I've built Ubuntu Server instances for DIGITS multiple times and have compiled a list of steps that seems to work well for me. Configuring all the dependencies and installing all the prerequisites can be tricky so I thought I'd share it in one place with some notes. It does depend somewhat on the compute capability of your Nvidia card.
You need an NVIDIA driver to begin with of course (details and instructions).
I do the typical installation using LVM to encrypt the whole drive. No automatic updates. Basic installation packages plus SSH server and really nothing else (at first).
sudo apt-get update
sudo apt-get upgrade
sudo apt-get install emacs #sorry vim fanboys
I like to try and request a specific IP. Insert the following lines into dhclient.conf. If you can get a static IP even better.
sudo emacs /etc/dhcp/dhclient.conf
#try to get a specific ip; dhclient -r -v to request again if it fails on startup
send dhcp-requested-address XXX.XX.XXX.XXX;
cd /tmp
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
CUDA_REPO_PKG=http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
ML_REPO_PKG=http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
wget "$CUDA_REPO_PKG" -O /tmp/cuda-repo.deb && sudo dpkg -i /tmp/cuda-repo.deb && rm -f /tmp/cuda-repo.deb
wget "$ML_REPO_PKG" -O /tmp/ml-repo.deb && sudo dpkg -i /tmp/ml-repo.deb && rm -f /tmp/ml-repo.deb
sudo apt-get update
sudo apt-get install cuda-9-0
sudo apt-mark hold cuda-9-0
wget https://developer.nvidia.com/compute/cuda/9.0/Prod/patches/2/cuda-repo-ubuntu1604-9-0-local-cublas-performance-update-2_1.0-1_amd64-deb
sudo dpkg -i cuda-repo-ubuntu1604-9-0-local-cublas-performance-update-2_1.0-1_amd64-deb
sudo apt-mark hold nvidia-390
Since we are using Nvidia, don't use Nouveau. Make a few changes to not try to boot to the GUI but command line instead.
sudo emacs /etc/modprobe.d/blacklist-nouveau.conf
blacklist nouveau
blacklist lbm-nouveau
alias nouveau off
alias lbm-nouveau off
sudo emacs /etc/default/grub
GRUB_CMDLINE_LINUX_DEFAULT="text"
GRUB_CMDLINE_LINUX="text"
sudo systemctl set-default multi-user.target
sudo reboot now
After reboot, double check. The first command should show stuff, the second not (if successful). The check packages and upgrade.
lsmod | grep nvidia
lsmod | grep nouveau
sudo apt-get update
sudo apt-get ugprade
Protobuf - in some cases I had trouble with git:// endpoints so I modified to always use https in the second line. 3.2.x is what appears to be compatible with DIGITS - newer versions gave me Caffe errors.
sudo apt-get install autoconf automake libtool curl make g++ git python-dev python-setuptools unzip
git config --global url."https://".insteadOf git://
export PROTOBUF_ROOT=/usr/local/protobuf
sudo -H git clone https://github.com/google/protobuf.git $PROTOBUF_ROOT -b '3.2.x'
cd $PROTOBUF_ROOT
sudo -H ./autogen.sh
sudo -H ./configure
sudo -H make "-j$(nproc)"
sudo -H make install
sudo -H ldconfig
cd python
sudo -H python setup.py install --cpp_implementation
protoc --version #should say "libprotoc 3.2.0"
Caffe. Update: the ubuntu libcudnn libraries are not found when you try and build Caffe. It's possible you could set environment variables to find them, but now I'm building from scratch using cudnn v. 7.0.5 for cuda 9.0 as follows. Detectnet requires caffe version 0.15. Note that you need to add an environment variable to the .bashrc file for whatever user will be running DIGITS so it knows where Caffe is.
# download ubuntu cudnn packages from https://developer.nvidia.com/rdp/cudnn-download to match your version of CUDA and Ubuntu
sudo dpkg -i libcudnn7_7.0.5.15-1+cuda9.0_amd64.deb
sudo dpkg -i libcudnn7-dev_7.0.5.15-1+cuda9.0_amd64.deb
sudo dpkg -i libcudnn7-doc_7.0.5.15-1+cuda9.0_amd64.deb
# test your cudnn installation
cd /tmp
cp -r /usr/src/cudnn_samples_v7/ .
cd cudnn_samples_v7/mnistCUDNN/
make clean
make
./mnistCUDNN #should say "Test Passed!"
rm -rf /tmp/cudnn_samples_v7
sudo -H apt-get install --no-install-recommends build-essential cmake git gfortran libatlas-base-dev libboost-filesystem-dev libboost-python-dev libboost-system-dev libboost-thread-dev libgflags-dev libgoogle-glog-dev libhdf5-serial-dev libleveldb-dev liblmdb-dev libopencv-dev libsnappy-dev python-all-dev python-dev python-h5py python-matplotlib python-numpy python-opencv python-pil python-pydot python-scipy python-skimage python-sklearn libnccl-dev libboost-regex-dev libturbojpeg libopenblas-dev
emacs ~/.bashrc
export CAFFE_ROOT=/usr/local/caffe
source ~/.bashrc
sudo -H pip install pip --upgrade
sudo -H git clone https://github.com/NVIDIA/caffe.git $CAFFE_ROOT -b 'caffe-0.15'
sudo -H pip install -r $CAFFE_ROOT/python/requirements.txt #you seem to need root access at least for some of these libraries
sudo -H pip install numpy --upgrade
cd $CAFFE_ROOT
sudo -H mkdir build
cd build
sudo -H cmake ..
sudo -H make -j"$(nproc)"
sudo -H make install
sudo apt-get install --no-install-recommends git software-properties-common
export TORCH_ROOT=/usr/local/torch
sudo git clone https://github.com/torch/distro.git $TORCH_ROOT --recursive
cd $TORCH_ROOT
sudo su
./install-deps
export TORCH_NVCC_FLAGS="-D__CUDA_NO_HALF_OPERATORS__" #something with cuda 9.0? needs to be in root environment
./install.sh -b
exit #exit root
sudo apt-get install --no-install-recommends libhdf5-serial-dev liblmdb-dev
source ~/.bashrc
sudo su
source /usr/local/torch/install/bin/torch-activate
git config --global url."https://".insteadOf git:// #because you need to do this as root as well for the following
luarocks install tds
luarocks install "https://raw.github.com/deepmind/torch-hdf5/master/hdf5-0-0.rockspec"
luarocks install "https://raw.github.com/Neopallium/lua-pb/master/lua-pb-scm-0.rockspec"
luarocks install lightningmdb 0.9.18.1-1 LMDB_INCDIR=/usr/include LMDB_LIBDIR=/usr/lib/x86_64-linux-gnu
luarocks install "https://raw.githubusercontent.com/ngimel/nccl.torch/master/nccl-scm-1.rockspec"
exit #exit root
Tensorflow. Using 1.5 currently. Some of this depends on the compute capability of your card - I think must be at least 3.5 for tensorflow 1.5.
sudo -H pip install tensorflow-gpu==1.5
#test tensorflow if you want
python
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello)) #should get "Hello, TensorFlow!"
DIGITS. Note that I've given control to the local user for the jobs directory and digits log file. This depends on what user will be running the DIGITS server.
DIGITS_ROOT=/usr/local/digits
sudo git clone https://github.com/NVIDIA/DIGITS.git $DIGITS_ROOT
sudo -H pip install -r $DIGITS_ROOT/requirements.txt
sudo -H pip install -e $DIGITS_ROOT
sudo apt-get install python-tk
sudo mkdir /usr/local/digits/digits/jobs
sudo chown <user>:<user> /usr/local/digits/digits/jobs
sudo touch /usr/local/digits/digits/digits.log
sudo chown <user>:<user> /usr/local/digits/digits/digits.log
Now you should have everything installed and can start DIGITS using the digits-devserver executable. Good idea to test here to be sure. I like to create a startup job as follows.
sudo emacs /lib/systemd/system/digits.service
[Unit]
Description=Start Digits
[Service]
User=<user>
Environment=CAFFE_ROOT=/usr/local/caffe
Environment=TORCH_ROOT=/usr/local/torch
WorkingDirectory=/usr/local/digits
ExecStart=/bin/bash digits-devserver
Restart=always
RestartSec=30
[Install]
WantedBy=multi-user.target
sudo systemctl daemon-reload
sudo systemctl enable digits
sudo systemctl start digits
Now that you're up and running, check out the Getting Started Guide.
If you are interested in developing for DIGITS or work with its source code, check out the Development Setup Guide
Most configuration options should have appropriate defaults. Read this doc for information about how to set a custom configuration for your server.