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Signed-off-by: Harrison Saturley-Hall <hsaturleyhal@nvidia.com>

feat: enhance GPT OSS frontend with improved harmony tool calling parser and reasoning parser (#2999)
Signed-off-by: zhongdaor <zhongdaor@nvidia.com>

Signed-off-by: Harrison Saturley-Hall <hsaturleyhal@nvidia.com>

Signed-off-by: Harrison King Saturley-Hall <hsaturleyhal@nvidia.com>

Co-authored-by: Graham King <grahamk@nvidia.com>

Signed-off-by: Ryan Olson <ryanolson@users.noreply.github.com>

Signed-off-by: Neelay Shah <neelays@nvidia.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Co-authored-by: Olga Andreeva <124622579+oandreeva-nv@users.noreply.github.com>

Signed-off-by: Dan Aloni <dan.aloni@vastdata.com>

feat: add a hierarchical Prometheus MetricsRegistry trait for DistributedRuntime, Namespace, Components, and Endpoint (#2008)
Co-authored-by: Keiven Chang <keivenchang@users.noreply.github.com>
Co-authored-by: Ryan Olson <rolson@nvidia.com>

Remove http and llmctl binaries. They have been unused for a while.

Co-authored-by: Keiven Chang <keivenchang@users.noreply.github.com>

Allow both password or TLS auth, if none of these is provided fallback to no auth

Closes #657

New vllm and sglang engines that run in a sub-process. Will hopefully replace the existing embedded python engines.
    
Why?
    
  - Pure Python, does not require knowing Rust to work on it. Much simpler to maintain.
  - No embedded Python interpreter which avoids linking libpython and avoids the MacOS virtualenv issues.
  - Should have better performance as it's "native" vllm / sglang.
  - Works with any version of vllm (including v1!) and sglang. Less upgrade struggle.

Part of https://github.com/ai-dynamo/dynamo/issues/743

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

Signed-off-by: Hongkuan Zhou <tedzhouhk@gmail.com>
Co-authored-by: ishandhanani <82981111+ishandhanani@users.noreply.github.com>
Co-authored-by: ishandhanani <ishandhanani@gmail.com>
Co-authored-by: Ubuntu <ubuntu@dev-inst-2w1vokvyuts83rzn4n1k7mnzew9.us-central1-a.c.brevdevprod.internal>
Co-authored-by: Biswa Panda <biswa.panda@gmail.com>
Co-authored-by: Anant Sharma <anants@nvidia.com>

Signed-off-by: Harrison Saturley-Hall <454891+saturley-hall@users.noreply.github.com>

This will allow an ingress-side pre-processor to see it without needing a model checkout.

Currently pre-processing is done in the worker, which has access to the model deployment card ("MDC") files (`config.json`, `tokenizer.json` and `tokenizer_config.json`) locally. We want to move the pre-processor to the ingress side to support KV routing. That requires ingress side (i.e the HTTP server), on a different machine than the worker to be able to see those three files.

To support that this PR makes the worker upload the contents of those files to the NATS object store, and publishes the MDC with those NATS urls to the key-value store. 

The key-value store has an interface so any store (nats, etcd, redis, etc) can be supported. Implementations for memory and NATS are provided.

Fetching the MDC from the store, doing pre-processing ingress side, and publishing a card backed by a GGUF, are all for a later commit.

Part of #743