Overview

isciml leverages distributed computing capabilities to handle large-scale data generation and model training efficiently. By utilizing SLURM (Simple Linux Utility for Resource Management) and multi-GPU setups, users can significantly accelerate their workflows and tackle complex 3D non-invasive imaging problems.

Distributed Data Generation

isciml's generate command supports distributed execution using MPI (Message Passing Interface), allowing you to parallelize data generation across multiple CPU cores or nodes. This is particularly useful when dealing with large datasets or complex physical models.

Key features:

• Utilize multiple cores on a single node or across multiple nodes

• Efficiently generate large volumes of synthetic data

• Leverage HPC resources for faster data preparation

Example SLURM script for distributed data generation:

#!/bin/bash
#SBATCH -N 2
#SBATCH -n 32
#SBATCH -t 4:00:00
#SBATCH -p <PARTITION>

singularity exec isciml.sif mpirun -np 32 isciml generate [OPTIONS]

Multi-GPU Training

For model training, isciml can utilize multiple GPUs to accelerate the process. This is implemented using PyTorch's Distributed Data Parallel (DDP) strategy, allowing for efficient scaling across multiple GPUs.

Key benefits:

• Faster training times for large models

• Ability to handle larger batch sizes

• Improved utilization of HPC resources

Example SLURM script for multi-GPU training:

#!/bin/bash
#SBATCH -N 1
#SBATCH -t 12:00:00
#SBATCH --gpus=4
#SBATCH -p gpu

singularity exec --nv isciml.sif isciml train \
    --n_gpus 4 \
    --strategy ddp \
    [OTHER_OPTIONS]

Considerations for Distributed Computing

1. Resource Allocation: Carefully consider the number of nodes, cores, and GPUs needed for your task to optimize resource usage.

2. Data Management: Ensure that your data is accessible from all compute nodes, typically by using a shared filesystem.

3. Scalability: Test your workflows with varying numbers of resources to find the optimal configuration for your specific problem.

4. Environment Compatibility: Make sure that your SLURM environment is compatible with the isciml Singularity container and has the necessary drivers (e.g., CUDA for GPU usage).

5. Monitoring and Optimization: Use SLURM's monitoring tools to track resource usage and job progress, and optimize your scripts accordingly.

By leveraging these distributed computing capabilities, isciml enables users to tackle larger and more complex 3D non-invasive imaging problems efficiently. Whether you're generating vast amounts of synthetic data or training sophisticated deep learning models, distributed computing with SLURM and multi-GPU setups can significantly enhance your productivity and the scale of problems you can address.