Merge pull request #301 from adithyaJRunpod/feature/tuned-configs

Add tuned configs,  CON-239

.runpod/README.md

@@ -33,6 +33,17 @@ All behaviour is controlled through environment variables:
 
 **Pass any vLLM engine arg** not listed above by setting an env var with the **UPPERCASED** field name (e.g. `MAX_MODEL_LEN=4096`, `ENABLE_CHUNKED_PREFILL=true`). The worker auto-discovers all `AsyncEngineArgs` fields from env. See the [vLLM engine args docs](https://docs.vllm.ai/en/latest/configuration/engine_args) for all available options.
 
+**Configuration file:** You can also supply a `config.yaml` instead of (or alongside) env vars. Mount it at `/vllm_config.yaml` in the container, or set `VLLM_CONFIG_FILE` to a custom path. Use the same key names as `vllm serve` — hyphens and underscores both work:
+
+```yaml
+model: meta-llama/Llama-3.1-8B-Instruct
+max-model-len: 8192
+gpu-memory-utilization: 0.90
+quantization: awq
+```
+
+Environment variables always override config file values.
+
 For complete configuration options, see the [full configuration documentation](https://github.com/runpod-workers/worker-vllm/blob/main/docs/configuration.md).
 
 ### Specify Transformers Version

Dockerfile

@@ -8,9 +8,20 @@ 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]==0.20.2" && \
+    uv pip install --system "vllm[flashinfer]==0.21.0" && \
     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)

README.md

@@ -78,6 +78,20 @@ Configure worker-vllm using environment variables:
 
 Any env var whose name matches a valid `AsyncEngineArgs` field (uppercased) is applied automatically. Backward-compat aliases: `MODEL_NAME`, `TOKENIZER_NAME`, `MAX_CONTEXT_LEN_TO_CAPTURE`. This lets you configure any vLLM option without waiting for explicit worker support.
 
+### Configuration File (config.yaml)
+
+As an alternative to environment variables, you can supply a `config.yaml` file using the same key names as `vllm serve` (hyphens or underscores both work):
+
+```yaml
+model: meta-llama/Llama-3.1-8B-Instruct
+max-model-len: 8192
+gpu-memory-utilization: 0.90
+quantization: awq
+tensor-parallel-size: 2
+```
+
+Mount the file into the container at `/vllm_config.yaml`, or point to a custom path with the `VLLM_CONFIG_FILE` env var. Environment variables always take precedence over config file values.
+
 For the complete list of all available environment variables, examples, and detailed descriptions: **[Configuration](docs/configuration.md)**
 
 ### Specify Transformers Version

builder/requirements.txt

@@ -1,9 +1,9 @@
 ray
 pandas
 pyarrow
-runpod==1.9.0
+runpod==1.9.1
 huggingface-hub
-lmcache==0.4.5
+lmcache==0.4.6
 packaging>=24.2
 typing-extensions>=4.8.0
 pydantic

configs/llama/llama3.1_8b_instruct.yaml

@@ -0,0 +1,10 @@
+model: meta-llama/Llama-3.1-8B-Instruct
+gpu-memory-utilization: 0.95
+max-model-len: 8192
+dtype: auto
+trust-remote-code: true
+quantization: fp8
+kv-cache-dtype: fp8
+enforce-eager: false
+enable-prefix-caching: true
+speculative-config: '{"model":"RedHatAI/Llama-3.1-8B-Instruct-speculator.eagle3","method":"eagle3","num_speculative_tokens":3}'

configs/qwen/qwen3_8b.yaml

@@ -0,0 +1,10 @@
+model: Qwen/Qwen3-8B
+gpu-memory-utilization: 0.95
+max-model-len: 8192
+dtype: auto
+trust-remote-code: true
+quantization: fp8
+kv-cache-dtype: fp8
+enforce-eager: false
+enable-prefix-caching: true
+speculative-config: '{"model":"RedHatAI/Qwen3-8B-speculator.eagle3","method":"eagle3","num_speculative_tokens":3}'

src/engine_args.py

@@ -404,6 +404,23 @@ def _resolve_cached_model_path(model_name: str) -> str:
     return resolved
 
 
+def _get_args_from_config_file() -> dict:
+    """Load engine args from a vLLM-style config.yaml.
+
+    Checks VLLM_CONFIG_FILE env var, then falls back to /vllm_config.yaml.
+    Keys use the same long-form names as vllm serve (hyphens converted to underscores).
+    """
+    import yaml
+    path = os.getenv("VLLM_CONFIG_FILE", "/vllm_config.yaml")
+    if not os.path.exists(path):
+        return {}
+    with open(path) as f:
+        raw = yaml.safe_load(f) or {}
+    normalized = {k.replace("-", "_"): v for k, v in raw.items()}
+    logging.info("Loaded engine args from config file %s: %s", path, list(normalized.keys()))
+    return normalized
+
+
 def get_local_args():
     """
     Retrieve local arguments from a JSON file.
@@ -429,6 +446,9 @@ def get_engine_args():
     # Start with worker custom defaults (only where we differ from vLLM)
     args = dict(DEFAULT_ARGS)
 
+    # Config file values sit above defaults but below env vars
+    args.update(_get_args_from_config_file())
+
     # Auto-discover: every AsyncEngineArgs field from env UPPERCASED (e.g. MAX_MODEL_LEN)
     args.update(_get_args_from_env_auto_discover())