intel-docker.md

# Use of Intel oneAPI docker image

If you want to run within a container, a set of predefined images are usable:
- [default oneAPI image](intel/oneapi-basekit) (4.26 GiB): see your Intel CPU and iGPU.
- [small one](gitlab-registry.in2p3.fr/codeursintensifs/grayscott/grayscottsyclsetup/jfalcou:2024.1) (2.29 GiB): only see your Intel CPU.
- [big image](gitlab-registry.in2p3.fr/codeursintensifs/grayscott/grayscottsyclsetup/dchamont:2024.1) (5.29 GiB): with CUDA and the NVidia codeplay plugin.

We recommend you to start your container with the following command, which sets some useful options for Intel GPU (`--device=/dev/dri`) and|or NVidia card (`--gpus all`), and mount the current directory as `/work` in the container:

```sh
IMG=gitlab-registry.in2p3.fr/codeursintensifs/grayscott/grayscottsyclsetup/dchamont:2024.1
docker pull ${IMG}
docker run --gpus all --device=/dev/dri --network host -it --rm -v ${PWD}:/work -w /work ${IMG}
```

First check that the installation is OK with `sycl-ls`, and `nvidia-smi` if you have a CUDA card.
Then you can go through the test program for CPU and|or CUDA:

```sh
# move to the top directory
cd GrayScottSyclSetup/

# check CPU nodes
IMG=gitlab-registry.in2p3.fr/codeursintensifs/grayscott/grayscottsyclsetup/jfalcou:2024.1
docker run --network host -it --rm -v ${PWD}:/work -w /work ${IMG}
cd CheckOneApi
./intel.bash   # give the list of available devices
./intel.bash 1 # check the results of device 1
exit

# Check GPU nodes
IMG=gitlab-registry.in2p3.fr/codeursintensifs/grayscott/grayscottsyclsetup/dchamont:2024.1
docker run --gpus all --device=/dev/dri --network host -it --rm -v ${PWD}:/work -w /work ${IMG}
cd CheckOneApi
nvidia-smi
./cuda.bash   # give the list of available devices
./cuda.bash 4 # give the list of available devices
exit
```

Have a look at `intel.bash` to see the various compile and run steps, and optionally adapt it to your own needs.