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Pre-configured installation (recommended)

It's strongly recommended that you use a Python distribution or package manager to install h5py along with its compiled dependencies. Here are some which are popular in the Python community:

conda install h5py  # Anaconda/Miniconda
enpkg h5py          # Canopy

Or, use your package manager:

  • apt-get (Linux/Debian, including Ubuntu)
  • yum (Linux/Red Hat, including Fedora and CentOS)
  • Homebrew (OS X)
  • pacman (Arch linux)

Source installation on Linux and OS X

You need, via apt-get, yum or Homebrew:

  • Python 2.6, 2.7, 3.3, 3.4, or 3.5 with development headers (python-dev or similar)
  • HDF5 1.8.4 or newer, shared library version with development headers (libhdf5-dev or similar)
  • NumPy 1.6.1 or later
$ pip install h5py

or, from a tarball or git :ref:`checkout <git_checkout>`

$ pip install -v .


$ python 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 Windows

Installing from source on Windows is effectively impossible because of the C library dependencies involved.

If you don't want to use Anaconda, Canopy, or PythonXY, download a third-party wheel from Chris Gohlke's excellent collection.

Custom installation

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

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

$ HDF5_DIR=/path/to/hdf5 pip install h5py
$ HDF5_VERSION=X.Y.Z pip install h5py
$ CC="mpicc" HDF5_MPI="ON" HDF5_DIR=/path/to/parallel-hdf5 pip install h5py

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

Option Via 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
$ python install

If you want access to the full Parallel HDF5 feature set in h5py (:ref:`parallel`), 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 configure or export HDF5_MPI="ON" beforehand:

$ export CC=mpicc
$ export HDF5_MPI="ON"
$ python configure
$ python build

See also :ref:`parallel`.