Merge pull request #307 from runpod-workers/fix/revert-0.20.0

revert to 0.20.2

.runpod/tests.json

@@ -30,7 +30,7 @@
     }
   ],
   "config": {
-    "gpuTypeId": "NNVIDIA L40",
+    "gpuTypeId": "NVIDIA L40",
     "gpuCount": 1,
     "env": [
       {

Dockerfile

@@ -8,20 +8,9 @@ ENV PATH="/root/.local/bin:$PATH"
 
 RUN ldconfig /usr/local/cuda-13.0/compat/
 
-# nixl_ep PyPI wheels are compiled against CUDA 12.x and require libcudart.so.12.
-# CUDA 13 runtime is ABI-compatible with CUDA 12, so symlinking is safe.
-# Symlink into /usr/local/cuda/lib64 (already in LD_LIBRARY_PATH) so the linker
-# finds it by filename scan rather than relying on ldcache SONAME lookup.
-RUN ln -sf /usr/local/cuda/lib64/libcudart.so.13 /usr/local/cuda/lib64/libcudart.so.12 && ldconfig
-
-# CUDA 13.0 containers return libs to /usr/local/nvidia/lib64 so container
-# providers (RunPod, Lambda, etc.) can mount host drivers there consistently.
-# See: https://github.com/vllm-project/vllm/issues/18859
-ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib64:/usr/local/cuda/lib64:$LD_LIBRARY_PATH
-
 # Install vLLM with FlashInfer - use CUDA 130 PyTorch wheels
 RUN uv pip install --system "packaging>=24.2" && \
-    uv pip install --system "vllm[flashinfer]==0.22.1" && \
+    uv pip install --system "vllm[flashinfer]==0.20.2" && \
     uv pip install --system git+https://github.com/deepseek-ai/DeepGEMM.git@714dd1a4a980f7937a74343d19a8eba4fe321480 --no-build-isolation
 
 # Install additional Python dependencies (after vLLM to avoid PyTorch version conflicts)

builder/requirements.txt

@@ -3,7 +3,7 @@ pandas
 pyarrow
 runpod==1.9.1
 huggingface-hub
-lmcache==0.4.6
+lmcache==0.4.5
 packaging>=24.2
 typing-extensions>=4.8.0
 pydantic