The NVIDIA Dynamo project uses containerized development and deployment to maintain consistent environments across different AI inference frameworks and deployment scenarios. This directory contains the tools for building and running Dynamo containers:
The NVIDIA Dynamo project uses containerized development and deployment to maintain consistent environments across different AI inference frameworks and deployment scenarios. This directory contains the tools for building and running Dynamo containers:
### Core Components
### Rendering Requirements:
-**`render.py`** - A render script used to generate Dockerfiles for AI inference frameworks (vLLM, TensorRT-LLM, SGLang). The generated Dockerfile includes the needed multi-stage steps for development vs production configurations
-**`run.sh`** - A container runtime manager that launches Docker containers with proper GPU access, volume mounts, and environment configurations. It supports different development workflows from root-based legacy setups to user-based development environments.
## Rendering Requirements:
- Python
- Python
- Python Packages:
- Python Packages:
- pyyaml
- pyyaml
- jinja2
- jinja2
### Core Components
-**`render.py`** - A render script used to generate Dockerfiles for AI inference frameworks (vLLM, TensorRT-LLM, SGLang) and the frontend image. The generated Dockerfile includes the needed multi-stage steps for development vs production configurations.
-**`run.sh`** - A container runtime manager that launches Docker containers with proper GPU access, volume mounts, and environment configurations. It supports different development workflows from root-based legacy setups to user-based development environments.
### Stage Summary for Frameworks
### Stage Summary for Frameworks
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<details>
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### 1. runtime target (runs as non-root dynamo user):
### 1. runtime target (runs as non-root dynamo user):