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+1
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@@ -935,7 +935,7 @@
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"name": "Max Concurrency",
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"type": "number",
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"description": "Max concurrent requests per worker. vLLM has an internal queue, so you don't have to worry about limiting by VRAM, this is for improving scaling/load balancing efficiency",
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"default": 300,
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"default": 30,
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"advanced": true
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}
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},
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@@ -12,7 +12,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
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python3 -m pip install --upgrade -r /requirements.txt
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# Install vLLM (switching back to pip installs since issues that required building fork are fixed and space optimization is not as important since caching) and FlashInfer
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RUN python3 -m pip install vllm==0.10.0 && \
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RUN python3 -m pip install vllm==0.11.0 && \
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python3 -m pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.3
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# Setup for Option 2: Building the Image with the Model included
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@@ -57,7 +57,7 @@ Configure worker-vllm using environment variables:
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| `ENABLE_AUTO_TOOL_CHOICE` | Enable automatic tool selection | false | boolean (true or false) |
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| `TOOL_CALL_PARSER` | Parser for tool calls | | "mistral", "hermes", "llama3_json", "granite", "deepseek_v3", etc. |
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| `OPENAI_SERVED_MODEL_NAME_OVERRIDE` | Override served model name in API | | String |
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| `MAX_CONCURRENCY` | Maximum concurrent requests | 300 | Integer |
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| `MAX_CONCURRENCY` | Maximum concurrent requests | 30 | Integer |
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For the complete list of all available environment variables, examples, and detailed descriptions: **[Configuration](docs/configuration.md)**
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@@ -119,7 +119,7 @@ The way this works is that the first request will have a batch size of `DEFAULT_
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| Variable | Default | Type/Choices | Description |
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| ---------------------- | ------- | ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `MAX_CONCURRENCY` | `300` | `int` | Max concurrent requests per worker. vLLM has an internal queue, so you don't have to worry about limiting by VRAM, this is for improving scaling/load balancing efficiency |
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| `MAX_CONCURRENCY` | `30` | `int` | Max concurrent requests per worker. vLLM has an internal queue, so you don't have to worry about limiting by VRAM, this is for improving scaling/load balancing efficiency |
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| `DISABLE_LOG_STATS` | False | `bool` | Enables or disables vLLM stats logging. |
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| `DISABLE_LOG_REQUESTS` | False | `bool` | Enables or disables vLLM request logging. |
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+3
-2
@@ -51,7 +51,8 @@ RunPod Request → handler.py → JobInput → Engine Selection → vLLM Generat
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- `src/engine_args.py`: Centralized configuration management
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- `src/constants.py`: Default values for core settings
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- `worker-config.json`: UI form generation for RunPod console
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- `.runpod/hub.json`: Hub UI configuration (CRITICAL: always update when changing defaults)
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- `worker-config.json`: UI form generation for RunPod console (if exists)
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## Core Development Concepts
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@@ -222,7 +223,7 @@ src/
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### 2. **Concurrency Patterns**
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- **Max Concurrency**: 300 concurrent requests by default
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- **Max Concurrency**: 30 concurrent requests by default
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- **vLLM Queuing**: Internal request batching and scheduling
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- **RunPod Integration**: Concurrency modifier for auto-scaling
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+1
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@@ -1,4 +1,4 @@
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DEFAULT_BATCH_SIZE = 50
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DEFAULT_MAX_CONCURRENCY = 300
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DEFAULT_MAX_CONCURRENCY = 30
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DEFAULT_BATCH_SIZE_GROWTH_FACTOR = 3
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DEFAULT_MIN_BATCH_SIZE = 1
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-1514
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