Add vLLM multimodal vision-language model support to Solaris #473
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This commit adds comprehensive support for vision-language models (VLMs) using vLLM, enabling multimodal analysis of satellite and aerial imagery with natural language. Features: - New VLLMInferer class in solaris/nets/vllm_infer.py for multimodal inference - CLI command solaris_vllm_infer for easy command-line usage - Support for any vLLM-compatible vision models (LLaVA, Qwen-VL, etc.) - Batch processing and multi-GPU inference via tensor parallelism - Geospatial metadata preservation (projection, geotransform) - Configurable prompts and sampling parameters - JSON output format for structured results New files: - solaris/nets/vllm_infer.py - Core vLLM inference module - solaris/bin/solaris_vllm_infer.py - CLI entry point - requirements-vllm.txt - vLLM-compatible dependencies (Python 3.8+) - environment-vllm.yml - Conda environment with vLLM support - VLLM_MULTIMODAL_GUIDE.md - Comprehensive usage guide - examples/vllm/ - Example configurations and scripts Modified files: - setup.py - Added solaris_vllm_infer CLI entry point - requirements.txt - Added note about vLLM optional dependencies Use cases: - Generate descriptions of overhead imagery - Land use classification with natural language - Building and infrastructure detection - Change detection and analysis - Visual question answering on satellite images Note: vLLM requires Python 3.8+, PyTorch 2.0+, and CUDA 11.8+ Use environment-vllm.yml or requirements-vllm.txt for installation. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Major refactoring to use LiteLLM instead of vLLM, focusing on US-based and NVIDIA vision-language models for multimodal satellite imagery analysis. Key Changes: - Replaced vLLM with LiteLLM for unified API access to multiple providers - Primary focus on Google Gemini (US-based) and NVIDIA models - Simplified deployment - no local GPU required for inference - API-based approach for better scalability and maintenance New Implementation: - solaris/nets/litellm_infer.py - LiteLLM-based multimodal inference * Supports Google Gemini (1.5-flash, 1.5-pro, pro-vision) * Supports NVIDIA vision models via API catalog * Preserves geospatial metadata (projection, geotransform) * Base64 image encoding for API calls * Robust error handling and logging Updated CLI: - solaris/bin/solaris_vllm_infer.py - Updated to use LiteLLM * Help text shows Gemini and NVIDIA model examples * Environment variable support (GEMINI_API_KEY, NVIDIA_API_KEY) * Maintains backward compatibility with existing command name Dependencies: - requirements-litellm.txt - LiteLLM and Google Generative AI packages - environment-litellm.yml - Conda environment for LiteLLM - Lighter weight than vLLM (no local model hosting required) Documentation: - LITELLM_MULTIMODAL_GUIDE.md - Comprehensive guide for LiteLLM usage * Google Gemini setup instructions * API key configuration * Model comparison (Flash vs Pro) * Cost estimation and optimization tips * Security and privacy considerations Example Configurations: - examples/vllm/litellm_config_gemini.yml - Gemini Flash (fast, efficient) - examples/vllm/litellm_config_gemini_pro.yml - Gemini Pro (most capable) - examples/vllm/litellm_config_nvidia.yml - NVIDIA models template - examples/vllm/example_litellm_inference.py - Python API examples Removed Files: - Old vLLM-specific files (vllm_infer.py, vllm configs, vllm guide) - No longer needed with API-based approach Benefits: - US-based models for security and compliance - No local GPU/CUDA requirements for inference - Production-ready APIs from Google and NVIDIA - Cost-effective (especially Gemini Flash) - Easier deployment and scaling - Regular model updates from providers Supported Use Cases: - Land use classification - Building and infrastructure detection - Environmental monitoring - Change detection analysis - General overhead imagery description API Key Setup: export GEMINI_API_KEY='your-key' # Get from https://makersuite.google.com/app/apikey export NVIDIA_API_KEY='your-key' # Get from https://build.nvidia.com/ 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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This commit adds comprehensive support for vision-language models (VLMs) using vLLM, enabling multimodal analysis of satellite and aerial imagery with natural language.
Features:
New files:
Modified files:
Use cases:
Note: vLLM requires Python 3.8+, PyTorch 2.0+, and CUDA 11.8+ Use environment-vllm.yml or requirements-vllm.txt for installation.
🤖 Generated with Claude Code
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