Mac OS Installation Instructions

There are various ways to install Aesara dependencies on a Mac. Here we describe the process in detail with Anaconda, Homebrew or MacPorts but if you did it differently and it worked, please let us know the details so that we can add alternative instructions.

Requirements

Note

We only support the installation of the requirements through conda.

Python == >= 3.7

The conda distribution is highly recommended. Python 2.4 was supported up to and including the release 0.6. Python 2.6 was supported up to and including the release 0.8.2. Python 3.3 was supported up to and including release 0.9. Python 3.6 was supported up to and including release 2.3.4.

NumPy >= 1.9.1

Earlier versions could work, but we don’t test it.

SciPy >= 0.14

Only currently required for sparse matrix and special functions support, but highly recommended. SciPy >=0.8 could work, but earlier versions have known bugs with sparse matrices.

BLAS installation (with Level 3 functionality)

• Recommended: MKL, which is free through Conda with mkl-service package.
• Alternatively, we suggest to install OpenBLAS, with the development headers (-dev, -devel, depending on your Linux distribution).

Optional requirements

clang (the system version)

Highly recommended. Aesara can fall back on a NumPy-based Python execution model, but a C compiler allows for vastly faster execution.

Sphinx >= 0.5.1, pygments

For building the documentation. LaTeX and dvipng are also necessary for math to show up as images.

pydot-ng

To handle large picture for gif/images.

NVIDIA CUDA drivers and SDK

Highly recommended Required for GPU code generation/execution on NVIDIA gpus. See instruction below.

libgpuarray

Required for GPU/CPU code generation on CUDA and OpenCL devices (see: GpuArray Backend).

pycuda and skcuda

Required for some extra operations on the GPU like fft and solvers. We use them to wrap cufft and cusolver. Quick install pip install pycuda scikit-cuda. For cuda 8, the dev version of skcuda (will be released as 0.5.2) is needed for cusolver: pip install pycuda; pip install git+https://github.com/lebedov/scikit-cuda.git#egg=scikit-cuda.

warp-ctc

Required for Aesara CTC implementation. It is faster then using an equivalent graph of Aesara ops.

Requirements installation through Conda (recommended)

Install Miniconda

Follow this link to install Miniconda.

Note

If you want fast compiled code (recommended), make sure you have Clang installed.

Install requirements and optional packages

conda install numpy scipy mkl pytest <sphinx> <pydot-ng>

• Arguments between <…> are optional.

Install and configure the GPU drivers (recommended)

Warning

OpenCL support is still minimal for now.

1. Install CUDA drivers

   • Follow this link to install the CUDA driver and the CUDA Toolkit.
   • You must reboot the computer after the driver installation.
   • Test that it was loaded correctly after the reboot, executing the command nvidia-smi from the command line.

   Note

   Sanity check: The bin subfolder should contain an nvcc program. This folder is called the cuda root directory.

2. Fix ‘lib’ path

   • Add the CUDA ‘lib’ subdirectory (and/or ‘lib64’ subdirectory if you have a 64-bit OS) to your $LD_LIBRARY_PATH environment variable. Example: /usr/local/cuda/lib64

Attention

For MacOS you should be able to follow the above instructions to setup CUDA, but be aware of the following caveats:

• If you want to compile the CUDA SDK code, you may need to temporarily revert back to Apple’s gcc (sudo port select gcc) as their Makefiles are not compatible with MacPort’s gcc.
• If CUDA seems unable to find a CUDA-capable GPU, you may need to manually toggle your GPU on, which can be done with gfxCardStatus.

Attention

Aesara officially supports only clang on OS X. This can be installed by getting XCode from the App Store and running it once to install the command-line tools.

Installation

Stable Installation

With conda

If you use conda, you can directly install both aesara and pygpu. Libgpuarray will be automatically installed as a dependency of pygpu.

conda install aesara pygpu

Warning

The Aesara developers do not maintain pygpu, so compatibility isn’t guaranteed.

With pip

If you use pip, you have to install Aesara and libgpuarray separately.

aesara

Install the latest stable version of Aesara with:

<sudo> pip install <--user> Aesara[test, doc]

• Any argument between <…> is optional.
• Use sudo for a root installation.
• Use user for a user installation without admin rights. It will install Aesara in your local site-packages.
• Use pip install -r requirements.txt to install the requirements for testing.
• Use pip install -r requirements-rtd.txt install the requirements for generating the documentation.

If you encountered any trouble, head to the Troubleshooting page.

libgpuarray

Download it with:

git clone https://github.com/Theano/libgpuarray.git
cd libgpuarray

and then follow the Step-by-step instructions.

Bleeding-Edge Installation (recommended)

Install the latest, bleeding-edge, development version of Aesara with:

<sudo> pip install <--user> <--no-deps> git+https://github.com/aesara-devs/aesara.git#egg=aesara

• Any argument between <…> is optional.
• Use sudo for a root installation.
• Use user for a user installation without admin rights. It will install Aesara in your local site-packages.
• Use no-deps when you don’t want the dependencies of Aesara to be installed through pip. This is important when they have already been installed as system packages.

If you encountered any trouble, head to the Troubleshooting page.

libgpuarray

Install the latest, development version of libgpuarray following the Step-by-step instructions.

Note

Currently, you need libgpuarray version 0.7.X that is not in conda default channel. But you can install it with our own channel mila-udem (that only supports Python 2.7, 3.5 and 3.6):

conda install -c mila-udem pygpu

Developer Installation

Install the developer version of Aesara with:

git clone git://github.com/aesara-devs/aesara.git
cd aesara
<sudo> pip install <--user> <--no-deps> -e .

• Any argument between <…> is optional.
• Use sudo for a root installation.
• Use user for a user installation without admin rights. It will install Aesara in your local site-packages.
• Use no-deps when you don’t want the dependencies of Aesara to be installed through pip. This is important when they have already been installed as system packages.
• -e makes your installation editable, i.e., it links it to your source directory.

If you encountered any trouble, head to the Troubleshooting page.

libgpuarray

See instructions for bleeding-edge installation about libgpuarray.

Requirements through Homebrew (not recommended)

Install python with homebrew:

$ brew install python # or python3 if you prefer

This will install pip. Then use pip to install numpy, scipy:

$ pip install numpy scipy

If you want to use openblas instead of Accelerate, you have to install numpy and scipy with hombrew:

$ brew tap homebrew/python
$ brew install numpy --with-openblas
$ brew install scipy --with-openblas

Requirements through MacPorts (not recommended)

Using MacPorts to install all required Aesara dependencies is easy, but be aware that it will take a long time (a few hours) to build and install everything.

• MacPorts requires installing XCode first (which can be found in the Mac App Store), if you do not have it already. If you can’t install it from the App Store, look in your MacOS X installation DVD for an old version. Then update your Mac to update XCode.

• Download and install MacPorts, then ensure its package list is up-to-date with sudo port selfupdate.

• Then, in order to install one or more of the required libraries, use port install, e.g. as follows:

  $ sudo port install py27-numpy +atlas py27-scipy +atlas py27-pip
  

  This will install all the required Aesara dependencies. gcc will be automatically installed (since it is a SciPy dependency), but be aware that it takes a long time to compile (hours)! Having NumPy and SciPy linked with ATLAS (an optimized BLAS implementation) is not mandatory, but recommended if you care about performance.

• You might have some different versions of gcc, SciPy, NumPy, Python installed on your system, perhaps via Xcode. It is a good idea to use either the MacPorts version of everything or some other set of compatible versions (e.g. provided by Xcode or Fink). The advantages of MacPorts are the transparency with which everything can be installed and the fact that packages are updated quite frequently. The following steps describe how to make sure you are using the MacPorts version of these packages.

• In order to use the MacPorts version of Python, you will probably need to explicitly select it with sudo port select python python27. The reason this is necessary is because you may have an Apple-provided Python (via, for example, an Xcode installation). After performing this step, you should check that the symbolic link provided by which python points to the MacPorts python. For instance, on MacOS X Lion with MacPorts 2.0.3, the output of which python is /opt/local/bin/python and this symbolic link points to /opt/local/bin/python2.7. When executing sudo port select python python27-apple (which you should not do), the link points to /usr/bin/python2.7.

• Similarly, make sure that you are using the MacPorts-provided gcc: use sudo port select gcc to see which gcc installs you have on the system. Then execute for instance sudo port select gcc mp-gcc44 to create a symlink that points to the correct (MacPorts) gcc (version 4.4 in this case).

• At this point, if you have not done so already, it may be a good idea to close and restart your terminal, to make sure all configuration changes are properly taken into account.

• Afterwards, please check that the scipy module that is imported in Python is the right one (and is a recent one). For instance, import scipy followed by print scipy.__version__ and print scipy.__path__ should result in a version number of at least 0.7.0 and a path that starts with /opt/local (the path where MacPorts installs its packages). If this is not the case, then you might have some old installation of scipy in your PYTHONPATH so you should edit PYTHONPATH accordingly.

• Please follow the same procedure with numpy.

• This is covered in the MacPorts installation process, but make sure that your PATH environment variable contains /opt/local/bin and /opt/local/sbin before any other paths (to ensure that the Python and gcc binaries that you installed with MacPorts are visible first).

• MacPorts does not automatically create pip symlinks pointing to the MacPorts version; you can add them yourself with

  $ sudo ln -s /opt/local/bin/pip-2.7 /opt/local/bin/pip