NumpyDL has a couple of prerequisites that need to be installed first, but it is not very picky about versions. The most important package is Numpy. At the same time, you should install some other useful packages, such as scipy and scikit-learn. Most importantly, these packages are not required to install the specific version to fit the version of NumpyDL you choose to install.
We strongly recommend you to install the Miniconda or a bigger installer Anaconda which is a leading open data science platform powered by Python and well integrated the efficient scientific computing platform MKL.
1.1.1. Python + pip¶
NumpyDL currently requires Python 3.3 or higher to run. Please install Python via the package manager of your operating system if it is not included already.
pip for installing additional modules that are not shipped
with your operating system, or shipped in an old version, and we will make use
of it below. We recommend installing these modules into your home directory
--user, or into a virtual environment
1.1.2. C compiler¶
Numpy/scipy require a C compiler if you install them via
pip. On Linux,
the default compiler is usually“gcc“, and on Mac OS, it’s
Windows, we recommend you to install the Miniconda
or Anaconda. Again, please install them via the
package manager of your operating system.
1.1.3. numpy/scipy + BLAS¶
NumpyDL requires numpy of version 1.6.2 or above, and sometimes also requires scipy 0.11 or above. Numpy/scipy rely on a BLAS library to provide fast linear algebra routines. They will work fine without one, but a lot slower, so it is worth getting this right (but this is less important if you plan to use a GPU).
If you install numpy and scipy via your operating system’s package manager,
they should link to the BLAS library installed in your system. If you install
numpy and scipy via
pip install numpy and
pip install scipy, make sure
to have development headers for your BLAS library installed (e.g., the
libopenblas-dev package on Debian/Ubuntu) while running the installation
command. Please refer to the numpy/scipy build instructions if in doubt.
1.2. Stable NumpyDL release¶
To install a version that is known to work, run the following command:
pip install -r https://github.com/oujago/NumpyDL/blob/master/requirements.txt
pip install npdl
If you do not use
--user to both commands to install
into your home directory instead. To upgrade from an earlier installation, add
1.3. Development installation¶
1.3.1. install from source¶
Alternatively, you can install NumpyDL from source, in a way that any changes to your local copy of the source tree take effect without requiring a reinstall. This is often referred to as editable or development mode. Firstly, you will need to obtain a copy of the source tree:
git clone https://github.com/oujago/NumpyDL.git
It will be cloned to a subdirectory called
NumpyDL. Make sure to place it
in some permanent location, as for an editable installation, Python will
import the module directly from this directory and not copy over the files.
Enter the directory and install the known good version of Theano:
cd NumpyDL pip install -r requirements.txt
To install the NumpyDL package itself, in editable mode, run:
pip install --editable
As always, add
--user to install it to your home directory instead.
Optional: If you plan to contribute to NumpyDL, you will need to fork the
NumpyDL repository on GitHub. This will create a repository under your user
account. Update your local clone to refer to the official repository as
upstream, and your personal fork as
git remote rename origin upstream git remote add origin https://github.com/<your-github-name>/NumpyDL.git
If you set up an SSH key, use the
SSH clone URL instead:
You can now use this installation to develop features and send us pull requests on GitHub, see Development!