caffe2--Install

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.

Platform:	MacOS X Ubuntu CentOS Windows iOS Android Raspbian Tegra
Install Type:	Build From Source Pre-Built Binaries Docker Images Cloud

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.

1 2	# for Ubuntu 14.04 sudo apt-get install -y --no-install-recommends libgflags2

1 2	# 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.

1	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.

1	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.

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

Troubleshooting#

Python errors
Python version	Python is core to run Caffe2. We currently require Python2.7. Ubuntu 14.04 and greater have Python built in by default, and that can be used to run Caffe2. To check your version: python --version
Solution	If you want the developer version of python, you could install the dev package for Python: sudo apt-get install python-dev
Python environment	You may have another version of Python installed or need to support Python version 3 for other projects.
Solution	Try virtualenv or Anaconda. The Anaconda platform provides a single script to install many of the necessary packages for Caffe2, including Python. Using Anaconda is outside the scope of these instructions, but if you are interested, it may work well for you.
pip version	If you plan to use Python with Caffe2 then you need pip.
Solution	sudo apt-get install python-pip and also try using pip2 instead of pip.

Building from source
OS version	Caffe2 requires Ubuntu 14.04 or greater.
git	While you can download the Caffe2 source code and submodules directly from GitHub as a zip, using git makes it much easier.
Solution	sudo apt-get install git
protobuf	You may experience an error related to protobuf during the make step.
Solution	Make sure you’ve installed protobuf in both of these two ways: sudo apt-get install libprotobuf-dev protobuf-compiler && sudo pip install protobuf
libgflags2 error	This optional dependency is for Ubuntu 14.04.
Solution	Use apt-get install libgflags-dev for Ubuntu 16.04.

GPU Support
GPU errors	Unsupported GPU or wrong version
Solution	You need to know the specific deb for your version of Linux. sudo dpkg -i cuda-repo-<distro>_<version>_<architecture>.deb Refer to NVIDIA’s installation guide.
Build issues	Be warned that installing CUDA and cuDNN will increase the size of your build by about 4GB, so plan to have at least 12GB for your Ubuntu disk size.

Caffe2 Python
Module not found	Verify that Caffe2 was installed correctly
Solution	Run the following: python -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure" An output of Success means you are ready to with Caffe2 - congratulations! An output of Failure usually means you have not installed one of the dependencies correctly.
Dependencies missing	It’s possible you’re trying to run something that was using an optional dependency.
Solution	sudo pip install setuptools flask jupyter matplotlib scipy pydot tornado python-nvd3 scikit-image pyyaml
matplotlib error	Sometimes you need setuptools first: sudo pip install -U pip setuptools && sudo pip install matplotlib
model downloader error	If you need to run it as sudo (because it’s trying to install the model in /usr/local/caffe2…), then PYTHONPATH might not be visible in that context.
Solution	sudo visudo then add this line: Defaults env_keep += "PYTHONPATH"
“AttributeError: ‘module’ object has no attribute ‘MakeArgument’”	Occurs when calling core.CreateOperator
Solution	Check your install directory (/usr/local/), and remove the folder /caffe2/python/utils

Other Errors
libdc1394 error	for some reason once opencv is installed you may get errors with libdc1394
Solution	ln /dev/null /dev/raw1394 but that’s not persistent so try sh -c 'ln -s /dev/null /dev/raw1394' or when instantiating the container use: --device /dev/null:/dev/raw1394
caffe2_pybind11_state_gpu	WARNING:root:Debug message: No module named caffe2_pybind11_state_gpu
Solution	ignore if you’re using CPU-only
Python kernel crashing	This happens when you try to call Jupyter server directly (like in a Docker container).
Solution	Use sh -c "jupyter notebook ..." to get around this problem.
Exception: “dot” not found in path	This happens in some of the tutorials when graphing.
Solution	Make sure you have graphviz and pydot. sudo apt-get install python-pydot and sudo pip install graphviz or brew install these to fix the problem.

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.

System Dependencies
cmake
git
gflags
glog: Google Logging Module
NumPy
protobuf: Google Protocol Buffers	version 3.2.0
Build tools for C++ 11	Xcode CLTs & automake (mac/iOS), build-essential (linux), Visual Studio (win), Android Studio (droid)

Python Dependencies
gflags
glog: Google Logging Module
NumPy
protobuf: Google Protocol Buffers	version 3.2.0

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.

Optional System Dependencies
cuDNN	if using GPU, this is needed for Caffe2’s cuDNN operators
Eigen 3
LevelDB
Nvidia CUDA	v6.5 or greater
OpenCV	for image-related operations; requires leveldb <= v1.19
OpenMPI	for MPI-related Caffe2 operators
RocksdB	for Caffe2’s RocksDB IO backend
ZeroMQ	needed for Caffe2’s ZmqDB IO backend (serving data through a socket)

Python Optional Dependencies
There are also various Python libraries that will be valuable in your experience with Caffe2. Many of these are required to run the tutorials.
Flask
Graphviz
Hypothesis
Jupyter	for the Jupyter Notebook
LevelDB
lmdb
Matplotlib
Pydot
Python-nvd3
pyyaml
requests
Scikit-Image
SciPy
setuptools
Tornado
ZeroMQ

What’s in Third Party?
Whether building from source or installing from the Python wheel, you also get complimentary tools installed as well.
Android cmake
benchmark
cnmem
cub
eigen
googletest
ios-cmake
nccl
nervanagpu
NNPACK	requires ninja and confu to build
Google Protocol Buffers (protobuf)
pybind11