Installation¶

It is highly recommended that you use a pre-built version of h5py, either from a Python Distribution, an OS-specific package manager, or a pre-built wheel from PyPI.

Be aware however that most pre-built versions lack MPI support, and that they are built against a specific version of HDF5. If you require MPI support, or newer HDF5 features, you will need to build from source.

After installing h5py, you should run the tests to be sure that everything was installed correctly. This can be done in the python interpreter via:

import h5py
h5py.run_tests()

On Python 2.6 and 2.7, unittest2 must be installed to run the tests.

Pre-built installation (recommended)¶

Pre-build h5py can be installed via many Python Distributions, OS-specific package managers, or via h5py wheels.

Python Distributions¶

If you do not already use a Python Distribution, we recommend either Anaconda/Miniconda or Enthought Canopy, both of which support most versions of Microsoft Windows, OSX/MacOS, and a variety of Linux Distributions. Installation of h5py can be done on the command line via:

$ conda install h5py

for Anaconda/MiniConda, and via:

$ enpkg h5py

for Canopy.

Wheels¶

If you have an existing Python installation (e.g. a python.org download, or one that comes with your OS), then on Windows, MacOS/OSX, and Linux on Intel computers, pre-built h5py wheels can be installed via pip from PyPI:

$ pip install h5py

Additionally, for Windows users, Chris Gohlke provides third-party wheels which use Intel’s MKL.

OS-Specific Package Managers¶

On OSX/MacOS, h5py can be installed via Homebrew, Macports, or Fink.

The current state of h5py in various Linux Distributions can be seen at https://pkgs.org/download/python-h5py, and can be installed via the package manager.

As far as the h5py developers know, none of the Windows package managers (e.g. Chocolatey, nuget) have h5py included, however they may assist in installing h5py’s requirements when building from source.

Source installation¶

To install h5py from source, you need three things installed:

A supported Python version with development headers
HDF5 1.8.4 or newer with development headers
A C compiler

On Unix platforms, you also need pkg-config unless you explicitly specify a path for HDF5 as described in Custom installation.

OS-specific instructions for installing HDF5, Python and a C compiler are in the next few sections.

Additional Python-level requirements should be installed automatically (which will require an internet connection).

The actual installation of h5py should be done via:

$ pip install --no-binary=h5py h5py

or, from a tarball or git checkout

$ pip install -v .

or

$ python setup.py install

If you are working on a development version and the underlying cython files change it may be necessary to force a full rebuild. The easiest way to achieve this is

$ git clean -xfd

from the top of your clone and then rebuilding.

Source installation on OSX/MacOS¶

HDF5 and Python are most likely in your package manager (e.g. Homebrew, Macports, or Fink). Be sure to install the development headers, as sometimes they are not included in the main package.

XCode comes with a C compiler (clang), and your package manager will likely have other C compilers for you to install.

Source installation on Linux/Other Unix¶

HDF5 and Python are most likely in your package manager. A C compiler almost definitely is, usually there is some kind of metapackage to install the default build tools, e.g. build-essential, which should be sufficient for our needs. Make sure that that you have the development headers, as they are usually not installed by default. They can usually be found in python-dev or similar and libhdf5-dev or similar.

Source installation on Windows¶

Installing from source on Windows is a much more difficult prospect than installing from source on other OSs, as not only are you likely to need to compile HDF5 from source, everything must be built with the correct version of Visual Studio. Additional patches are also needed to HDF5 to get HDF5 and Python to work together.

We recommend examining the appveyor build scripts, and using those to build and install HDF5 and h5py.

Custom installation¶

Important

Remember that pip installs wheels by default. To perform a custom installation with pip, you should use:

$ pip install --no-binary=h5py h5py

or build from a git checkout or downloaded tarball to avoid getting a pre-built version of h5py.

You can specify build options for h5py with the configure option to setup.py. Options may be given together or separately:

$ python setup.py configure --hdf5=/path/to/hdf5
$ python setup.py configure --hdf5-version=X.Y.Z
$ python setup.py configure --mpi

Note the --hdf5-version option is generally not needed, as h5py auto-detects the installed version of HDF5 (even for custom locations).

Once set, build options apply to all future builds in the source directory. You can reset to the defaults with the --reset option:

$ python setup.py configure --reset

You can also configure h5py using environment variables. This is handy when installing via pip, as you don’t have direct access to setup.py:

$ HDF5_DIR=/path/to/hdf5 pip install --no-binary=h5py h5py
$ HDF5_VERSION=X.Y.Z pip install --no-binary=h5py h5py
$ CC="mpicc" HDF5_MPI="ON" HDF5_DIR=/path/to/parallel-hdf5 pip install --no-binary=h5py h5py

Here’s a list of all the configure options currently supported:

Option	Via setup.py	Via environment variable
Custom path to HDF5	`--hdf5=/path/to/hdf5`	`HDF5_DIR=/path/to/hdf5`
Force HDF5 version	`--hdf5-version=X.Y.Z`	`HDF5_VERSION=X.Y.Z`
Enable MPI mode	`--mpi`	`HDF5_MPI=ON`

Building against Parallel HDF5¶

If you just want to build with mpicc, and don’t care about using Parallel HDF5 features in h5py itself:

$ export CC=mpicc
$ pip install --no-binary=h5py h5py

If you want access to the full Parallel HDF5 feature set in h5py (Parallel HDF5), you will further have to build in MPI mode. This can either be done with command-line options from the h5py tarball or by:

$ export HDF5_MPI="ON"

You will need a shared-library build of Parallel HDF5 (i.e. built with ./configure –enable-shared –enable-parallel).

To build in MPI mode, use the --mpi option to setup.py configure or export HDF5_MPI="ON" beforehand:

$ export CC=mpicc
$ export HDF5_MPI="ON"
$ pip install --no-binary=h5py h5py