To install Caffe with the python interface, PyCaffe (Recommended) you need to give the paths to your python include libs and the path where you have numpy stored. Path to numpy include folder must be given with great caution. You might land into unnecessary trouble by specifying this path incorrectly. Installing caffe The instructions are mostly the same as the official installation instructions except for a few modifications specified below. First, install the usual system dependencies as described in here. Then, install the usual python dependecies using pip, but first update the requirements.txt file to use protobuf 3.0 alpha.
![]()
1conda install pytorch-nightly-cpu -c pytorchThis does NOT include libraries that are necessary to run the tutorials, such as jupyter. See the page for the list of required packages needed to run the tutorials.NOTE: This will install Caffe2 and all of its required dependencies into the current conda environment. We strongly suggest that you create a new conda environment and install Caffe2 into that. A conda environment is like a separate python installation and so won’t have problems with your other conda environments. You can learn more about conda environments. Prebuilt Caffe2 Python WheelThis is in beta mode, but you can try. 1cd && python -c 'from caffe2.python import core' 2/dev/null && echo 'Success' echo 'Failure'If this fails, then get a better error message by running Python in your home directory and then running from caffe2.python import core inside Python.
Then see the page for help. GPU SupportIn the instance that you have a NVIDIA supported GPU in your Mac, then you should visit the NVIDIA website for and and install the provided binaries. Also see this on setting up your GPU correctly. Caffe2 requires CUDA 6.5 or greater.Once CUDA and CuDNN (and optionally NCCL) are installed, please verify that your CUDA installation is working as expected, and then continue with your preferred Caffe2 installation path.After Caffe2 is installed, you should NOT see the following error when you try to import caffe2.python.core in Python. 12WARNING:root:This caffe2 python run does not have GPU support. Will run in CPU only mode.WARNING:root:Debug message: No module named 'caffe2.python.caffe2pybind11stategpu'If you see this error then your GPU installation did not work correctly.We only support Anaconda packages at the moment. If you do not wish to use Anaconda, then you must build Caffe2 from.
Anaconda packagesWe build Linux packages without CUDA support, with CUDA 9.0 support, and with CUDA 8.0 support, for both Python 2.7 and Python 3.6. These packages are built on Ubuntu 16.04, but they will probably work on Ubuntu14.04 as well (if they do not, please tell us by creating an issue on our ). To install Caffe2 with Anaconda, simply activate your desired conda environment and then run one of the following commands:If you do not have a GPU. 1conda install pytorch-nightly cuda80 -c pytorchThis does NOT include libraries that are necessary to run the tutorials, such as jupyter. See the page for the list of required packages needed to run the tutorials.NOTE: This will install Caffe2 and all of its required dependencies into the current conda environment. We strongly suggest that you create a new conda environment and install Caffe2 into that. A conda environment is like a separate python installation and so won’t have problems with your other conda environments.
![]()
You can learn more about conda environments.You can easily try out Caffe2 by using the Cloud services. Caffe2 is available as AWS (Amazon Web Services) Deep Learning AMI and Microsoft Azure Virtual Machine offerings. You can run run Caffe2 in the Cloud at any scale.We test the latest code on. Ubuntu 14.04.
Ubuntu 16.04Install Dependencies. 021222324sudo apt-get updatesudo apt-get install -y -no-install-recommends build-essential git libgoogle-glog-dev libgtest-dev libiomp-dev libleveldb-dev liblmdb-dev libopencv-dev libopenmpi-dev libsnappy-dev libprotobuf-dev openmpi-bin openmpi-doc protobuf-compiler python-dev python-pippip install -user future numpy protobuf typing hypothesisNote libgflags2 is for Ubuntu 14.04. Libgflags-dev is for Ubuntu 16.04.
1cd && python -c 'from caffe2.python import core' 2/dev/null && echo 'Success' echo 'Failure'If this fails, then get a better error message by running Python in your home directory and then running from caffe2.python import core inside Python.If this fails with a message about not finding caffe2.python or not finding libcaffe2.so, please see on how Caffe2 installs in Python.If you installed with GPU support, test that the GPU build was a success with this command (run from the top level pytorch directory). You will get a test output either way, but it will warn you at the top of the output if CPU was used instead of GPU, along with other errors such as missing libraries.
1ssh -N -f -L localhost:8888:localhost:8889 -i 'your-public-cert.pem'We only support Anaconda packages at the moment. If you do not wish to use Anaconda, then you must build Caffe2 from. Anaconda packagesWe build Linux packages without CUDA support, with CUDA 9.0 support, and with CUDA 8.0 support, for both Python 2.7 and Python 3.6. These packages are built on Ubuntu 16.04, but they will probably work on CentOS as well (if they do not, please tell us by creating an issue on our ). To install Caffe2 with Anaconda, simply activate your desired conda environment and then run one of the following commands:If you do not have a GPU.
1conda install pytorch-nightly cuda80 -c pytorchThis does NOT include libraries that are necessary to run the tutorials, such as jupyter. See the page for the list of required packages needed to run the tutorials.NOTE: This will install Caffe2 and all of its required dependencies into the current conda environment. We strongly suggest that you create a new conda environment and install Caffe2 into that. A conda environment is like its own python installation that won’t have library version problems with your other conda environments.
You can learn more about conda environments.Check the cloud instructions for a general guideline on building from source for CentOS.The installation instructions for will probably also work in most cases. AWS Cloud Setup Amazon Linux AMI with NVIDIA GRID and TESLA GPU DriverThe above AMI had been tested with Caffe2 + GPU support on a G2.2xlarge instance that uses a NVIDIA GRID K520 GPU. This AMI comes with CUDA v7.5, and no cuDNN, so we install that manually. The installation is currently a little tricky, but we hope over time this can be smoothed out a bit.
![]()
This AMI is great though because it supports the. Installation GuideNote that this guide will help you install Caffe2 on any CentOS distribution. Amazon uses their own flavor of RHEL and they’ve installed CUDA in different spots than normally expected, so keep that in mind if you have to do some troubleshooting.
Some of these steps will not be required on vanilla CentOS because things will go in their normal places. Get your repos setMany of the required dependencies don’t show up in Amazon’s enabled repositories. Epel is already provided in this image, but the repo is disabled. You need to enable it by editing the repo config to turn it on.
Set enabled=1 in the epel.repo file. This enables you to find cmake3 leveldb-devel lmdb-devel. 0git clone && cd gflags && mkdir build && cd build && cmake3 -DBUILDSHAREDLIBS=ON -DCMAKECXXFLAGS='-fPIC'. && make -j 8 && sudo make install && cd./. && git clone && cd glog && mkdir build && cd build && cmake3 -DBUILDSHAREDLIBS=ON -DCMAKECXXFLAGS='-fPIC'.
&& make -j 8 && sudo make install && cd./.Python DependenciesNow we need the Python dependencies. Note the troubleshooting info below the install path with Python can get difficult. 123456$ sudo easyinstall -upgrade pipPassword:Searching for pipReading match: pip 9.0.1Note that in this example, the upgrade was to 9.0.1.
Use vim to open the /usr/bin/pip file and change the instances of 7.1.0 to 9.0.1, and this solves the pip error and will allow you to install the dependencies. 021$ cat /proc/driver/nvidia/versionNVRM version: NVIDIA UNIX x8664 Kernel Module 352.99 Mon Jul 4 23:52:14 PDT 2016GCC version: gcc version 4.8.3 20140911 (Red Hat 4.8.3-9) (GCC)$ nvcc -Vnvcc: NVIDIA (R) Cuda compiler driverCopyright (c) 2005-2015 NVIDIA CorporationBuilt on TueAug1114:27:32CDT2015Cuda compilation tools, release 7.5, V7.5.17$ nvidia-smi -q headNVSMI LOGTimestamp: Fri Mar 10 23:Driver Version: 352.99Attached GPUs: 1GPU 0000:00:03.0Product Name: GRID K520Product Brand: GridThat’s it.
You’ve successfully built Caffe2! Setting Up Tutorials & Jupyter ServerIf 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. 1ssh -N -f -L localhost:8888:localhost:8889 -i 'your-public-cert.pem'Troubleshootingcaffe2.python not found You may have some PATH or PYTHONPATH issues.
Add /home/ec2-user/caffe2/build to your path and that can take care of those problems.error while loading shared libraries: libCaffe2CPU.so: cannot open shared object file: No such file or directory Try updating your LDLIBRARYPATH with export LDLIBRARYPATH=/usr/local/lib:$LDLIBRARYPATHundefined reference to `ncclReduceScatter’ This does not occur on Caffe2 building, but on linking with “libCaffe2GPU.so” in some external projects. To solve this, you may install NCCL from its source bundled with Caffe2: (under the Caffe2 project directory) cd thirdparty/nccl && make -j 8 && sudo make installWindows 10 or greater is required to run Caffe2.
PrebuiltThere are no pre-built binaries available for Windows yet. Please install from.Windows build is in testing and beta mode. For the easiest route, use the docker images for now in CPU-only mode. Required DependenciesThe first thing you want to do is to assess whether or not you’re going to use GPU acceleration with Caffe2.
If you have an and you plan on training some neural networks, then it’s probably worth the extra installation an effort. If you’re just going to play around with pre-trained models then skip the video drivers and NVIDIA CUDA/cuDNN installation steps.
Update your video drivers: assuming you have an NVIDIA card, use NVIDIA GeForce Experience to run the latest update.: if you have GPU(s) then go ahead and install. (registration required; it is a zip file, not installer, so you need to copy the contents of the zip file to the cuda folder which is C:Program FilesNVIDIA GPU Computing ToolkitCUDAv8.0 by default). Python 2.7.6 to. Python 3 support is experimental.
You can use regular Python or Anaconda Python. Just note that you might have issues with package location and versioning with Anaconda. Some Anaconda notes are provided below the Regular Python notes.
Take care to install the 64 bits version of Python, since we will be going to compile for 64 bits. Install a C compiler such as. When installing VS 2017, install Desktop Development with C (on the right select: C/CLI support) and v140 (on the right select: VC 2015.3 v140 toolset). InstallSetup Python, Install Python Packages, Build Regular Python InstallInstall and.Assuming you have already added C:Python27 and C:Python27scripts to your Path environment variable, you can go ahead and use pip to install the Python dependencies. 1buildwindows.batFor VS15 and VS17 users with GitHub Desktop:.
Install the. From within Visual Studio you can open/clone the GitHub repository. From the Getting Started page under Open, you should have GitHub as an option. Login, and then either choose Caffe2 from the list (if you’ve forked it) or browse to where you cloned it.
Default location hereinafter is referencing C:UsersusernameSourceReposcaffe2.Python ConfigurationYou will find the Caffe2 binary in $USERSourceRepos (if that’s where you put the caffe2 source) pytorchbuildcaffe2pythonCopy caffe2pybind11state.pyd to Python’s DLL folder $USERAppDataLocalContinuumAnaconda2DLLs. If you’re not using Anaconda, then put it in your Python27 or python-2713 folder.Now you can run python from pytorchbuild directory and successfully import caffe2 and other modules.
Anaconda Python. this install path needs correction / confirmation.: download the Python 2.7 version. Run Anaconda Prompt as Administrator. Go to the search bar, search for “anaconda prompt” and right-click it and choose “Run as Administrator”. Install Python packages. 12cd caffe2./scripts/buildios.shThere are no pre-built binaries available for iOS yet.
Please install from. Install Caffe2 for your development platformIf you want to build Caffe2 for use on Android, first follow the instructions to setup Caffe2 on your given development platform using the toggler above, and then: Android Studiowill install all the necessary NDK, etc. Components to build Caffe2 for Android use. This can be done on a Mac via brew install automake libtool or on Ubuntu via sudo apt-get install automake libtool. Download Caffe2 SourceIf you have not done so already, download the Caffe2 source code from GitHub. 12cd pytorch./scripts/buildandroid.sh -DANDROIDABI=arm64-v8a -DANDROIDTOOLCHAIN=clangThere are no pre-built binaries available for Android yet.
Please install from. Docker ImagesDocker images are currently in testing. If you would like to build an image yourself, follow the instructions further below. For a quick install try the following commands (assuming you have already).Get caffe2ai/caffe2Visit our for a full list of different Docker options. Currently we have CPU and GPU support for both 14.04 and 16.04 Ubuntu.If you wish to use GPU with Docker use nvidia-docker to run your image instead of regular docker.You can.For the latest Docker image using GPU support and optional dependencies like IPython & OpenCV. 12345docker pull caffe2ai/caffe2:cpu-minimal-ubuntu14.04# to testdocker run -it caffe2ai/caffe2:cpu-minimal-ubuntu14.04 python -m caffe2.python.operatortest.reluoptest# to interactdocker run -it caffe2ai/caffe2:cpu-minimal-ubuntu14.04 /bin/bashSee below for instructions on usage. Build From DockerfileInside repo’s /docker folder are subfolders with a Dockerfile that contain the minimal dependencies and optional ones.
You may remove specific optional dependencies if you wish. The folder’s name describes the defaults that will be installed by that dockerfile. For example, if you run the command below from the ubuntu-14.04-cpu-all-options folder you will get a docker image around 1.5GB that has many optional libraries like OpenCV, for the minimal install, ubuntu-14.04-cpu-minimal, it is about 1GB and is just enough to run Caffe2, and finally for the gpu dockerfile, ubuntu-14.04-gpu-all-options, it is based on the NVIDIA CUDA docker image about 3.2GB and contains all of the optional dependencies.In a terminal window in one of those folders, simply run the following. 12cd /caffe2/docker/ubuntu-14.04-cpu-all-optionsdocker build -t caffe2:cpu-optionals.Don’t miss the. As it is pointing to the Dockerfile in your current directory.
Also, you can name docker image whatever you want. The -t denotes tag followed by the repository name you want it called, in this case cpu-optionals.Once the build process is complete you can run it by its name or by the last unique ID that was provided upon completion. In this example case, this ID is 5ee1fb669aef. To run the image in a container and get to bash you can launch it interactively using the following where you call it by its repository name. 1nvidia-docker run -it caffe2 python -m caffe2.python.operatortest.reluoptestYou may also try fetching some models directly and running them as described in this.
Jupyter from DockerIf you want to run your Jupyter server from a Docker container, then you’ll need to run the container with several additional flags. The first new one (versus running it locally) for Docker is -p 8888:8888 which “publishes” the 8888 port on the container and maps it to your host’s 8888 port. You also need to launch jupyter with -ip 0.0.0.0 so that you can hit that port from your host’s browser, otherwise it will only be available from within the container which isn’t very helpful. Of course you’ll want to swap out the caffe2ai/caffe2:cpu-fulloptions-ubuntu14.04 with your own repo:tag for the image you want to launch.In this case we’re running jupyter with sh -c. This solves a problem with the Python kernel crashing constantly when you’re running notebooks. 1docker run -it -p 8888:8888 caffe2ai/caffe2:cpu-fulloptions-ubuntu14.04 sh -c 'jupyter notebook -no-browser -ip 0.0.0.0 /caffe2tutorials'Your output will be along these lines below.
You just need to copy the provided URL/token combo into your browser and you should see the folder with tutorials. Note the if you installed caffe2 in a different spot, then update the optional path that is in the command /caffe2tutorials to match where the tutorials are located.In some situations you can’t access the Jupyter server on your browser via 0.0.0.0 or localhost. You need to pull the Docker IP address (run docker-machine ip) and use that to access the Jupyter server.Docker - Ubuntu 14.04 with full dependencies notes:. librocksdb-dev not found. (May have to install this yourself if you want it.)Troubleshootingcommongpu.cc:42Found an unknown error - this may be due to an incorrectly set up environment, e.g. Changing env variable CUDAVISIBLEDEVICES after program start.
I will set the available devices to be zero.SolutionThis may be a Docker-specific error where you need to launch the images while passing in GPU device flags: sudo docker run -ti -device /dev/nvidia0:/dev/nvidia0 -device /dev/nvidiactl:/dev/nvidiactl -device /dev/nvidia-uvm:/dev/nvidia-uvm mydocker-repo/mytag /bin/bash. You will need to update those devices according to your hardware (however this should match a 1-GPU build) and you need to swap out mydocker-repo/mytag with the ID or the repo/tag of your Docker image.HyperV is not available on Home editions. Please use Docker Toolbox.Docker for Windows only works on Professional versions of Windows.SolutionInstall. Don’t worry, the Caffe2 images should still work for you!An error occurred trying to connectvarious errors just after installing Docker ToolboxSolutionrun docker-machine env default then follow the instructions run each of the commands that setup the docker environment then try docker version and you shouldn’t see the errors again and will be able to docker pull caffe2ai/caffe2.For Raspbian, clone the Caffe2 source, run scripts/buildraspbian.sh on the Raspberry Pi. Download Caffe2 SourceIf you have not done so already, download the Caffe2 source code from GitHub. 12cd caffe2./scripts/buildraspbian.shThere are no pre-built binaries available for Raspbian yet. Please install from.There are no Docker images for Raspbian available at this time.
Please install from.To install Caffe2 on NVidia’s Tegra X1 platform, simply install the latest system with the, clone the Caffe2 source, and then run scripts/buildtegrax1.sh on the Tegra device. Install JetPack.Download Caffe2 SourceIf you have not done so already, download the Caffe2 source code from GitHub.
. Install Visual Studio Express 2015 (Not Visual Studio Express 2017, CUDA 8.0 doesn’t support Visual Studio Express 2017, yet). Install CUDA 8.0. Install cuDNN v5.
Install Python 2.7 in Anaconda Environment (This version of caffe for windows uses Python 2.7). Download caffe for windows from. Follow the instructions.
![]() Comments are closed.
|
AuthorWrite something about yourself. No need to be fancy, just an overview. Archives
March 2023
Categories |