Challenges Solved
- Model Deployment: Deployed Octo-1.5 robotics foundation model on Hugging Face Spaces.
- Inference Optimization: Optimized inference latency for real-time control capability via API endpoints.
Signal
MLOps / Model Serving
Project Overview
Octo-1.5 is a state-of-the-art vision-language-action (VLA) model for robotics. This project focuses on the MLOps challenge of serving such a large-scale model with enough performance for interactive use.
Features
- Standardized API: Created a REST API using FastAPI to handle multimodal inputs (images + text commands).
- Hugging Face Deployment: Configured and deployed the stack on Hugging Face Spaces using custom Docker containers.
- Inference Pipeline: Built an optimized pipeline using Hugging Face's
transformers and accelerate libraries.
Technical Depth
- Mixed-Precision Inference: Utilized BF16/FP16 mixed precision to balance memory usage and inference speed.
- Dynamic Batching: Implemented request queueing and dynamic batching to handle multiple concurrent users efficiently.
- Resource Management: Fine-tuned GPU resource allocation within the Space to prevent OOM errors while maintaining throughput.
Links