Caffe2 v0.8.1

This incremental release provides support for cuDNN7.

Caffe2 v0.8.0

Caffe2 v0.8.0 Release Notes
July 21, 2017

Major Changes

• Caffe2 Brew API: now you can easily brew a variety of Caffe2 models with this help of this API
• Predictor Exporter and Mobile Exporter: export model state to a database or for mobile use; see the MNIST tutorial and the predictor code for examples of this and brew.
• 16-bit FP: Caffe2 and the NVIDIA Volta platform have added 16-bit Floating Point Training Support

Docker

docker pull caffe2ai/caffe2

Caffe2 v0.7.0

Caffe2 v0.7.0 Release Notes

Installation

This build is confirmed for:

• Ubuntu 14.04
• Ubuntu 16.06

Required Dependencies

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 and cuDNN, 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

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

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)

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

Optional Dependencies

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

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

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

# 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 \
      graphviz \
      hypothesis \
      jupyter \
      matplotlib \
      pydot python-nvd3 \
      pyyaml \
      requests \
      scikit-image \
      scipy \
      setuptools \
      tornado

Clone & Build

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.

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 below.

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.

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.

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.
"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

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
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 v0.6.0

Caffe2 v0.6.0 Release Notes

Installation

Note: the release archive will not include third_party submodules which you will need in order to build Caffe2. We've uploaded an archive with the source and the submodules and attached it to this release. See the bottom of this page for the link!

This build is confirmed for:

• Ubuntu 14.04
• Ubuntu 16.06

Required Dependencies

sudo apt-get update
sudo apt-get install python-dev python-pip git build-essential cmake libprotobuf-dev protobuf-compiler libgoogle-glog-dev
sudo pip install numpy protobuf

Optional GPU Support

If you plan to use GPU instead of CPU only, then you should install NVIDIA CUDA and cuDNN, 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

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

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)

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

Optional Dependencies

sudo apt-get install libgtest-dev libgflags2 libgflags-dev liblmdb-dev libleveldb-dev libsnappy-dev libopencv-dev libiomp-dev openmpi-bin openmpi-doc libopenmpi-dev python-pydot
sudo pip install flask graphviz hypothesis jupyter matplotlib pydot python-nvd3 pyyaml requests scikit-image scipy setuptools tornado

• Note for Ubuntu 16.04 libgflags2 should be replaced with libgflags-dev.

Clone & Build

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.

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 below.

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.

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.

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
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.