the default working directory. This can be overridden using the `--workdir=` Docker command line option. Note that you need to prepare dataset before training. | ||
|
|
||
| # Other use-cases | ||
|
|
||
| Although running the `caffe` command in the docker containers as described above serves many purposes, the container can also be used for more interactive use cases. For example, specifying `bash` as the command instead of `caffe` yields a shell that can be used for interactive tasks. (Since the caffe build requirements are included in the container, this can also be used to build and run local versions of caffe). | ||
|
|
||
| Another use case is to run python scripts that depend on `caffe`'s Python modules. Using the `python` command instead of `bash` or `caffe` will allow this, and an interactive interpreter can be started by running: | ||
| ``` | ||
| docker run -ti caffe:cpu python | ||
| ``` | ||
| (`ipython` is also available in the container). | ||
|
|
||
| Since the `caffe/python` folder is also added to the path, the utility executable scripts defined there can also be used as executables. This includes `draw_net.py`, `classify.py`, and `detect.py` | ||
|
|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,56 +1,56 @@ | ||
| FROM centos:7 | ||
| MAINTAINER [email protected] | ||
|
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| #ENV http_proxy proxy:port | ||
| #ENV https_proxy proxy:port | ||
|
|
||
| RUN rpm -iUvh http://download.fedoraproject.org/pub/epel/7/x86_64/e/epel-release-7-8.noarch.rpm | ||
|
|
||
| RUN yum install -y \ | ||
| redhat-rpm-config \ | ||
| tar \ | ||
| findutils \ | ||
| make \ | ||
| gcc-c++ \ | ||
| cmake \ | ||
| git \ | ||
| wget \ | ||
| atlas-devel \ | ||
| boost-devel \ | ||
| gflags-devel \ | ||
| glog-devel \ | ||
| hdf5-devel \ | ||
| leveldb-devel \ | ||
| lmdb-devel \ | ||
| opencv-devel \ | ||
| protobuf-devel \ | ||
| snappy-devel \ | ||
| protobuf-compiler \ | ||
| freetype-devel \ | ||
| libpng-devel \ | ||
| python-devel \ | ||
| python-numpy \ | ||
| python-pip \ | ||
| python-scipy \ | ||
| gcc-gfortran \ | ||
| libjpeg-turbo-devel | ||
|
|
||
| RUN yum clean all | ||
| ENV CAFFE_ROOT=/opt/caffe | ||
| WORKDIR $CAFFE_ROOT | ||
|
|
||
| # FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this. | ||
| ENV CLONE_TAG=master | ||
|
|
||
| RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/intel/caffe.git . && \ | ||
| for req in $(cat python/requirements.txt) pydot; do pip --no-cache-dir install $req; done && \ | ||
| mkdir build && cd build && \ | ||
| cmake -DCPU_ONLY=1 -DCMAKE_BUILD_TYPE=Release .. && \ | ||
| make all -j"$(nproc)" | ||
|
|
||
| ENV PYCAFFE_ROOT $CAFFE_ROOT/python | ||
| ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH | ||
| ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH | ||
| RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig | ||
|
|
||
| WORKDIR /workspace | ||
| FROM centos:7 | ||
| MAINTAINER [email protected] | ||
|
|
||
| #ENV http_proxy proxy:port | ||
| #ENV https_proxy proxy:port | ||
|
|
||
| RUN rpm -iUvh http://download.fedoraproject.org/pub/epel/7/x86_64/e/epel-release-7-8.noarch.rpm | ||
|
|
||
| RUN yum install -y \ | ||
| redhat-rpm-config \ | ||
| tar \ | ||
| findutils \ | ||
| make \ | ||
| gcc-c++ \ | ||
| cmake \ | ||
| git \ | ||
| wget \ | ||
| atlas-devel \ | ||
| boost-devel \ | ||
| gflags-devel \ | ||
| glog-devel \ | ||
| hdf5-devel \ | ||
| leveldb-devel \ | ||
| lmdb-devel \ | ||
| opencv-devel \ | ||
| protobuf-devel \ | ||
| snappy-devel \ | ||
| protobuf-compiler \ | ||
| freetype-devel \ | ||
| libpng-devel \ | ||
| python-devel \ | ||
| python-numpy \ | ||
| python-pip \ | ||
| python-scipy \ | ||
| gcc-gfortran \ | ||
| libjpeg-turbo-devel | ||
|
|
||
| RUN yum clean all | ||
| ENV CAFFE_ROOT=/opt/caffe | ||
| WORKDIR $CAFFE_ROOT | ||
|
|
||
| # FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this. | ||
| ENV CLONE_TAG=master | ||
|
|
||
| RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/intel/caffe.git . && \ | ||
| for req in $(cat python/requirements.txt) pydot; do pip --no-cache-dir install $req; done && \ | ||
| mkdir build && cd build && \ | ||
| cmake -DCPU_ONLY=1 -DCMAKE_BUILD_TYPE=Release .. && \ | ||
| make all -j"$(nproc)" | ||
|
|
||
| ENV PYCAFFE_ROOT $CAFFE_ROOT/python | ||
| ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH | ||
| ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH | ||
| RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig | ||
|
|
||
| WORKDIR /workspace |
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