You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
If the make command failed, try compiling with only one thread.
If you insist using conda. Make sure the following packages are correctly installed. Anaconda packages maybe conflict with the system libraries, recommend uninstall both (system libraries and conda packages). I recommend installing them manually from source.
git clone https://github.com/gflags/gflags.git
cd gflags
# Compilation using CMake
mkdir build &&cd build
cmake -DBUILD_SHARED_LIBS=ON -DGFLAGS_NAMESPACE=google -DCMAKE_CXX_FLAGS=-fPIC ..
make && sudo make install
sudo ldconfig
glog
git clone https://github.com/google/glog.git
cd glog
# Compilation using Make
make clean
./autogen.sh
./configure
export LDFLAGS='-L/usr/local/lib'
make
make test
sudo make install
sudo ldconfig
# Compilation using CMake
mkdir build &&cd build
cmake -DBUILD_SHARED_LIBS=ON -DCMAKE_CXX_FLAGS="-std=c++11 -fPIC" ..
make && sudo make install
sudo ldconfig
ceres-solver
Start by installing all the dependencies: CMake, google-glog & gflags, BLAS & LAPACK, Eigen3
SuiteSparse and CXSparse (optional)
If you want to build Ceres as a static library (the default) you can use the SuiteSparse package in the main Ubuntu package repository:
sudo apt-get install libsuitesparse-dev
However, if you want to build Ceres as a shared library, you must add the following PPA:
We are now ready to build, test, and install Ceres.
# Compilation using CMake
tar zxf ceres-solver-1.14.0.tar.gz
mkdir ceres-bin
cd ceres-bin
cmake -gl=ON ../ceres-solver-1.14.0
make -j3
make test
sudo make install
sudo ldconfig
mkdir build_cmake &&cd build_cmake
ccmake ..
make
sudo make install
sudo ldconfig
ALLOW_LMDB_NOLOCK = OFF
BLAS = Atlas
BOOST_THREAD_LIBRARY = /usr/lib/x86_64-linux-gnu/libpthread.so
BUILD_SHARED_LIBS = ON
BUILD_docs = ON
BUILD_matlab = OFF
BUILD_only_testes = (empty)
BUILD_python = ON
BUILD_python_layer = ON
CAFFE_TARGET_SOVERSION = 1.0.0
CAFFE_TARGET_VERSION = 1.0.0
CMAKE_BUILD_TYPE = (empty)
CMAKE_INSTALL_PREFIX = /home/olorin/.local/install/caffe/build_cmake/install
CMAKE_INSTALL_RPATH_USE_LINK_PATH = TRUE
CPU_ONLY = OFF
CUDA_HOST_COMPILER = /usr/bin/cc
CUDA_TOOLKIT_ROOT_DIR = /usr/local/cuda-9.0
CUDA_USE_STATIC_CUDA_RUNTIME = ON
CUDA_rt_LIBRARY = /usr/lib/x86_64-linux-gnu/librt.so
DOXYGEN_config_file = /home/olorin/.local/install/caffe/.Doxyfle
HDF5_DIR = HDF5_DIR-NOTFOUND
OpenCV_DIR = /usr/local/share/OpenCV
USE_CUDNN = ON
USE_HDF5 = ON
USE_LEVELDB = ON
USE_LMDB = ON
USE_NCCL = OFF
USE_OPENCV = OFF
USE_OPENMP = OFF
USE_PROJECT_FOLDERS = OFF
python_version = 3
Note: It was necessary to disable OpenCV, because some files were compiled using an older version of CUDA. I recommend recompiling OpenCV with the correct CUDA Toolkit and recompilling Caffe.
To import caffe Python module after completing the installation, add the module directory to your $PYTHONPATH variable by using export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH. You should not import the module in the caffe/python/caffe directory!
mkdir .local/install/caffe
cd .local/install/caffe/python
forreqin$(cat requirements.txt);do pip install $req;donecd ..
make all
make test
make runtest
sudo make install
sudo ldconfig
With the availability of huge amount of data for research and powerfull machines to run your code on, Machine Learning and Neural Networks is gaining their foot again and impacting us more than ever in our everyday lives. With huge players like Google opensourcing part of their Machine Learning systems like the TensorFlow software library for numerical computation, there are many options for someone interested in starting off with Machine Learning/Neural Nets to choose from. Caffe, a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and its contributors, comes to the play with a fresh cup of coffee.
Installation Instructions (Ubuntu 14 Trusty)
The following section is divided in to two parts. Caffe's documentation suggests you to install Anaconda Python distribution to make sure that you've installed necessary packages, with ease. If you're someone who do not want to install Anaconda in your system for some reason, I've covered that too. So in the first part you'll find information on how to install Caffe with Anaconda and in the second part you'll find the information for installing Caffe without Anaconda .
Please note that the following instructions were tested on my local machine and in two Chameleon Cloud Instances. However I cannot garuntee success for anyone. Please be ready to see some errors on the way, but I hope you won't stumble into any if you follow the directions as is.
My local machine and the instances I used are NOT equipped with GPU's. So the installation instrucions are strictly for non-GPU based or more clearly CPU-only systems running Ubuntu 14 trusty. However, to install it in a GPU based system, you just have to install CUDA and necessary drivers for your GPU. You can find the instructions in Stack Overflow or in the always go to friend Google.
For systems without GPU's (CPU_only)
1. Caffe + Anaconda
Anaconda python distribution includes scientific and analytic Python packages which are extremely useful. The complete list of packages can be found here.
To install Anaconda, you have to first download the Installer to your machine. Go to this website to download the Installer. Scroll to the 'Anaconda for Linux' section and choose the installer to download depending on your system architecture.
Once you have the Installer in your machine, run the following code to install Anaconda.
bash Anaconda2-2.5.0-Linux-x86_64.sh
If you fail to read the few lines printed after installation, you'll waste a good amount of your produtive time on trying to figure out what went wrong. An important line reads:
For this change to become active, you have to open a new terminal.
So, once the Anaconda installation is over, please open a new terminal. Period.
After opening a new terminal, to verify the installation type:
conda -V
This should give you the current version of conda, thus verifying the installation. Now that's done !
Now we will install OpenBLAS.
sudo apt-get install libopenblas-dev
Next go ahead and install Boost. More info on boost here
I faced a problem while installing boost in all my machines. I fixed it by including multiverse repository into the sources.list. Since playing with sources.list is not reccomended, follow the steps for a better alternative.
The repo is saved to a temporary list named 'multiverse.list' in the /tmp folder. It is then copied to /etc/apt/sources.list.d/ folder. The file in /tmp folder is then removed. I found this fix in Stack Exchange fourm.
We will edit the configuration file of Caffe now. We need to do it to specify that we are using a CPU-only system. (Tell compiler to disable GPU, CUDA etc). For this, make a copy of the Makefile.config.example.
cp Makefile.config.example Makefile.config
Great ! Now go ahead and open the Makefile.config in your favourite text editor (vi or vim or gedit or ...). Change the following:
1. Uncomment (No space in the beginning):
CPU_ONLY := 1
2. Change:
BLAS := atlas to BLAS := open
3. Comment out:
PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/lib/python2.7/dist-packages/numpy/core/include
4. Uncomment:
ANACONDA_HOME := $(HOME)/anaconda2
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
$(ANACONDA_HOME)/include/python2.7 \
$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
5. Comment:
PYTHON_LIB := /usr/lib
6. Uncomment:
PYTHON_LIB := $(ANACONDA_HOME)/lib
7. Uncomment:
USE_PKG_CONFIG := 1
Your Makefile.config should look something like this now: Makefile.config
Now that's done, let me share with you an error I came across. Our Makefile.config is okay. But while 'make'-ing / building the installation files, the hf5 dependeny gave me an error. This might not apply to you. I can't say for sure. The build required two files libhdf5_h1.so.10 and libhd5.so.10 but the files in the system were libhdf5_h1.so.7 and libhd5.so.7. I fixed this by doing the following:
cd /usr/lib/x86_64-linux-gnu/
sudo cp libhdf5_hl.so.7 libhdf5_hl.so.10
sudo cp libhdf5.so.7 libhdf5.so.10
We will now install the libraries listed in the requirements.txt file.
Now, we can safely build the files in the caffe directory. We will run the make process as 4 jobs by specifying it like -j4. More on it here
cd ~/caffe
cmake .. -DCMAKE_CXX_COMPILER=/usr/bin/g++-4.8 -DCMAKE_CXX_FLAGS="-std=c++11 -O3"
sudo make all -j4
I hope the make process went well. If not, please see which package failed by checking the logs or from terminal itself. Feel free to comment, I will help to the best of my knowledge. You can seek help from your go to friend Google or Stack Exchange as mentioned above.
Provided that the make process was successfull, continue with the rest of the installation process.
We will now make the Pycaffe files. Pycaffe is the Python interface of Caffe which allows you to use Caffe inside Python. More on it here. We will also make distribute. This is explained in Caffe website.
sudo make pycaffe
sudo make distribute
Awesome! We are almost there. We just need to test whether everything went fine. For that make the files for testing and run the test.
sudo make test
sudo make runtest
If you succeed in all the tests then you've successfully installed Caffe in your system ! One good reason to smile !
Finally, we need to add the correct path to our installed modules. Using your favourite text editor, add the following to the .bashrc file in your /home/user/ folder for Caffe to work properly. Please make sure you replace the < username > with your system's username.
#Anaconda if not present already
export PATH=/home/<username>/anaconda2/bin:$PATH
#Caffe Root
export CAFFE_ROOT=/home/<username>/caffe/
export PYTHONPATH=/home/<username>/caffe/distribute/python:$PYTHONPATH
export PYTHONPATH=/home/<username>/caffe/python:$PYTHONPATH
CHEERS ! You're done ! Now let's test if it really works.
Restart/reboot your system to ensure everything loads perfect.
sudo reboot
Open Python and type:
import caffe
You should be able to successfully load caffe.
Now let's start coding :)
2. Caffe without installing Anaconda
By preference, if you don't want to install Anaconda in your system, you can install Caffe by following the steps below. As mentioned earlier, installing all the dependencies can be difficult. If this tutorial does not work for you, please look into the errors, use our trusted friends.
To start with, we will update and upgrade the packages in our system. Then we will have to install the dependencies one by one on the machine. Type the following to get started.
Next go ahead and install Boost. More info on boost here
I faced a problem while installing boost in all my machines. I fixed it by including multiverse repository into the sources.list. Since playing with sources.list is not reccomended, follow the steps for a better alternative.
The repo is saved to a temporary list named 'multiverse.list' in the /tmp folder. It is then copied to /etc/apt/sources.list.d/ folder. The file in /tmp folder is then removed. I found this fix in Stack Exchange fourm.
If later in the installation process you find that any of the boost related files are missing, run the following command. You can skip this one for now but won't hurt if you do it either.
Now, we need to install ffmpeg. Let us also make sure that the ffmpeg version is one which OpenCV and Caffe approves. We will remove any previous versions of ffmpeg and install new ones.
The following code will remove ffmpeg and related packages:
sudo apt-get -y remove ffmpeg x264 libx264-dev
The mc3man repository hosts ffmpeg packages. I came to know about it from Stack Exchange forums. To include the repo, type this:
Now, we can install OpenCV. First let us install the dependencies. Building OpenCV can be challenging at first, but if you have all the dependencies correct it will be done in no time.
Go ahead and run the following lines:
sudo apt-get install build-essential
The 'build-essential' ensures that we have the compilers ready. Now we will install some required packages. Run:
Installing Pydot will be beneficial to view our net by saving it off in an image file.
sudo apt-get install python-pydot
Now that all the dependencies are installed, we will go ahead and download the Caffe installation files. Go ahead and run:
git clone https://github.com/BVLC/caffe
Go into the caffe folder and copy and rename the Makefile.config.example file to Makefile.config.
cd caffe
cp Makefile.config.example Makefile.config
Great ! Now go ahead and open the Makefile.config in your favourite text editor (vi or vim or gedit or ...). Change the following:
1. Uncomment (No space in the beginning):
CPU_ONLY := 1
2. Uncomment:
USE_PKG_CONFIG := 1
We will install the packages listed in Caffe's requirements.txt file as well; just in case.
cd ~/caffe/python
sudo pip install -r requirements.txt
Now, we can safely build the files in the caffe directory. We will run the make process as 4 jobs by specifying it like -j4. More on it here
cd ~/caffe
sudo make all -j4
I hope the make process went well. If not, please see which package failed by checking the logs or from terminal itself. Feel free to comment, I will help to the best of my knowledge. You can seek help from your go to friend Google or Stack Exchange as mentioned above.
Provided that the make process was successfull, continue with the rest of the installation process.
We will now make the Pycaffe files. Pycaffe is the Python interface of Caffe which allows you to use Caffe inside Python. More on it here. We will also make distribute. This is explained in Caffe website.
sudo make pycaffe
sudo make distribute
Awesome! We are almost there. We just need to test whether everything went fine. For that make the files for testing and run the test.
sudo make test
sudo make runtest
If you succeed in all the tests then you've successfully installed Caffe in your system ! One good reason to smile !
Finally, we need to add the correct path to our installed modules. Using your favourite text editor, add the following to the .bashrc file in your /home/user/ folder for Caffe to work properly. Please make sure you replace the < username > with your system's username.
CHEERS ! You're done ! Now let's test if it really works.
Restart/reboot your system to ensure everything loads perfect.
sudo reboot
Open Python and type:
import caffe
You should be able to successfully load caffe. Now let's start coding :)
Appendix
Makefile.config
For Caffe + Anaconda
## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!
# cuDNN acceleration switch (uncomment to build with cuDNN).
# USE_CUDNN := 1
# CPU-only switch (uncomment to build without GPU support).
CPU_ONLY := 1
# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0
# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
# You should not set this flag if you will be reading LMDBs with any
# possibility of simultaneous read and write
# ALLOW_LMDB_NOLOCK := 1
# Uncomment if you're using OpenCV 3
# OPENCV_VERSION := 3
# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++
# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr
# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
-gencode arch=compute_20,code=sm_21 \
-gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_50,code=sm_50 \
-gencode arch=compute_50,code=compute_50
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := open
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas
# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib
# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app
# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
#PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
ANACONDA_HOME := $(HOME)/anaconda2
PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
$(ANACONDA_HOME)/include/python2.7 \
$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
# /usr/lib/python3.5/dist-packages/numpy/core/include
# We need to be able to find libpythonX.X.so or .dylib.
#PYTHON_LIB := /usr/lib
PYTHON_LIB := $(ANACONDA_HOME)/lib
# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib
# Uncomment to support layers written in Python (will link against Python libs)
# WITH_PYTHON_LAYER := 1
# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib
# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
USE_PKG_CONFIG := 1
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
# enable pretty build (comment to see full commands)
Q ?= @
For Caffe without Anaconda
## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!
# cuDNN acceleration switch (uncomment to build with cuDNN).
# USE_CUDNN := 1
# CPU-only switch (uncomment to build without GPU support).
CPU_ONLY := 1
# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0
# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
# You should not set this flag if you will be reading LMDBs with any
# possibility of simultaneous read and write
# ALLOW_LMDB_NOLOCK := 1
# Uncomment if you're using OpenCV 3
# OPENCV_VERSION := 3
# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++
# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr
# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
-gencode arch=compute_20,code=sm_21 \
-gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_50,code=sm_50 \
-gencode arch=compute_50,code=compute_50
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := open
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas
# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib
# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app
# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
# ANACONDA_HOME := $(HOME)/anaconda
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
# $(ANACONDA_HOME)/include/python2.7 \
# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
# /usr/lib/python3.5/dist-packages/numpy/core/include
# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib
# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib
# Uncomment to support layers written in Python (will link against Python libs)
# WITH_PYTHON_LAYER := 1
# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib
# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0
# enable pretty build (comment to see full commands)
Q ?= @
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
