March 2026: Key deliveries across three repositories to stabilize, modernize, and extend inference capabilities in DJL and SageMaker ecosystems, with a clear focus on business value and developer efficiency. Key features/updates: - deepjavalibrary/djl-serving: Internal stability and code clarity improvements through a vLLM upgrade and tokenizer interface refactor. vLLM upgraded to 0.16.0 (commit de45404ec1f85dd5b1940500a061466184f632a7); tokenizer rename from AnyTokenizer to TokenizerLike (commit aede2e65b9282ae0b409a24c565d8c051fbd93eb). - aws/deep-learning-containers: LMI image configuration updates for SageMaker GPU inference, adding v21/v22 configurations and simplifying engine tags for consistency (commits 46bf3b5ffe41d5e69a5c248bbcd50259274fb583; eeb2d6ca324f09465180bccf81b7da8acee9dfb4; 8fd3f9bc3e6c90cdd9f727e170a189633edfd05e). Also DJL-serving compatibility and vLLM upgrade to drop support for DJL-serving < 0.32 and update vLLM across files (commit 42d7d614e73d9e8258da586587a4a5534d012867). - aws/sagemaker-python-sdk: SDK Image Version Alignment for SDK v3.x by using latest LMI images (LMI latest and LMIv22) to improve compatibility and feature access (commits 9e7c062b833a54d33edb372d61959deadf7503ac; 02e864dabc93922c5a80a4ae02c9bee425be0139). Overall impact: - Stability, clarity, and maintainability improvements reduce operational risk and simplify future feature adoption. - Expanded GPU inference support and compatibility across the DJL/SageMaker stack, enabling faster time-to-value for customers. - Strong alignment of SDK deployment images with the latest LMI assets to unlock new capabilities and smoother upgrade paths. Technologies/skills demonstrated: - Dependency management and targeted refactoring (vLLM, tokenizer interface) - Cloud image configuration and deployment pipelines (LMI configurations, tagging conventions) - Compatibility engineering and version gating (dropping legacy support for DJL-serving < 0.32) - Cross-repo traceability via conventional commits and cohesive release planning Business value: - Reduced risk of runtime instability, improved model serving clarity, and accelerated access to newer inference features for customers on SageMaker and DJL platforms.

February 2026 (2026-02) monthly summary for deepjavalibrary/djl-serving focused on production deployment performance and build reliability. Key features delivered: LMIv21 Production Deployment Performance Enhancements with updated VLLM wheel, tuned NVIDIA H100 configs, and aligned dependency versions. Major bugs fixed: Docker Build Stability Fix by pinning setuptools to a specific version to avoid installation issues. Overall impact: improved model serving throughput and reliability in production, plus more predictable container builds and smoother deployments. Technologies demonstrated: wheel management, container tuning for NVIDIA GPUs, Docker build stabilization, and dependency/version control.