GitLab

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

Co-authored-by: root <root@kkranen-dt.nvidia.com>

Removed the hard coded sleeps, explained what we're testing.

Closes https://github.com/ai-dynamo/dynamo/issues/1132

The race condition is that `apply_event` sends a message on a channel, it does not directly apply the event. At some later point the tokio runtime schedules the task running the channel receiver, which applies the event. If that had not happened yet the test would fail.

Llama 4 has a very large context length (aka n_ctx, model_max_length, max_model_len), and vllm won't start unless it can allocate enough KV cache for the entire context.

Allow passing `--context-length <N>` to `dynamo-run` to limit it so long-context models will fit.

Future todo:
- Restrict every request's `max_tokens` to below the context length. Our pre-processor should do this by setting stop_conditions.max_tokens. mistralrs engine wrapper must do it itself because it does not use the pre-processor.
- mistralrs and llamacpp currently have a hard-coded max context length if one is not provided on the command line. Change those to be the model's built-in max, read from the GGUF or tokenizer_config.json.

Co-authored-by: Suman Tatiraju <statiraju@statiraju-mlt.client.nvidia.com>

Previously any error would cause us to halt. Most of them are recoverable. So now we print the error and return to the prompt.

Co-authored-by: Hannah Zhang <hannahz@nvidia.com>

Signed-off-by: Suman Tatiraju <167138127+statiraju@users.noreply.github.com>
Signed-off-by: Anant Sharma <anants@nvidia.com>
Co-authored-by: Anant Sharma <anants@nvidia.com>
Co-authored-by: Dmitry Tokarev <dtokarev@nvidia.com>
Co-authored-by: ishandhanani <82981111+ishandhanani@users.noreply.github.com>
Co-authored-by: Kristen Kelleher <kkelleher@nvidia.com>
Co-authored-by: Suman Tatiraju <statiraju@statiraju-mlt.client.nvidia.com>
Co-authored-by: Hannah Zhang <hannahz@nvidia.com>

- Stop advertising a model when it's last instance stops. Previously was when any instance stops.
- Faster locks on model manager.
- Move discovery code out of http, as it is used by all inputs.

Signed-off-by: Yan Ru Pei <yanrpei@gmail.com>

Remove RouterType and ModelMetaData in `lib/runtime/src/protocols.rs`, which are unused (no outside reference). It is because that the routing has been moved to its own module, `pipeline/network/egress/push_router.rs`. Therefore, the legacy definition of RouterType in `protocols.rs` is no longer used.

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.

Co-authored-by: Rohan Varma <rohanv@rohanv-mlt.client.nvidia.com>
Co-authored-by: Julien Mancuso <jmancuso@nvidia.com>
Co-authored-by: julienmancuso <161955438+julienmancuso@users.noreply.github.com>

Implements OpenAI embeddings (interface only).

- Adds ModelType::Embedding
- Adds OpenAI embedding request/response structs
- Adds support for embedding model discovery