case-env

Clinical Agents for Seeking Evidence - the conda environment package for case-8b training and benchmark evaluation in the paper From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoning. A single compressed tar that extracts directly into a ready environment, bit-identical to the stack that produced all published case-8b runs - no dependency resolution, no rebuild.

Contents: Python 3.10, PyTorch 2.11.0 (CUDA 13.0), vLLM 0.26.0, transformers 5.14.1, Ray 2.55.1, LangGraph 1.2.9 (multi-turn tool-agent orchestration), verl 0.7.0 (rebuilt for agentic RL training: agent-loop state machine and related adaptations, based on upstream verl 0.7.0), flash-attn / FLA / FlashInfer stack
Size: 12.6 GB tar; about 12 GB on disk after extraction
Install: the tar's top-level directory is case/, and bin/ shebangs are pre-rewritten to /opt/conda/envs/case, so tar -xf case_env_0904.tar -C /opt/conda/envs yields a ready env named case.
Checksum: sha256 abf5016f171887449c41fb6d7aaec95e2eaa79439e35fd2443736f3713a8fea8
Verify: sha256sum -c case_env_0904.tar.sha256 then /opt/conda/envs/case/bin/python -c "import torch, vllm, verl"
Runtime exports (already baked into the released run scripts): PYTHONPATH=<repo>/scripts/vllm_patch and VLLM_USE_FLASHINFER_SAMPLER=0 FLA_CACHE_RESULTS=0 OMP_NUM_THREADS=16
Requirements: Linux x86_64, NVIDIA driver with CUDA 13.0 support (>= 580 series), system gcc (for Triton runtime JIT); ~25 GB free disk during install (12.6 GB tar + about 12 GB extracted)
Docker alternative: case_0904_image.tar.gz -> docker load -> docker run --gpus all -it case:0904 bash
Data: UK Biobank-derived data is access-controlled and not included in this environment. Research use only.