| # 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. |
| |
