An in-depth look into getting Deep Learning frameworks installed on Ubuntu 18.04 and using your Nvidia GPU.
Ignore my old dusty PS3, I was able to find an orange Nvidia logo to match!
This article and info is current/updated as of June 25th, 2018
Building and training deep learning models is laborious task. Ensuring your environments, packages, and GPUs work in harmony adds to that effort. This guide gets fairly in-depth to help users that are relatively new to Linux. Ubuntu’s latest long term support (LTS) 18.04 was released around the end of April, so I decided to wipe my current Linux box and start from scratch. Including getting my deep learning environments installed. As 18.04 is relatively new, there may be some confusion when determining which packages to install. Hopefully this post helps alleviates those worries. I have been using this set up for a little over a month without any issues, but if you have or want to use Ubuntu 16.04 LTS these instructions should work fine as well. Either-way, I walk through this process slowly, and provide all terminal inputs and most of the output. I also not just show what I am doing, but try to explain why for newcomers to Linux/deep learning.
Hopefully you find this helpful! Let’s get into it…
Preamble
Hardware of Choice
- Nvidia GTX 1070 (I assume you have an Nvidia GPU)
- Asus Micro-ATX B150M-A D3
- 16 gigs memory
- Intel Core I5–6500 3.2GHz
Software of Choice
- Ubuntu 18.04 LTS (Desktop Version)
- Miniconda (you can also use Anaconda — the main difference)
- Python 3.6 (through miniconda)
- Terminator for my terminal
- Tmux Terminal multiplexer (an essential tool for deep learning)
- SublimeText As my text editor
- VSCode As my main code editor. Use to be Atom…
I use the following conventions in this post
- Anything proceeding a $ and is bold is a command for you to type into the terminal and run. Do not include the dollar sign.
- Anything proceeding a # and is italicized is a comment and is there to provide more info for clarity. Do not add these to the terminal.
- Any lines that are tabbed over are console output. Basically what you might see after running a command. I will not include some output, especially if it is long, or not very constructive.
Here is an example:
$ run this command #comment about the command, do not include this# comment about the proceeding output from the above command some output that is potentially important…ack! test failed!!!Why did it fail? who knows… StackExchange Knows!
GPU Compatibility
First order of business is ensuring your GPU has a high enough compute score. TensorFlow’s documentation states:
GPU card with CUDA Compute Capability 3.0 or higher for building from source and 3.5 or higher for our binaries.
We will not be building TensorFlow from source, but rather using their prebuilt binaries. Therefore you need a score of 3.5 or higher. Open your terminal and run:
$ sudo lshw -C display | grep productproduct: GP104 [GeForce GTX 1070]
Now that you have your GPU model you can cross reference it with Nvidia’s site to determine your compute capability. In my case it is a score of 6.1, So I am good to go.
Next we want to ensure all the packages available to Ubuntu are the latest and greatest and then we want to install/upgrade them. Run:
$ sudo apt update$ sudo apt upgrade #will ask you Y/n if you want to upgrade
Next we will install GPU drivers.
Nvidia Drivers
Ubuntu comes prepackaged with generic drivers for GPUs. These drivers are not highly optimized, and we don’t want to sacrifice any performance. You will first need to find and install the latest Nvidia Drivers for your GPU. There are a few options as outlined in this good StackExchange post:
Nvidia PPA — Great Performance. It works out of the box for most cards by using the driver included in the PPA (Which are different for each generation of Nvidia cards (This is explained below).
Ubuntu Default Recommended Driver*— Ubuntu does an amazing job in figuring out which Nvidia driver you need depending on the card you are using.
Nouveau — This is the open source implementation of the Nvidia driver. They also do an amazing job and although they are not on par with the official drivers or the ones in the PPA (Even more so with the latest Nvidia cards), the speed of development, the commitment, dedication and advancements that they do per week, gives confident that this is fact an option to have and promote.
Official Nvidia Site — This are the official drivers (Same as the ones in the PPA), but the difference is that they do not upgrade automatically and have some issues when updating, uninstalling and installing (Very rare but it happens).
After looking into which driver would be best to use, I went with Ubuntu’s PPA. The official Nvidia driver had known issues with X Server and would require more effort to get it working. No thanks… The default Ubuntu driver lacks performance. And the Nouveau driver, while it looks good and open source is nice, it isn’t as performant. Adding the Ubuntu PPA is the best option as they have the latest Nvidia official drivers and have been tested on Ubuntu, so it should be a smooth installation. Ok, to do this run the following command:
$ sudo add-apt-repository ppa:graphics-drivers/ppa
This command will add the PPA repository to Ubuntu’s packaging system. What does this mean? Ubuntu is built off of Debian Linux and thus uses Debian’s dpkg packaging system. This system provides Ubuntu with applications to install. Advanced Package Tool (APT) allows you to easily interact with dpkg from the terminal. It oversees package dependencies and upgrading a lot of the software in your OS.
Next you need to determine which driver you need to install. Go to the PPA repository and scroll down to the bottom of the page. Based on Ubuntu 18.04 I had a choice between nvidia-driver-390 and nvidia-driver-396. I choose 390 because it had been out in the wild longer and thus more potential bugs and issues would have been uncovered by now.
Run the following command to install the driver.
$ sudo apt install nvidia-driver-390
Go ahead and bookmark your tabs, it’s time to reboot your computer! I will still be here when you’re back up and running…
Great you’re back. Hopefully! Ok let’s check to make sure the driver was installed correctly:
$ nvidia-smi
Your output should look similar to the one below, except your driver version number may be different, as well as your GPU and processes. If it does congratulations! You have Nvidia drivers.
output of nvidia-smi
To wrap up the driver portion, you are going to make sure APT doesn’t upgrade your driver. Remember you added the PPA to APT and now it is responsible for managing this software. It is important that you stop the software we are installing from upgrading.
Getting GPU support to work requires a symphony of different hardware and software. To ensure compatibility we are going to ask Ubuntu to not update certain software. You can remove this hold at any time. Run:
$ sudo apt-mark hold nvidia-driver-390nvidia-driver-390 set on hold.# To reverse this operation run:# sudo apt-mark unhold package_name
Now onto to CUDA!
Installing CUDA 9.0
The question is CUDA 9.0 vs 9.1. From what I have read it seems that version 9.1 works if building from source, whereas 9.0 works out of the box when installing from the binaries. Installing from binaries makes this process just that less tedious, let’s stick with that for this go around.
Go ahead and open up the CUDA docs. These instructions can be fairly involved to follow, but let’s step through them one-by-one.
First we are going to check what gcc and g++ version you have installed.
$ gcc — versiongcc (Ubuntu 7.3.0–16ubuntu3) 7.3.0$ g++ — versiong++ (Ubuntu 7.3.0–16ubuntu3) 7.3.0
When you installed the PPA Nvidia drivers, the drivers also installed gcc and g++ version 7.x. This is fine, however TensorFlow and cuDNN requires version 6.x for compatibility. We will leave this issue alone for now, but at some point a test will fail, we will resolve this issue then after installing CUDA.
Let’s install the kernel headers. For more info on what Linux headers are, go here and especially here. Essentially, kernel headers are files used to compile the kernel and any other applications/software (in this case CUDA) that depend on information within those header files. To install run:
$ sudo apt install linux-headers-$(uname -r)Reading package lists… DoneBuilding dependency treeReading state information… Donelinux-headers-4.15.0–23-generic is already the newest version (4.15.0–23.25).linux-headers-4.15.0–23-generic set to manually installed.
Now you need to download CUDA and install it. You can grab CUDA 9.0 from this official Nvidia archive. Look at the image below to see which options to select.
Once it is finished downloading run the following:
$ cd Downloads/ _#navigate to where the .deb file downloaded_$ sudo dpkg -i cuda-repo-ubuntu1704–9–0-local_9.0.176–1_amd64.debSelecting previously unselected package cuda-repo-ubuntu1704–9–0-local.(Reading database … 224086 files and directories currently installed.)Preparing to unpack cuda-repo-ubuntu1704–9–0-local_9.0.176–1_amd64.deb …Unpacking cuda-repo-ubuntu1704–9–0-local (9.0.176–1) …Setting up cuda-repo-ubuntu1704–9–0-local (9.0.176–1) …The public CUDA GPG key does not appear to be installed.To install the key, run this command:sudo apt-key add /var/cuda-repo-9–0-local/7fa2af80.pub _# run the command the command they provide to install the GPG key_$ sudo apt-key add /var/cuda-repo-9–0-local/7fa2af80.pub$ sudo apt update$ sudo apt install cuda #might take a few minutes to finish
Congratulations! CUDA 9.0 is installed. Let’s hold this package as well from updates:
$ sudo apt-mark hold cuda
Lastly you need your PATH variable in your .bashrc file. This hidden file is located in your home directory. You need to add the following line to the end of your .bashrc file:
export PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}}
You can use any text editor you like, or nano or vim. but open your .bashrc file and add that to the end of the file save it and close. You will need to start a new terminal session (close and reopen your terminal) for the .bashrc file changes to get loaded. .bashrc is a shell script that bash runs every time a session is started. You are adding the PATH variable so that CUDA can be found in your PATH.
$ subl .bashrc _#opens .bashrc file in sublime text, I copy pasta the above line of code then save and close sublime._$ cat .bashrc | grep cudaexport PATH=/usr/local/cuda-9.0/bin${PATH:+:${PATH}} #you should see this output
Great CUDA is ready to be tested!
$ cd _#navigate back to home_$ cat /proc/driver/nvidia/versionNVRM version: NVIDIA UNIX x86_64 Kernel Module 390.67 Fri Jun 1 04:04:27 PDT 2018GCC version: gcc version 7.3.0 (Ubuntu 7.3.0–16ubuntu3)$ nvcc — versionnvcc: NVIDIA (R) Cuda compiler driverCopyright © 2005–2017 NVIDIA CorporationBuilt on Fri_Sep__1_21:08:03_CDT_2017Cuda compilation tools, release 9.0, V9.0.176
If this is all good, then CUDA is installed and working correctly. GCC is still at version 7.x, but we will fix this soon. For now let’s tackle cuDNN.
cuDNN
Check out the official docs on cuDNN. We will be stepping through these. First you need to make sure you have created a free account with Nvidia’s Developer Program. Once you sign up, verify your email and are ready to go, you can sign in from this link and it should take you directly to the download cuDNN page. Follow the steps in the images below to find the specific cuDNN version. I choose cuDNN version 7.0.5 over 7.1.4 based on what TensorFlow suggested for optimal compatibility at the time.
Once at the Download page agree to the terms and then look at the bottom of the list for a link to archived cuDNN releases. 7.0.5 is an archived stable release.
This will take you to the archives, where you can now look for cuDNN v7.0.5 for CUDA 9.0.
Download all three of the following .deb files:
- cuDNN v7.0.5 Runtime Library for Ubuntu16.04 (Deb)
- cuDNN v7.0.5 Developer Library for Ubuntu16.04 (Deb)
- cuDNN v7.0.5 Code Samples and User Guide for Ubuntu16.04 (Deb)
After you have downloaded them, run these next commands:
$ cd Downloads/ _#navigate to where you downloaded them# Start with the runtime file_$ sudo dpkg -i libcuDNN7_7.0.5.15–1+cuda9.0_amd64.debSelecting previously unselected package libcuDNN7.(Reading database … 236355 files and directories currently installed.)Preparing to unpack libcuDNN7_7.0.5.15–1+cuda9.0_amd64.deb …Unpacking libcuDNN7 (7.0.5.15–1+cuda9.0) …Setting up libcuDNN7 (7.0.5.15–1+cuda9.0) …Processing triggers for libc-bin (2.27–3ubuntu1) …/sbin/ldconfig.real: /usr/local/cuda-9.0/targets/x86_64-linux/lib/libcuDNN.so.7 is not a symbolic link
_# then the developer file — dev typically includes the source code_$ sudo dpkg -i libcuDNN7-dev_7.0.5.15–1+cuda9.0_amd64.deb(Reading database … 236362 files and directories currently installed.)Preparing to unpack libcuDNN7-dev_7.0.5.15–1+cuda9.0_amd64.debUnpacking libcuDNN7-dev (7.0.5.15–1+cuda9.0) over (7.0.5.15 1+cuda9.0) …Setting up libcuDNN7-dev (7.0.5.15–1+cuda9.0) …update-alternatives: using /usr/include/x86_64-linux gnu/cuDNN_v7.h to provide /usr/include/cuDNN.h (libcuDNN) in auto mode
_# finally the doc file — necessary for running the installation test_$ sudo dpkg -i libcuDNN7-doc_7.0.5.15–1+cuda9.0_amd64.debSelecting previously unselected package libcuDNN7-doc.(Reading database … 236362 files and directories currently installed.)Preparing to unpack libcuDNN7-doc_7.0.5.15–1+cuda9.0_amd64.deb …Unpacking libcuDNN7-doc (7.0.5.15–1+cuda9.0) …Setting up libcuDNN7-doc (7.0.5.15–1+cuda9.0) …
Great, that wasn’t too bad. Let’s test the cuDNN installation.
$ cd _#back to home_$ cp -r /usr/src/cuDNN_samples_v7/ $HOME _#copy the test mnist files over to home._$ cd $HOME/cuDNN_samples_v7/mnistcuDNN$ make clean && makerm -rf *orm -rf mnistcuDNN/usr/local/cuda/bin/nvcc -ccbin g++ -I/usr/local/cuda/include -IFreeImage/include -m64 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_53,code=sm_53 -gencode arch=compute_53,code=compute_53 -o fp16_dev.o -c fp16_dev.cuIn file included from /usr/local/cuda/include/host_config.h:50:0,from /usr/local/cuda/include/cuda_runtime.h:78,from <command-line>:0:/usr/local/cuda/include/crt/host_config.h:119:2: error: #error — unsupported GNU version! gcc versions later than 6 are not supported!#error — unsupported GNU version! gcc versions later than 6 are not supported! ^~~~~Makefile:203: recipe for target ‘fp16_dev.o’ failedmake: *** [fp16_dev.o] Error 1
And now we run into trouble with gcc/++ 7. Now we will need to mitigate this gcc issue. Lets install gcc 6.x and g++ 6.x Then we can create a symlink to tell cuDNN to look for version 6. We can leave version 7 installed as well.
$ sudo apt install gcc-6 g++-6_# ln -s makes a symbolic link so cuda looks for gcc version 6 where we tell it to look._$ sudo ln -s /usr/bin/gcc-6 /usr/local/cuda/bin/gcc$ sudo ln -s /usr/bin/g++-6 /usr/local/cuda/bin/g++
What exactly is a symbolic link?
A symbolic link, also termed a soft link, is a special kind of file that points to another file, much like a shortcut in Windows or a Macintosh alias. Unlike a hard link, a symbolic link does not contain the data in the target file. It simply points to another entry somewhere in the file system. This difference gives symbolic links certain qualities that hard links do not have, such as the ability to link to directories, or to files on remote computers networked through NFS. Also, when you delete a target file, symbolic links to that file become unusable, whereas hard links preserve the contents of the file https://kb.iu.edu/d/abbe
Alright, let’s try this test again and make sure gcc6 and g++6 is working as intended.
$ make clean && makerm -rf *orm -rf mnistcuDNN/usr/local/cuda/bin/nvcc -ccbin g++ -I/usr/local/cuda/include -IFreeImage/include -m64 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_53,code=sm_53 -gencode arch=compute_53,code=compute_53 -o fp16_dev.o -c fp16_dev.cug++ -I/usr/local/cuda/include -IFreeImage/include -o fp16_emu.o -c fp16_emu.cppg++ -I/usr/local/cuda/include -IFreeImage/include -o mnistcuDNN.o -c mnistcuDNN.cpp/usr/local/cuda/bin/nvcc -ccbin g++ -m64 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_53,code=sm_53 -gencode arch=compute_53,code=compute_53 -o mnistcuDNN fp16_dev.o fp16_emu.o mnistcuDNN.o -LFreeImage/lib/linux/x86_64 -LFreeImage/lib/linux -lcudart -lcublas -lcuDNN -lfreeimage -lstdc++ -lm
so far so good, and finally, cuDNN has an internal feed forward convolutional network test using the famous MNIST numbers images dataset:
$ ./mnistcuDNNcuDNNGetVersion() : 7005 , cuDNN_VERSION from cuDNN.h : 7005 (7.0.5)Host compiler version : GCC 6.4.0There are 1 CUDA capable devices on your machine :device 0 : sms 15 Capabilities 6.1, SmClock 1784.5 Mhz, MemSize (Mb) 8116, MemClock 4004.0 Mhz, Ecc=0, boardGroupID=0Using device 0Testing single precisionLoading image data/one_28x28.pgmPerforming forward propagation …Testing cuDNNGetConvolutionForwardAlgorithm …Fastest algorithm is Algo 1Testing cuDNNFindConvolutionForwardAlgorithm …^^^^ cuDNN_STATUS_SUCCESS for Algo 1: 0.027648 time requiring 3464 memory_#… lots of additional lines ...# ... Final few lines should say ..._Result of classification: 1 3 5Test passed!
Awesome! cuDNN is working. Next order of business… you guessed it. Hold these packages from being updated and remove (you can delete the .deb files as well in your Downloads/) the copied samples directory we used for the previous MNIST test:
$ sudo apt-mark hold libcuDNN7 libcuDNN7-dev libcuDNN7-doc #These are the three .deb files we installed libcuDNN7 set on hold.libcuDNN7-dev set on hold.libcuDNN7-doc set on hold.$ rm -r cuDNN_samples_v7/
Now we are getting into the home stretch. Time to install our deep learning frameworks. First up is TensorFlow, as it is generally more picky then pytorch.
Deep Learning Frameworks
First create a new virtual environment with conda. I called it ‘tensorflow’. Below I make a comment about initializing the env with pip and not installing pip after the env has been created. There used to be (might still be) an insidious bug where if you installed pip after you created an env pip installs would install packages globally regardless if you were in an activated env. This is highly undesirable behavior. The solution was to initialize with pip. This bug might be mitigated by now.
$ cd$ conda create -n tensorflow python=3.6 pip numpy _#remember to initialize the env with pip here.# activate your conda environment_$ conda source activate tensorflow# now your user and hostname will be prepended with a (tensorflow) e.g. below# keil@yuki-bot:~$# (tensorflow) keil@yuki-bot:~$
Now for TensorFlow. I got the url below from TensorFlow’s website. Important to make sure you are installing the package: tensorflow-gpu and NOT tensorflow. Otherwise you won’t be doing much GPU computing…
I grabbed the python 3.6 GPU support url, found at the bottom of the page. Let’s install it!
$ pip install — ignore-installed — upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.8.0-cp36-cp36m-linux_x86_64.whl$ conda list | grep tensor #should show something like the following tensorboard 1.8.0 <pip>tensorflow-gpu 1.8.0 <pip> #this should say tensorflow-gpu… not tensorflow
Now we can test our tensorflow install by starting a python repl in the console and importing the tensorflow library.
$ pythonPython 3.6.5 |Anaconda, Inc.| (default, Apr 29 2018, 16:14:5[GCC 7.2.0] on linuxType “help”, “copyright”, “credits” or “license” for more information.>>> import tensorflow as tf>>> hello = tf.constant(‘Hello, TensorFlow!’)>>> sess = tf.Session()_# some warning messages and other verbose output..._ tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate(GHz): 1.7845pciBusID: 0000:01:00.0totalMemory: 7.93GiB freeMemory: 7.46GiB2018–06–18 15:54:49.570548: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 02018–06–18 15:54:49.763041: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:2018–06–18 15:54:49.763069: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929] 02018–06–18 15:54:49.763089: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0: N2018–06–18 15:54:49.763303: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7205 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)
>>> print(sess.run(hello))b’Hello, TensorFlow!’>>> exit()
Great all looks good. Congratulations, you have TensorFlow installed with GPU support. And you do not need to hold tensorflow with APT, conda is in charge of managing the upgrade for tensorflow.
now deactivate your tensorflow conda env and create one for pytorch:
$ source deactivate tensorflow$ conda create -n pytorch python=3.6 pip numpy$ conda source activate pytorch
Head over to the pytorch website and generate a command to install pytorch. The below image shows the options I selected:
$ conda install pytorch torchvision cuda90 -c pytorch$ conda list | grep torch# packages in environment at /home/keil/miniconda3/envs/pytorch: cuda90 1.0 h6433d27_0 pytorchpytorch 0.4.0 py36_cuda9.0.176_cuDNN7.1.2_1 [cuda90] pytorchtorchvision 0.2.1 py36_1 pytorch$ python #start the python interpreter to test pytorch for GPU support Python 3.6.5 |Anaconda, Inc.| (default, Apr 29 2018, 16:14:56)[GCC 7.2.0] on linuxType “help”, “copyright”, “credits” or “license” for more information.
>>> import torch>>> torch.cuda.current_device()0>>> torch.cuda.device(0)<torch.cuda.device object at 0x7faff01a8860>>>> torch.cuda.get_device_name(0)‘GeForce GTX 1070’>>> exit()$ source deactivate pytorch
Awesome! Pytorch is looking good to go.
Well that is it. You are all done. You have installed tensorflow and pytorch, both with GPU support in their own conda virtual envs. You installed CUDA 9.0, cuDNN 7.0.5, gcc/g++ 6.0, latest official nvidia GPU drivers for Ubuntu, and the latest kernel headers.
Hopefully all of this worked for you and you learned a few things along the way. The biggest challenge with getting GPU support for deep learning frameworks are making sure all the different versions of everything play nice together. That just requires some research and reading the official docs from Ubuntu, Nvidia, TensorFlow and Pytorch. Other than that it is mostly straightforward copy pasta of commands.
Useful sites for good quality info I found were StackExchange and especially the Nvidia developer forms. When reading online forums just make sure the info is up to date. A lot of the answers and questions are old and no longer suitable. I am sure this post will be old soon too. I will try to update it when necessary.
Thanks for slogging through this post. Have fun deep learning!
Cheers,
Kyle