The `k8s-launcher` repository provides a comprehensive set of tools and scripts to facilitate the pretraining, fine-tuning, and evaluation of large language models, specifically designed to work with NVIDIA NeMo models. This toolkit is aimed at researchers and developers who seek to harness the power of Kubernetes (K8s) for distributed computing to train and optimize language models efficiently.
The field of natural language processing (NLP) has experienced significant advancements, thanks to the development of large language models and innovative training techniques. However, the training of such models often requires significant computational resources and time. With the growing scale of NLP models, distributed computing has become a necessity to meet the demand for faster training and evaluation.
The motivation behind `k8s-launcher` is to provide a solution that streamlines the deployment and management of language model training pipelines on Kubernetes clusters. Leveraging Kubernetes for distributed training not only accelerates the training process but also allows researchers to efficiently utilize available hardware resources, including multiple GPUs across multiple nodes.
- Kubernetes Integration: Leverage the scalability and distributed computing capabilities of Kubernetes to efficiently train large language models on clusters. By using Kubernetes, `k8s-launcher` simplifies the deployment of complex training jobs and ensures fault tolerance, automatic scaling, and container orchestration.
- NVIDIA NeMo Compatibility: Seamless integration with NVIDIA NeMo, a powerful neural model training framework developed by NVIDIA. By building on top of NeMo, `k8s-launcher` capitalizes on the flexibility and performance optimization provided by NeMo's model implementations.
- Distributed Training: Utilize distributed training techniques for faster convergence and efficient resource utilization. By dividing the training workload across multiple GPUs and nodes, `k8s-launcher` optimizes training times, making it possible to train large models within reasonable time frames.
- Scalability: The architecture of `k8s-launcher` is designed to accommodate models of various sizes and data scales. Researchers can easily scale up or down depending on the complexity of the models and the available resources.
- Customization: The repository provides customizable configuration files to fine-tune the training process according to specific requirements, such as batch size, learning rate, and optimization strategies.