Install

Welcome to Caffe2! Get started with deep learning today by following the step by step guide on how to download and install Caffe2.

Select your preferred platform and install type.

This build is confirmed for:

• Ubuntu 14.04
• Ubuntu 16.04

Required Dependencies#

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sudo apt-get update
sudo apt-get install -y --no-install-recommends       build-essential       cmake       git       libgoogle-glog-dev       libprotobuf-dev       protobuf-compiler       python-dev       python-pip                          
sudo pip install numpy protobuf

Optional GPU Support#

If you plan to use GPU instead of CPU only, then you should install NVIDIA CUDA 8 and cuDNN v5.1 or v6.0, a GPU-accelerated library of primitives for deep neural networks. NVIDIA’s detailed instructions or if you’re feeling lucky try the quick install set of commands below.

Update your graphics card drivers first! Otherwise you may suffer from a wide range of difficult to diagnose errors.

For Ubuntu 14.04

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sudo apt-get update && sudo apt-get install wget -y --no-install-recommends
wget "http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_8.0.61-1_amd64.deb"
sudo dpkg -i cuda-repo-ubuntu1404_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda

For Ubuntu 16.04

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sudo apt-get update && sudo apt-get install wget -y --no-install-recommends
wget "http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda

Install cuDNN (all Ubuntu versions)#

Version 5.1

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CUDNN_URL="http://developer.download.nvidia.com/compute/redist/cudnn/v5.1/cudnn-8.0-linux-x64-v5.1.tgz"
wget ${CUDNN_URL}
sudo tar -xzf cudnn-8.0-linux-x64-v5.1.tgz -C /usr/local
rm cudnn-8.0-linux-x64-v5.1.tgz && sudo ldconfig

Version 6.0 Visit NVIDIA’s cuDNN download to register and download the archive. Follow the same instructions above switching out for the updated library.

Optional Dependencies#

Note libgflags2 is for Ubuntu 14.04. libgflags-dev is for Ubuntu 16.04.

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# for Ubuntu 14.04
sudo apt-get install -y --no-install-recommends libgflags2

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# for Ubuntu 16.04
sudo apt-get install -y --no-install-recommends libgflags-dev

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# for both Ubuntu 14.04 and 16.04
sudo apt-get install -y --no-install-recommends       libgtest-dev       libiomp-dev       libleveldb-dev       liblmdb-dev       libopencv-dev       libopenmpi-dev       libsnappy-dev       openmpi-bin       openmpi-doc       python-pydot
sudo pip install       flask       future       graphviz       hypothesis       jupyter       matplotlib       pydot python-nvd3       pyyaml       requests       scikit-image       scipy       setuptools       six       tornado

Clone & Build#

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git clone --recursive https://github.com/caffe2/caffe2.git && cd caffe2
make && cd build && sudo make install
python -c ‘from caffe2.python import core‘ 2>/dev/null && echo "Success" || echo "Failure"

Run this command below to test if your GPU build was a success. You will get a test output either way, but it will warn you at the top of the output if CPU was used instead along with other errors like missing libraries.

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python -m caffe2.python.operator_test.relu_op_test

Environment Variables#

These environment variables may assist you depending on your current configuration. When using the install instructions above on the AWS Deep Learning AMI you don’t need to set these variables. However, our Docker scripts built on Ubuntu-14.04 or NVIDIA’s CUDA images seem to benefit from having these set. If you ran into problems with the build tests above then these are good things to check. Echo them first and see what you have and possibly append or replace with these directories. Also visit the Troubleshooting section.

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echo $PYTHONPATH
# export PYTHONPATH=/usr/local:$PYTHONPATH
# export PYTHONPATH=$PYTHONPATH:/home/ubuntu/caffe2/build
echo $LD_LIBRARY_PATH
# export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH

Setting Up Tutorials & Jupyter Server#

If you’re running this all on a cloud computer, you probably won’t have a UI or way to view the IPython notebooks by default. Typically, you would launch them locally with ipython notebook and you would see a localhost:8888 webpage pop up with the directory of notebooks running. The following example will show you how to launch the Jupyter server and connect to remotely via an SSH tunnel.

First configure your cloud server to accept port 8889, or whatever you want, but change the port in the following commands. On AWS you accomplish this by adding a rule to your server’s security group allowing a TCP inbound on port 8889. Otherwise you would adjust iptables for this.

Next you launch the Juypter server.

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jupyter notebook --no-browser --port=8889

Then create the SSH tunnel. This will pass the cloud server’s Jupyter instance to your localhost 8888 port for you to use locally. The example below is templated after how you would connect AWS, where your-public-cert.pem is your own public certificate and ubuntu@super-rad-GPU-instance.compute-1.amazonaws.com is your login to your cloud server. You can easily grab this on AWS by going to Instances > Connect and copy the part after ssh and swap that out in the command below.

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ssh -N -f -L localhost:8888:localhost:8889 -i "your-public-cert.pem" ubuntu@super-rad-GPU-instance.compute-1.amazonaws.com

Troubleshooting#

Dependencies#

Try to keep the system and python dependencies at the same version. We’ve encountered issues when the python version is more updated than the system version or vice versa.

Strictly speaking, the core dependencies above are all you need to run the core Caffe2 successfully. However, for real-world deep learning (e.g., image processing, mathematical operations, etc), there are other dependencies that you will want to install in order to experience the full features of Caffe2.

caffe2--Install