This test case contains examples and configurations for running distributed training with NVIDIA NeMo 2.0.
NVIDIA NeMo is a cloud-native framework for training and deploying generative AI models, optimized for architectures ranging from billions to trillions of parameters. NeMo 2.0 introduces a Python-based configuration system, providing enhanced flexibility, better IDE integration, and streamlined customization for large language model training.
- Comprehensive development tools for data preparation, model training, and deployment.
- Advanced customization for fine-tuning models to specific use cases.
- Optimized infrastructure with multi-GPU and multi-node support.
- Enterprise-grade features such as parallelism techniques, memory optimization, and deployment pipelines.
NeMo 2.0 introduces a Python-based configuration system, providing enhanced flexibility, better IDE integration, and streamlined customization.
The slurm directory provides implementation examples for running NeMo 2.0 using Slurm as the workload manager. This approach leverages AWS's purpose-built infrastructure for large-scale AI training. See the README in the slurm directory for detailed setup and usage instructions.