Yann is built on top of Theano. Theano and all its pre-requisites are mandatory. Once theano and its pre-requisites are setup you may setup and run this toolbox. Theano setup is documented in the theano toolbox documentation. Yann is built with theanoo 0.8 but should be forward compatible unless theano makes a drastic release.
Quick fire Installation¶
Now before going through the full-fledged installation procedure, you can run through the entire installation in one command that will install the basics required to run the toolbox. To install the toolbox quickly do the following:
pip install git+git://github.com/ragavvenkatesan/yann.git
If it showed any errors, install
skdata has some issue that requires
installed first. If you use anaconda, just install the numpy and scipy using
pip install. This will setup the toolbox for all intentions and purposes.
Verify that the installation of theano is indeed version 0.9 or greater by doing the following in a python shell
import theano theano.__version__
If the version was not 0.9, you can install 0.9 by doing the following:
pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
For a full-fledged installation procedure, don’t do the above but run through the following set of instructions. If you want to install all other supporting features like datasets, visualizers and others, do the following:
pip install -r requirements_full.txt pip install git+git://github.com/ragavvenkatesan/yann.git
Python + pip / conda¶
Yann needs Python 2.7. Please install it for your OS.. Some modules that are required don’t come with default python. But don’t worry python comes with a package installer called pip. You can use pip to install additional packages.
For a headache free installation, the anaconda distribution of python is very strongly recommended because it comes with a lot of goodies pre-packaged.
You need a C compiler, not because yann needs C, but theano and probably numpy requires C compilers. Make sure that your OS has one. Apple osX or macOS users, if you are using Cuda and cuDNN, prefer using command line tools 7.x+. 8 doesn’t work with cuDNN at the moment of writing this documentation. You can download older versions of xcode and command line tools here.
Numpy 1.6 and Scipy 0.11 are needed for yann. Make sure these work well with a blas system. Prefer Intel MKL for blas, which is also availabe from anaconda. MKL is free for students and researchers and is available for a small price for others.
If you use pip usepip install numpy pip install scipy
to install these. If you use anaconda, useconda install mkl conda install numpy conda install scipy
to set these up. If not, yann installer will
pip install numpy scipyanyway as part of its requirements.
Once all the pre-requisites are setup, install theano version 0.8 or higher.
.theanorc configuration can be used as a sample normally,
but you may choose other options. As an example one can use the following:
[global] floatX=float32 device=cuda0 optimizer_including=cudnn mode = FAST_RUN [nvcc] nvcc.fastmath=True allow_gc=False [cuda] root=/usr/local/cuda/ [blas] ldflags = -lmkl [lib] cnmem = 0.5
If you use the libgpuarray
backend instead of the CUDA backend, use
device=cuda0 or whichever device you want to run on.
If you are using CUDA backed use
device=gpu0. Refer theano documentation for more on this.
These are some optional dependencies that yann doesn’t use directly but are used by yann’s dependencies like theano. I highly recommend these before installing theano.
This is an optional dependency. If you need the capability of a Nvidia GPU, you will need a suitable CUDA toolkit and drivers. If you do not have this dependency installed, you won’t be able to run the code on Nvidia GPUs.Some compoenents of the code depend on cuDNN for speeding things up, so cuDNN is highly recommended although optional. Nvidia has the awesome cuDNN library that is free as long as you register as a developer. If you didn’t install CUDA, you can still run the toolbox, but it will be much slower running on a CPU.
libgpuarray is now fully supported, cuda backend is strongly recommended for macOS, but for the Pascal architecture of GPUs,
libgpuarrayseems to be performing much better. This is also an optional but highly recommended tool
Yann also needs the following as additional dependencies that opens up additional features.
For those who are networking geeks, a neural network is a directed acyclic graph. So Yann internally has the ability for every network to create a
networkxstyle graph and do things with it if you need. Networkx is a tremendously popular tool for network realted tasks and we are still exploring and testing its capabilities. This might only ever be used for visualization of network purposes, but some researcher somewhere might use this once in the future networks get sophisticated, we never know. This is an optional dependency, not having this dependency doesn’t affect the toolbox, except for the purposes it is needed for.
You can install
networkxas follows:pip install networkx
Used as a port for datasets. This is Needed if you are using some common benchmark datasets. Although this is an additional dependency, skdata is the core of the datasets module and most datasets in this toolbox are ported through skdata unless you have matlab. Work is on-going in integrating with fuel and other ports.
Install by using the following command:
pip install skdata
Yann uses progressbar for aesthetic printing. You can install it easily by usingpip install progressbar
If you don’t have progressbar, yann will simply ignore it and print progress on terminal.
Dependencies for visualization¶
Theano needs pydot and graphviz for visualization. We use theano’s visualization for printing theano functions as shown here.
These visualizations are highly useful during debugging. If you want the capability of producing these for your networks, install the dependencises using the following commands:apt-get install graphviz pip install graphviz pip install pydot pydot-ng
Not needed now, but might need in future. Yann will switch from openCV to matplotlib or browser matplotlib for visualization. Install it bypip insall matplotlib
cPickle, gzip and hdf5py¶
Most often the case is that cPickle and gzip these come with the python installation, if not please install them. Yann uses these for saving down models and such.
For datasets, at the moment, yann uses cpickle. In the future, yann will migrate to hdf5 for datasets. We don’t use hdf5py at the moment. Install hdf5py by running either,conda install h5py
or,pip install h5py
Yann Toolbox Setup¶
Finally to install the toolbox run,
pip install git+git://github.com/ragavvenkatesan/yann.git
If you have already setup the toolbox and want to just update to the bleeding-edge use,
pip install --upgrade git+git://github.com/ragavvenkatesan/yann.git
If you want to build by yourself you may clone from git and then run using setuptools. Ensure that you have setuptools installed first.
pip install git setuptools
Once you are done, you clone the repository from git.
git clone http://github.com/ragavvenkatesan/yann
Once cloned, enter the directory and run installer.
cd yann python setup.py install
You can run a bunch of tests ( working on it ) by running the following code:
python setup.py test