Move much of what was in the `dynamo-run` crate into `dynamo-llm` so that everyone can use it.
Example usage:
1. Create a `LocalModel`:
```
let local_model = LocalModelBuilder::default()
.model_path("Qwen/Qwen3-0.6B")
.http_port(8080)
.build().await?;
```
2. Make an engine:
```
let engine_config = EngineConfig::StaticFull {
engine: dynamo_engine_mistralrs::make_engine(&local_model).await?,
model: Box::new(local_model),
};
```
3. Connect it to an input and run it
```
dynamo_llm::entrypoint::input::run_input(Input::Http, runtime, engine_config).await?;
```
For https://github.com/ai-dynamo/dynamo/issues/1647
Code Rabbit summary, thanks:
* Introduced a flexible builder pattern for local model configuration, allowing advanced customization and easier initialization.
* Added new input modes and unified input handling, supporting interactive chat, HTTP server, batch file, and distributed endpoint modes.
* Centralized engine configuration and routing, enabling more extensible and maintainable engine management.
* Simplified and modularized the codebase by moving input and engine logic into dedicated modules.
* Replaced direct model construction with an asynchronous builder for improved clarity and extensibility.
* Streamlined configuration and validation for flags and router settings.
* Added validation to prevent incompatible input and output combinations in endpoint and dynamic modes.
Example:
```
dynamo-run out=<engine> <model> --kv-cache-block-size 64
```
In a distributed system this goes on the worker node and is propagated to ingress via the model deployment card.
Previously hard coded to 16, which is now the default.
- Load context_length from model. Closes#1172
- Store context length and KV cache block size in Model Deployment Card #1170
We can now do this:
- Node 1:
```
dynamo-run in=http out=dyn
```
- Node 2 and 3, two instances of component 'backend' in the nemotron_ultra pipeline:
```
dynamo-run in=dyn://nemotron_ultra.backend.generate out=vllm /data/models/NemotronUltra
```
- Node 4 and 5, two instances of the 'backend' component in nemotron_super pipeline:
```
dynamo-run in=dyn://nemotron_super.backend.generate out=vllm /data/models/NemotronSuper
```
The ingress node will discover all four instances and route correctly. We have been planning for this for a long time now.
As part of this auto-discovery is now always `out=dyn`, with no extra URL parts. Previously it could only route to a single pipeline.
Also:
- Refactor endpoint / instance naming now that I understand them
- Fix removing models when their instance stops.
Router:
```
dynamo-run in=http out=dyn://dynamo.endpoint.generate --router-mode kv
```
Worker (* N):
```
dynamo-run in=dyn://dynamo.endpoint.generate out=vllm /data/llms/Qwen/Qwen3-4B
```
You need patched vllm and the C bindings `.so`. Full docs in the updated guide: `docs/guides/dynamo_run.md`.
This gives us a pure-Rust ingress node: OpenAI compliant HTTP server + Pre-processor + KV-aware router.
Adding this to a Python script makes it register on the network so that `dynamo-run` can discover it and send it requests:
```
from dynamo.llm import register_llm
MODEL = "Qwen/Qwen2.5-0.5B-Instruct"
await register_llm(endpoint, MODEL, 3)
```
Full vllm example, with pre-processing in dynamo:
- `dynamo-run in=text out=dyn://dynamo.backend.generate`
- `cd lib/bindings/python/examples/hello_world`
- `python server_vllm.py`
This builds on top of the work to move pre-processor to ingress side. It means we can decouple Rust and Python using NATS as the bus.
The `register_llm` call does this:
- Download the model from HF if necessary
- Load the model deployment card from the HF folder or extract from GGUF
- Push the tokenizer config etc into NATS object store so ingress can access it from a different machine
- Publish the model deployment card to ETCD
In a distributed system we don't know if the remote workers need pre-processing done ingress-side or not. Previously Client required us to decide this before discovering the remote endpoints, which was fine because pre-processing was worker-side.
As part of moving pre-processing back to ingress-side we need to split this into two steps:
- Client discovers the endpoints, and (later PR) will fetch their Model Deployment Card.
- PushRouter will use the Model Deployment Card to decide if they need pre-processing or not, which affects the types of the generic parameters.
Part of #743
Keiven C