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.
In order to test the Caffe image, run:
docker run -ti caffe:cpu caffe --version
which should show a message like:
caffe version 1.1.0
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.
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