Legate NumPy is a Legate library that aims to provide a distributed and accelerated drop-in replacement for the NumPy API on top of the Legion runtime. Using Legate NumPy you do things like run the final example of the Python CFD course completely unmodified on 2048 A100 GPUs in a DGX SuperPOD and achieve good weak scaling.
Legate NumPy works best for programs that have very large arrays of data that cannot fit in the memory of a single GPU or a single node and need to span multiple nodes and GPUs. While our implementation of the current NumPy API is still incomplete, programs that use unimplemented features will still work (assuming enough memory) by falling back to the canonical NumPy implementation.
Users must have a working installation of the
library prior to installing Legate NumPy.
Legate NumPy requires Python >= 3.6. We provide a
conda environment file that
installs all needed dependencies in one step. Use the following command to
create a conda environment with it:
conda env create -n legate -f conda/legate_numpy_dev.yml
Installation of Legate NumPy is done with either
setup.py for simple
uses cases or
install.py for more advanced use cases. The most common
installation command is:
python setup.py --with-core <path-to-legate-core-installation>
This will build Legate NumPy against the Legate Core installation and then
install Legate NumPy into the same location. Users can also install Legate NumPy
into an alternative location with the canonical
--prefix flag as well.
python setup.py --prefix <install-dir> --with-core <path-to-legate-core-installation>
Note that after the first invocation of
setup.py this repository will remember
which Legate Core installation to use and the
--with-core option can be
omitted unless the user wants to change it.
Advanced users can also invoke
install.py --help to see options for
configuring Legate NumPy by invoking the
install.py script directly.
Of particular interest to Legate NumPy users will likely be the option for
specifying an installation of OpenBLAS to use.
If you already have an installation of OpenBLAS on your machine you can
install.py script about its location using the
python setup.py --with-openblas /path/to/open/blas/
Usage and Execution
Using Legate NumPy as a replacement for NumPy is easy. Users only need
import numpy as np
import legate.numpy as np
These programs can then be run by the Legate driver script described in the
Legate Core documentation.
For execution with multiple nodes (assuming Legate Core is installed with GASNet support)
users can supply the
--nodes flag. For execution with GPUs, users can use the
--gpus flags to specify the number of GPUs to use per node. We encourage all users
to familiarize themselves with these resource flags as described in the Legate Core
documentation or simply by passing
--help to the
legate driver script.
Supported and Planned Features
Legate NumPy is currently a work in progress and we are gradually adding support for
additional NumPy operators. Unsupported NumPy operations will provide a
warning that we are falling back to canonical NumPy. Please report unimplemented
features that are necessary for attaining good performance so that we can triage
them and prioritize implementation appropriately. The more users that report an
unimplemented feature, the more we will prioritize it. Please include a pointer
to your code if possible too so we can see how you are using the feature in context.
Supported Types and Dimensions
Legate NumPy currently supports the following NumPy types:
Legate currently also only works on up to 3D arrays at the moment. We're currently working
on support for N-D arrays. If you have a need for arrays with more than three
dimensions please let us know about it.
A complete list of available features can is provided in the API
There are three primary directions that we plan to investigate
with Legate NumPy going forward:
- More features: we plan to identify a few key lighthouse applications
and use the demands of these applications to drive the addition of
new features to Legate NumPy.
- We plan to add support for sharded file I/O for loading and
storing large data sets that could never be loaded on a single node.
Initially this will begin with native support for h5py
but will grow to accommodate other formats needed by our lighthouse
- Strong scaling: while Legate NumPy is currently implemented in a way that
enables weak scaling of codes on larger data sets, we would also like
to make it possible to strong-scale Legate applications for a single
problem size. This will require leveraging some of the more advanced
features of Legion from inside the Python interpreter.
We are open to comments, suggestions, and ideas.