sklearn-numba-dpex 0.1.0.dev0

Computational Engine for scikit-learn based on numba-dpex

sklearn-numba-dpex

Experimental plugin for scikit-learn to be able to run (some estimators) on Intel GPUs via numba-dpex.

DISCLAIMER: this is work in progres, do not expect this repo to be in a workable state at the moment.

This requires working with the following branch of scikit-learn which is itself not yet in a working state at the moment:

• wip-engines branch on https://github.com/ogrisel/scikit-learn

Getting started:

Step 1: Installing a numba_dpex environment

Getting started requires a working environment for using numba_dpex. Currently a conda install or a docker image are available. The most stable and recommended environment at the moment is using the docker image.

Using a conda installation

TODO: update the instructions to install everything from non-dev conda packages once available.

A conda env with Intel Python libraries (1/2)

Let's create a dedicated conda env with the development versions of the Intel Python libraries (from the dppy/label/dev channel).

conda create -n dppy-dev dpnp numba cython spirv-tools -c dppy/label/dev -c intel --override-channels

Let's activate it and introspect the available hardware:

conda activate dppy-dev
python -c "import dpctl; print(dpctl.get_devices())"

If you do not not see any CPU device, try again with the following after setting the SYCL_ENABLE_HOST_DEVICE=1 environment variable, for instance:

SYCL_ENABLE_HOST_DEVICE=1 python -c "import dpctl; print(dpctl.get_devices())"

If you have an Intel GPU and it is not detected, check the following steps:

• Make sure you have installed the latest GPU drivers

• On Linux, check that the i915 driver is properly loaded:

  $ lspci -nnk | grep i915
        Kernel driver in use: i915
        Kernel modules: i915
  

• On Linux, check that the current user is in the render group, for instance:

  $ groups
  myuser adm cdrom sudo dip plugdev render lpadmin lxd sambashare docker
  

  If not, add it with sudo adduser $USER render, logout and log back in, and check again.

• Install recent versions of the runtime libraries (not yet available as conda packages)

For more in-depth information, you can refer to the guides for system configuration for Intel hardware and software and to the discussions in this github issue https://github.com/IntelPython/dpnp/issues/1149

The dpctl.lsplatform() can also list version informations on your SYCL runtime environment:

python -c "import dpctl; dpctl.lsplatform()"

Install numba-dpex from source (2/2)

Install and activate the Intel oneAPI DPC++ compiler shipped with the Intel oneAPI base toolkit.

For instance, in Ubuntu, once the apt repo has been configured:

sudo apt update
sudo apt install intel-basekit
source /opt/intel/oneapi/compiler/latest/env/vars.sh

Ensure that the icx command can be found in the PATH with the command:

which icx

Install numba-dpex in the same conda env as previously:

git clone https://github.com/IntelPython/numba-dpex/
cd numba-dpex
pip install -e . --no-build-isolation

Important: in order to use numba-dpex, the llvm-spirv compiler is required to be in the PATH. This can be achieved with:

$ export PATH=/opt/intel/oneapi/compiler/latest/linux/bin-llvm:$PATH

Using the docker image

A docker image is available and provides an up-to-date, one-command install environment. You can either build it from the Dockerfile :

$ cd docker
$ docker build . -t my_tag

or pull the docker image from this publicly available repository:

$ docker pull jjerphan/numba_dpex_dev:latest

Run the container in interactive mode with your favorite docker flags, for example:

$ docker run --name my_container_name -it -v /my/host/volume/:/mounted/volume --device=/dev/dri my_tag

where my_tag would be jjerphan/numba_dpex_dev:latest if you pulled from the repository.

⚠ The flag --device=/dev/dri is mandatory to enable the gpu within the container, also the user starting the docker run command must have access to the gpu, e.g. by being a member of the render group.

Unless using the flag --rm when starting a container, you can restart it after it was exited, with the command:

sudo docker start -a -i my_container_name

Once inside the container, you can check that the environment works: the command

python -c "import dpctl; print(dpctl.get_devices())"

will introspect the available hardware, and should display working opencl cpu and gpu devices, and level_zero gpu devices.

Step 2: install the wip-engines branch of scikit-learn

Once you have loaded into a numba_dpex development environment, following one of the two previous guides, follow those instructions:

git clone https://github.com/ogrisel/scikit-learn
cd scikit-learn
git checkout wip-engines
pip install -e . --no-build-isolation
cd ..

Step 3: install this plugin

git clone https://github.com/soda-inria/sklearn-numba-dpex
cd sklearn-numba-dpex
pip install -e . --no-build-isolation

Intended usage

See the sklearn_numba_dpex/tests folder for example usage.

TODO: write some doc here instead.

Running the benchmarks

Repeat the pip installation step exposed in step 3 with the following edit:

pip install -e .[benchmark] --no-build-isolation

(i.e adding the benchmark extra-require), followed by:

cd benckmark
python ./kmeans.py

to run a benchmark for different k-means implementations and print a short summary of the performance.

Some parameters in the __main__ section of the file ./benchmark/kmeans.py are exposed for quick edition (n_clusters, max_iter, skip_slow, ...).

Notes about the preferred floating point precision

In many machine learning applications, operations using single-precision (float32) floating point data require twice as less memory that double-precision (float64), are regarded as faster, accurate enough and more suitable for GPU compute. Besides, most GPUs used in machine learning projects are significantly faster with float32 than with double-precision (float64) floating point data.

To leverage the full potential of GPU execution, it's strongly advised to use a data loader that loads float32 data. By default, unless specified otherwise numpy array are created with type float64, so be especially careful to the type whenever the loader does not explicitly document the type nor expose a type option.

Although it's less recommended to prevent avoidable data copies, it's also possible to transform float64 numpy arrays into float32 arrays using the numpy.ndarray.astype type converter following this example:

X = my_data_loader()
X_float32 = X.astype(float32)
my_gpu_compute(X_float32)