Imageprocessing with 10M rows successful run #20
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- Update Ray base image to 2.51.1 and vLLM to 0.11.0 - Add boto3 dependency for S3 operations - Update transformers to 4.57.1 for compatibility - Configure compute resources with auto-selection (max 520 CPU, 128 GPU) - Add disk size configuration options for customer-hosted deployments - Implement robust URL validation and error handling - Add base64 image encoding for Arrow serialization - Add JPEG format validation and 128x128 image resizing - Scale model replicas from 1 to 32 for higher throughput - Optimize batch sizes and memory usage for large-scale processing - Implement session pooling for HTTP requests with retry logic - Add timestamp-based output paths to /mnt/shared_storage - Add run.sh script for job submission with HF_TOKEN
- Switch from synchronous requests to async aiohttp for better performance - Update num_model_replicas to 96 for higher throughput - Implement concurrent image downloading with semaphore control - Add async image processing with proper event loop handling - Update compute config: CPU max to 1024, GPU min to 96, GPU max to 128 - Add RAY_DEFAULT_OBJECT_STORE_MEMORY_PROPORTION env var - Simplify pipeline by removing base64 encoding intermediate step - Process images as bytes directly in vision preprocessing
- Add detailed overview of the 3-stage pipeline (download, preprocess, inference) - Document key features: scale, throughput, fault tolerance, cost optimization - Provide clear setup instructions with HuggingFace token requirements - Include configuration details for compute, model, and batch processing - Use human-friendly tone for Anyscale users
- Update max CPU limit from 1024 to 530 in job.yaml and README - Update max GPU limit from 128 to 48, set min GPU to 0 for better resource allocation - Change from fixed num_model_replicas to dynamic min/max GPU range (32-96) - Use concurrency tuple (min_gpu_num, max_gpu_num) for autoscaling - Simplify async event loop handling (remove run_async_in_thread wrapper) - Update batch sizes: 100 -> 50 for better memory management - Add concurrency=1024 to image_download for higher throughput - Fix code formatting (quotes, spacing, line endings)
- Add detailed docstring explaining pipeline architecture and goals - Document GPU autoscaling configuration with best practice notes - Annotate async I/O patterns for image downloading (semaphores, connection pooling) - Explain ThreadPoolExecutor usage for CPU-bound image processing - Document vLLM configuration with tuning recommendations - Add inline comments explaining Ray Data performance tuning - Provide reasoning for resource allocation decisions (CPU, memory, GPU) - Include best practice notes for batch processing, error handling, and filtering - Add comments explaining why certain patterns are used (fail fast, graceful degradation) - Make code suitable as educational reference for building large-scale pipelines
- Remove extensive inline documentation and docstrings - Add concise 5-step pipeline overview comment - Keep code clean and readable without excessive comments - All documentation is now in README.md for better maintainability
- Pin vLLM concurrency to 64 L4 replicas to match g6.xlarge worker pool - Configure explicit g6.xlarge worker nodes (min 0, max 64) in job.yaml - Switch accelerator_type from A10G to L4 for proper GPU targeting - Reduce max_num_batched_tokens from 2048 to 1024 for L4 memory constraints - Update README to reflect L4-based scaling (up to 64 replicas) - Reduce num_cpus from 1 to 0.5 for process_image_bytes batch processing
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10M rows successful run