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Setup Instructions on the Satori Cluster

0. Log in

We will use RHEL8 nodes to setup SALIENT and run experiments. Log in satori-login-001.mit.edu.

1. Install Conda

On the login node, follow instructions on the Satori user documentation (step 1 therein) to install Conda.

Then, create a Conda environment (for example, call it salient):

conda create -n salient python=3.9 -y
conda activate salient

2. Check Conda Channels

Check that Conda has the following channels:

$ conda config --show channels
channels:
  - https://opence.mit.edu
  - https://public.dhe.ibm.com/ibmdl/export/pub/software/server/ibm-ai/conda/
  - defaults
  - conda-forge

If some channels are missing:

conda config --prepend channels [missing_channel]

3. Install PyTorch & CUDA

conda install pytorch==1.9.0 cudatoolkit=10.2

Note: Before cudatoolkit=11.2 is compatible with the PyTorch available on Satori, we will need to use a lower version and hack some of the subsequent installation steps.

4. Log in an Interactive Compute Node

Request a GPU compute node (RHEL8) without exclusive access:

srun --gres=gpu:1 -N 1 --mem=1T --time 8:00:00 -p sched_system_all_8 --pty /bin/bash

or request the full node:

srun --gres=gpu:4 -N 1 -c 40 --exclusive --mem=1T --time 8:00:00 -p sched_system_all_8 --pty /bin/bash

After getting on the node, activate the Conda environment again:

conda activate salient

The subsequent steps are done on the compute node.

5. Install PyTorch-Geometric (PyG)

5.1 Load CUDA Module

module load cuda/11.2

Note: This module has the nvcc compiler needed subsequently. The compiler version does not match the cudatoolkit version (see step 3). However, CUDA 10.2 does not come with cublas_v2.h, which is needed to compile some of the packages subsequently. Hence, we load CUDA 11.2 instead.

5.2 Install PyG Dependencies

export FORCE_CUDA=1
pip install git+git://github.com/rusty1s/[email protected]
pip install git+git://github.com/rusty1s/[email protected]
pip install git+git://github.com/rusty1s/[email protected]
pip install git+git://github.com/rusty1s/pytorch_sparse.git@master

Note: We install from source here (there are no pre-built wheels for PowerPC).

After pip install, start python and try to load a package (e.g., torch-scatter):

>>> import torch
>>> import torch_scatter

An error will occur:

RuntimeError: Detected that PyTorch and torch_scatter were compiled with different CUDA versions. PyTorch has CUDA version 10.2 and torch_scatter has CUDA version 11.2. Please reinstall the torch_scatter that matches your PyTorch install.

This error is raised by the __init__.py file of this package. Open this file and comment the block that raises the error:

    if t_major != major:
        raise RuntimeError(
            f'Detected that PyTorch and torch_scatter were compiled with '
            f'different CUDA versions. PyTorch has CUDA version '
            f'{t_major}.{t_minor} and torch_scatter has CUDA version '
            f'{major}.{minor}. Please reinstall the torch_scatter that '
            f'matches your PyTorch install.')

Do the same hack for all packages torch_scatter, torch_cluster, torch_spline_conv, and torch_sparse.

5.3 Install PyG

pip install torch-geometric

6. Install OGB

pip install ogb

7. Install SALIENT's fast_sampler

Go to the folder fast_sampler and install:

cd fast_sampler
python setup.py install
cd ..

To check that it is properly installed, start python and type:

>>> import torch
>>> import fast_sampler
>>> help(fast_sampler)

One should see information of the package.

8. Install Other Dependencies

conda install prettytable -c conda-forge

9. Try Examples

Congratulations! SALIENT has been installed. The folder examples contains several example scripts to use SALIENT.

Tips: Create a folder under /nobackup/ to store the datasets. Pass the folder path to --dataset_root in the example scripts. The first time an OGB dataset is used, it will be automatically downloaded to that folder (which may take some time depending on size).

Alternatively, to pre-download an OGB dataset before trying the examples, start python and type:

>>> name = # type dataset name here, such as 'ogbn-arxiv'
>>> root = # type dataset root here, such as '/nobackup/users/username/dataset'
>>> from ogb.nodeproppred import PygNodePropPredDataset
>>> dataset = PygNodePropPredDataset(name=name, root=root)

Note: When an OGB dataset is used the first time, SALIENT will process it after downloading and will store the processed data under a processed subfolder of that dataset. Subsequent uses of SALIENT will directly load the processed data.

Tips: To see all command-line arguments of SALIENT, set PYTHONPATH to be the root of SALIENT and type

python -m driver.main --help

8.1 Interactive Job on One Compute Node

Log in an interactive compute node (RHEL8) with exclusive access (see step 4). Under the folder examples, read example_Satori_interactive.sh with care, edit as appropriate (e.g., set the correct SALIENT_ROOT and DATASET_ROOT), and run:

./example_Satori_interactive.sh

8.2 Batch Job on One Compute Node

On a login node, under the folder examples, read example_Satori_batch_1_node.slurm with care, edit as appropriate (e.g., set the correct SALIENT_ROOT and DATASET_ROOT), and submit the job:

sbatch example_Satori_batch_1_node.slurm

8.3 Batch Job on Multiple Compute Nodes

On a login node, under the folder examples, read example_Satori_batch_2_nodes.slurm with care, edit as appropriate (e.g., set the correct SALIENT_ROOT and DATASET_ROOT), and submit the job:

sbatch example_Satori_batch_2_nodes.slurm