LLM Workshop on Databricks Lakehouse AI

This repository was created as a supporting material around scaling your GenAI(LLM) workloads using Databricks Lakehouse AI Platform.

Structure

The repository is divided into multiple folders:

• databricks_llm (utils and scripts used)
• ds_configs (configuration files for Deep Speed (for distributed training))
• notebooks
  • data_prep
  • composer_library
  • optimized_inference
  • prompt_engineering (work in progress)
  • vectordb_rag
  • gpu_serving
  • mlflow_gateway (work in progress)
  • ds_fine_tuning
  • model_serving (work in progress)
• old_notebooks

Important Notes

This code has been tested on the following cloud provider's instances:

Cloud Provider	Instance Type	GPU's RAM	Comments
AWS	A10(1 GPU)	24 Gb	-
AWS	A10(4GPU)	4*24 Gb	-
AWS	A100(8GPU)	320 Gb	-
Azure	A10(1 GPU)	24 Gb	-
Azure	A10(2 GPU)	48 Gb	-
Azure	A100(1 GPU)	40 Gb	-
Azure	V100(1 GPU)	16 Gb	-

Resources

1. Official ML Examples from the Databricks on LLM's : https://github.com/databricks/databricks-ml-examples/blob/master/llm-tutorials/batch-inference/transformer-batch-inference.ipynb
2. Model Serving examples : https://github.com/ahdbilal/Databricks-GPU-Serving-Examples/

Contributors:

• Puneet Jain
• Michael Shtelma
• Anastasia Prokaieva

Issues

If you encounter any issues or have questions please raise an issue.