Release v1.0.0

Release Notes - v1.0.0

We're thrilled to announce the initial release of sagemaker-hyperpod-recipes!

🎉 Features

• Unified Job Submission: Submit training and fine-tuning workflows to SageMaker HyperPod or SageMaker training jobs using a single entry point
• Flexible Configuration: Customize your training jobs with three types of configuration files:
  • General Configuration (ex: recipes_collection/config.yaml)
  • Cluster Configuration (ex: recipes_collection/cluster/slurm.yaml)
  • Recipe Configuration (ex: recipes_collection/recipes/training/llama/hf_llama3_8b_seq16k_gpu_p5x16_pretrain.yaml)
• Pre-defined LLM Recipes: Access a collection of ready-to-use recipes for training Large Language Models
• Cluster Agnostic: Compatible with SageMaker HyperPod (with Slurm or Amazon EKS orchestrators) and SageMaker training jobs
• Built on Nvidia NeMo Framework: Leverages the Nvidia NeMo Framework Launcher for efficient job management

🗂️ Repository Structure

• main.py: Primary entry point for submitting training jobs
• launcher_scripts/: Collection of commonly used scripts for LLM training
• recipes_collection/: Pre-defined LLM recipes provided by developers

🔧 Key Components

1. General Configuration: Common settings like default parameters and environment variables
2. Cluster Configuration: Cluster-specific settings (e.g., volume, label for Kubernetes; job name for Slurm)
3. Recipe Configuration: Training job settings including model types, sharding degree, and dataset paths

📚 Documentation

• Refer to the README.md for detailed usage instructions and examples

🤝 Contributing

We welcome contributions to enhance the capabilities of sagemaker-hyperpod-recipes. Please refer to our contributing guidelines for more information.

Thank you for choosing sagemaker-hyperpod-recipes for your large-scale language model training needs!