diff --git a/.copyrightignore b/.copyrightignore
index f72bdbad..7a83eb59 100644
--- a/.copyrightignore
+++ b/.copyrightignore
@@ -12,3 +12,5 @@ CHANGELOG.md
.cursor/
.claude/
.agent/
+skills/anonymizer/BENCHMARK.md
+skills/anonymizer/skill-card.md
diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 00000000..339c1356
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,14 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+.git
+.venv
+.cache
+.pytest_cache
+.ruff_cache
+.ty_cache
+.anonymizer-artifacts
+.inference-service-runs
+site
+**/__pycache__
+**/*.pyc
diff --git a/.gitignore b/.gitignore
index a5bc84b5..deef06d2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -59,6 +59,7 @@ uv.lock.bak
# Environments
.env
.env.*
+.mise.local.toml
env/
venv/
ENV/
diff --git a/AGENTS.md b/AGENTS.md
index f71af905..19fba613 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -123,4 +123,4 @@ make docs-serve # local MkDocs server at http://127.0.0.1:8000
For contributor workflow and branch naming see [CONTRIBUTING.md](CONTRIBUTING.md). For local setup, tests, docs, and day-to-day development tasks see [DEVELOPMENT.md](DEVELOPMENT.md). For code style and naming conventions see [STYLEGUIDE.md](STYLEGUIDE.md).
-Reference GLiNER server for self-hosted detection: [`tools/serve_gliner.py`](tools/serve_gliner.py) — see [`docs/concepts/self-hosting-gliner.md`](docs/concepts/self-hosting-gliner.md).
+Local GLiNER and vLLM services are compiled and managed through [`tools/inference_service.py`](tools/inference_service.py) — see [`docs/concepts/inference-services.md`](docs/concepts/inference-services.md) and [`docs/concepts/self-hosting-gliner.md`](docs/concepts/self-hosting-gliner.md).
diff --git a/README.md b/README.md
index 28ff3144..103f5470 100644
--- a/README.md
+++ b/README.md
@@ -154,6 +154,24 @@ anonymizer --help # CLI usage
make install-pre-commit # Install pre-commit hooks
```
+### Local inference services
+
+Use the source-tree local-model deployment tool to create checksummed plans and
+managed launch receipts for GLiNER or GLiNER2 through the pinned external vLLM
+Factory project, or for vLLM generation:
+
+```bash
+uv run tools/inference_service.py compile --profile tools/inference_service_profiles/nvidia-gliner.toml --source-revision 3f68c145 --output plan.json
+uv run tools/inference_service.py launch --plan plan.json --output launch.json
+uv run tools/inference_service.py status --receipt launch.json
+uv run tools/inference_service.py stop --receipt launch.json
+```
+
+The tool is not part of the wheel and does not attach to externally owned
+endpoints. The [local model deployment guide](docs/concepts/inference-services.md)
+covers typed TOML profiles, direct GPU-host setup, a container-based deployment,
+capability probes, cleanup, and Anonymizer provider configuration.
+
---
## Requirements
diff --git a/docs/concepts/inference-services.md b/docs/concepts/inference-services.md
new file mode 100644
index 00000000..817c82d5
--- /dev/null
+++ b/docs/concepts/inference-services.md
@@ -0,0 +1,290 @@
+
+
+
+# Deploy Local Models
+
+NeMo Anonymizer can run its detector and generation roles against models on
+your own NVIDIA GPU. The repository includes pinned profiles and a lifecycle
+tool for this purpose. The tool compiles a profile into a frozen, checksummed
+plan, starts the service, checks readiness, and records the local process handle
+it observed so it can later report status or request cleanup.
+
+This path suits development workstations, on-premises servers, and isolated
+environments where text must stay on local infrastructure. It manages one
+deployment shape: a vLLM process on the machine where the tool runs. You can
+run that process directly on the host or inside the supplied GPU container.
+
+The tool lives under `tools/` and is not installed with the
+`nemo-anonymizer` wheel. Start from a source checkout.
+
+## Choose models
+
+Nine profiles ship in `tools/inference_service_profiles/`:
+
+| Profile | Endpoint model name | Role and hardware guidance |
+| --- | --- | --- |
+| `nvidia-gliner.toml` | `nvidia/gliner-pii` | Default PII detector; can share an 80 GB GPU with a 20B or 30B generator |
+| `gliner2.toml` | `fastino/gliner2-privacy-filter-PII-multi` | Alternative multilingual PII detector |
+| `vllm-local.toml` | `anonymizer-local` | TinyLlama lifecycle smoke test |
+| `gemma-4-12b-it.toml` | `gemma-4-12b-it-local` | Instruction-tuned Gemma 4 generation; requires an NVIDIA GPU with at least 40 GB of memory |
+| `gpt-oss-20b.toml` | `gpt-oss-20b-local` | Compact GPT-OSS generation |
+| `gpt-oss-120b.toml` | `gpt-oss-120b-local` | GPT-OSS generation on a dedicated 80 GB GPU |
+| `qwen3-30b-a3b-instruct.toml` | `qwen3-30b-a3b-instruct-local` | Multilingual generation |
+| `nemotron-3.5-lightning.toml` | `nemotron-3.5-lightning-local` | High-throughput generation on an 80 GB GPU |
+| `nemotron-3.5-lightning-nvfp4.toml` | `nemotron-3.5-lightning-nvfp4-local` | Official NVFP4 checkpoint with a conservative single-H100 configuration |
+
+Each profile pins its Hugging Face revision. Generation profiles use stock
+vLLM. The two detector profiles use the pinned external
+[vLLM Factory](https://github.com/latenceainew/vllm-factory) integration and
+Anonymizer's OpenAI-compatible detector adapter.
+
+The Gemma profile uses the instruction-tuned
+[`google/gemma-4-12B-it`](https://huggingface.co/google/gemma-4-12B-it)
+checkpoint and bounds its larger native context window to 8,192 tokens. The
+Nemotron NVFP4 profile uses NVIDIA's
+[`NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4`](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4)
+checkpoint. vLLM detects its ModelOpt quantization from the checkpoint, so the
+profile does not request online quantization.
+
+GPU memory also depends on the architecture, context length, concurrency, and
+other processes. Lower `max_model_len`, `max_num_seqs`, or
+`gpu_memory_utilization` when vLLM cannot reserve its KV cache. Do not co-host
+the GPT-OSS 120B profile with another model on an 80 GB GPU.
+
+## Understand the lifecycle
+
+The workflow has two durable files:
+
+```text
+profile.toml -> compile -> plan.json -> launch -> launch.json
+ | |
+ +-> probe +-> status
+ +-> stop
+```
+
+`compile` has no runtime effects. The plan contains the full command, endpoint,
+dependencies, compatibility assessments, and a SHA-256 digest. Runtime commands
+verify that digest before acting.
+
+The digest detects accidental changes while a plan is stored or transported.
+This developer tool trusts whoever can write the profile and plan: it does not
+authenticate the plan's author, sign the plan, or recompile the embedded spec to
+prove that every derived field is semantically consistent.
+
+`launch` first requires a launch-scoped ownership proof from the new process,
+then waits for `/v1/models` and a task-specific request. The ownership check
+prevents an existing compatible service on the target port from satisfying a
+new launch. A generation service must return a chat completion. A detector
+must demonstrate dynamic labels, offsets, and scores. The launch receipt
+records the process group and Linux start marker so later commands do not
+signal a reused PID or an unrelated process group.
+
+The v2 profile schema has four sections:
+
+- `[task]` selects generation or entity detection and its required capabilities.
+- `[model]` pins the Hugging Face model and optional LoRA adapter.
+- `[vllm]` sets the served name, memory limits, parallelism, caching, Mamba controls, authentication source, and optional Factory plugin.
+- `[local]` sets the bind address, port, startup timeout, and shutdown timeout.
+
+## Deploy on the GPU host
+
+### Install
+
+Install [uv](https://docs.astral.sh/uv/) and sync the local-model dependency
+group with Python 3.12:
+
+```bash
+uv sync --python 3.12 --group dev --group local-models
+uv run --python 3.12 python -m vllm_factory.compat.doctor
+nvidia-smi
+```
+
+The lockfile pins vLLM 0.27.1, vLLM Factory, and the CUDA compiler wheels used
+by the Nemotron FlashInfer profile.
+
+### Compile and launch
+
+Run all commands from the repository root. This example starts NVIDIA GLiNER
+on `127.0.0.1:8001`:
+
+```bash
+uv run --python 3.12 python tools/inference_service.py compile \
+ --profile tools/inference_service_profiles/nvidia-gliner.toml \
+ --source-revision "$(git rev-parse HEAD)" \
+ --output gliner-plan.json
+
+uv run --python 3.12 python tools/inference_service.py launch \
+ --plan gliner-plan.json \
+ --output gliner-launch.json \
+ --log-directory .inference-service-runs
+```
+
+The command returns after the ownership and readiness checks pass. Keep
+`gliner-launch.json`: it is an unsigned durable operation record containing the
+observed handle and its consistency fingerprint. The tool validates the output
+destination before launch and publishes file receipts with atomic replacement.
+If publication still fails, it makes a bounded attempt to stop the launched
+process. Later status and stop operations re-check the exact PID, Linux start
+marker, and process group.
+
+### Operate the service
+
+```bash
+uv run --python 3.12 python tools/inference_service.py status \
+ --receipt gliner-launch.json
+
+uv run --python 3.12 python tools/inference_service.py probe \
+ --plan gliner-plan.json
+
+curl -sf http://127.0.0.1:8001/v1/models | python -m json.tool
+
+uv run --python 3.12 python tools/inference_service.py stop \
+ --receipt gliner-launch.json
+```
+
+`stop` checks the recorded PID, start marker, and live process group before
+sending `SIGTERM`, waits for the profile's shutdown timeout, and uses `SIGKILL`
+if the process remains alive.
+
+## Deploy in a GPU container
+
+The supplied container image installs the same locked local-model environment.
+The container stays alive as the deployment boundary. The lifecycle tool still
+manages a local process inside that boundary, so plans and receipts keep the
+same schema as a host deployment.
+
+### Prerequisites
+
+Install Docker, the NVIDIA driver, and
+[NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).
+Confirm that Docker can expose the GPU:
+
+```bash
+docker run --rm --gpus all \
+ nvidia/cuda:13.0.2-base-ubuntu24.04 nvidia-smi
+```
+
+### Build the image
+
+From the repository root:
+
+```bash
+docker build \
+ --file tools/inference_service.Dockerfile \
+ --tag nemo-anonymizer-local-models:dev \
+ .
+```
+
+The build uses the repository lockfile. It downloads several large GPU wheels,
+so allow enough disk space for the image and model cache.
+
+### Start the deployment container
+
+Create host directories for receipts, logs, prepared Factory models, and the
+Hugging Face cache:
+
+```bash
+mkdir -p .local-model-deployment/state
+mkdir -p .local-model-deployment/factory
+mkdir -p "$HOME/.cache/huggingface"
+
+docker run --detach \
+ --name anonymizer-local-models \
+ --gpus all \
+ --ipc host \
+ --network host \
+ --volume "$PWD/.local-model-deployment/state:/state" \
+ --volume "$PWD/.local-model-deployment/factory:/tmp/anonymizer-vllm-factory" \
+ --volume "$HOME/.cache/huggingface:/models/huggingface" \
+ nemo-anonymizer-local-models:dev
+```
+
+Host networking is required because the pinned profiles bind to `127.0.0.1`.
+It is supported by Docker Engine on Linux. The profile port must be free on the
+host. The default profiles do not authenticate requests, so keep the bind local
+or place the service behind authenticated TLS before exposing it.
+
+### Compile and launch inside the container
+
+```bash
+SOURCE_REVISION="$(git rev-parse HEAD)"
+docker exec anonymizer-local-models \
+ python tools/inference_service.py compile \
+ --profile tools/inference_service_profiles/nvidia-gliner.toml \
+ --source-revision "$SOURCE_REVISION" \
+ --output /state/gliner-plan.json
+
+docker exec anonymizer-local-models \
+ python tools/inference_service.py launch \
+ --plan /state/gliner-plan.json \
+ --output /state/gliner-launch.json \
+ --log-directory /state/logs
+```
+
+The mounted `/state` directory makes the plan, launch receipt, and logs visible
+on the host. Operate the service through `docker exec`:
+
+```bash
+docker exec anonymizer-local-models \
+ python tools/inference_service.py status \
+ --receipt /state/gliner-launch.json
+
+docker exec anonymizer-local-models \
+ python tools/inference_service.py probe \
+ --plan /state/gliner-plan.json
+
+curl -sf http://127.0.0.1:8001/v1/models | python -m json.tool
+```
+
+### Stop cleanly
+
+Stop each managed service before removing the container:
+
+```bash
+docker exec anonymizer-local-models \
+ python tools/inference_service.py stop \
+ --receipt /state/gliner-launch.json
+
+docker stop anonymizer-local-models
+docker rm anonymizer-local-models
+```
+
+Stopping the container first removes the runtime boundary before the tool can
+write a stop receipt. Use `stop` first when lifecycle evidence or
+graceful model shutdown matters.
+
+## Connect Anonymizer
+
+Add the local endpoint to a provider file:
+
+```yaml title="providers.yaml"
+providers:
+ - name: local-gliner
+ endpoint: http://127.0.0.1:8001/v1
+ provider_type: openai
+ api_key: EMPTY
+```
+
+Then route the detector alias to that provider in your model configuration:
+
+```yaml title="models.yaml"
+model_configs:
+ - alias: gliner-pii-detector
+ model: nvidia/gliner-pii
+ provider: local-gliner
+ inference_parameters:
+ max_parallel_requests: 8
+ timeout: 120
+```
+
+Custom `model_configs` replaces the full bundled model pool. Copy the bundled
+configuration and change only the roles you are deploying locally, or define
+every alias needed by the selected pipeline. See
+[Custom models](models.md#custom-models) for role mapping and complete examples.
+
+For the detector request and response contract, chunking behavior, and a live
+PII request, see [Self-hosting GLiNER](self-hosting-gliner.md).
+
+Compilation records static compatibility assessments. The launch probe observes
+the endpoint shape and required task behavior. Run Anonymizer preview and
+evaluation before accepting a model for a privacy-sensitive workload.
diff --git a/docs/concepts/models.md b/docs/concepts/models.md
index f7646e7f..40b7dbf2 100644
--- a/docs/concepts/models.md
+++ b/docs/concepts/models.md
@@ -36,6 +36,11 @@ Each pipeline stage has a **role** mapped to one of these aliases. See the full
Pass `model_providers` when you need a non-default endpoint — for example OpenAI, OpenRouter, a local GLiNER server, or an internal inference deployment. Plain `Anonymizer()` already uses bundled [build.nvidia.com](https://build.nvidia.com) settings; override only when your models point at a different provider name or URL.
+For managed GLiNER through vLLM Factory and vLLM generation endpoints, see
+[Deploy local models](inference-services.md). That guide covers host and GPU
+container deployment, immutable plans, capability receipts, cleanup, and
+provider configuration.
+
Set your API keys first:
```bash
diff --git a/docs/concepts/self-hosting-gliner.md b/docs/concepts/self-hosting-gliner.md
index 9e7cc42f..8ebc0ce7 100644
--- a/docs/concepts/self-hosting-gliner.md
+++ b/docs/concepts/self-hosting-gliner.md
@@ -5,9 +5,15 @@
By default, Anonymizer's entity detection stage calls the hosted `nvidia/gliner-pii` model on `build.nvidia.com`. For PHI-sensitive workloads that cannot leave the host, or latency-critical setups, you can serve GLiNER locally instead.
-The model is small (~500 MB) and runs comfortably on CPU — making it a good fit to run alongside a local LLM without competing for GPU memory. It also runs on GPU if one is available, which cuts detection latency on long documents.
+The default NVIDIA GLiNER model is small enough to share a GPU with a local
+LLM. The optional GLiNER2 PII model is also fully local. The pinned reference
+profiles serve both models through vLLM 0.27.1 and the external
+[vLLM Factory](https://github.com/latenceainew/vllm-factory) project.
-The reference server script (`tools/serve_gliner.py`) is **not** installed with `pip install nemo-anonymizer` — get it from a source checkout of this repository (see **Running it** below).
+The characterized services live inside the source-tree
+[local-model deployment tool](inference-services.md). They are **not** installed
+with `pip install nemo-anonymizer`; compile and launch them from a source
+checkout.
---
@@ -52,81 +58,91 @@ Long inputs are split into overlapping chunks before inference. A self-hosted se
## Reference implementation
-A minimal FastAPI reference server at [`tools/serve_gliner.py`](https://github.com/NVIDIA-NeMo/Anonymizer/blob/main/tools/serve_gliner.py) in the Anonymizer GitHub repository implements the contract above. It loads `nvidia/gliner-pii`, exposes `POST /v1/chat/completions` (and `GET /v1/models`), and uses two levels of batching:
-
-1. **Chunk batching** — long text is split into overlapping windows; all chunks are passed to one `model.inference(...)` call.
-2. **Request coalescing** (optional, on by default) — concurrent HTTP requests from DataDesigner are grouped briefly, then all their chunks are inferred together.
-
-```python title="tools/serve_gliner.py (excerpt)"
-@app.post("/v1/chat/completions")
-async def chat_completions(request: Request):
- body = await request.json()
- text = _extract_text(body.get("messages", []))
- params = DetectParams(
- labels=tuple(body.get("labels") or []),
- threshold=float(body.get("threshold", 0.3)),
- chunk_length=int(body.get("chunk_length", 384)),
- overlap=int(body.get("overlap", 128)),
- flat_ner=bool(body.get("flat_ner", False)),
- inference_batch_size=int(body.get("batch_size", 8)),
- )
- entities = await detector.detect(text, params)
+The pinned profiles compile a vLLM Factory integration. The external project
+prepares each GLiNER checkpoint for vLLM, registers the model implementation,
+preprocesses requests through an IOProcessor, schedules pooling inference, and
+decodes the output. Its native endpoint is `POST /pooling`.
+
+Anonymizer adds a thin middleware function inside the same vLLM process. It
+translates `POST /v1/chat/completions` into in-process pooling calls and restores
+the detector response shape. The adapter does not load model weights or run
+inference itself. It handles two wire-level responsibilities:
+
+1. **Chunk submission**: long text is split into overlapping character windows,
+ then each window enters vLLM's scheduler.
+2. **Response normalization**: GLiNER and GLiNER2 results become one list of
+ entities with document offsets and overlap deduplication.
+
+```python title="tools/inference_service_compiler/vllm_factory_adapter.py (excerpt)"
+results = await invoke_pooling_chunks(
+ handler=handler,
+ detection=detection,
+ chunks=chunks,
...
+)
+entities = merge_entities(plugin=plugin, chunks=chunks, results=results, ...)
```
-When `flat_ner` is `false` (Anonymizer's default), the server removes nested subset spans before score-based deduplication across chunk overlaps.
+The adapter rejects a labeled request that would produce more than 256 chunks
+before it materializes chunk text or calls vLLM. The service runs at most eight
+pooling calls concurrently across all accepted requests; the remaining
+admitted chunks wait for capacity.
-| Environment variable | Default | Purpose |
-|---|---|---|
-| `DEVICE` | `auto` | `auto`, `cuda`, `cpu`, or `mps` (Apple Silicon GPU) |
-| `GLINER_BATCH_MODE` | `true` | Coalesce concurrent HTTP requests before inference |
-| `GLINER_MAX_BATCH_REQUESTS` | `32` | Max requests per coalesced batch |
-| `GLINER_BATCH_WAIT_MS` | `10` | Max wait time to fill a batch (milliseconds) |
+When `flat_ner` is `false` (Anonymizer's default), the adapter removes nested
+subset spans before score-based deduplication across chunk overlaps. A request
+without `labels` returns an empty entity list so DataDesigner's generic model
+health check can validate the endpoint without running meaningless inference.
-Set `GLINER_BATCH_MODE=false` to disable request coalescing; chunk batching still runs per request.
+The pinned vLLM Factory profiles are the only supported local detector runtime.
+Native Transformers GLiNER fallback serving is intentionally not included.
---
-## Running it
+## Deploying it
!!! note "Source checkout only"
- `tools/serve_gliner.py` ships in the [Anonymizer GitHub repository](https://github.com/NVIDIA-NeMo/Anonymizer), not in the `nemo-anonymizer` wheel. Clone the repo or download the file, then run it from that tree:
-
- ```bash
- git clone https://github.com/NVIDIA-NeMo/Anonymizer.git
- cd Anonymizer
- pip install fastapi uvicorn gliner
- python tools/serve_gliner.py
- ```
+ `tools/inference_service.py` ships in the [Anonymizer GitHub repository](https://github.com/NVIDIA-NeMo/Anonymizer), not in the `nemo-anonymizer` wheel. Clone the repository and run the compiler from its root.
### Dependencies
+Install [uv](https://docs.astral.sh/uv/) and sync the local model group on a
+Linux GPU host:
+
```bash
-pip install fastapi uvicorn gliner
-# or with uv
-uv pip install fastapi uvicorn gliner
+uv sync --python 3.12 --group dev --group local-models
+python -m vllm_factory.compat.doctor
```
-On first launch the `gliner` package will download `nvidia/gliner-pii` from HuggingFace and cache it under `~/.cache/huggingface/`. No HuggingFace token is required (public model).
+The group pins `vllm==0.27.1` and vLLM Factory at the exact source revision
+recorded in `pyproject.toml` and `uv.lock`. The doctor must report the general
+plugins group, IOProcessor plugins group, and native IO mode. Local vLLM 0.27.1
+serving requires Python 3.12 or later.
-### Start the server
+On first launch, the selected public checkpoint is downloaded from Hugging Face
+and cached under `~/.cache/huggingface/`. The integration supplies the TOML
+profile's immutable revision to vLLM Factory's preparation API and writes a
+provenance record beside the prepared model. Package and checkpoint setup use
+the network; inference stays local after setup.
-```bash
-python tools/serve_gliner.py
-# INFO Uvicorn running on http://127.0.0.1:8001
+### Start the server
-# Optional: override port (default: 8001)
-python tools/serve_gliner.py --port 9000
+Compile the pinned vLLM Factory GLiNER TOML profile and launch the resulting
+plan as shown in [Deploy local models](inference-services.md). That guide has
+complete host and GPU container workflows. NVIDIA GLiNER uses
+`deberta_gliner`; GLiNER2 uses `deberta_gliner2`.
-# Optional: listen on all interfaces — no auth; use only on trusted networks
-python tools/serve_gliner.py --host 0.0.0.0
+Launch writes a versioned receipt only after the new process proves launch
+ownership and the model-list and detection contract probes pass. Use that
+receipt with the compiler's `status` and `stop` commands instead of supervising
+the internal server module directly.
-# Optional: pick device explicitly (auto prefers mps, then cuda, then cpu)
-DEVICE=cuda python tools/serve_gliner.py
-```
+The model families do not use identical label vocabularies. The request example below targets the default NVIDIA model and uses `user_name`; the default GLiNER2 PII checkpoint uses `username` for that category.
-The reference server has **no authentication**. The default bind address is `127.0.0.1` so detection traffic stays on localhost. Use `--host 0.0.0.0` only when Anonymizer runs on another host in a trusted environment.
+The reference profiles have **no authentication**. The default bind address is
+`127.0.0.1` so detection traffic stays on localhost. Set `api_key_env` in the
+`[vllm]` section or place the endpoint behind authenticated TLS before exposing
+it to another host.
Verify the server is reachable:
@@ -195,9 +211,8 @@ model_configs:
- alias: gliner-pii-detector
model: nvidia/gliner-pii
provider: local-gliner
- skip_health_check: true # the default health check sends no `labels`, which GLiNER can't handle
inference_parameters:
- max_parallel_requests: 8 # send concurrent rows; the reference server batches them
+ max_parallel_requests: 8 # vLLM continuously batches concurrent detector calls
timeout: 120
- alias: gpt-oss-120b
@@ -230,15 +245,16 @@ anonymizer = Anonymizer(
)
```
-Set `skip_health_check: true` on the detector alias: Anonymizer's default probe sends `prompt="Hello!"` with no `labels` field, which is not a valid GLiNER request.
-
---
## Performance notes
-- **Batch mode**: The reference server coalesces concurrent detector requests by default. Pair it with a higher `max_parallel_requests` on the `gliner-pii-detector` alias (see YAML above) so DataDesigner sends multiple rows at once and the server fills GPU batches efficiently.
-- On CPU, detection of a ~1000-character note with ~30 candidate labels takes **5–20 ms** per request on a modern x86 core. For typical Anonymizer workflows this is a rounding error compared to the LLM roles that follow, and keeping GLiNER on CPU frees GPU memory for the LLM.
-- On GPU the same request drops to roughly **1–3 ms** — worth it when you're processing tens of thousands of documents in a batch workflow, or when the host has spare GPU memory next to the LLM.
-- Choose device with the `DEVICE` environment variable (`auto`, `cuda`, `mps`, `cpu`). `auto` prefers Apple Silicon GPU (MPS), then NVIDIA CUDA, then CPU.
+- **Continuous batching**: Pair vLLM Factory with a higher
+ `max_parallel_requests` on the detector alias so DataDesigner supplies enough
+ concurrent work for vLLM's scheduler.
+- **GPU memory**: `gpu_memory_utilization` is a vLLM memory budget. Size it with
+ any colocated generation service in mind.
+- **Native fallback**: Use the native engine when CPU or MPS deployment matters
+ more than vLLM scheduling.
- The default GLiNER threshold is `0.3`. Lower values detect more spans (higher recall, more false positives); higher values improve precision but miss edge cases. Tune via `Detect(gliner_threshold=...)`.
- Each request loads the FULL list of candidate labels passed from `Detect.entity_labels`. If you only need a subset (e.g. a clinical-only deployment), narrowing that list materially speeds up detection.
diff --git a/docs/devnotes/posts/self-hosted-anonymizer-b300.md b/docs/devnotes/posts/self-hosted-anonymizer-b300.md
index f20e26ca..ced7fad1 100644
--- a/docs/devnotes/posts/self-hosted-anonymizer-b300.md
+++ b/docs/devnotes/posts/self-hosted-anonymizer-b300.md
@@ -133,16 +133,50 @@ The `CUDA_ROOT` path above is specific to the Brev B300 SXM6 environment used fo
`--gpu-memory-utilization 0.45` was a conservative co-location setting, not a compute throttle. In vLLM it controls the GPU memory budget for model weights and KV cache. Qwen could use more memory if the run needed a larger KV cache, but this setting left headroom for the GLiNER server on the same GPU and still completed the measured batches with zero failures.
-GLiNER ran on the same machine. The command uses `tools/serve_gliner.py`, the reference GLiNER server from an Anonymizer source checkout; see [Self-hosting GLiNER](../../concepts/self-hosting-gliner.md) for the server contract and setup details.
+GLiNER ran on the same machine. Current reruns use the source-tree local-model
+deployment tool described in [Self-hosting GLiNER](../../concepts/self-hosting-gliner.md).
+It records the exact model, vLLM settings, endpoint, and observed process
+coordinates in versioned plans and receipts. The current detector path uses
+vLLM Factory.
+
+```toml title="gliner-b300.toml"
+schema_version = "inference-service.local-spec/v2"
+
+[task]
+kind = "entity-detection"
+dynamic_labels = true
+offsets = true
+scores = true
+
+[model]
+model_id = "nvidia/gliner-pii"
+revision = "bd23e8ef4425fd04e34c5204ab49ffaa706eae79"
+
+[vllm]
+python_executable = ".venv/bin/python"
+gpu_memory_utilization = 0.45
+max_model_len = 512
+
+[vllm.factory]
+plugin = "deberta_gliner"
+prepared_model_root = "/tmp/anonymizer-vllm-factory"
+
+[local]
+host = "127.0.0.1"
+port = 9000
+startup_timeout_seconds = 300
+shutdown_timeout_seconds = 30
+```
```bash
-mkdir -p logs
-
-DEVICE=cuda \
-GLINER_MAX_BATCH_REQUESTS=64 \
-GLINER_BATCH_WAIT_MS=10 \
-nohup .venv/bin/python tools/serve_gliner.py --port 9000 \
- > logs/gliner.log 2>&1 &
+uv run --python 3.12 python tools/inference_service.py compile \
+ --profile gliner-b300.toml \
+ --source-revision 3f68c145 \
+ --output gliner-b300-plan.json
+uv run --python 3.12 python tools/inference_service.py launch \
+ --plan gliner-b300-plan.json \
+ --output gliner-b300-launch.json \
+ --log-directory logs
```
For archival reruns, pin the GLiNER model as well. The server default used here is `nvidia/gliner-pii`, which Hugging Face resolved as `nvidia/gliner-PII` revision `bd23e8ef4425fd04e34c5204ab49ffaa706eae79` as of this write-up; serving a newer detector snapshot can change entity counts.
diff --git a/mkdocs.yml b/mkdocs.yml
index f75c218a..f4ee3b08 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -161,6 +161,7 @@ nav:
- Choosing a Strategy: concepts/choosing-a-strategy.md
- Evaluation: concepts/evaluation.md
- Self-hosting GLiNER: concepts/self-hosting-gliner.md
+ - Deploy Local Models: concepts/inference-services.md
- Troubleshooting: troubleshooting.md
- Tutorials:
- Overview: tutorials/index.md
diff --git a/pyproject.toml b/pyproject.toml
index 4c5c4b82..cf999ea7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -53,6 +53,13 @@ notebooks = [
"jupytext>=1.16.0,<2",
"pillow>=12.0.0,<13",
]
+local-models = [
+ "vllm==0.27.1; sys_platform == 'linux' and python_version >= '3.12'",
+ "vllm-factory[gliner] @ git+https://github.com/latenceainew/vllm-factory.git@7d6ff68ce68f9f7c0a9d72f9645bcf6d335d02f0; sys_platform == 'linux' and python_version >= '3.12'",
+ "nvidia-cuda-nvcc==13.0.88; sys_platform == 'linux' and python_version >= '3.12'",
+ "nvidia-cuda-crt==13.0.88; sys_platform == 'linux' and python_version >= '3.12'",
+ "nvidia-nvvm==13.0.88; sys_platform == 'linux' and python_version >= '3.12'",
+]
[build-system]
requires = ["hatchling", "uv-dynamic-versioning"]
@@ -82,6 +89,7 @@ constraint-dependencies = [
[tool.pytest.ini_options]
testpaths = ["tests"]
+pythonpath = ["tools"]
markers = [
"e2e: end-to-end tests",
"integration: integration tests",
@@ -92,7 +100,7 @@ include = ["src", "tests", "tests_e2e", "docs", "scripts", "tools"]
[tool.ty.environment]
python-version = "3.11"
-root = ["src", ".", "tools/measurement"]
+root = ["src", ".", "tools", "tools/measurement"]
[tool.coverage.run]
omit = []
diff --git a/ruff.toml b/ruff.toml
index d7b98193..20659382 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -34,7 +34,7 @@ fixable = ["ALL"]
unfixable = []
[lint.isort]
-known-first-party = ["anonymizer"]
+known-first-party = ["anonymizer", "inference_service_compiler"]
[lint.flake8-tidy-imports]
ban-relative-imports = "all"
diff --git a/skills/anonymizer/BENCHMARK.md b/skills/anonymizer/BENCHMARK.md
index 027917f8..7c4d1199 100644
--- a/skills/anonymizer/BENCHMARK.md
+++ b/skills/anonymizer/BENCHMARK.md
@@ -1,85 +1,110 @@
-
-
+# Skill Benchmark: anonymizer
-# Evaluation Report
+> ✅ **Overall verdict: PASS — Recommended for publication**
-Evaluation report for the `anonymizer` skill before publication through
-NVSkills-Eval.
+## Publication Recommendation
-This benchmark file records the publication-ready evaluation plan and task
-composition for NeMo Anonymizer. The external NVSkills-Eval run has not been
-executed in this local workspace, so this branch intentionally reports no
-Anonymizer scores.
+Recommended for publication based on the completed evaluation evidence in this report.
-## Evaluation Summary
+## Evaluation Metadata
- Skill: `anonymizer`
-- Evaluation date: pending external `/nvskills-ci` run
-- NVSkills-Eval profile: external
-- Environment: external NVSkills-Eval runner
-- Dataset: 6 evaluation tasks
-- Attempts per task: recorded by external NVSkills-Eval after execution
-- Pass threshold: recorded by external NVSkills-Eval after execution
-- Overall verdict: pending external NVSkills-Eval run
+- Evaluation date: 2026-08-26
+- Evaluator version: `1.3.2`
+- Agents: Claude Code (`aws/anthropic/bedrock-claude-opus-4-8`), Codex (`openai/openai/gpt-5.5`)
+- Tasks: 6 evaluation tasks (4 positive, 2 negative)
+- Dataset digest: `sha256:2426de4eaba6137e3e0514953becf6a0eff19516d8d63bd790df3455ff8c8b32` (skill-evaluator-dataset-snapshot/1)
+- Attempts per task: 1
+- Environment: `local`
+- Tier 3 evidence: required for publication
-## Agents Used
+Tasks ran on the trusted local host; local mode is not sandboxed.
-Agent-level measured results are pending the external NVSkills-Eval run.
+## Execution and Provenance
-## Metrics Used
+- Validation status: `passed`
+- Report generation: `complete`
+- Evaluator version: `1.3.2`
+- Git commit: `0117bc2e3e54da4244a656466526c5b1b5a559ea`
+- Content type: requested `auto`, detected `skill`
+- Container image: `gitlab-master.nvidia.com:5005/nvcarps/ci-group/nvcarps-ci/skillevaluator-ci:sha-0117bc2e3e54da4244a656466526c5b1b5a559ea`
+- Container image digest: `not recorded`
+- Tier 3: requested `true`, executed `true`, status `succeeded`
-Reported benchmark dimensions:
+## What This Report Answers
-- Security: checks whether skill-assisted execution avoids unsafe behavior such
- as secret leakage, destructive commands, or unauthorized access.
-- Correctness: checks whether the agent follows the expected workflow and
- produces the correct final output.
-- Discoverability: checks whether the agent loads the skill when relevant and
- avoids using it when irrelevant.
-- Effectiveness: checks whether the agent performs measurably better with the
- skill than without it.
-- Efficiency: checks whether the agent uses fewer tokens and avoids redundant
- work.
+The three-tier evaluation checks whether the skill:
-Underlying evaluation signals will be recorded from the external
-NVSkills-Eval output after execution.
+- is safe to use;
+- produces correct answers;
+- is discovered and activated when needed;
+- helps the agent complete the user's goal and expected workflow; and
+- avoids wasted skill and tool usage.
-## Test Tasks
+## Results at a Glance
-The benchmark dataset contains 6 evaluation tasks:
+| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
+|---|---:|---:|
+| Overall | 62% → 92% (+30 points) | 71% → 95% (+24 points) |
+| Security | 100% → 100% (±0 points) | 100% → 100% (±0 points) |
+| Correctness | 57% → 90% (+33 points) | 77% → 97% (+20 points) |
+| Discoverability | 50% → 99% (+49 points) | 67% → 94% (+27 points) |
+| Effectiveness | 53% → 78% (+26 points) | 63% → 87% (+24 points) |
+| Efficiency | 50% → 92% (+42 points) | 50% → 100% (+50 points) |
-- Positive tasks: 4 tasks where the skill is expected to activate.
-- Negative tasks: 2 tasks where no skill is expected.
-- Unlabeled tasks: 0 tasks where positive/negative intent cannot be inferred.
+**How to read this table:** baseline is the same task attempted without the target skill. Uplift is `skill score - baseline score`, shown in percentage points.
-Entries with `should_trigger: true` and `expected_skill: "anonymizer"` are
-positive skill-activation cases. Entries with `should_trigger: false` and
-`expected_skill: null` are negative activation cases.
+Example: `47% → 92% (+45 points)` means the skill-assisted run scored 92%, 45 percentage points above its 47% no-skill baseline.
-## Results
+## Tier Status
-External NVSkills-Eval execution is pending. No copied or locally inferred
-Anonymizer results are reported here.
+| Tier | Purpose | Status | Evidence |
+|---|---|---|---|
+| Tier 1 | Static validation | **PASSED WITH OBSERVATIONS** | 1 validator(s); 2 finding(s) |
+| Tier 2 | Semantic deduplication | **NOT RUN** | No result was recorded |
+| Tier 3 | Live agent evaluation | **PASS** | 2 agent(s); 6 task(s) |
-| Dimension | Tasks | Result |
-|---|---:|---|
-| Security | 6 | Pending external NVSkills-Eval run |
-| Correctness | 6 | Pending external NVSkills-Eval run |
-| Discoverability | 6 | Pending external NVSkills-Eval run |
-| Effectiveness | 6 | Pending external NVSkills-Eval run |
-| Efficiency | 6 | Pending external NVSkills-Eval run |
+## Findings and Observations
-## Tier 1: Static Validation Summary
+
+Show detailed findings and successful checks
-Local static validation is covered by this branch's validation evidence. The
-external NVSkills-Eval Tier 1 result is pending the `/nvskills-ci` run.
+- **MEDIUM** SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (`skills/anonymizer/SKILL.md`)
+- **MEDIUM** SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (`skills/anonymizer/SKILL.md`)
-## Tier 2: Deduplication Summary
+
-External NVSkills-Eval deduplication results are pending.
+## Scoring Methodology
-## Publication Recommendation
+
+Show dimension definitions, source signals, and thresholds
+
+| Dimension | Question | Scored signals |
+|---|---|---|
+| Security | Is it safe to use? | `security` (100%) |
+| Correctness | Is the answer correct? | `accuracy` (100%) |
+| Discoverability | Was the right skill loaded when needed? | `skill_execution` (100%) |
+| Effectiveness | Did the skill help complete the task? | `goal_accuracy` (50%) + `behavior_check` (50%) |
+| Efficiency | Did it avoid wasted tool or skill usage? | `skill_efficiency` (100%) |
+
+- Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.
+- Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.
+- Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.
+- The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.
+- Effectiveness is the equal-weight mean of goal completion (`goal_accuracy`) and expected workflow adherence (`behavior_check`).
+- Token efficiency is a separate report-only signal. It does not change a dimension score or the overall verdict.
+
+Signals present in this run:
+
+- `security` (Security): unsafe operations, secret leakage, and unauthorized access.
+- `skill_execution` (Skill Execution): whether the expected skill was found and executed.
+- `skill_efficiency` (Efficiency): routing quality, workspace-aware skill reads, and productive tool use.
+- `accuracy` (Accuracy): final-answer correctness against the reference answer.
+- `goal_accuracy` (Goal Accuracy): whether the user's goal was achieved.
+- `behavior_check` (Behavior Check): whether the expected workflow behavior was followed.
+
+
+
+## Freshness
-Proceed to external NVSkills-Eval and signing. Publication should depend on the
-external evaluation and signing results rather than this local preparation
-branch alone.
+Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.
diff --git a/skills/anonymizer/SKILL.md b/skills/anonymizer/SKILL.md
index 30642118..b040b1ec 100644
--- a/skills/anonymizer/SKILL.md
+++ b/skills/anonymizer/SKILL.md
@@ -48,7 +48,7 @@ regulatory and business context.
- **`risk_tolerance` only applies to Rewrite mode**, not Replace.
- **`PrivacyGoal.protect` and `.preserve` must each be 10–1000 chars and at least 3 words.** Be specific (categories, named identifiers, structural facets); avoid generic phrasing like "preserve meaning".
- **Validator pool is the only model role with built-in load-spreading.** Set `entity_validator: [a, b, c]` in `models.yaml` if rate limits drop rows. Other roles (rewriter, evaluator, etc.) are single-alias.
-- **Self-hosted GLiNER:** When detection must not call `build.nvidia.com` (PHI on-prem, air-gapped, latency), run the reference server from a **source checkout** with `python tools/serve_gliner.py`. The server is not installed by `pip install nemo-anonymizer`. Add a provider with `endpoint: http://localhost:8001/v1`, then route `entity_detector` through a `gliner-pii-detector` alias with `provider: local-gliner` and `skip_health_check: true`. Match any custom `--port` or `--host` in the provider endpoint. `model_configs` is a **complete** model pool, not an overlay. Copy `src/anonymizer/config/default_model_configs/models.yaml` and change only the detector entry, keeping `gpt-oss-120b` and `nemotron-30b-thinking`. See [`docs/concepts/self-hosting-gliner.md`](../../docs/concepts/self-hosting-gliner.md) or the [published self-hosting guide](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/self-hosting-gliner/).
+- **Self-hosted GLiNER:** When detection must not call `build.nvidia.com` (PHI on-prem, air-gapped, latency), use `tools/inference_service.py` from a **source checkout** to compile the pinned `tools/inference_service_profiles/nvidia-gliner.toml` profile and launch its managed plan. The profile uses vLLM 0.27.1 and the external vLLM Factory project at a pinned source revision. Local serving requires Python 3.12 or later. Deploy on the GPU host or build `tools/inference_service.Dockerfile` and run the same lifecycle inside a GPU container. The tool and runtime are not installed by `pip install nemo-anonymizer`. Add a provider with `endpoint: http://localhost:8001/v1`, then route `entity_detector` through a `gliner-pii-detector` alias with `provider: local-gliner`. Match the compiled host and port in the provider endpoint. `model_configs` is a **complete** model pool, not an overlay. Copy `src/anonymizer/config/default_model_configs/models.yaml` and change only the detector entry, keeping `gpt-oss-120b` and `nemotron-30b-thinking`. See [`docs/concepts/inference-services.md`](../../docs/concepts/inference-services.md) or the [published deployment guide](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/inference-services/), plus [`docs/concepts/self-hosting-gliner.md`](../../docs/concepts/self-hosting-gliner.md) or the [published self-hosting guide](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/self-hosting-gliner/).
- **The evaluation judges use their own model roles** (`entity_coverage_judge`, `detection_validity_judge`, `replace_type_fidelity_judge`, `replace_relational_consistency_judge`, `replace_attribute_fidelity_judge`, `rewrite_judge`), configured in the `evaluate` section of `models.yaml`. They are **not** consumed by `preview()` / `run()`, so a config that anonymizes fine can still fail validation at `evaluate()` if those roles are unset. Defaults ship in `src/anonymizer/config/default_model_configs/evaluate.yaml` (`entity_coverage_judge` defaults to `nemotron-super`).
- **Verdict columns are null when the judge was unavailable** — `None` means "unscored", never a pass. `entity_coverage` is a `0–1` float (`1.0` = no missed candidate values or no PII found) or `None`; `missed_entities` lists unique candidate values the anonymizer failed to detect. Replace verdict columns (`type_fidelity_valid`, etc.) are `True` / `False` / `None`. Rewrite `detection_valid` is a `0–1` float fraction (or `None` if unscored). Inspect verdicts per record with `evaluated.display_record(i)`.
- **`EvaluateConfig` has one knob today: `compute_detection_validity`** (default `False`). Plain `anonymizer.evaluate(result)` runs entity coverage + the mode's quality judges; pass `EvaluateConfig(compute_detection_validity=True)` only to additionally score detection validity (an internal-facing tag-precision metric).
@@ -73,7 +73,7 @@ read `docs/troubleshooting.md` or the
- **`anonymizer` not installed:** Tell the user `nemo-anonymizer` is not in this Python environment (requires Python ≥ 3.11). Ask if they want you to install it (`pip install nemo-anonymizer`) or do it themselves. Do not install without permission.
- **Model/provider setup:** Plain `Anonymizer()` ships with bundled `models.yaml` and `providers.yaml` (see `src/anonymizer/config/default_model_configs/`). For the default path, confirm `NVIDIA_API_KEY` is set. Pass custom `model_configs` or `model_providers` only for non-default endpoints or model pools. See `docs/concepts/models.md` or the [published models guide](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/models/).
- **LLM calls failing at preview:** Check for a missing or invalid API key, a network problem, or a wrong endpoint URL. See `docs/troubleshooting.md` "Validation passed but `preview` errors at LLM call" or the [published troubleshooting guide](https://nvidia-nemo.github.io/Anonymizer/dev/troubleshooting/).
-- **Local / on-prem GLiNER:** Clone or download `tools/serve_gliner.py` from the Anonymizer repo, start the server, add a provider with `endpoint: http://localhost:8001/v1`, and point `gliner-pii-detector` at `provider: local-gliner` with `skip_health_check: true`. Preflight errors about missing aliases usually mean `model_configs` lists only the detector. Include the full default pool. A wrong endpoint or stopped server surfaces as a detection failure during preview. See [`docs/concepts/self-hosting-gliner.md`](../../docs/concepts/self-hosting-gliner.md) or the [published self-hosting guide](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/self-hosting-gliner/).
+- **Local / on-prem GLiNER:** Clone the Anonymizer repository, then deploy `tools/inference_service_profiles/nvidia-gliner.toml` on the GPU host or through `tools/inference_service.Dockerfile`. Add a provider with the plan's endpoint (normally `http://localhost:8001/v1`) and point `gliner-pii-detector` at `provider: local-gliner`. The vLLM Factory adapter supports DataDesigner's health check, so do not suppress it. Preflight errors about missing aliases usually mean `model_configs` lists only the detector. Include the full default pool. Use the launch receipt with `status` to observe the managed process or `stop` to clean it up before stopping its container. See [`docs/concepts/inference-services.md`](../../docs/concepts/inference-services.md) or the [published deployment guide](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/inference-services/), plus [`docs/concepts/self-hosting-gliner.md`](../../docs/concepts/self-hosting-gliner.md) or the [published self-hosting guide](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/self-hosting-gliner/).
# Output Template
diff --git a/skills/anonymizer/evals/evals.json b/skills/anonymizer/evals/evals.json
index 277b61fa..840ad034 100644
--- a/skills/anonymizer/evals/evals.json
+++ b/skills/anonymizer/evals/evals.json
@@ -53,13 +53,13 @@
"expected_skill": "anonymizer",
"should_trigger": true,
"expected_script": null,
- "ground_truth": "The agent loads the anonymizer skill and explains the self-hosted GLiNER path: run tools/serve_gliner.py from a source checkout, add an OpenAI-compatible provider at http://localhost:8001/v1, point the gliner-pii-detector/entity_detector alias at provider local-gliner with skip_health_check true, and keep model_configs as a complete model pool copied from defaults rather than a partial overlay.",
+ "ground_truth": "The agent loads the anonymizer skill and explains the self-hosted GLiNER path: use tools/inference_service.py from a source checkout with the local-models dependency group to compile the pinned tools/inference_service_profiles/nvidia-gliner.toml vLLM Factory profile and launch its managed plan, add an OpenAI-compatible provider at the plan endpoint (normally http://localhost:8001/v1), point the gliner-pii-detector/entity_detector alias at provider local-gliner, retain the health check supported by the adapter, and keep model_configs as a complete model pool copied from defaults rather than a partial overlay.",
"expected_behavior": [
"The agent read skills/anonymizer/SKILL.md before answering",
- "The answer says the reference GLiNER server comes from a source checkout, not pip-installed package files",
+ "The answer says the compiler and adapter come from a source checkout and require the local-models dependency group",
"The answer includes the localhost OpenAI-compatible endpoint",
"The answer routes entity_detector through the gliner-pii-detector alias using a local provider",
- "The answer sets or mentions skip_health_check for the local GLiNER provider",
+ "The answer identifies the pinned external vLLM Factory integration and keeps the supported health check enabled",
"The answer warns that model_configs must include the full default model pool",
"The answer cites the self-hosting GLiNER docs with the published Anonymizer docs fallback"
]
diff --git a/skills/anonymizer/skill-card.md b/skills/anonymizer/skill-card.md
index 57b5aa7c..769f8e27 100644
--- a/skills/anonymizer/skill-card.md
+++ b/skills/anonymizer/skill-card.md
@@ -1,139 +1,86 @@
-
-
+## Description:
+Use when the user wants to anonymize a text dataset, redact PII, de-identify free-text data, or rewrite text to remove sensitive or inferable identifying information. Produces a runnable Python script that calls the NeMo Anonymizer pipeline (detection → replace or rewrite).
-## Description
-
-Use NeMo Anonymizer through an interactive agent workflow: inspect text data,
-choose Replace or Rewrite, select a replacement strategy, draft a runnable
-Python script, preview before full execution, diagnose failed records first, and
-configure self-hosted GLiNER when detection must stay local.
-
-This skill package is prepared for NVSkills publication review. External
-NVSkills-Eval results are pending and no Anonymizer scores are reported in this
-branch.
+This skill is ready for commercial/non-commercial use.
## Owner
+NVIDIA
-NVIDIA
-
-### License/Terms of Use
-
-Apache 2.0
-
-## Use Case
-
-Developers, privacy engineers, and data practitioners using NeMo Anonymizer to
-detect, replace, redact, hash, annotate, or rewrite sensitive entities in text
-datasets while keeping a durable script for review and reruns.
-
-### Deployment Geography for Use
-
-Global
-
-## Known Risks and Mitigations
-
-Risk: Users may overinterpret anonymized output as a privacy guarantee.
-
-Mitigation: The skill instructs agents to describe Anonymizer as best-effort,
-preview before full execution, inspect failed records, and call out human review
-for rewrite outputs that need it.
-
-Risk: Agent-generated scripts may target the wrong source file, text column, or
-model-provider configuration.
-
-Mitigation: The workflow requires data inspection, explicit user confirmation
-of mode and key configuration choices, and preview execution before a full run.
-
-Risk: An incorrect provider or model alias may send detection requests to an
-unintended endpoint.
-
-Mitigation: The skill directs agents to configure the local GLiNER provider
-explicitly, keep the full model pool, verify the endpoint, preview, and consult
-the self-hosting documentation.
-
-## Reference(s)
-
-- [Interactive workflow](references/interactive.md)
-- [Choosing a Strategy](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/choosing-a-strategy/)
-- [Detection](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/detection/)
-- [Evaluation](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/evaluation/)
-- [Models](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/models/)
-- [Self-hosting GLiNER](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/self-hosting-gliner/)
-- [Troubleshooting](https://nvidia-nemo.github.io/Anonymizer/dev/troubleshooting/)
-
-## Skill Output
-
-**Output Type(s):** Python scripts, shell commands, configuration guidance,
-diagnostic guidance
+### License/Terms of Use:
+Apache 2.0
+## Use Case:
+Developers and engineers who need to de-identify or anonymize text datasets containing PII before sharing, model training, or analytics.
-**Output Format:** A runnable Python script plus concise Markdown guidance for
-previewing, diagnosing failures, and running the full pipeline
+### Deployment Geography for Use:
+Global
-**Output Parameters:** Dataset path, text column, data summary, mode
-(`Replace` or `Rewrite`), replacement strategy when applicable, privacy goal,
-risk tolerance, entity labels, and optional model-provider paths
+## Requirements / Dependencies:
+**Requires API Key or External Credential:** [Yes]
+**Credential Type(s):** [API key]
-**Other Properties Related to Output:** The generated script previews by
-default, exits on failed records, optionally evaluates output with
-LLM-as-judge, and leaves full dataset execution under explicit user control.
+Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate.
-## Evaluation Agents Used
+## Known Risks and Mitigations:
+Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills.
+Mitigation: Review and scan skill before deployment.
-The external NVSkills-Eval run is pending. Agent-level measured results will be
-reported from the external `/nvskills-ci` evaluation output after it runs.
+## Reference(s):
+- [NeMo Anonymizer Documentation](https://nvidia-nemo.github.io/Anonymizer/)
+- [Choosing a Strategy](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/choosing-a-strategy/)
+- [Detection](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/detection/)
+- [Evaluation](https://nvidia-nemo.github.io/Anonymizer/dev/concepts/evaluation/)
+- [Troubleshooting](https://nvidia-nemo.github.io/Anonymizer/dev/troubleshooting/)
+- [Interactive Reference](references/interactive.md)
-## Evaluation Tasks
-The prepared evaluation dataset contains 6 NVSkills-Eval tasks: 4 positive
-activation cases and 2 negative activation cases. The positive tasks cover mode
-choice, stable cross-record replacement with `Hash`, failed-record-first
-diagnosis, and self-hosted GLiNER. The negative tasks cover a general privacy
-explainer and repository source development.
+## Skill Output:
+**Output Type(s):** [Code]
+**Output Format:** [Python script]
+**Output Parameters:** [1D]
+**Other Properties Related to Output:** [None]
-## Evaluation Metrics Used
+## Evaluation Agents Used:
+- Claude Code (`aws/anthropic/bedrock-claude-opus-4-8`)
+- Codex (`openai/openai/gpt-5.5`)
-Metrics will be reported by the external NVSkills-Eval run. Expected benchmark
-dimensions are:
-- Security: Checks whether skill-assisted execution avoids unsafe behavior such
- as secret leakage, destructive commands, or unauthorized access.
-- Correctness: Checks whether the agent follows the expected workflow and
- produces the correct final output.
-- Discoverability: Checks whether the agent loads the skill when relevant and
- avoids using it when irrelevant.
-- Effectiveness: Checks whether the agent performs measurably better with the
- skill than without it.
-- Efficiency: Checks whether the agent uses fewer tokens and avoids redundant
- work.
-## Evaluation Results
+## Evaluation Tasks:
+6 evaluation tasks (4 positive, 2 negative) from a curated skill-evaluator dataset snapshot.
-External NVSkills-Eval execution is pending. This publication branch does not
-include local or copied Anonymizer benchmark scores.
+## Evaluation Metrics Used:
+Reported benchmark dimensions:
+- Security: Checks for unsafe operations, secret leakage, and unauthorized access.
+- Correctness: Checks final-answer correctness against the reference answer.
+- Discoverability: Checks whether the expected skill was found and executed when needed.
+- Effectiveness: Checks whether the skill helped complete the user's goal and followed the expected workflow.
+- Efficiency: Checks routing quality, workspace-aware skill reads, and productive tool use.
-| Dimension | Tasks | Result |
-|---|---:|---|
-| Security | 6 | Pending external NVSkills-Eval run |
-| Correctness | 6 | Pending external NVSkills-Eval run |
-| Discoverability | 6 | Pending external NVSkills-Eval run |
-| Effectiveness | 6 | Pending external NVSkills-Eval run |
-| Efficiency | 6 | Pending external NVSkills-Eval run |
+Underlying evaluation signals used in this run:
+- `security`: Detects unsafe operations, secret leakage, and unauthorized access.
+- `accuracy`: Verifies final-answer correctness against the reference answer.
+- `skill_execution`: Verifies the expected skill was found and executed.
+- `goal_accuracy`: Verifies whether the user's goal was achieved.
+- `behavior_check`: Verifies expected workflow behavior was followed.
+- `skill_efficiency`: Verifies routing quality and productive tool use.
-## Skill Version(s)
-Publication candidate from this repository branch. The released skill version
-should be recorded after review, external evaluation, and signing.
-## Ethical Considerations
+## Evaluation Results:
+| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
+|---|---:|---:|
+| Overall | 62% → 92% (+30 points) | 71% → 95% (+24 points) |
+| Security | 100% → 100% (±0 points) | 100% → 100% (±0 points) |
+| Correctness | 57% → 90% (+33 points) | 77% → 97% (+20 points) |
+| Discoverability | 50% → 99% (+49 points) | 67% → 94% (+27 points) |
+| Effectiveness | 53% → 78% (+26 points) | 63% → 87% (+24 points) |
+| Efficiency | 50% → 92% (+42 points) | 50% → 100% (+50 points) |
-NVIDIA believes Trustworthy AI is a shared responsibility and has established
-policies and practices to enable development for a wide array of AI
-applications. When downloaded or used in accordance with our terms of service,
-developers should work with their internal team to ensure this skill meets
-requirements for the relevant industry and use case and addresses foreseeable
-product misuse.
+## Skill Version(s):
+2cf0c90 (source: git SHA, committed 2026-08-20)
-(For Release on NVIDIA Platforms Only)
+## Ethical Considerations:
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
-Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns
-[here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail).
+(For Release on NVIDIA Platforms Only)
+Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail).
diff --git a/skills/anonymizer/skill.oms.sig b/skills/anonymizer/skill.oms.sig
new file mode 100644
index 00000000..cf005971
--- /dev/null
+++ b/skills/anonymizer/skill.oms.sig
@@ -0,0 +1 @@
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diff --git a/tests/tools/test_inference_service.py b/tests/tools/test_inference_service.py
new file mode 100644
index 00000000..adf293a2
--- /dev/null
+++ b/tests/tools/test_inference_service.py
@@ -0,0 +1,735 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Observable contracts for the local-process vLLM inference host."""
+
+from __future__ import annotations
+
+import asyncio
+import json
+import stat
+from pathlib import Path
+from types import SimpleNamespace
+from unittest import mock
+
+import httpx
+import pytest
+from pydantic import ValidationError
+
+from inference_service_compiler import cli, compiler, lifecycle, models, runtime
+from inference_service_compiler.profiles import load_profile
+
+ROOT = Path(__file__).resolve().parents[2]
+TOOLS = ROOT / "tools"
+PROFILES = TOOLS / "inference_service_profiles"
+CLI = TOOLS / "inference_service.py"
+
+
+def generation(**vllm: object) -> models.LocalInferenceServiceSpec:
+ return models.LocalInferenceServiceSpec(
+ task=models.Generation(),
+ model=models.HuggingFaceModel(model_id="openai/gpt-oss-20b", revision="abc"),
+ vllm=models.Vllm.model_validate(vllm),
+ local=models.LocalProcess(port=8000, startup_timeout_seconds=3, shutdown_timeout_seconds=0.01),
+ )
+
+
+def launch_receipt(
+ plan: models.RunPlan,
+ handle: models.LocalProcessHandle,
+) -> models.LaunchReceipt:
+ return models.LaunchReceipt(
+ plan_digest=plan.plan_digest,
+ launched_at="2026-08-07T00:00:00+00:00",
+ shutdown_timeout_seconds=0.01,
+ handle=handle,
+ probe=models.CapabilityProbeReceipt(
+ plan_digest=plan.plan_digest,
+ endpoint=plan.endpoint,
+ observed_at="2026-08-07T00:00:00+00:00",
+ models=(plan.served_model_name,),
+ observed_capabilities=plan.required_capabilities,
+ passed=True,
+ ),
+ )
+
+
+def test_cli_remains_a_directly_executable_source_entrypoint() -> None:
+ assert CLI.is_file()
+ assert CLI.stat().st_mode & stat.S_IXUSR
+
+
+def test_all_shipped_profiles_compile() -> None:
+ plans = [
+ compiler.compile_profile(load_profile(path), source_revision="test") for path in sorted(PROFILES.glob("*.toml"))
+ ]
+ assert plans
+ assert all(plan.schema_version == "inference-service.run-plan/v2" for plan in plans)
+ assert all(plan.readiness.path == "/models" for plan in plans)
+ assert all(plan.served_model_name for plan in plans)
+
+
+@pytest.mark.parametrize(
+ ("filename", "model_id", "revision", "served_model_name", "valued_flags", "switches"),
+ [
+ (
+ "gemma-4-12b-it.toml",
+ "google/gemma-4-12B-it",
+ "707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7",
+ "gemma-4-12b-it-local",
+ (("--gpu-memory-utilization", "0.9"), ("--max-model-len", "8192"), ("--max-num-seqs", "16")),
+ ("--enable-prefix-caching", "--async-scheduling"),
+ ),
+ (
+ "nemotron-3.5-lightning-nvfp4.toml",
+ "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4",
+ "cc84af2fe71647d87f4486c064f320e1e7535243",
+ "nemotron-3.5-lightning-nvfp4-local",
+ (
+ ("--gpu-memory-utilization", "0.88"),
+ ("--max-model-len", "8192"),
+ ("--max-num-seqs", "16"),
+ ("--mamba-backend", "flashinfer"),
+ ("--mamba-ssm-cache-dtype", "float16"),
+ ("--mamba-cache-philox-rounds", "5"),
+ ),
+ (
+ "--enable-prefix-caching",
+ "--async-scheduling",
+ "--enable-mamba-cache-stochastic-rounding",
+ ),
+ ),
+ ],
+)
+def test_dedicated_generation_profiles_are_pinned_and_compile_expected_flags(
+ filename: str,
+ model_id: str,
+ revision: str,
+ served_model_name: str,
+ valued_flags: tuple[tuple[str, str], ...],
+ switches: tuple[str, ...],
+) -> None:
+ profile_path = PROFILES / filename
+ assert profile_path.is_file(), f"missing dedicated profile: {filename}"
+
+ plan = compiler.compile_profile(load_profile(profile_path), source_revision="test")
+ argv = plan.command.render_argv()
+
+ assert plan.spec.model.model_id == model_id
+ assert plan.spec.model.revision == revision
+ assert plan.served_model_name == served_model_name
+ assert plan.required_capabilities == ("chat-completions",)
+ assert argv[2] == model_id
+ for flag, expected_value in (("--revision", revision), ("--tokenizer-revision", revision), *valued_flags):
+ flag_index = argv.index(flag)
+ assert argv[flag_index + 1] == expected_value
+ assert all(switch in argv for switch in switches)
+ assert "--vllm-factory-plugin" not in argv
+
+
+def test_plan_keeps_one_endpoint_address_and_one_served_model_vocabulary() -> None:
+ plan = compiler.compile_profile(generation(served_model_name="local-generator"), source_revision="test")
+
+ serialized = plan.model_dump(mode="json")
+ assert plan.endpoint.url == "http://127.0.0.1:8000/v1"
+ assert plan.readiness.path == "/models"
+ assert plan.served_model_name == "local-generator"
+ assert "host" not in serialized["readiness"]
+ assert "port" not in serialized["readiness"]
+ assert "intent_digest" not in serialized
+ assert "declared_capabilities" not in serialized
+
+
+def test_compile_command_writes_a_digest_verified_plan(tmp_path: Path) -> None:
+ output = tmp_path / "plan.json"
+ with pytest.raises(SystemExit) as exc_info:
+ cli.app(
+ [
+ "compile",
+ "--profile",
+ str(PROFILES / "vllm-local.toml"),
+ "--source-revision",
+ "test",
+ "--output",
+ str(output),
+ ]
+ )
+ assert exc_info.value.code == 0
+ plan = compiler.load_plan(output.read_text(encoding="utf-8"))
+ assert plan.served_model_name == "anonymizer-local"
+
+
+def test_compile_command_translates_non_directory_profile_paths(tmp_path: Path) -> None:
+ """Filesystem path-shape errors retain the documented bad-input exit."""
+ not_a_directory = tmp_path / "profile-file"
+ not_a_directory.write_text("not a directory", encoding="utf-8")
+
+ with pytest.raises(SystemExit) as exc_info:
+ cli.app(
+ [
+ "compile",
+ "--profile",
+ str(not_a_directory / "profile.toml"),
+ "--source-revision",
+ "test",
+ ]
+ )
+
+ assert exc_info.value.code == 125
+
+
+def test_compile_command_translates_empty_source_revision() -> None:
+ """Known compiler input errors retain the documented bad-input exit."""
+ with pytest.raises(SystemExit) as exc_info:
+ cli.app(
+ [
+ "compile",
+ "--profile",
+ str(PROFILES / "vllm-local.toml"),
+ "--source-revision",
+ "",
+ ]
+ )
+
+ assert exc_info.value.code == 125
+
+
+def test_generation_argv_keeps_local_vllm_controls_and_omits_defaults() -> None:
+ plan = compiler.compile_profile(
+ generation(api_key_env="LOCAL_KEY", tensor_parallel_size=2, max_model_len=4096, eager=True),
+ source_revision="test",
+ )
+ argv = plan.command.render_argv()
+ assert argv[:3] == (".venv/bin/python", "tools/inference_service_compiler/vllm_server.py", "openai/gpt-oss-20b")
+ assert ("--tensor-parallel-size", "2") == argv[
+ argv.index("--tensor-parallel-size") : argv.index("--tensor-parallel-size") + 2
+ ]
+ assert "--enforce-eager" in argv and "--enable-prefix-caching" not in argv
+ assert plan.command.secret_sources == ("LOCAL_KEY",)
+ assert plan.command.render_environment() == {"VLLM_API_KEY": ""}
+ assert plan.command.resolve_environment({"LOCAL_KEY": "secret-value"}) == {"VLLM_API_KEY": "secret-value"}
+
+
+def test_factory_detection_is_task_bounded() -> None:
+ valid = models.LocalInferenceServiceSpec(
+ task=models.EntityDetection(dynamic_labels=True, offsets=True, scores=True),
+ model=models.HuggingFaceModel(model_id="nvidia/gliner-pii", revision="abc"),
+ vllm=models.Vllm(factory=models.VllmFactoryIntegration(plugin="deberta_gliner")),
+ local=models.LocalProcess(),
+ )
+ assert "--vllm-factory-plugin" in compiler.compile_profile(valid, source_revision="test").command.render_argv()
+ with pytest.raises(compiler.CompilationError, match="does not support"):
+ compiler.compile_profile(
+ generation(factory=models.VllmFactoryIntegration(plugin="deberta_gliner")), source_revision="test"
+ )
+
+
+def test_factory_plugin_is_closed_at_the_spec_boundary() -> None:
+ """Profiles cannot select an uncharacterized Factory plugin."""
+ with pytest.raises(ValidationError):
+ models.VllmFactoryIntegration.model_validate({"plugin": "unsupported"})
+
+
+def test_factory_detection_requires_a_pin_and_characterized_model() -> None:
+ for model_id, revision, message in (
+ ("nvidia/gliner-pii", None, "pinned model revision"),
+ ("unknown/model", "abc", "not characterized"),
+ ):
+ spec = models.LocalInferenceServiceSpec(
+ task=models.EntityDetection(dynamic_labels=True, offsets=True, scores=True),
+ model=models.HuggingFaceModel(model_id=model_id, revision=revision),
+ vllm=models.Vllm(factory=models.VllmFactoryIntegration(plugin="deberta_gliner")),
+ local=models.LocalProcess(),
+ )
+ with pytest.raises(compiler.CompilationError, match=message):
+ compiler.compile_profile(spec, source_revision="test")
+
+
+def test_removed_domains_are_invalid_profile_fields() -> None:
+ with pytest.raises(ValidationError):
+ models.LocalInferenceServiceSpec.model_validate(
+ {
+ "schema_version": "inference-service.local-spec/v2",
+ "task": {"kind": "generation"},
+ "model": {"model_id": "x"},
+ "vllm": {},
+ "local": {},
+ "placement": {"kind": "docker"},
+ }
+ )
+
+
+def test_plan_digest_detects_transport_mutation() -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ changed = json.loads(plan.model_dump_json())
+ changed["endpoint"]["port"] = 9000
+ with pytest.raises(compiler.PlanIntegrityError, match="plan digest mismatch"):
+ compiler.load_plan(json.dumps(changed))
+
+
+def test_lora_is_rendered_as_a_model_artifact() -> None:
+ spec = models.LocalInferenceServiceSpec(
+ task=models.Generation(),
+ model=models.HuggingFaceModel(
+ model_id="openai/gpt-oss-20b",
+ adapter=models.LoraAdapter(path="/models/privacy-adapter", name="privacy"),
+ ),
+ vllm=models.Vllm(),
+ local=models.LocalProcess(),
+ )
+ argv = compiler.compile_profile(spec, source_revision="test").command.render_argv()
+ assert argv[-3:] == ("--enable-lora", "--lora-modules", "privacy=/models/privacy-adapter")
+
+
+def test_probe_payload_is_task_aware_and_reasoning_safe() -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ requests: list[httpx.Request] = []
+
+ def handler(request: httpx.Request) -> httpx.Response:
+ requests.append(request)
+ if request.url.path == "/v1/models":
+ return httpx.Response(200, json={"data": [{"id": "openai/gpt-oss-20b"}]})
+ return httpx.Response(200, json={"choices": [{"message": {"content": "ok", "reasoning_content": "r"}}]})
+
+ with httpx.Client(transport=httpx.MockTransport(handler)) as client:
+ receipt = runtime.probe_endpoint(plan, client=client)
+ assert client.is_closed is False
+ assert receipt.passed and receipt.observed_capabilities == ("chat-completions",)
+ payload = json.loads(requests[-1].content)
+ assert payload["max_tokens"] == 128
+ assert payload["chat_template_kwargs"] == {"enable_thinking": False, "reasoning_effort": "low"}
+
+
+def test_probe_uses_bearer_secret_without_serializing_it() -> None:
+ plan = compiler.compile_profile(generation(api_key_env="LOCAL_KEY"), source_revision="test")
+
+ def handler(request: httpx.Request) -> httpx.Response:
+ assert request.headers["authorization"] == "Bearer test-secret"
+ if request.url.path == "/v1/models":
+ return httpx.Response(200, json={"data": [{"id": plan.served_model_name}]})
+ return httpx.Response(200, json={"choices": [{"message": {"content": "ready"}}]})
+
+ with httpx.Client(transport=httpx.MockTransport(handler)) as client:
+ receipt = runtime.probe_endpoint(plan, client=client, secret_values={"LOCAL_KEY": "test-secret"})
+ assert receipt.passed
+ assert "test-secret" not in receipt.model_dump_json()
+
+
+def test_probe_rejects_wrong_model_and_status() -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+
+ def wrong_model(request: httpx.Request) -> httpx.Response:
+ if request.url.path == "/v1/models":
+ return httpx.Response(200, json={"data": [{"id": "other/model"}]})
+ return httpx.Response(200, json={"choices": [{"message": {"content": "ready"}}]})
+
+ with httpx.Client(transport=httpx.MockTransport(wrong_model)) as client:
+ assert runtime.probe_endpoint(plan, client=client).passed is False
+ with httpx.Client(transport=httpx.MockTransport(lambda _request: httpx.Response(201, json={}))) as client:
+ with pytest.raises(runtime.RuntimeEffectError, match="status 201"):
+ runtime.probe_endpoint(plan, client=client)
+
+
+def test_plan_integrity_is_enforced_before_launch(tmp_path: Path) -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ with pytest.raises(compiler.PlanIntegrityError):
+ runtime.launch_plan(
+ plan.model_copy(update={"source_revision": "changed"}), secret_values={}, log_directory=tmp_path
+ )
+
+
+def test_status_and_stop_reject_a_reused_pid_without_signaling(monkeypatch: pytest.MonkeyPatch) -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ handle = models.LocalProcessHandle(
+ external_id="1:x", pid=1, process_group_id=1, start_marker="old", stdout_path="out", stderr_path="err"
+ )
+ launch = launch_receipt(plan, handle)
+ monkeypatch.setattr(runtime, "read_process_start_marker", lambda _pid: "new")
+
+ with pytest.raises(runtime.RuntimeEffectError) as status_error:
+ runtime.status_run(launch)
+ with (
+ mock.patch.object(runtime.os, "killpg") as killpg,
+ pytest.raises(runtime.RuntimeEffectError) as stop_error,
+ ):
+ runtime.stop_run(launch)
+
+ assert status_error.value.diagnostic.code == "process-identity-mismatch"
+ assert stop_error.value.diagnostic.code == "process-identity-mismatch"
+ killpg.assert_not_called()
+
+
+def test_stop_rejects_a_mismatched_process_group_without_signaling() -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ handle = models.LocalProcessHandle(
+ external_id="4242:100",
+ pid=4242,
+ process_group_id=4242,
+ start_marker="100",
+ stdout_path="out",
+ stderr_path="err",
+ )
+ launch = launch_receipt(plan, handle)
+
+ with (
+ mock.patch.object(runtime, "read_process_start_marker", return_value="100"),
+ mock.patch.object(runtime, "read_process_state", return_value="S"),
+ mock.patch.object(runtime.os, "getpgid", return_value=9000),
+ mock.patch.object(runtime.os, "kill"),
+ mock.patch.object(runtime.os, "killpg") as killpg,
+ mock.patch.object(runtime.time, "monotonic", side_effect=[0.0, 1.0]),
+ pytest.raises(runtime.RuntimeEffectError) as exc_info,
+ ):
+ runtime.stop_run(launch)
+
+ assert exc_info.value.diagnostic.code == "process-identity-mismatch"
+ killpg.assert_not_called()
+
+
+def test_launch_records_process_identity_and_resolves_secrets(tmp_path: Path) -> None:
+ plan = compiler.compile_profile(generation(api_key_env="LOCAL_KEY"), source_revision="test")
+ probe = models.CapabilityProbeReceipt(
+ plan_digest=plan.plan_digest,
+ endpoint=plan.endpoint,
+ observed_at="2026-08-07T00:00:00+00:00",
+ models=(plan.served_model_name,),
+ observed_capabilities=("chat-completions",),
+ passed=True,
+ )
+ process = mock.Mock(pid=4242)
+ with (
+ mock.patch.object(runtime.subprocess, "Popen", return_value=process) as popen,
+ mock.patch.object(runtime, "wait_for_readiness", return_value=probe),
+ mock.patch.object(runtime, "read_process_start_marker", return_value="100"),
+ ):
+ receipt = runtime.launch_plan(plan, secret_values={"LOCAL_KEY": "test-secret"}, log_directory=tmp_path)
+ assert tuple(popen.call_args.args[0]) == plan.command.render_argv()
+ assert popen.call_args.kwargs["env"]["VLLM_API_KEY"] == "test-secret"
+ assert "test-secret" not in popen.call_args.args[0]
+ assert receipt.handle.external_id == "4242:100"
+
+
+def test_launch_requires_scoped_ownership_evidence(tmp_path: Path) -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ handle = models.LocalProcessHandle(
+ external_id="4242:100",
+ pid=4242,
+ process_group_id=4242,
+ start_marker="100",
+ stdout_path="out",
+ stderr_path="err",
+ )
+ probe = launch_receipt(plan, handle).probe
+ observed_environment: dict[str, str] = {}
+ observed_token: str | None = None
+
+ def launch_process(
+ _plan: models.RunPlan,
+ _argv: tuple[str, ...],
+ environment: dict[str, str],
+ _log_directory: Path,
+ ) -> models.LocalProcessHandle:
+ observed_environment.update(environment)
+ return handle
+
+ def wait_for_readiness(
+ _plan: models.RunPlan,
+ *,
+ secret_values: object,
+ handle: models.LocalProcessHandle,
+ launch_token: str | None = None,
+ ) -> models.CapabilityProbeReceipt:
+ del secret_values, handle
+ nonlocal observed_token
+ observed_token = launch_token
+ return probe
+
+ with (
+ mock.patch.object(runtime, "_launch_process", side_effect=launch_process),
+ mock.patch.object(runtime, "wait_for_readiness", side_effect=wait_for_readiness),
+ ):
+ receipt = runtime.launch_plan(plan, secret_values={}, log_directory=tmp_path)
+
+ environment_token = observed_environment.get("ANONYMIZER_INFERENCE_LAUNCH_TOKEN")
+ assert environment_token
+ assert observed_token == environment_token
+ assert environment_token not in receipt.model_dump_json()
+
+
+def test_launch_ownership_endpoint_requires_and_proves_the_scoped_token() -> None:
+ async def next_handler(_request: object) -> object:
+ return mock.sentinel.next_response
+
+ def request(path: str, token: str | None) -> SimpleNamespace:
+ headers = {} if token is None else {lifecycle.LAUNCH_OWNERSHIP_HEADER: token}
+ return SimpleNamespace(url=SimpleNamespace(path=path), headers=headers)
+
+ with mock.patch.dict(
+ runtime.os.environ,
+ {lifecycle.LAUNCH_TOKEN_ENVIRONMENT_VARIABLE: "launch-secret"},
+ clear=True,
+ ):
+ wrong = asyncio.run(lifecycle.launch_ownership(request(lifecycle.LAUNCH_OWNERSHIP_PATH, "wrong"), next_handler))
+ owned = asyncio.run(
+ lifecycle.launch_ownership(request(lifecycle.LAUNCH_OWNERSHIP_PATH, "launch-secret"), next_handler)
+ )
+ passed_through = asyncio.run(lifecycle.launch_ownership(request("/v1/models", None), next_handler))
+
+ assert wrong.status_code == 404
+ assert json.loads(owned.body) == {
+ lifecycle.LAUNCH_OWNERSHIP_PROOF_FIELD: lifecycle.launch_token_proof("launch-secret")
+ }
+ assert passed_through is mock.sentinel.next_response
+
+
+def test_compatible_preexisting_endpoint_cannot_prove_launch_ownership() -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ compatible_response = httpx.Response(200, json={"data": [{"id": plan.served_model_name}]})
+
+ with (
+ mock.patch.object(runtime.httpx, "get", return_value=compatible_response),
+ pytest.raises(runtime.RuntimeEffectError) as exc_info,
+ ):
+ runtime._probe_launch_ownership(plan, "launch-secret")
+
+ assert exc_info.value.diagnostic.code == "launch-ownership-not-observed"
+
+
+def test_launch_ownership_probe_receives_endpoint_authentication() -> None:
+ plan = compiler.compile_profile(generation(api_key_env="LOCAL_KEY"), source_revision="test")
+ probe = launch_receipt(
+ plan,
+ models.LocalProcessHandle(
+ external_id="4242:100",
+ pid=4242,
+ process_group_id=4242,
+ start_marker="100",
+ stdout_path="out",
+ stderr_path="err",
+ ),
+ ).probe
+ observed_secrets: object = None
+
+ def ownership_probe(
+ _plan: models.RunPlan,
+ _launch_token: str,
+ secret_values: object = None,
+ ) -> None:
+ nonlocal observed_secrets
+ observed_secrets = secret_values
+
+ with (
+ mock.patch.object(runtime, "_probe_launch_ownership", side_effect=ownership_probe),
+ mock.patch.object(runtime, "probe_endpoint", return_value=probe),
+ ):
+ runtime.wait_for_readiness(
+ plan,
+ secret_values={"LOCAL_KEY": "endpoint-secret"},
+ launch_token="launch-secret",
+ )
+
+ assert observed_secrets == {"LOCAL_KEY": "endpoint-secret"}
+
+
+@pytest.mark.parametrize("failure", [RuntimeError("unexpected readiness failure"), KeyboardInterrupt()])
+def test_post_spawn_readiness_failures_always_clean_up(tmp_path: Path, failure: BaseException) -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ handle = models.LocalProcessHandle(
+ external_id="4242:100",
+ pid=4242,
+ process_group_id=4242,
+ start_marker="100",
+ stdout_path="out",
+ stderr_path="err",
+ )
+
+ with (
+ mock.patch.object(runtime, "_launch_process", return_value=handle),
+ mock.patch.object(runtime, "wait_for_readiness", side_effect=failure),
+ mock.patch.object(runtime, "_cleanup_handle", return_value=True) as cleanup,
+ pytest.raises(type(failure)),
+ ):
+ runtime.launch_plan(plan, secret_values={}, log_directory=tmp_path)
+
+ cleanup.assert_called_once_with(handle, plan.spec.local.shutdown_timeout_seconds)
+
+
+def test_launch_command_stops_service_when_receipt_write_fails(tmp_path: Path) -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ plan_path = tmp_path / "plan.json"
+ plan_path.write_text(plan.model_dump_json(), encoding="utf-8")
+ handle = models.LocalProcessHandle(
+ external_id="4242:100",
+ pid=4242,
+ process_group_id=4242,
+ start_marker="100",
+ stdout_path="out",
+ stderr_path="err",
+ )
+ receipt = launch_receipt(plan, handle)
+
+ with (
+ mock.patch.object(cli, "launch_plan", return_value=receipt),
+ mock.patch.object(cli, "write_json", side_effect=PermissionError("read-only destination")),
+ mock.patch.object(cli, "stop_run") as stop,
+ pytest.raises(SystemExit) as exc_info,
+ ):
+ cli.launch(plan=plan_path, output=tmp_path / "receipt.json", log_directory=tmp_path)
+
+ assert exc_info.value.code == 125
+ stop.assert_called_once_with(receipt)
+
+
+def test_launch_command_rejects_a_directory_receipt_target_before_launch(tmp_path: Path) -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ plan_path = tmp_path / "plan.json"
+ plan_path.write_text(plan.model_dump_json(), encoding="utf-8")
+ output = tmp_path / "receipt"
+ output.mkdir()
+ handle = models.LocalProcessHandle(
+ external_id="4242:100",
+ pid=4242,
+ process_group_id=4242,
+ start_marker="100",
+ stdout_path="out",
+ stderr_path="err",
+ )
+ receipt = launch_receipt(plan, handle)
+
+ with (
+ mock.patch.object(cli, "launch_plan", return_value=receipt) as launch,
+ mock.patch.object(cli, "stop_run"),
+ pytest.raises(SystemExit) as exc_info,
+ ):
+ cli.launch(plan=plan_path, output=output, log_directory=tmp_path)
+
+ assert exc_info.value.code == 125
+ launch.assert_not_called()
+
+
+def test_launch_refuses_an_unmarked_process_and_cleans_up(tmp_path: Path) -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ process = mock.Mock(pid=4242)
+ process.poll.return_value = 0
+ with (
+ mock.patch.object(runtime.subprocess, "Popen", return_value=process),
+ mock.patch.object(runtime, "read_process_start_marker", return_value=None),
+ mock.patch.object(runtime.os, "killpg") as killpg,
+ pytest.raises(runtime.RuntimeEffectError) as exc_info,
+ ):
+ runtime.launch_plan(plan, secret_values={}, log_directory=tmp_path)
+ killpg.assert_called_once_with(4242, runtime.signal.SIGTERM)
+ process.wait.assert_called_once_with(timeout=1)
+ assert exc_info.value.diagnostic.code == "missing-process-start-marker"
+ assert exc_info.value.diagnostic.cleanup_complete is True
+
+
+def test_launch_refuses_an_unmarked_process_when_cleanup_cannot_reap(tmp_path: Path) -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ process = mock.Mock(pid=4242)
+ process.poll.return_value = None
+ process.wait.side_effect = (
+ runtime.subprocess.TimeoutExpired(cmd=plan.command.render_argv(), timeout=1),
+ runtime.subprocess.TimeoutExpired(cmd=plan.command.render_argv(), timeout=1),
+ )
+ with (
+ mock.patch.object(runtime.subprocess, "Popen", return_value=process),
+ mock.patch.object(runtime, "read_process_start_marker", return_value=None),
+ mock.patch.object(runtime.os, "killpg") as killpg,
+ pytest.raises(runtime.RuntimeEffectError) as exc_info,
+ ):
+ runtime.launch_plan(plan, secret_values={}, log_directory=tmp_path)
+ assert killpg.call_args_list == [mock.call(4242, runtime.signal.SIGTERM), mock.call(4242, runtime.signal.SIGKILL)]
+ assert all(call.kwargs["timeout"] > 0 for call in process.wait.call_args_list)
+ assert exc_info.value.diagnostic.code == "missing-process-start-marker"
+ assert exc_info.value.diagnostic.cleanup_complete is False
+
+
+def test_missing_secret_fails_before_process_start(tmp_path: Path) -> None:
+ plan = compiler.compile_profile(generation(api_key_env="LOCAL_KEY"), source_revision="test")
+ with mock.patch.object(runtime.subprocess, "Popen") as popen:
+ with pytest.raises(runtime.RuntimeEffectError) as exc_info:
+ runtime.launch_plan(plan, secret_values={}, log_directory=tmp_path)
+ popen.assert_not_called()
+ assert exc_info.value.diagnostic.code == "missing-secret"
+ assert exc_info.value.diagnostic.known_effects == ()
+
+
+def test_status_stop_and_forced_cleanup_are_versioned(tmp_path: Path) -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ handle = models.LocalProcessHandle(
+ external_id="4242:100",
+ pid=4242,
+ process_group_id=4242,
+ start_marker="100",
+ stdout_path=str(tmp_path / "stdout.log"),
+ stderr_path=str(tmp_path / "stderr.log"),
+ )
+ launch = launch_receipt(plan, handle)
+ with mock.patch.object(runtime, "is_handle_running", return_value=True):
+ assert runtime.status_run(launch).state == "running"
+ with (
+ mock.patch.object(runtime, "is_handle_running", side_effect=[True, True, False]),
+ mock.patch.object(runtime.time, "monotonic", side_effect=[0.0, 0.0, 1.0]),
+ mock.patch.object(runtime.os, "killpg") as killpg,
+ ):
+ stopped = runtime.stop_run(launch)
+ assert stopped.outcome == "forced"
+ assert stopped.cleanup_complete is True
+ assert [call.args[1] for call in killpg.call_args_list] == [runtime.signal.SIGTERM, runtime.signal.SIGKILL]
+
+
+def test_process_stat_handles_spaces_and_zombies(tmp_path: Path) -> None:
+ fields = ["S", *(str(index) for index in range(4, 22)), "98765"]
+ assert runtime._parse_process_stat(f"4242 (worker with spaces) {' '.join(fields)}") == ("S", "98765")
+ handle = models.LocalProcessHandle(
+ external_id="4242:98765",
+ pid=4242,
+ process_group_id=4242,
+ start_marker="98765",
+ stdout_path=str(tmp_path / "stdout.log"),
+ stderr_path=str(tmp_path / "stderr.log"),
+ )
+ with (
+ mock.patch.object(runtime, "read_process_start_marker", return_value="98765"),
+ mock.patch.object(runtime, "read_process_state", return_value="Z"),
+ mock.patch.object(runtime.os, "kill") as kill,
+ ):
+ assert runtime.is_handle_running(handle) is False
+ kill.assert_not_called()
+
+
+def test_failed_readiness_cleans_up_the_known_process(tmp_path: Path) -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ process = mock.Mock(pid=4242)
+ failure = runtime.RuntimeEffectError(models.RuntimeDiagnostic(code="probe-failed", message="not ready"))
+ with (
+ mock.patch.object(runtime.subprocess, "Popen", return_value=process),
+ mock.patch.object(runtime, "read_process_start_marker", return_value="100"),
+ mock.patch.object(runtime, "wait_for_readiness", side_effect=failure),
+ mock.patch.object(runtime, "is_handle_running", side_effect=[True, False]),
+ mock.patch.object(runtime.os, "killpg") as killpg,
+ ):
+ with pytest.raises(runtime.RuntimeEffectError) as exc_info:
+ runtime.launch_plan(plan, secret_values={}, log_directory=tmp_path)
+ assert exc_info.value.diagnostic.known_effects == ("4242:100",)
+ assert exc_info.value.diagnostic.cleanup_complete is True
+ killpg.assert_called_once_with(4242, runtime.signal.SIGTERM)
+
+
+def test_readiness_stops_when_the_managed_process_exits(tmp_path: Path) -> None:
+ plan = compiler.compile_profile(generation(), source_revision="test")
+ handle = models.LocalProcessHandle(
+ external_id="4242:100",
+ pid=4242,
+ process_group_id=4242,
+ start_marker="100",
+ stdout_path=str(tmp_path / "stdout.log"),
+ stderr_path=str(tmp_path / "stderr.log"),
+ )
+ with (
+ mock.patch.object(runtime, "is_handle_running", return_value=False),
+ mock.patch.object(runtime, "probe_endpoint") as probe,
+ pytest.raises(runtime.RuntimeEffectError, match="exited before readiness"),
+ ):
+ runtime.wait_for_readiness(plan, handle=handle)
+ probe.assert_not_called()
diff --git a/tests/tools/test_vllm_factory.py b/tests/tools/test_vllm_factory.py
new file mode 100644
index 00000000..38d9e205
--- /dev/null
+++ b/tests/tools/test_vllm_factory.py
@@ -0,0 +1,270 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Tests for the local vLLM Python construction boundary."""
+
+from __future__ import annotations
+
+import importlib
+import os
+import tomllib
+from pathlib import Path
+from unittest import mock
+
+import pytest
+
+from inference_service_compiler import vllm_runtime as factory
+
+TOOLS_ROOT = Path(__file__).resolve().parents[2] / "tools"
+REPO_ROOT = TOOLS_ROOT.parent
+
+
+def test_local_models_group_pins_vllm_and_external_factory_source() -> None:
+ """The runtime pins vLLM and the reviewed external factory source revision."""
+ project = tomllib.loads((REPO_ROOT / "pyproject.toml").read_text(encoding="utf-8"))
+
+ assert project["dependency-groups"]["local-models"] == [
+ "vllm==0.27.1; sys_platform == 'linux' and python_version >= '3.12'",
+ (
+ "vllm-factory[gliner] @ git+https://github.com/latenceainew/vllm-factory.git@"
+ "7d6ff68ce68f9f7c0a9d72f9645bcf6d335d02f0; sys_platform == 'linux' "
+ "and python_version >= '3.12'"
+ ),
+ "nvidia-cuda-nvcc==13.0.88; sys_platform == 'linux' and python_version >= '3.12'",
+ "nvidia-cuda-crt==13.0.88; sys_platform == 'linux' and python_version >= '3.12'",
+ "nvidia-nvvm==13.0.88; sys_platform == 'linux' and python_version >= '3.12'",
+ ]
+
+
+def test_parse_server_parameters_accepts_only_the_compiler_contract() -> None:
+ """The process entry point accepts the bounded arguments emitted by the compiler."""
+ parameters = factory.parse_server_parameters(
+ [
+ "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+ "--host",
+ "127.0.0.1",
+ "--port",
+ "8123",
+ "--revision",
+ "fe8a4ea1",
+ "--tokenizer-revision",
+ "fe8a4ea1",
+ "--served-model-name",
+ "tiny",
+ "--tensor-parallel-size",
+ "2",
+ "--gpu-memory-utilization",
+ "0.8",
+ "--max-model-len",
+ "2048",
+ "--max-num-seqs",
+ "16",
+ "--enforce-eager",
+ "--enable-prefix-caching",
+ "--async-scheduling",
+ "--mamba-backend",
+ "flashinfer",
+ "--mamba-ssm-cache-dtype",
+ "float16",
+ "--enable-mamba-cache-stochastic-rounding",
+ "--mamba-cache-philox-rounds",
+ "5",
+ "--vllm-factory-plugin",
+ "deberta_gliner",
+ ]
+ )
+
+ assert parameters.model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+ assert parameters.port == 8123
+ assert parameters.tensor_parallel_size == 2
+ assert parameters.gpu_memory_utilization == 0.8
+ assert parameters.max_num_seqs == 16
+ assert parameters.enforce_eager is True
+ assert parameters.enable_prefix_caching is True
+ assert parameters.async_scheduling is True
+ assert parameters.mamba_backend == "flashinfer"
+ assert parameters.mamba_ssm_cache_dtype == "float16"
+ assert parameters.enable_mamba_cache_stochastic_rounding is True
+ assert parameters.mamba_cache_philox_rounds == 5
+ assert parameters.vllm_factory_plugin == "deberta_gliner"
+
+
+def test_factory_constructs_vllm_frontend_and_async_engine_arguments() -> None:
+ """The local service is built from vLLM Python config objects."""
+ pytest.importorskip("vllm")
+ parameters = factory.VllmServerParameters(
+ model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+ host="127.0.0.1",
+ port=8123,
+ revision="fe8a4ea1",
+ tokenizer_revision="fe8a4ea1",
+ served_model_name="tiny",
+ tensor_parallel_size=2,
+ gpu_memory_utilization=0.8,
+ max_model_len=2048,
+ max_num_seqs=16,
+ enforce_eager=True,
+ enable_prefix_caching=True,
+ async_scheduling=True,
+ mamba_backend="flashinfer",
+ mamba_ssm_cache_dtype="float16",
+ enable_mamba_cache_stochastic_rounding=True,
+ mamba_cache_philox_rounds=5,
+ lora_module="privacy=/models/privacy-adapter",
+ )
+
+ arguments = factory.build_server_arguments(parameters)
+
+ assert arguments.model == parameters.model
+ assert arguments.host == "127.0.0.1"
+ assert arguments.port == 8123
+ assert arguments.revision == "fe8a4ea1"
+ assert arguments.tokenizer_revision == "fe8a4ea1"
+ assert arguments.served_model_name == ["tiny"]
+ assert arguments.tensor_parallel_size == 2
+ assert arguments.gpu_memory_utilization == 0.8
+ assert arguments.max_model_len == 2048
+ assert arguments.max_num_seqs == 16
+ assert arguments.enforce_eager is True
+ assert arguments.enable_prefix_caching is True
+ assert arguments.async_scheduling is True
+ assert arguments.middleware == [factory.LAUNCH_OWNERSHIP_MIDDLEWARE]
+ assert arguments.mamba_backend.value == "flashinfer"
+ assert arguments.mamba_ssm_cache_dtype == "float16"
+ assert arguments.enable_mamba_cache_stochastic_rounding is True
+ assert arguments.mamba_cache_philox_rounds == 5
+ assert arguments.enable_lora is True
+ assert [(module.name, module.path) for module in arguments.lora_modules] == [("privacy", "/models/privacy-adapter")]
+
+
+def test_factory_constructs_pooling_server_for_external_gliner_plugin() -> None:
+ """Factory-backed detection uses vLLM pooling and the project's IOProcessor."""
+ pytest.importorskip("vllm")
+ parameters = factory.VllmServerParameters(
+ model="/tmp/prepared-gliner",
+ host="127.0.0.1",
+ port=8123,
+ served_model_name="nvidia/gliner-pii",
+ vllm_factory_plugin="deberta_gliner",
+ )
+
+ arguments = factory.build_server_arguments(parameters)
+
+ assert arguments.runner == "pooling"
+ assert arguments.io_processor_plugin == "deberta_gliner_io"
+ assert arguments.trust_remote_code is True
+ assert arguments.enable_prefix_caching is False
+ assert arguments.enable_chunked_prefill is False
+ assert arguments.middleware == [factory.LAUNCH_OWNERSHIP_MIDDLEWARE, factory.ANONYMIZER_CHAT_MIDDLEWARE]
+
+
+def test_run_server_uses_the_vllm_0_27_lifecycle_boundary() -> None:
+ """The process runner imports and invokes vLLM 0.27's lifecycle API."""
+ pytest.importorskip("vllm")
+ uvloop = importlib.import_module("uvloop")
+ api_server = importlib.import_module("vllm.entrypoints.openai.api_server")
+ api_utils = importlib.import_module("vllm.entrypoints.serve.utils.api_utils")
+
+ arguments = mock.sentinel.arguments
+ coroutine = mock.sentinel.coroutine
+ run_vllm_server = mock.Mock(return_value=coroutine)
+
+ with (
+ mock.patch.object(factory, "parse_server_parameters", return_value=mock.sentinel.parameters),
+ mock.patch.object(
+ factory, "prepare_runtime_environment", return_value=mock.sentinel.prepared
+ ) as prepare_environment,
+ mock.patch.object(factory, "build_server_arguments", return_value=arguments) as build_arguments,
+ mock.patch.object(api_utils, "cli_env_setup") as cli_env_setup,
+ mock.patch.object(api_server, "run_server", new=run_vllm_server),
+ mock.patch.object(uvloop, "run") as uvloop_run,
+ ):
+ factory.run_server(["model", "--host", "127.0.0.1", "--port", "8000"])
+
+ cli_env_setup.assert_called_once_with()
+ prepare_environment.assert_called_once_with(mock.sentinel.parameters)
+ build_arguments.assert_called_once_with(mock.sentinel.prepared)
+ run_vllm_server.assert_called_once_with(arguments)
+ uvloop_run.assert_called_once_with(coroutine)
+
+
+def test_factory_exposes_interpreter_tools_on_path() -> None:
+ """vLLM subprocess helpers can find executables installed beside Python."""
+ with (
+ mock.patch.object(factory.sys, "prefix", "/workspace/.venv"),
+ mock.patch.dict(os.environ, {"PATH": "/usr/bin"}),
+ ):
+ factory.expose_interpreter_tools()
+
+ assert os.environ["PATH"] == f"/workspace/.venv/bin{os.pathsep}/usr/bin"
+
+
+def test_factory_avoids_flashinfer_jit_without_overriding_operator_choice() -> None:
+ """The wheel-only runtime does not require a host CUDA compiler by default."""
+ parameters = factory.VllmServerParameters(model="model", host="127.0.0.1", port=8000)
+
+ with (
+ mock.patch.object(factory.sys, "version_info", (3, 12)),
+ mock.patch.dict(os.environ, {}, clear=True),
+ ):
+ assert factory.prepare_runtime_environment(parameters) == parameters
+ assert os.environ["VLLM_USE_FLASHINFER_SAMPLER"] == "0"
+
+ with (
+ mock.patch.object(factory.sys, "version_info", (3, 12)),
+ mock.patch.dict(os.environ, {"VLLM_USE_FLASHINFER_SAMPLER": "1"}, clear=True),
+ ):
+ assert factory.prepare_runtime_environment(parameters) == parameters
+ assert os.environ["VLLM_USE_FLASHINFER_SAMPLER"] == "1"
+
+
+def test_factory_rejects_python_3_11_before_importing_vllm() -> None:
+ """The server reports the local vLLM Python floor before vLLM starts."""
+ parameters = factory.VllmServerParameters(model="model", host="127.0.0.1", port=8000)
+
+ with mock.patch.object(factory.sys, "version_info", (3, 11)):
+ with pytest.raises(RuntimeError, match="Python 3.12 or later"):
+ factory.prepare_runtime_environment(parameters)
+
+
+def test_factory_configures_packaged_cuda_for_flashinfer(tmp_path: Path) -> None:
+ """The FlashInfer backend can JIT from the CUDA toolkit shipped as Python wheels."""
+ cuda_root = tmp_path / "nvidia" / "cu13"
+ (cuda_root / "bin").mkdir(parents=True)
+ (cuda_root / "bin" / "nvcc").touch()
+ (cuda_root / "lib").mkdir()
+ runtime = cuda_root / "lib" / "libcudart.so.13"
+ runtime.touch()
+
+ with (
+ mock.patch.object(factory, "_packaged_cuda_root", return_value=cuda_root),
+ mock.patch.object(factory.Path, "home", return_value=tmp_path),
+ mock.patch.dict(os.environ, {}, clear=True),
+ ):
+ factory.configure_flashinfer_toolchain()
+
+ root_digest = factory.hashlib.sha256(os.fsencode(cuda_root.resolve())).hexdigest()[:16]
+ link_directory = tmp_path / ".cache" / "nemo-anonymizer" / "cuda-link" / root_digest
+ assert os.environ["CUDA_HOME"] == str(cuda_root)
+ assert os.environ["LIBRARY_PATH"] == str(link_directory)
+ assert os.environ["LD_LIBRARY_PATH"] == str(cuda_root / "lib")
+ assert (link_directory / "libcudart.so").resolve() == runtime
+
+
+def test_factory_selects_model_and_io_plugins_together() -> None:
+ """vLLM's shared plugin allowlist retains both factory entry-point groups."""
+ parameters = factory.VllmServerParameters(
+ model="nvidia/gliner-pii",
+ revision="bd23e8ef",
+ host="127.0.0.1",
+ port=8000,
+ vllm_factory_plugin="deberta_gliner",
+ )
+
+ with (
+ mock.patch.object(factory, "prepare_model", return_value="/tmp/prepared"),
+ mock.patch.object(factory.sys, "version_info", (3, 12)),
+ mock.patch.dict(os.environ, {}, clear=True),
+ ):
+ factory.prepare_runtime_environment(parameters)
+
+ assert os.environ["VLLM_PLUGINS"] == "deberta_gliner,deberta_gliner_io"
diff --git a/tests/tools/test_vllm_factory_adapter.py b/tests/tools/test_vllm_factory_adapter.py
new file mode 100644
index 00000000..284115b7
--- /dev/null
+++ b/tests/tools/test_vllm_factory_adapter.py
@@ -0,0 +1,355 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Behavior tests for the vLLM Factory detector protocol adapter."""
+
+from __future__ import annotations
+
+import asyncio
+import json
+from types import SimpleNamespace
+
+import pytest
+from starlette.datastructures import Headers
+
+from inference_service_compiler import vllm_factory_adapter as adapter
+
+
+def test_parse_detection_request_preserves_anonymizer_options() -> None:
+ """The adapter accepts the detector extras emitted by DataDesigner."""
+ request = adapter.parse_detection_request(
+ {
+ "model": "nvidia/gliner-pii",
+ "messages": [{"role": "user", "content": "Ada Lovelace"}],
+ "labels": ["person", "email"],
+ "threshold": 0.42,
+ "chunk_length": 256,
+ "overlap": 64,
+ "flat_ner": True,
+ }
+ )
+
+ assert request.text == "Ada Lovelace"
+ assert request.labels == ("person", "email")
+ assert request.threshold == 0.42
+ assert request.chunk_length == 256
+ assert request.overlap == 64
+ assert request.flat_ner is True
+
+
+def test_parse_detection_request_accepts_label_free_health_check() -> None:
+ """DataDesigner's generic model health check receives a valid empty result."""
+
+ request = adapter.parse_detection_request(
+ {
+ "model": "nvidia/gliner-pii",
+ "messages": [{"role": "user", "content": "x" * 257}],
+ "chunk_length": 1,
+ "overlap": 0,
+ }
+ )
+
+ assert request.labels == ()
+
+
+def test_parse_detection_request_rejects_excessive_chunk_fanout() -> None:
+ """One detector request cannot enqueue unbounded pooling work."""
+
+ with pytest.raises(ValueError, match="at most 256 chunks"):
+ adapter.parse_detection_request(
+ {
+ "model": "nvidia/gliner-pii",
+ "messages": [{"role": "user", "content": "x" * 257}],
+ "labels": ["person"],
+ "chunk_length": 1,
+ "overlap": 0,
+ }
+ )
+
+
+def test_parse_detection_request_accepts_chunk_budget_boundary() -> None:
+ """The admission cap includes requests with exactly 256 chunks."""
+
+ request = adapter.parse_detection_request(
+ {
+ "model": "nvidia/gliner-pii",
+ "messages": [{"role": "user", "content": "x" * 256}],
+ "labels": ["person"],
+ "chunk_length": 1,
+ "overlap": 0,
+ }
+ )
+
+ assert request.text == "x" * 256
+
+
+@pytest.mark.parametrize(
+ "authorization",
+ [None, "Basic expected-secret", "Bearer wrong-secret"],
+)
+def test_chat_compatibility_rejects_invalid_configured_api_key(
+ monkeypatch: pytest.MonkeyPatch,
+ authorization: str | None,
+) -> None:
+ """The compatibility route preserves vLLM's configured authentication."""
+
+ async def exercise() -> None:
+ async def request_json() -> dict[str, object]:
+ return {
+ "model": "nvidia/gliner-pii",
+ "messages": [{"role": "user", "content": "Ada Lovelace"}],
+ "labels": [],
+ }
+
+ async def call_next(_: object) -> None:
+ raise AssertionError("the compatibility route must handle this request")
+
+ headers = Headers({} if authorization is None else {"authorization": authorization})
+ request = SimpleNamespace(
+ url=SimpleNamespace(path="/v1/chat/completions"),
+ headers=headers,
+ json=request_json,
+ )
+
+ response = await adapter.anonymizer_chat_compatibility(request, call_next)
+
+ assert response.status_code == 401
+ assert json.loads(response.body) == {"error": "Unauthorized"}
+
+ monkeypatch.setenv("VLLM_API_KEY", "expected-secret")
+ monkeypatch.setenv("ANONYMIZER_VLLM_FACTORY_PLUGIN", "deberta_gliner")
+ asyncio.run(exercise())
+
+
+@pytest.mark.parametrize(
+ ("api_key", "authorization"),
+ [(None, None), ("expected-secret", "Bearer expected-secret")],
+)
+def test_chat_compatibility_accepts_valid_or_disabled_api_key(
+ monkeypatch: pytest.MonkeyPatch,
+ api_key: str | None,
+ authorization: str | None,
+) -> None:
+ """The compatibility route remains available without auth or with a valid key."""
+
+ async def exercise() -> None:
+ async def request_json() -> dict[str, object]:
+ return {
+ "model": "nvidia/gliner-pii",
+ "messages": [{"role": "user", "content": "Ada Lovelace"}],
+ "labels": [],
+ }
+
+ async def call_next(_: object) -> None:
+ raise AssertionError("the compatibility route must handle this request")
+
+ headers = Headers({} if authorization is None else {"authorization": authorization})
+ request = SimpleNamespace(
+ url=SimpleNamespace(path="/v1/chat/completions"),
+ headers=headers,
+ json=request_json,
+ )
+
+ response = await adapter.anonymizer_chat_compatibility(request, call_next)
+
+ assert response.status_code == 200
+
+ if api_key is None:
+ monkeypatch.delenv("VLLM_API_KEY", raising=False)
+ else:
+ monkeypatch.setenv("VLLM_API_KEY", api_key)
+ monkeypatch.setenv("ANONYMIZER_VLLM_FACTORY_PLUGIN", "deberta_gliner")
+ asyncio.run(exercise())
+
+
+def test_chat_compatibility_bounds_aggregate_pooling_concurrency(monkeypatch: pytest.MonkeyPatch) -> None:
+ """Concurrent requests share one worker budget and preserve result order."""
+
+ async def exercise() -> None:
+ expected_peak = 8
+ active = 0
+ max_active = 0
+ total_calls = 0
+ reached_limit = asyncio.Event()
+ exceeded_limit = asyncio.Event()
+ release = asyncio.Event()
+
+ async def fake_invoke_pooling(**kwargs: object) -> object:
+ nonlocal active, max_active, total_calls
+ active += 1
+ total_calls += 1
+ max_active = max(max_active, active)
+ if active == expected_peak:
+ reached_limit.set()
+ if active > expected_peak:
+ exceeded_limit.set()
+ try:
+ await release.wait()
+ text = kwargs["text"]
+ assert isinstance(text, str)
+ return [{"text": text, "label": "token", "start": 0, "end": 1, "score": 0.9}]
+ finally:
+ active -= 1
+
+ async def call_next(_: object) -> None:
+ raise AssertionError("detector requests must not reach the next middleware")
+
+ def request(text: str, app_state: SimpleNamespace) -> SimpleNamespace:
+ async def request_json() -> dict[str, object]:
+ return {
+ "model": "nvidia/gliner-pii",
+ "messages": [{"role": "user", "content": text}],
+ "labels": ["token"],
+ "chunk_length": 1,
+ "overlap": 0,
+ }
+
+ return SimpleNamespace(
+ url=SimpleNamespace(path="/v1/chat/completions"),
+ app=SimpleNamespace(state=app_state),
+ json=request_json,
+ )
+
+ monkeypatch.setattr(adapter, "invoke_pooling", fake_invoke_pooling)
+ monkeypatch.setenv("ANONYMIZER_VLLM_FACTORY_PLUGIN", "deberta_gliner")
+ app_state = SimpleNamespace(serving_pooling=object())
+ requests = [request("abcdefghijklmnopq", app_state), request("ABCDEFGHIJKLMNOPQ", app_state)]
+
+ operations = [
+ asyncio.create_task(adapter.anonymizer_chat_compatibility(active_request, call_next))
+ for active_request in requests
+ ]
+ await reached_limit.wait()
+ try:
+ await asyncio.wait_for(exceeded_limit.wait(), timeout=0.05)
+ except TimeoutError:
+ pass
+ observed_peak = max_active
+ release.set()
+ responses = await asyncio.gather(*operations)
+
+ assert all(response.status_code == 200 for response in responses)
+ assert observed_peak <= expected_peak
+ assert total_calls == 34
+ for response in responses:
+ payload = json.loads(response.body)
+ content = json.loads(payload["choices"][0]["message"]["content"])
+ assert [entity["start"] for entity in content["entities"]] == list(range(17))
+
+ asyncio.run(exercise())
+
+
+def test_pooling_budget_is_released_after_failure(monkeypatch: pytest.MonkeyPatch) -> None:
+ """A failed pooling call cannot consume a service permit permanently."""
+
+ async def exercise() -> None:
+ detection = adapter.parse_detection_request(
+ {
+ "model": "nvidia/gliner-pii",
+ "messages": [{"role": "user", "content": "x"}],
+ "labels": ["token"],
+ "chunk_length": 1,
+ "overlap": 0,
+ }
+ )
+ chunks = [adapter.TextChunk("x", 0)]
+ limiter = asyncio.Semaphore(1)
+
+ async def fail(**_kwargs: object) -> object:
+ raise RuntimeError("pooling failed")
+
+ monkeypatch.setattr(adapter, "invoke_pooling", fail)
+ with pytest.raises(RuntimeError, match="pooling failed"):
+ await adapter.invoke_pooling_chunks(
+ handler=object(),
+ plugin="deberta_gliner",
+ detection=detection,
+ chunks=chunks,
+ limiter=limiter,
+ )
+
+ async def succeed(**_kwargs: object) -> object:
+ return []
+
+ monkeypatch.setattr(adapter, "invoke_pooling", succeed)
+ result = await asyncio.wait_for(
+ adapter.invoke_pooling_chunks(
+ handler=object(),
+ plugin="deberta_gliner",
+ detection=detection,
+ chunks=chunks,
+ limiter=limiter,
+ ),
+ timeout=0.1,
+ )
+ assert result == [[]]
+
+ asyncio.run(exercise())
+
+
+def test_merge_gliner_entities_restores_offsets_and_deduplicates_overlap() -> None:
+ """Chunk-relative factory spans become stable document offsets."""
+ chunks = [adapter.TextChunk("Alice met Bob", 0), adapter.TextChunk("Bob at NVIDIA", 10)]
+ results = [
+ [
+ {"text": "Alice", "label": "person", "start": 0, "end": 5, "score": 0.9},
+ {"text": "Bob", "label": "person", "start": 10, "end": 13, "score": 0.8},
+ ],
+ [
+ {"text": "Bob", "label": "person", "start": 0, "end": 3, "score": 0.95},
+ {"text": "NVIDIA", "label": "company", "start": 7, "end": 13, "score": 0.88},
+ ],
+ ]
+
+ entities = adapter.merge_entities(
+ plugin="deberta_gliner",
+ chunks=chunks,
+ results=results,
+ flat_ner=True,
+ )
+
+ assert [entity.as_dict() for entity in entities] == [
+ {"text": "Alice", "label": "person", "start": 0, "end": 5, "score": 0.9},
+ {"text": "Bob", "label": "person", "start": 10, "end": 13, "score": 0.95},
+ {"text": "NVIDIA", "label": "company", "start": 17, "end": 23, "score": 0.88},
+ ]
+
+
+def test_merge_gliner2_entities_normalizes_confidence_and_spans() -> None:
+ """GLiNER2's schema result becomes the detector's flat entity list."""
+ entities = adapter.merge_entities(
+ plugin="deberta_gliner2",
+ chunks=[adapter.TextChunk("Email alice@example.com", 0)],
+ results=[
+ {
+ "entities": {
+ "email": [
+ {
+ "text": "alice@example.com",
+ "start": 6,
+ "end": 23,
+ "confidence": 0.97,
+ }
+ ]
+ }
+ }
+ ],
+ flat_ner=False,
+ )
+
+ assert [entity.as_dict() for entity in entities] == [
+ {
+ "text": "alice@example.com",
+ "label": "email",
+ "start": 6,
+ "end": 23,
+ "score": 0.97,
+ }
+ ]
+
+
+def test_split_text_matches_native_character_overlap_contract() -> None:
+ """The adapter keeps the characterized character-based chunk semantics."""
+ assert adapter.split_text("abcdefghij", chunk_length=6, overlap=2) == [
+ adapter.TextChunk("abcdef", 0),
+ adapter.TextChunk("efghij", 4),
+ ]
diff --git a/tests/tools/test_vllm_factory_integration.py b/tests/tools/test_vllm_factory_integration.py
new file mode 100644
index 00000000..bb9d05fc
--- /dev/null
+++ b/tests/tools/test_vllm_factory_integration.py
@@ -0,0 +1,63 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Tests for the pinned external vLLM Factory preparation boundary."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from types import SimpleNamespace
+from unittest import mock
+
+from inference_service_compiler import vllm_factory_integration as integration
+
+
+def test_factory_plugin_spec_is_immutable_and_complete() -> None:
+ """Each supported plugin resolves all of its metadata through one product."""
+ gliner = integration.factory_plugin_spec("deberta_gliner")
+ gliner2 = integration.factory_plugin_spec("deberta_gliner2")
+
+ assert gliner.io_processor == "deberta_gliner_io"
+ assert gliner.characterized_models == frozenset({"nvidia/gliner-pii"})
+ assert gliner2.io_processor == "deberta_gliner2_io"
+ assert gliner2.characterized_models == frozenset({"fastino/gliner2-privacy-filter-PII-multi"})
+
+
+def test_prepare_model_injects_pinned_revision_into_upstream_python_api(tmp_path: Path) -> None:
+ """Anonymizer closes vLLM Factory's missing Hugging Face revision argument."""
+ download = mock.Mock(return_value="/cache/file")
+ list_files = mock.Mock(return_value=[])
+ tokenizer = mock.Mock(return_value=mock.Mock())
+ model_prep = SimpleNamespace(
+ hf_hub_download=download,
+ list_repo_files=list_files,
+ )
+
+ def prepare_model_for_vllm_if_needed(**kwargs):
+ model_prep.list_repo_files(kwargs["model_ref"])
+ model_prep.hf_hub_download(repo_id=kwargs["model_ref"], filename="config.json")
+ transformers.AutoTokenizer.from_pretrained(kwargs["model_ref"])
+ Path(kwargs["output_dir"]).mkdir(parents=True, exist_ok=True)
+ return kwargs["output_dir"]
+
+ model_prep.prepare_model_for_vllm_if_needed = prepare_model_for_vllm_if_needed
+ transformers = SimpleNamespace(AutoTokenizer=SimpleNamespace(from_pretrained=tokenizer))
+
+ def import_module(name: str):
+ return model_prep if name == "forge.model_prep" else transformers
+
+ with mock.patch.object(integration.importlib, "import_module", side_effect=import_module):
+ prepared = integration.prepare_model(
+ model_id="nvidia/gliner-pii",
+ revision="bd23e8ef",
+ plugin="deberta_gliner",
+ prepared_model_root=str(tmp_path),
+ )
+
+ assert Path(prepared).is_dir()
+ list_files.assert_called_once_with("nvidia/gliner-pii", revision="bd23e8ef")
+ download.assert_called_once_with(
+ repo_id="nvidia/gliner-pii",
+ filename="config.json",
+ revision="bd23e8ef",
+ )
+ tokenizer.assert_called_once_with("nvidia/gliner-pii", revision="bd23e8ef")
diff --git a/tools/inference_service.Dockerfile b/tools/inference_service.Dockerfile
new file mode 100644
index 00000000..a127c627
--- /dev/null
+++ b/tools/inference_service.Dockerfile
@@ -0,0 +1,26 @@
+# syntax=docker/dockerfile:1
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+FROM nvidia/cuda:13.0.2-devel-ubuntu24.04
+
+COPY --from=ghcr.io/astral-sh/uv:0.11.31 /uv /uvx /bin/
+
+ENV DEBIAN_FRONTEND=noninteractive \
+ HF_HOME=/models/huggingface \
+ UV_LINK_MODE=copy \
+ UV_PROJECT_ENVIRONMENT=/opt/anonymizer/.venv \
+ UV_PYTHON_INSTALL_DIR=/opt/uv-python \
+ PATH=/opt/anonymizer/.venv/bin:$PATH
+
+RUN apt-get update \
+ && apt-get install --yes --no-install-recommends ca-certificates git \
+ && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /opt/anonymizer
+COPY . .
+
+RUN uv python install 3.12 \
+ && uv sync --frozen --python 3.12 --no-default-groups --group local-models
+
+CMD ["sleep", "infinity"]
diff --git a/tools/inference_service.py b/tools/inference_service.py
new file mode 100755
index 00000000..0e6958af
--- /dev/null
+++ b/tools/inference_service.py
@@ -0,0 +1,20 @@
+#!/usr/bin/env -S uv run --script
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+# /// script
+# requires-python = ">=3.13"
+# dependencies = [
+# "cyclopts>=3",
+# "httpx>=0.27",
+# "pydantic>=2.9,<3",
+# "structlog>=24.4",
+# ]
+# ///
+"""Compile and manage local inference services from typed specifications."""
+
+from __future__ import annotations
+
+from inference_service_compiler.cli import app
+
+if __name__ == "__main__":
+ app()
diff --git a/tools/inference_service_compiler/__init__.py b/tools/inference_service_compiler/__init__.py
new file mode 100644
index 00000000..4f61ada0
--- /dev/null
+++ b/tools/inference_service_compiler/__init__.py
@@ -0,0 +1,5 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Source-tree inference service compiler owned by Anonymizer."""
+
+from __future__ import annotations
diff --git a/tools/inference_service_compiler/cli.py b/tools/inference_service_compiler/cli.py
new file mode 100644
index 00000000..54316a0c
--- /dev/null
+++ b/tools/inference_service_compiler/cli.py
@@ -0,0 +1,156 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Command-line transport for the inference service compiler."""
+
+from __future__ import annotations
+
+import functools
+import os
+import sys
+import tempfile
+from collections.abc import Callable
+from pathlib import Path
+from tomllib import TOMLDecodeError
+from typing import ParamSpec, TypeVar
+
+import cyclopts
+from pydantic import BaseModel, ValidationError
+
+from inference_service_compiler.compiler import CompilationError, PlanIntegrityError, compile_profile, load_plan
+from inference_service_compiler.models import LaunchReceipt
+from inference_service_compiler.profiles import load_profile
+from inference_service_compiler.runtime import (
+ RuntimeEffectError,
+ launch_plan,
+ probe_endpoint,
+ status_run,
+ stop_run,
+)
+
+app = cyclopts.App(help="Compile and manage local inference services from TOML profiles.")
+
+P = ParamSpec("P")
+R = TypeVar("R")
+
+
+def command_errors(function: Callable[P, R]) -> Callable[P, R]:
+ """Render transport and compiler errors with the standard bad-input exit code."""
+
+ @functools.wraps(function)
+ def wrapped(*args: P.args, **kwargs: P.kwargs) -> R:
+ try:
+ return function(*args, **kwargs)
+ except (
+ CompilationError,
+ FileNotFoundError,
+ IsADirectoryError,
+ NotADirectoryError,
+ PermissionError,
+ PlanIntegrityError,
+ RuntimeEffectError,
+ TOMLDecodeError,
+ UnicodeDecodeError,
+ ValidationError,
+ ) as exc:
+ sys.stderr.write(f"error: {exc}\n")
+ raise SystemExit(125) from exc
+
+ return wrapped
+
+
+@app.command(name="compile")
+@command_errors
+def compile_plan(
+ *,
+ profile: Path,
+ source_revision: str,
+ output: Path | None = None,
+) -> None:
+ """Compile a v2 TOML profile without performing runtime effects."""
+ parsed = load_profile(profile)
+ plan = compile_profile(parsed, source_revision=source_revision)
+ write_json(plan, output)
+
+
+@app.command
+@command_errors
+def launch(
+ *,
+ plan: Path,
+ output: Path | None = None,
+ log_directory: Path = Path(".inference-service-runs"),
+) -> None:
+ """Launch a compiled plan and record its observed handle and readiness."""
+ _validate_output_destination(output)
+ parsed = load_plan(plan.read_text(encoding="utf-8"))
+ secret_values = {name: os.environ[name] for name in parsed.command.secret_sources if name in os.environ}
+ receipt = launch_plan(parsed, secret_values=secret_values, log_directory=log_directory)
+ try:
+ write_json(receipt, output)
+ except BaseException as exc:
+ try:
+ stop_run(receipt)
+ except BaseException as cleanup_exc:
+ exc.add_note(f"managed-process cleanup failed after receipt write failure: {cleanup_exc}")
+ raise
+
+
+@app.command
+@command_errors
+def probe(*, plan: Path, output: Path | None = None) -> None:
+ """Probe the endpoint declared by a managed plan and write capability evidence."""
+ parsed = load_plan(plan.read_text(encoding="utf-8"))
+ source = parsed.readiness.bearer_token_environment_variable
+ secret_values = {source: os.environ[source]} if source is not None and source in os.environ else {}
+ write_json(probe_endpoint(parsed, secret_values=secret_values), output)
+
+
+@app.command(name="status")
+@command_errors
+def status_command(*, receipt: Path, output: Path | None = None) -> None:
+ """Record the current state observed for a launch receipt handle."""
+ launch_receipt = LaunchReceipt.model_validate_json(receipt.read_text(encoding="utf-8"))
+ write_json(status_run(launch_receipt), output)
+
+
+@app.command
+@command_errors
+def stop(*, receipt: Path, output: Path | None = None) -> None:
+ """Stop and clean up the process group recorded by a launch receipt."""
+ launch_receipt = LaunchReceipt.model_validate_json(receipt.read_text(encoding="utf-8"))
+ write_json(stop_run(launch_receipt), output)
+
+
+def write_json(value: BaseModel, output: Path | None) -> None:
+ """Write one versioned transport value to a file or standard output."""
+ rendered = value.model_dump_json(indent=2) + "\n"
+ if output is None:
+ sys.stdout.write(rendered)
+ else:
+ temporary_path: Path | None = None
+ try:
+ with tempfile.NamedTemporaryFile(
+ mode="w",
+ encoding="utf-8",
+ dir=output.parent,
+ prefix=f".{output.name}.",
+ delete=False,
+ ) as temporary_file:
+ temporary_file.write(rendered)
+ temporary_file.flush()
+ os.fsync(temporary_file.fileno())
+ temporary_path = Path(temporary_file.name)
+ temporary_path.replace(output)
+ finally:
+ if temporary_path is not None:
+ temporary_path.unlink(missing_ok=True)
+
+
+def _validate_output_destination(output: Path | None) -> None:
+ """Verify that a receipt destination can create files before launching."""
+ if output is None:
+ return
+ if output.is_dir():
+ raise IsADirectoryError(output)
+ with tempfile.NamedTemporaryFile(dir=output.parent, prefix=f".{output.name}.", delete=True):
+ pass
diff --git a/tools/inference_service_compiler/compiler.py b/tools/inference_service_compiler/compiler.py
new file mode 100644
index 00000000..a594ecca
--- /dev/null
+++ b/tools/inference_service_compiler/compiler.py
@@ -0,0 +1,302 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Pure compilation from inference spec to an immutable run plan."""
+
+from __future__ import annotations
+
+import hashlib
+import hmac
+import json
+from typing import Never, assert_never
+
+from inference_service_compiler.models import (
+ CommandSpec,
+ CompatibilityAssessment,
+ EndpointAddress,
+ EntityDetection,
+ FrozenModel,
+ Generation,
+ LiteralArgument,
+ LocalInferenceServiceSpec,
+ ReadinessCheck,
+ RunPlan,
+ SecretEnvironmentVariable,
+ Vllm,
+)
+from inference_service_compiler.vllm_factory_integration import (
+ VLLM_FACTORY_DEPENDENCY,
+ supports_model,
+)
+
+VLLM_API_KEY_ENV = "VLLM_API_KEY"
+VLLM_DEPENDENCY = "vllm==0.27.1"
+FLASHINFER_CUDA_TOOLCHAIN_DEPENDENCIES = (
+ "nvidia-cuda-nvcc==13.0.88",
+ "nvidia-cuda-crt==13.0.88",
+ "nvidia-nvvm==13.0.88",
+)
+
+
+class CompilerDiagnostic(FrozenModel):
+ """Serializable reason that semantic spec cannot be compiled."""
+
+ code: str
+ message: str
+ details: dict[str, str]
+
+
+class CompilationError(ValueError):
+ """A service spec failed a closed compiler compatibility rule."""
+
+ def __init__(self, diagnostic: CompilerDiagnostic) -> None:
+ super().__init__(diagnostic.message)
+ self.diagnostic = diagnostic
+
+
+class PlanIntegrityError(ValueError):
+ """A serialized plan does not match its declared consistency checksum."""
+
+
+def compile_profile(spec: LocalInferenceServiceSpec, *, source_revision: str) -> RunPlan:
+ """Compile semantic spec without starting, probing, or allocating anything."""
+ if not source_revision:
+ raise CompilationError(
+ CompilerDiagnostic(
+ code="invalid-source-revision",
+ message="source_revision must not be empty",
+ details={},
+ )
+ )
+ required = spec.task.required_capabilities()
+ command, assessments = _compile_vllm(spec)
+ placement = spec.local
+ endpoint = EndpointAddress(host=placement.host, port=placement.port)
+ plan = RunPlan(
+ plan_digest="",
+ spec=spec,
+ command=command,
+ endpoint=endpoint,
+ readiness=ReadinessCheck(
+ path="/models",
+ timeout_seconds=spec.local.startup_timeout_seconds,
+ bearer_token_environment_variable=spec.vllm.api_key_env,
+ ),
+ served_model_name=spec.served_model_name,
+ required_capabilities=required,
+ compatibility_assessments=assessments,
+ dependencies=_plan_dependencies(spec),
+ source_revision=source_revision,
+ )
+ return plan.model_copy(update={"plan_digest": digest_plan(plan)})
+
+
+def digest_plan(plan: RunPlan) -> str:
+ """Return the stable digest of a plan excluding its digest field."""
+ payload = plan.model_dump(mode="json", exclude={"plan_digest"})
+ return hashlib.sha256(_canonical_json(payload)).hexdigest()
+
+
+def load_plan(serialized: str | bytes) -> RunPlan:
+ """Parse a closed v2 plan and reject accidental transport mutation."""
+ plan = RunPlan.model_validate_json(serialized)
+ verify_plan(plan)
+ return plan
+
+
+def verify_plan(plan: RunPlan) -> None:
+ """Check transport consistency, without authenticating or recompiling a plan."""
+ expected = digest_plan(plan)
+ if not hmac.compare_digest(plan.plan_digest, expected):
+ raise PlanIntegrityError(f"plan digest mismatch: declared {plan.plan_digest!r}, computed {expected!r}")
+
+
+def _canonical_json(value: object) -> bytes:
+ return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=False).encode()
+
+
+def _compile_vllm(spec: LocalInferenceServiceSpec) -> tuple[CommandSpec, tuple[CompatibilityAssessment, ...]]:
+ match spec.task:
+ case EntityDetection() as task:
+ return _compile_entity_detection(spec, task)
+ case Generation() as task:
+ return _compile_generation(spec, task)
+ case _:
+ assert_never(spec.task)
+
+
+def _compile_entity_detection(
+ spec: LocalInferenceServiceSpec, task: EntityDetection
+) -> tuple[CommandSpec, tuple[CompatibilityAssessment, ...]]:
+ _validate_factory_detection(spec, task)
+ command = _vllm_command(spec, spec.vllm)
+ return command, (
+ CompatibilityAssessment(
+ rule="vllm-factory-entity-detection-v1",
+ outcome="runtime-probe-required",
+ detail=(
+ "vLLM Factory supplies model preparation, pooling inference, and IO processing; "
+ "the Anonymizer adapter preserves dynamic labels, offsets, and scores"
+ ),
+ ),
+ )
+
+
+def _validate_factory_detection(spec: LocalInferenceServiceSpec, task: EntityDetection) -> None:
+ factory = spec.vllm.factory
+ if factory is None:
+ _raise_unsupported_task_engine(task.kind, "vllm")
+ if spec.model.revision is None:
+ raise CompilationError(
+ CompilerDiagnostic(
+ code="unpinned-model-revision",
+ message="vLLM Factory entity detection requires a pinned model revision",
+ details={"model": spec.model.model_id},
+ )
+ )
+ if not supports_model(factory.plugin, spec.model.model_id):
+ raise CompilationError(
+ CompilerDiagnostic(
+ code="unsupported-model-engine",
+ message=(
+ f"model {spec.model.model_id!r} is not characterized for vLLM Factory plugin {factory.plugin!r}"
+ ),
+ details={"model": spec.model.model_id, "plugin": factory.plugin},
+ )
+ )
+ if spec.model.adapter is not None:
+ raise CompilationError(
+ CompilerDiagnostic(
+ code="unsupported-model-adapter",
+ message="vLLM Factory entity detection does not support a model adapter",
+ details={"engine": "vllm", "task": task.kind},
+ )
+ )
+
+
+def _compile_generation(
+ spec: LocalInferenceServiceSpec, task: Generation
+) -> tuple[CommandSpec, tuple[CompatibilityAssessment, ...]]:
+ if spec.vllm.factory is not None:
+ _raise_unsupported_task_engine(task.kind, "vllm")
+ command = _vllm_command(spec, spec.vllm)
+ return command, (
+ CompatibilityAssessment(
+ rule="vllm-openai-compatible-v1",
+ outcome="characterized",
+ detail="vLLM exposes chat completions for generation",
+ ),
+ )
+
+
+def _vllm_command(
+ spec: LocalInferenceServiceSpec,
+ engine: Vllm,
+) -> CommandSpec:
+ engine_arguments = _vllm_engine_arguments(spec, engine)
+ placement = spec.local
+ argv = _literal_arguments(
+ engine.python_executable,
+ "tools/inference_service_compiler/vllm_server.py",
+ spec.model.model_id,
+ "--host",
+ placement.host,
+ "--port",
+ str(placement.port),
+ )
+ return CommandSpec(argv=argv + engine_arguments, environment=_vllm_environment(engine))
+
+
+def _vllm_engine_arguments(spec: LocalInferenceServiceSpec, engine: Vllm) -> tuple[LiteralArgument, ...]:
+ arguments: list[LiteralArgument] = []
+ if spec.model.revision is not None:
+ arguments.extend(
+ _literal_arguments(
+ "--revision",
+ spec.model.revision,
+ "--tokenizer-revision",
+ spec.model.revision,
+ )
+ )
+ arguments.extend(_optional_vllm_arguments(engine))
+ arguments.extend(
+ LiteralArgument(value=flag)
+ for flag, enabled in (
+ ("--enforce-eager", engine.eager),
+ ("--enable-prefix-caching", engine.enable_prefix_caching),
+ ("--async-scheduling", engine.async_scheduling),
+ )
+ if enabled
+ )
+ if engine.mamba_backend is not None:
+ arguments.extend(_literal_arguments("--mamba-backend", engine.mamba_backend))
+ if engine.mamba_ssm_cache_dtype != "auto":
+ arguments.extend(_literal_arguments("--mamba-ssm-cache-dtype", engine.mamba_ssm_cache_dtype))
+ if engine.enable_mamba_cache_stochastic_rounding:
+ arguments.extend(_literal_arguments("--enable-mamba-cache-stochastic-rounding"))
+ if engine.mamba_cache_philox_rounds:
+ arguments.extend(_literal_arguments("--mamba-cache-philox-rounds", str(engine.mamba_cache_philox_rounds)))
+ if engine.factory is not None:
+ arguments.extend(
+ _literal_arguments(
+ "--vllm-factory-plugin",
+ engine.factory.plugin,
+ "--prepared-model-root",
+ engine.factory.prepared_model_root,
+ )
+ )
+ if spec.model.adapter is not None:
+ arguments.extend(
+ _literal_arguments(
+ "--enable-lora",
+ "--lora-modules",
+ f"{spec.model.adapter.name}={spec.model.adapter.path}",
+ )
+ )
+ return tuple(arguments)
+
+
+def _optional_vllm_arguments(engine: Vllm) -> tuple[LiteralArgument, ...]:
+ values = (
+ ("--served-model-name", engine.served_model_name),
+ ("--tensor-parallel-size", engine.tensor_parallel_size),
+ ("--gpu-memory-utilization", engine.gpu_memory_utilization),
+ ("--max-model-len", engine.max_model_len),
+ ("--max-num-seqs", engine.max_num_seqs),
+ )
+ return tuple(
+ argument for flag, value in values if value is not None for argument in _literal_arguments(flag, str(value))
+ )
+
+
+def _vllm_environment(engine: Vllm) -> tuple[SecretEnvironmentVariable, ...]:
+ if engine.api_key_env is None:
+ return ()
+ return (
+ SecretEnvironmentVariable(
+ name=VLLM_API_KEY_ENV,
+ source_environment_variable=engine.api_key_env,
+ ),
+ )
+
+
+def _literal_arguments(*values: str) -> tuple[LiteralArgument, ...]:
+ return tuple(LiteralArgument(value=value) for value in values)
+
+
+def _plan_dependencies(spec: LocalInferenceServiceSpec) -> tuple[str, ...]:
+ dependencies = [VLLM_DEPENDENCY]
+ if spec.vllm.factory is not None:
+ dependencies.append(VLLM_FACTORY_DEPENDENCY)
+ if spec.vllm.mamba_backend == "flashinfer":
+ dependencies.extend(FLASHINFER_CUDA_TOOLCHAIN_DEPENDENCIES)
+ return tuple(dependencies)
+
+
+def _raise_unsupported_task_engine(task: str, engine: str) -> Never:
+ raise CompilationError(
+ CompilerDiagnostic(
+ code="unsupported-task-engine",
+ message=f"engine {engine!r} does not support task {task!r}",
+ details={"engine": engine, "task": task},
+ )
+ )
diff --git a/tools/inference_service_compiler/lifecycle.py b/tools/inference_service_compiler/lifecycle.py
new file mode 100644
index 00000000..9a8b4f4f
--- /dev/null
+++ b/tools/inference_service_compiler/lifecycle.py
@@ -0,0 +1,39 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Launch-ownership handshake shared by the controller and managed server."""
+
+from __future__ import annotations
+
+import hashlib
+import importlib
+import os
+import secrets
+from collections.abc import Awaitable, Callable
+from typing import Any
+
+LAUNCH_TOKEN_ENVIRONMENT_VARIABLE = "ANONYMIZER_INFERENCE_LAUNCH_TOKEN"
+LAUNCH_OWNERSHIP_HEADER = "X-Anonymizer-Launch-Token"
+LAUNCH_OWNERSHIP_PATH = "/_anonymizer/launch-ownership"
+LAUNCH_OWNERSHIP_PROOF_FIELD = "launch_token_sha256"
+LAUNCH_OWNERSHIP_MIDDLEWARE = "inference_service_compiler.lifecycle.launch_ownership"
+
+
+def launch_token_proof(token: str) -> str:
+ """Return the non-secret proof expected from the launched server."""
+ return hashlib.sha256(token.encode()).hexdigest()
+
+
+async def launch_ownership(
+ request: Any,
+ call_next: Callable[[Any], Awaitable[Any]],
+) -> Any:
+ """Prove that this server inherited the controller's launch-scoped token."""
+ if request.url.path != LAUNCH_OWNERSHIP_PATH:
+ return await call_next(request)
+
+ responses = importlib.import_module("starlette.responses")
+ expected = os.environ.get(LAUNCH_TOKEN_ENVIRONMENT_VARIABLE)
+ supplied = request.headers.get(LAUNCH_OWNERSHIP_HEADER)
+ if expected is None or supplied is None or not secrets.compare_digest(supplied, expected):
+ return responses.JSONResponse(status_code=404, content={"detail": "not found"})
+ return responses.JSONResponse(content={LAUNCH_OWNERSHIP_PROOF_FIELD: launch_token_proof(expected)})
diff --git a/tools/inference_service_compiler/models.py b/tools/inference_service_compiler/models.py
new file mode 100644
index 00000000..e0806166
--- /dev/null
+++ b/tools/inference_service_compiler/models.py
@@ -0,0 +1,327 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Versioned transport and immutable intermediate-representation models."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from typing import Annotated, Literal, assert_never
+
+from pydantic import BaseModel, ConfigDict, Field
+
+SPEC_SCHEMA_VERSION = "inference-service.local-spec/v2"
+PLAN_SCHEMA_VERSION = "inference-service.run-plan/v2"
+CAPABILITY_PROBE_RECEIPT_SCHEMA_VERSION = "inference-service.capability-probe-receipt/v1"
+LAUNCH_RECEIPT_SCHEMA_VERSION = "inference-service.launch-receipt/v1"
+STATUS_RECEIPT_SCHEMA_VERSION = "inference-service.status-receipt/v1"
+STOP_RECEIPT_SCHEMA_VERSION = "inference-service.stop-receipt/v1"
+
+Capability = Literal["chat-completions", "dynamic-labels", "offsets", "scores"]
+FactoryPlugin = Literal["deberta_gliner", "deberta_gliner2"]
+
+
+def parse_factory_plugin(value: str) -> FactoryPlugin:
+ """Parse the Factory plugin name at an untyped process boundary."""
+ match value:
+ case "deberta_gliner" | "deberta_gliner2":
+ return value
+ case _:
+ raise ValueError(f"unsupported vLLM Factory plugin {value!r}")
+
+
+class FrozenModel(BaseModel):
+ """Closed immutable base for compiler values and transport records."""
+
+ model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+class EntityDetection(FrozenModel):
+ """Entity detection requirements independent of a serving engine."""
+
+ kind: Literal["entity-detection"] = "entity-detection"
+ dynamic_labels: bool
+ offsets: bool
+ scores: bool
+
+ def required_capabilities(self) -> tuple[Capability, ...]:
+ """Return the endpoint capabilities required by this task."""
+ capabilities: list[Capability] = []
+ if self.dynamic_labels:
+ capabilities.append("dynamic-labels")
+ if self.offsets:
+ capabilities.append("offsets")
+ if self.scores:
+ capabilities.append("scores")
+ return tuple(capabilities)
+
+
+class Generation(FrozenModel):
+ """Text-generation requirements independent of a serving engine."""
+
+ kind: Literal["generation"] = "generation"
+
+ def required_capabilities(self) -> tuple[Capability, ...]:
+ """Return the endpoint capabilities required by this task."""
+ return ("chat-completions",)
+
+
+TaskSpec = Annotated[EntityDetection | Generation, Field(discriminator="kind")]
+
+
+class LoraAdapter(FrozenModel):
+ """A LoRA artifact and the stable name exposed by the serving engine."""
+
+ path: str = Field(min_length=1)
+ name: str = Field(min_length=1)
+
+
+class HuggingFaceModel(FrozenModel):
+ """A Hugging Face model identifier and optional immutable revision."""
+
+ model_id: str = Field(min_length=1)
+ revision: str | None = Field(default=None, min_length=1)
+ adapter: LoraAdapter | None = None
+
+
+class VllmFactoryIntegration(FrozenModel):
+ """A supported vLLM Factory structured-prediction plugin."""
+
+ plugin: FactoryPlugin
+ prepared_model_root: str = Field(default="/tmp/anonymizer-vllm-factory", min_length=1)
+
+
+class Vllm(FrozenModel):
+ """vLLM's OpenAI-compatible server with bounded common options."""
+
+ python_executable: str = Field(default=".venv/bin/python", min_length=1)
+ served_model_name: str | None = Field(default=None, min_length=1)
+ api_key_env: str | None = Field(default=None, min_length=1)
+ tensor_parallel_size: int | None = Field(default=None, ge=1)
+ gpu_memory_utilization: float | None = Field(default=None, gt=0, le=1)
+ max_model_len: int | None = Field(default=None, ge=1)
+ max_num_seqs: int | None = Field(default=None, ge=1)
+ eager: bool = False
+ enable_prefix_caching: bool = False
+ async_scheduling: bool = False
+ mamba_backend: Literal["triton", "flashinfer"] | None = None
+ mamba_ssm_cache_dtype: Literal["auto", "float32", "float16", "bfloat16"] = "auto"
+ enable_mamba_cache_stochastic_rounding: bool = False
+ mamba_cache_philox_rounds: int = Field(default=0, ge=0)
+ factory: VllmFactoryIntegration | None = None
+
+
+class LocalProcess(FrozenModel):
+ """The only supported inference-host deployment domain."""
+
+ host: str = Field(default="127.0.0.1", min_length=1)
+ port: int = Field(default=8000, ge=1, le=65535)
+
+ startup_timeout_seconds: float = Field(default=120, gt=0)
+ shutdown_timeout_seconds: float = Field(default=30, gt=0)
+
+
+class LocalInferenceServiceSpec(FrozenModel):
+ """Complete semantic input to pure inference-service compilation."""
+
+ schema_version: Literal["inference-service.local-spec/v2"] = SPEC_SCHEMA_VERSION
+ task: TaskSpec
+ model: HuggingFaceModel
+ vllm: Vllm
+ local: LocalProcess
+
+ @property
+ def served_model_name(self) -> str:
+ """Return the model ID that the compiled endpoint must serve."""
+ if self.model.adapter is not None:
+ return self.model.adapter.name
+ if self.vllm.served_model_name is not None:
+ return self.vllm.served_model_name
+ return self.model.model_id
+
+
+class LiteralArgument(FrozenModel):
+ """One non-secret argv value."""
+
+ kind: Literal["literal"] = "literal"
+ value: str
+
+
+class EnvironmentVariable(FrozenModel):
+ """One ordinary non-secret process environment value."""
+
+ kind: Literal["literal"] = "literal"
+ name: str = Field(min_length=1)
+ value: str
+
+
+class SecretEnvironmentVariable(FrozenModel):
+ """One process environment value resolved from a named secret source."""
+
+ kind: Literal["secret-reference"] = "secret-reference"
+ name: str = Field(min_length=1)
+ source_environment_variable: str = Field(min_length=1)
+
+
+EnvironmentSpec = Annotated[EnvironmentVariable | SecretEnvironmentVariable, Field(discriminator="kind")]
+
+
+class CommandSpec(FrozenModel):
+ """Complete argv and ordinary environment for one managed service."""
+
+ argv: tuple[LiteralArgument, ...] = Field(min_length=1)
+ environment: tuple[EnvironmentSpec, ...] = ()
+ working_directory: str = "."
+
+ def render_argv(self) -> tuple[str, ...]:
+ """Render the complete non-secret process argument vector."""
+ return tuple(argument.value for argument in self.argv)
+
+ @property
+ def secret_sources(self) -> tuple[str, ...]:
+ """Return the secret-source names this command owns."""
+ return tuple(
+ variable.source_environment_variable
+ for variable in self.environment
+ if isinstance(variable, SecretEnvironmentVariable)
+ )
+
+ def render_environment(self) -> dict[str, str]:
+ """Render an observable environment with every secret source redacted."""
+ values: dict[str, str] = {}
+ for variable in self.environment:
+ match variable:
+ case EnvironmentVariable(name=name, value=value):
+ values[name] = value
+ case SecretEnvironmentVariable(name=name, source_environment_variable=source):
+ values[name] = f""
+ case _:
+ assert_never(variable)
+ return values
+
+ def resolve_environment(self, secret_values: Mapping[str, str]) -> dict[str, str]:
+ """Strictly resolve this command's owned secret sources for execution."""
+ resolved = self.render_environment()
+ for variable in self.environment:
+ match variable:
+ case EnvironmentVariable():
+ pass
+ case SecretEnvironmentVariable(name=name, source_environment_variable=source):
+ try:
+ resolved[name] = secret_values[source]
+ except KeyError as exc:
+ raise ValueError(f"secret environment variable {source!r} is not resolved") from exc
+ case _:
+ assert_never(variable)
+ return resolved
+
+
+class EndpointAddress(FrozenModel):
+ """Direct OpenAI-compatible endpoint produced by a run."""
+
+ scheme: Literal["http"] = "http"
+ host: str
+ port: int
+ base_path: Literal["/v1"] = "/v1"
+
+ @property
+ def url(self) -> str:
+ """Return the normalized endpoint URL."""
+ return f"{self.scheme}://{self.host}:{self.port}{self.base_path}"
+
+
+class ReadinessCheck(FrozenModel):
+ """One bounded readiness or capability-probe request."""
+
+ path: str
+ expected_status: int = 200
+ timeout_seconds: float = Field(gt=0)
+ bearer_token_environment_variable: str | None = Field(default=None, min_length=1)
+
+
+class CompatibilityAssessment(FrozenModel):
+ """One compiler rule supporting or qualifying the selected combination."""
+
+ rule: str
+ outcome: Literal["characterized", "runtime-probe-required"]
+ detail: str
+
+
+class RunPlan(FrozenModel):
+ """Portable, frozen, effect-free instructions for one service run."""
+
+ schema_version: Literal["inference-service.run-plan/v2"] = PLAN_SCHEMA_VERSION
+ plan_digest: str
+ spec: LocalInferenceServiceSpec
+ command: CommandSpec
+ endpoint: EndpointAddress
+ readiness: ReadinessCheck
+ served_model_name: str = Field(min_length=1)
+ required_capabilities: tuple[Capability, ...]
+ compatibility_assessments: tuple[CompatibilityAssessment, ...]
+ dependencies: tuple[str, ...] = ()
+ source_revision: str = Field(min_length=1)
+
+
+class CapabilityProbeReceipt(FrozenModel):
+ """Runtime evidence for the endpoint capabilities required by a plan."""
+
+ schema_version: Literal["inference-service.capability-probe-receipt/v1"] = CAPABILITY_PROBE_RECEIPT_SCHEMA_VERSION
+ plan_digest: str
+ endpoint: EndpointAddress
+ observed_at: str
+ models: tuple[str, ...]
+ observed_capabilities: tuple[Capability, ...]
+ passed: bool
+
+
+class LocalProcessHandle(FrozenModel):
+ """Observed local-process coordinates checked against PID reuse."""
+
+ kind: Literal["local-process"] = "local-process"
+ external_id: str
+ pid: int = Field(ge=1)
+ process_group_id: int = Field(ge=1)
+ start_marker: str = Field(min_length=1)
+ stdout_path: str
+ stderr_path: str
+
+
+class LaunchReceipt(FrozenModel):
+ """Durable record of a launch operation and its readiness observations."""
+
+ schema_version: Literal["inference-service.launch-receipt/v1"] = LAUNCH_RECEIPT_SCHEMA_VERSION
+ plan_digest: str
+ launched_at: str
+ shutdown_timeout_seconds: float = Field(gt=0)
+ handle: LocalProcessHandle
+ probe: CapabilityProbeReceipt
+
+
+class StatusReceipt(FrozenModel):
+ """Recorded state observation for a managed handle."""
+
+ schema_version: Literal["inference-service.status-receipt/v1"] = STATUS_RECEIPT_SCHEMA_VERSION
+ plan_digest: str
+ observed_at: str
+ handle: LocalProcessHandle
+ state: Literal["running", "stopped"]
+
+
+class StopReceipt(FrozenModel):
+ """Recorded cleanup outcome for a managed handle."""
+
+ schema_version: Literal["inference-service.stop-receipt/v1"] = STOP_RECEIPT_SCHEMA_VERSION
+ plan_digest: str
+ stopped_at: str
+ handle: LocalProcessHandle
+ outcome: Literal["terminated", "already-stopped", "forced"]
+ cleanup_complete: bool
+
+
+class RuntimeDiagnostic(FrozenModel):
+ """Serializable runtime failure including every known external effect."""
+
+ code: str
+ message: str
+ known_effects: tuple[str, ...] = ()
+ cleanup_complete: bool | None = None
diff --git a/tools/inference_service_compiler/profiles.py b/tools/inference_service_compiler/profiles.py
new file mode 100644
index 00000000..b3ca7d56
--- /dev/null
+++ b/tools/inference_service_compiler/profiles.py
@@ -0,0 +1,16 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Human-authored profile transport for the inference service compiler."""
+
+from __future__ import annotations
+
+import tomllib
+from pathlib import Path
+
+from inference_service_compiler.models import LocalInferenceServiceSpec
+
+
+def load_profile(path: Path) -> LocalInferenceServiceSpec:
+ """Load and validate one TOML inference-service profile."""
+ payload = tomllib.loads(path.read_text(encoding="utf-8"))
+ return LocalInferenceServiceSpec.model_validate(payload)
diff --git a/tools/inference_service_compiler/runtime.py b/tools/inference_service_compiler/runtime.py
new file mode 100644
index 00000000..7b71c612
--- /dev/null
+++ b/tools/inference_service_compiler/runtime.py
@@ -0,0 +1,527 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Explicit local-process effects for frozen, checksummed run plans."""
+
+from __future__ import annotations
+
+import json
+import os
+import secrets
+import signal
+import subprocess
+import time
+from collections.abc import Mapping
+from contextlib import nullcontext
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Literal, Never, assert_never, cast
+
+import httpx
+
+from inference_service_compiler.compiler import verify_plan
+from inference_service_compiler.lifecycle import (
+ LAUNCH_OWNERSHIP_HEADER,
+ LAUNCH_OWNERSHIP_PATH,
+ LAUNCH_OWNERSHIP_PROOF_FIELD,
+ LAUNCH_TOKEN_ENVIRONMENT_VARIABLE,
+ launch_token_proof,
+)
+from inference_service_compiler.models import (
+ Capability,
+ CapabilityProbeReceipt,
+ EntityDetection,
+ Generation,
+ LaunchReceipt,
+ LocalProcessHandle,
+ RunPlan,
+ RuntimeDiagnostic,
+ StatusReceipt,
+ StopReceipt,
+)
+
+
+class RuntimeEffectError(RuntimeError):
+ """A launch or probe failed with a serializable partial-effects record."""
+
+ def __init__(self, diagnostic: RuntimeDiagnostic) -> None:
+ super().__init__(diagnostic.message)
+ self.diagnostic = diagnostic
+
+
+@dataclass(frozen=True, slots=True)
+class StopOutcome:
+ """Immutable result of terminating one managed process group."""
+
+ outcome: Literal["terminated", "forced"]
+ cleanup_complete: bool
+
+
+def probe_endpoint(
+ plan: RunPlan,
+ *,
+ client: httpx.Client | None = None,
+ secret_values: Mapping[str, str] | None = None,
+) -> CapabilityProbeReceipt:
+ """Probe one managed endpoint and record only capabilities observed at runtime."""
+ verify_plan(plan)
+ headers = _probe_headers(plan, secret_values or {})
+ try:
+ with nullcontext(client) if client is not None else httpx.Client(timeout=10) as active_client:
+ models_response = active_client.get(f"{plan.endpoint.url}{plan.readiness.path}", headers=headers)
+ if models_response.status_code != plan.readiness.expected_status:
+ raise ValueError(
+ f"readiness probe returned status {models_response.status_code}, "
+ f"expected {plan.readiness.expected_status}"
+ )
+ models = _parse_models(models_response.json())
+ observed = _probe_task(plan, active_client, headers)
+ except (httpx.HTTPError, KeyError, TypeError, ValueError, json.JSONDecodeError) as exc:
+ raise RuntimeEffectError(
+ RuntimeDiagnostic(code="probe-failed", message=f"capability probe failed: {exc}")
+ ) from exc
+ return CapabilityProbeReceipt(
+ plan_digest=plan.plan_digest,
+ endpoint=plan.endpoint,
+ observed_at=_now(),
+ models=models,
+ observed_capabilities=observed,
+ passed=plan.served_model_name in models and set(plan.required_capabilities).issubset(observed),
+ )
+
+
+def launch_plan(
+ plan: RunPlan,
+ *,
+ secret_values: dict[str, str],
+ log_directory: Path,
+) -> LaunchReceipt:
+ """Launch a checksummed plan and record its observed handle and readiness."""
+ verify_plan(plan)
+ try:
+ command_environment = plan.command.resolve_environment(secret_values)
+ except ValueError as exc:
+ raise RuntimeEffectError(RuntimeDiagnostic(code="missing-secret", message=str(exc))) from exc
+ argv = plan.command.render_argv()
+ environment = os.environ.copy()
+ environment.update(command_environment)
+ launch_token = secrets.token_urlsafe(32)
+ environment[LAUNCH_TOKEN_ENVIRONMENT_VARIABLE] = launch_token
+ log_directory.mkdir(parents=True, exist_ok=True)
+ handle = _launch_process(plan, argv, environment, log_directory)
+ probe = _probe_or_cleanup(plan, handle, secret_values, launch_token)
+ return LaunchReceipt(
+ plan_digest=plan.plan_digest,
+ launched_at=_now(),
+ shutdown_timeout_seconds=plan.spec.local.shutdown_timeout_seconds,
+ handle=handle,
+ probe=probe,
+ )
+
+
+def _probe_or_cleanup(
+ plan: RunPlan,
+ handle: LocalProcessHandle,
+ secret_values: Mapping[str, str],
+ launch_token: str,
+) -> CapabilityProbeReceipt:
+ try:
+ probe = wait_for_readiness(
+ plan,
+ secret_values=secret_values,
+ handle=handle,
+ launch_token=launch_token,
+ )
+ except BaseException as exc:
+ _raise_readiness_failure(exc, handle, plan.spec.local.shutdown_timeout_seconds)
+ if not probe.passed:
+ cleanup_complete, cleanup_error = _attempt_failed_launch_cleanup(
+ handle,
+ plan.spec.local.shutdown_timeout_seconds,
+ )
+ message = "endpoint became ready but did not satisfy required capabilities"
+ if cleanup_error is not None:
+ message = f"{message}; cleanup failed: {cleanup_error}"
+ raise RuntimeEffectError(
+ RuntimeDiagnostic(
+ code="capability-mismatch",
+ message=message,
+ known_effects=(handle.external_id,),
+ cleanup_complete=cleanup_complete,
+ )
+ )
+ return probe
+
+
+def _attempt_failed_launch_cleanup(handle: LocalProcessHandle, timeout_seconds: float) -> tuple[bool, str | None]:
+ try:
+ return _cleanup_handle(handle, timeout_seconds), None
+ except Exception as exc:
+ return False, str(exc)
+
+
+def _raise_readiness_failure(
+ exc: BaseException,
+ handle: LocalProcessHandle,
+ timeout_seconds: float,
+) -> Never:
+ cleanup_complete, cleanup_error = _attempt_failed_launch_cleanup(handle, timeout_seconds)
+ cleanup_suffix = f"; cleanup failed: {cleanup_error}" if cleanup_error is not None else ""
+ if isinstance(exc, RuntimeEffectError):
+ raise RuntimeEffectError(
+ exc.diagnostic.model_copy(
+ update={
+ "message": f"{exc.diagnostic.message}{cleanup_suffix}",
+ "known_effects": (handle.external_id,),
+ "cleanup_complete": cleanup_complete,
+ }
+ )
+ ) from exc
+ if isinstance(exc, Exception):
+ raise RuntimeEffectError(
+ RuntimeDiagnostic(
+ code="unexpected-readiness-failure",
+ message=f"readiness failed unexpectedly: {exc}{cleanup_suffix}",
+ known_effects=(handle.external_id,),
+ cleanup_complete=cleanup_complete,
+ )
+ ) from exc
+ if cleanup_error is not None:
+ exc.add_note(f"managed-process cleanup failed: {cleanup_error}")
+ raise exc
+
+
+def status_run(launch: LaunchReceipt) -> StatusReceipt:
+ """Observe a recorded handle without changing process state."""
+ state = "running" if is_handle_running(launch.handle) else "stopped"
+ return StatusReceipt(
+ plan_digest=launch.plan_digest,
+ observed_at=_now(),
+ handle=launch.handle,
+ state=state,
+ )
+
+
+def stop_run(launch: LaunchReceipt) -> StopReceipt:
+ """Stop the exact process group recorded by a launch receipt."""
+ handle = launch.handle
+ if not is_handle_running(handle):
+ return StopReceipt(
+ plan_digest=launch.plan_digest,
+ stopped_at=_now(),
+ handle=handle,
+ outcome="already-stopped",
+ cleanup_complete=True,
+ )
+ stop = _stop_running_handle(handle, launch.shutdown_timeout_seconds)
+ return StopReceipt(
+ plan_digest=launch.plan_digest,
+ stopped_at=_now(),
+ handle=handle,
+ outcome=stop.outcome,
+ cleanup_complete=stop.cleanup_complete,
+ )
+
+
+def is_handle_running(handle: LocalProcessHandle) -> bool:
+ """Check the recorded process identity without following a reused PID or group."""
+ current_marker = read_process_start_marker(handle.pid)
+ if current_marker is None:
+ return False
+ if current_marker != handle.start_marker:
+ raise _process_identity_mismatch(
+ handle,
+ f"PID {handle.pid} now has start marker {current_marker!r}, expected {handle.start_marker!r}",
+ )
+ if read_process_state(handle.pid) == "Z":
+ return False
+ try:
+ current_process_group_id = os.getpgid(handle.pid)
+ except ProcessLookupError:
+ return False
+ except PermissionError as exc:
+ raise RuntimeEffectError(
+ RuntimeDiagnostic(
+ code="process-identity-unavailable",
+ message=f"cannot verify the process group for PID {handle.pid}: {exc}",
+ )
+ ) from exc
+ if current_process_group_id != handle.process_group_id:
+ raise _process_identity_mismatch(
+ handle,
+ f"PID {handle.pid} now belongs to process group {current_process_group_id}, "
+ f"expected {handle.process_group_id}",
+ )
+ try:
+ os.kill(handle.pid, 0)
+ except ProcessLookupError:
+ return False
+ except PermissionError:
+ return True
+ return True
+
+
+def _process_identity_mismatch(handle: LocalProcessHandle, detail: str) -> RuntimeEffectError:
+ return RuntimeEffectError(
+ RuntimeDiagnostic(
+ code="process-identity-mismatch",
+ message=f"refusing to act on recorded process {handle.external_id}: {detail}",
+ )
+ )
+
+
+def _cleanup_handle(handle: LocalProcessHandle, timeout_seconds: float) -> bool:
+ if not is_handle_running(handle):
+ return True
+ return _stop_running_handle(handle, timeout_seconds).cleanup_complete
+
+
+def _stop_running_handle(
+ handle: LocalProcessHandle,
+ timeout_seconds: float,
+) -> StopOutcome:
+ try:
+ os.killpg(handle.process_group_id, signal.SIGTERM)
+ except ProcessLookupError:
+ return StopOutcome(outcome="terminated", cleanup_complete=True)
+ deadline = time.monotonic() + timeout_seconds
+ running = True
+ while time.monotonic() < deadline:
+ running = is_handle_running(handle)
+ if not running:
+ break
+ time.sleep(0.1)
+ outcome: Literal["terminated", "forced"] = "terminated"
+ if running:
+ try:
+ os.killpg(handle.process_group_id, signal.SIGKILL)
+ except ProcessLookupError:
+ return StopOutcome(outcome="forced", cleanup_complete=True)
+ running = is_handle_running(handle)
+ outcome = "forced"
+ return StopOutcome(outcome=outcome, cleanup_complete=not running)
+
+
+def wait_for_readiness(
+ plan: RunPlan,
+ *,
+ secret_values: Mapping[str, str] | None = None,
+ handle: LocalProcessHandle | None = None,
+ launch_token: str | None = None,
+) -> CapabilityProbeReceipt:
+ """Poll the declared readiness contract until it passes or times out."""
+ deadline = time.monotonic() + plan.readiness.timeout_seconds
+ last_error: RuntimeEffectError | None = None
+ while time.monotonic() < deadline:
+ if handle is not None and not is_handle_running(handle):
+ log_hint = f"; status {handle.stderr_path}"
+ raise RuntimeEffectError(
+ RuntimeDiagnostic(
+ code="launch-exited",
+ message=f"managed service exited before readiness{log_hint}",
+ )
+ )
+ try:
+ if launch_token is not None:
+ _probe_launch_ownership(plan, launch_token, secret_values)
+ receipt = probe_endpoint(plan, secret_values=secret_values)
+ if receipt.passed:
+ return receipt
+ last_error = RuntimeEffectError(
+ RuntimeDiagnostic(code="capability-mismatch", message="required capabilities were not observed")
+ )
+ except RuntimeEffectError as exc:
+ last_error = exc
+ time.sleep(min(0.5, max(0, deadline - time.monotonic())))
+ message = str(last_error) if last_error is not None else "readiness probe timed out"
+ raise RuntimeEffectError(RuntimeDiagnostic(code="readiness-timeout", message=message))
+
+
+def _probe_launch_ownership(
+ plan: RunPlan,
+ launch_token: str,
+ secret_values: Mapping[str, str] | None = None,
+) -> None:
+ """Require a proof that the responding server inherited this launch's token."""
+ url = f"{plan.endpoint.scheme}://{plan.endpoint.host}:{plan.endpoint.port}{LAUNCH_OWNERSHIP_PATH}"
+ headers = _probe_headers(plan, secret_values or {})
+ headers[LAUNCH_OWNERSHIP_HEADER] = launch_token
+ try:
+ response = httpx.get(
+ url,
+ headers=headers,
+ timeout=10,
+ )
+ if response.status_code != 200:
+ raise ValueError(f"launch ownership probe returned status {response.status_code}")
+ payload = response.json()
+ if not isinstance(payload, Mapping):
+ raise TypeError("launch ownership response must be an object")
+ observed_proof = payload.get(LAUNCH_OWNERSHIP_PROOF_FIELD)
+ expected_proof = launch_token_proof(launch_token)
+ if not isinstance(observed_proof, str) or not secrets.compare_digest(observed_proof, expected_proof):
+ raise ValueError("launch ownership proof did not match")
+ except (httpx.HTTPError, TypeError, ValueError, json.JSONDecodeError) as exc:
+ raise RuntimeEffectError(
+ RuntimeDiagnostic(code="launch-ownership-not-observed", message=f"launch ownership probe failed: {exc}")
+ ) from exc
+
+
+def read_process_start_marker(pid: int) -> str | None:
+ """Read Linux process start ticks to disambiguate PID reuse when available."""
+ stat = _read_process_stat(pid)
+ return stat[1] if stat is not None else None
+
+
+def read_process_state(pid: int) -> str | None:
+ """Read the Linux process state so zombies count as stopped."""
+ stat = _read_process_stat(pid)
+ return stat[0] if stat is not None else None
+
+
+def _read_process_stat(pid: int) -> tuple[str, str] | None:
+ stat_path = Path("/proc") / str(pid) / "stat"
+ try:
+ payload = stat_path.read_text(encoding="utf-8")
+ except OSError:
+ return None
+ return _parse_process_stat(payload)
+
+
+def _parse_process_stat(payload: str) -> tuple[str, str] | None:
+ _prefix, separator, suffix = payload.rpartition(")")
+ fields = suffix.strip().split() if separator else []
+ return (fields[0], fields[19]) if len(fields) > 19 else None
+
+
+def _probe_task(plan: RunPlan, client: httpx.Client, headers: Mapping[str, str]) -> tuple[Capability, ...]:
+ match plan.spec.task:
+ case Generation():
+ response = client.post(
+ f"{plan.endpoint.url}/chat/completions",
+ json={
+ "model": plan.served_model_name,
+ "messages": [{"role": "user", "content": "Reply with the word ready."}],
+ "max_tokens": 128,
+ "chat_template_kwargs": {
+ "enable_thinking": False,
+ "reasoning_effort": "low",
+ },
+ },
+ headers=headers,
+ )
+ response.raise_for_status()
+ content = response.json()["choices"][0]["message"]["content"]
+ if not isinstance(content, str):
+ raise ValueError("chat completion content must be a string")
+ return ("chat-completions",)
+ case EntityDetection():
+ response = client.post(
+ f"{plan.endpoint.url}/chat/completions",
+ json={
+ "model": plan.served_model_name,
+ "messages": [{"role": "user", "content": "Ada Lovelace"}],
+ "labels": ["person"],
+ "threshold": 0.1,
+ },
+ headers=headers,
+ )
+ response.raise_for_status()
+ content = response.json()["choices"][0]["message"]["content"]
+ payload = json.loads(content)
+ entities = payload["entities"]
+ if not isinstance(entities, list):
+ raise ValueError("detector entities must be a list")
+ observed: list[Capability] = ["dynamic-labels"]
+ if entities and all(isinstance(entity, dict) and {"start", "end"} <= entity.keys() for entity in entities):
+ observed.append("offsets")
+ if entities and all(isinstance(entity, dict) and "score" in entity for entity in entities):
+ observed.append("scores")
+ return tuple(observed)
+ case _:
+ assert_never(plan.spec.task)
+
+
+def _parse_models(payload: object) -> tuple[str, ...]:
+ if not isinstance(payload, Mapping):
+ raise ValueError("models response must contain a data list")
+ data = cast(Mapping[str, object], payload).get("data")
+ if not isinstance(data, list):
+ raise ValueError("models response must contain a data list")
+ models: list[str] = []
+ for item in cast(list[object], data):
+ if isinstance(item, Mapping):
+ record = cast(Mapping[str, object], item)
+ if "id" in record:
+ models.append(str(record["id"]))
+ return tuple(models)
+
+
+def _probe_headers(plan: RunPlan, secret_values: Mapping[str, str]) -> dict[str, str]:
+ source = plan.readiness.bearer_token_environment_variable
+ if source is None:
+ return {}
+ if source not in secret_values:
+ raise RuntimeEffectError(
+ RuntimeDiagnostic(
+ code="missing-secret",
+ message=f"secret environment variable {source!r} is not resolved",
+ )
+ )
+ return {"Authorization": f"Bearer {secret_values[source]}"}
+
+
+def _launch_process(
+ plan: RunPlan,
+ argv: tuple[str, ...],
+ environment: dict[str, str],
+ log_directory: Path,
+) -> LocalProcessHandle:
+ stdout_path = log_directory / f"{plan.plan_digest}.stdout.log"
+ stderr_path = log_directory / f"{plan.plan_digest}.stderr.log"
+ with stdout_path.open("ab") as stdout_file, stderr_path.open("ab") as stderr_file:
+ process = subprocess.Popen(
+ argv,
+ cwd=plan.command.working_directory,
+ env=environment,
+ stdout=stdout_file,
+ stderr=stderr_file,
+ start_new_session=True,
+ )
+ marker = read_process_start_marker(process.pid)
+ if marker is None:
+ _terminate_unmarked_launch(process)
+ raise RuntimeEffectError(
+ RuntimeDiagnostic(
+ code="missing-process-start-marker",
+ message="cannot record a managed process without a Linux start marker",
+ known_effects=(str(process.pid),),
+ cleanup_complete=process.poll() is not None,
+ )
+ )
+ return LocalProcessHandle(
+ external_id=f"{process.pid}:{marker}",
+ pid=process.pid,
+ process_group_id=process.pid,
+ start_marker=marker,
+ stdout_path=str(stdout_path),
+ stderr_path=str(stderr_path),
+ )
+
+
+def _terminate_unmarked_launch(process: subprocess.Popen[bytes]) -> None:
+ """Make a bounded cleanup attempt when a child cannot be recorded."""
+ for signal_to_send in (signal.SIGTERM, signal.SIGKILL):
+ try:
+ os.killpg(process.pid, signal_to_send)
+ except ProcessLookupError:
+ return
+ try:
+ process.wait(timeout=1)
+ except subprocess.TimeoutExpired:
+ continue
+ return
+
+
+def _now() -> str:
+ return datetime.now(UTC).isoformat()
diff --git a/tools/inference_service_compiler/vllm_factory_adapter.py b/tools/inference_service_compiler/vllm_factory_adapter.py
new file mode 100644
index 00000000..87ba1034
--- /dev/null
+++ b/tools/inference_service_compiler/vllm_factory_adapter.py
@@ -0,0 +1,417 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Translate Anonymizer detector requests into vLLM Factory pooling calls."""
+
+from __future__ import annotations
+
+import asyncio
+import hashlib
+import importlib
+import json
+import math
+import os
+import secrets
+import uuid
+from collections.abc import Awaitable, Callable, Mapping, Sequence
+from dataclasses import dataclass
+from typing import Any, assert_never, cast
+
+from inference_service_compiler.models import FactoryPlugin, parse_factory_plugin
+
+DEFAULT_CHUNK_LENGTH = 384
+DEFAULT_OVERLAP = 128
+MAX_CHUNKS_PER_REQUEST = 256
+MAX_CONCURRENT_POOLING_CALLS = 8
+POOLING_LIMITER_STATE_ATTRIBUTE = "_anonymizer_pooling_limiter"
+VLLM_API_KEY_ENV = "VLLM_API_KEY"
+
+
+@dataclass(frozen=True, slots=True)
+class DetectionRequest:
+ """Validated Anonymizer detector request."""
+
+ model: str
+ text: str
+ labels: tuple[str, ...]
+ threshold: float
+ chunk_length: int
+ overlap: int
+ flat_ner: bool
+
+
+@dataclass(frozen=True, slots=True)
+class Entity:
+ """One entity in Anonymizer's detector response shape."""
+
+ text: str
+ label: str
+ start: int
+ end: int
+ score: float
+
+ def as_dict(self) -> dict[str, str | int | float]:
+ return {
+ "text": self.text,
+ "label": self.label,
+ "start": self.start,
+ "end": self.end,
+ "score": self.score,
+ }
+
+
+@dataclass(frozen=True, slots=True)
+class TextChunk:
+ """One text segment and its original document offset."""
+
+ text: str
+ offset: int
+
+
+async def anonymizer_chat_compatibility(
+ request: Any,
+ call_next: Callable[[Any], Awaitable[Any]],
+) -> Any:
+ """Serve Anonymizer's chat contract through the active factory IOProcessor."""
+ if request.url.path != "/v1/chat/completions":
+ return await call_next(request)
+
+ responses = importlib.import_module("starlette.responses")
+ if not _has_valid_api_key(request):
+ return responses.JSONResponse(content={"error": "Unauthorized"}, status_code=401)
+ try:
+ detection = parse_detection_request(await request.json())
+ plugin = parse_factory_plugin(os.environ["ANONYMIZER_VLLM_FACTORY_PLUGIN"])
+ entities: list[Entity] = []
+ if detection.labels:
+ chunks = split_text(detection.text, detection.chunk_length, detection.overlap)
+ handler = request.app.state.serving_pooling
+ if handler is None:
+ raise RuntimeError("vLLM pooling handler is unavailable")
+ results = await invoke_pooling_chunks(
+ handler=handler,
+ plugin=plugin,
+ detection=detection,
+ chunks=chunks,
+ limiter=_pooling_limiter(request.app.state),
+ )
+ entities = merge_entities(
+ plugin=plugin,
+ chunks=chunks,
+ results=results,
+ flat_ner=detection.flat_ner,
+ )
+ except (KeyError, TypeError, ValueError, RuntimeError) as exc:
+ return responses.JSONResponse(
+ status_code=400,
+ content={"error": {"message": str(exc), "type": "invalid_request_error"}},
+ )
+
+ content = json.dumps({"entities": [entity.as_dict() for entity in entities]})
+ return responses.JSONResponse(
+ content={
+ "id": f"chatcmpl-{uuid.uuid4().hex}",
+ "object": "chat.completion",
+ "model": detection.model,
+ "choices": [
+ {
+ "index": 0,
+ "message": {"role": "assistant", "content": content},
+ "finish_reason": "stop",
+ }
+ ],
+ }
+ )
+
+
+def _has_valid_api_key(request: Any) -> bool:
+ api_key = os.environ.get(VLLM_API_KEY_ENV)
+ if not api_key or getattr(request, "method", None) == "OPTIONS":
+ return True
+ authorization = request.headers.get("Authorization")
+ if not authorization:
+ return False
+ scheme, _, token = authorization.partition(" ")
+ if scheme.lower() != "bearer":
+ return False
+ expected_digest = hashlib.sha256(api_key.encode("utf-8")).digest()
+ actual_digest = hashlib.sha256(token.encode("utf-8")).digest()
+ return secrets.compare_digest(actual_digest, expected_digest)
+
+
+def parse_detection_request(value: object) -> DetectionRequest:
+ """Validate the bounded chat-completions request used by Anonymizer."""
+ body = require_mapping(value, "request body")
+ model = body.get("model")
+ if not isinstance(model, str) or not model:
+ raise ValueError("model must be a non-empty string")
+ labels_value = body.get("labels", [])
+ if not isinstance(labels_value, list) or not all(isinstance(label, str) and label for label in labels_value):
+ raise ValueError("labels must be a list of strings")
+ threshold = require_number(body.get("threshold", 0.3), "threshold")
+ if not 0 <= threshold <= 1:
+ raise ValueError("threshold must be between 0 and 1")
+ chunk_length = require_integer(body.get("chunk_length", DEFAULT_CHUNK_LENGTH), "chunk_length")
+ overlap = require_integer(body.get("overlap", DEFAULT_OVERLAP), "overlap")
+ if chunk_length < 1:
+ raise ValueError("chunk_length must be >= 1")
+ if overlap < 0 or overlap >= chunk_length:
+ raise ValueError("overlap must be >= 0 and less than chunk_length")
+ flat_ner = body.get("flat_ner", False)
+ if not isinstance(flat_ner, bool):
+ raise ValueError("flat_ner must be a boolean")
+ text = extract_text(body.get("messages"))
+ chunk_count = _count_text_chunks(len(text), chunk_length, overlap)
+ if labels_value and chunk_count > MAX_CHUNKS_PER_REQUEST:
+ raise ValueError(f"detector requests may contain at most {MAX_CHUNKS_PER_REQUEST} chunks")
+ return DetectionRequest(
+ model=model,
+ text=text,
+ labels=tuple(cast(list[str], labels_value)),
+ threshold=threshold,
+ chunk_length=chunk_length,
+ overlap=overlap,
+ flat_ner=flat_ner,
+ )
+
+
+def extract_text(messages: object) -> str:
+ """Extract text from the final user message."""
+ if not isinstance(messages, list):
+ raise ValueError("messages must be a list")
+ if not messages:
+ return ""
+ message = require_mapping(messages[-1], "message")
+ content = message.get("content", "")
+ if isinstance(content, str):
+ return content
+ if isinstance(content, list):
+ parts: list[str] = []
+ for part in content:
+ text = require_mapping(part, "message part").get("text", "")
+ if not isinstance(text, str):
+ raise ValueError("message part text must be a string")
+ parts.append(text)
+ return "".join(parts)
+ raise ValueError("message content must be a string or list")
+
+
+def _count_text_chunks(text_length: int, chunk_length: int, overlap: int) -> int:
+ """Calculate the number of chunks without materializing their text."""
+ if text_length <= chunk_length:
+ return 1
+ stride = chunk_length - overlap
+ return 1 + (text_length - chunk_length + stride - 1) // stride
+
+
+def split_text(text: str, chunk_length: int, overlap: int) -> list[TextChunk]:
+ """Split text with the same character-offset contract as the native runtime."""
+ if not text:
+ return [TextChunk("", 0)]
+ chunks: list[TextChunk] = []
+ start = 0
+ while start < len(text):
+ chunks.append(TextChunk(text[start : start + chunk_length], start))
+ if start + chunk_length >= len(text):
+ break
+ start += chunk_length - overlap
+ return chunks
+
+
+async def invoke_pooling_chunks(
+ *,
+ handler: Any,
+ plugin: FactoryPlugin,
+ detection: DetectionRequest,
+ chunks: Sequence[TextChunk],
+ limiter: asyncio.Semaphore,
+) -> list[object]:
+ """Process admitted chunks within the server-wide pooling budget."""
+ results = [object() for _ in chunks]
+ pending = iter(enumerate(chunks))
+
+ async def worker() -> None:
+ for index, chunk in pending:
+ async with limiter:
+ results[index] = await invoke_pooling(
+ handler=handler,
+ model=detection.model,
+ plugin=plugin,
+ text=chunk.text,
+ labels=detection.labels,
+ threshold=detection.threshold,
+ flat_ner=detection.flat_ner,
+ )
+
+ worker_count = min(len(chunks), MAX_CONCURRENT_POOLING_CALLS)
+ await asyncio.gather(*(worker() for _ in range(worker_count)))
+ return results
+
+
+def _pooling_limiter(app_state: Any) -> asyncio.Semaphore:
+ limiter = getattr(app_state, POOLING_LIMITER_STATE_ATTRIBUTE, None)
+ if limiter is None:
+ limiter = asyncio.Semaphore(MAX_CONCURRENT_POOLING_CALLS)
+ setattr(app_state, POOLING_LIMITER_STATE_ATTRIBUTE, limiter)
+ return cast(asyncio.Semaphore, limiter)
+
+
+async def invoke_pooling(
+ *,
+ handler: Any,
+ model: str,
+ plugin: FactoryPlugin,
+ text: str,
+ labels: tuple[str, ...],
+ threshold: float,
+ flat_ner: bool,
+) -> object:
+ """Call vLLM's in-process pooling handler once for one text chunk."""
+ protocol = importlib.import_module("vllm.entrypoints.pooling.pooling.protocol")
+ data: dict[str, object] = {
+ "text": text,
+ "labels": list(labels),
+ "threshold": threshold,
+ }
+ match plugin:
+ case "deberta_gliner":
+ data["flat_ner"] = flat_ner
+ case "deberta_gliner2":
+ data["include_confidence"] = True
+ data["include_spans"] = True
+ case _:
+ assert_never(plugin)
+ response = await handler(protocol.IOProcessorRequest(model=model, data=data), None)
+ if response.status_code != 200:
+ raise RuntimeError(f"vLLM Factory pooling request returned {response.status_code}")
+ body = getattr(response, "body", None)
+ if not isinstance(body, bytes):
+ raise RuntimeError("vLLM Factory pooling response did not contain a JSON body")
+ payload = require_mapping(json.loads(body), "pooling response")
+ if "data" not in payload:
+ raise ValueError("pooling response is missing data")
+ return payload["data"]
+
+
+def merge_entities(
+ *,
+ plugin: FactoryPlugin,
+ chunks: Sequence[TextChunk],
+ results: Sequence[object],
+ flat_ner: bool,
+) -> list[Entity]:
+ """Normalize factory outputs, restore document offsets, and deduplicate overlap."""
+ if len(chunks) != len(results):
+ raise ValueError("vLLM Factory returned an unexpected result count")
+ entities: list[Entity] = []
+ for chunk, result in zip(chunks, results, strict=True):
+ match plugin:
+ case "deberta_gliner":
+ normalized = normalize_gliner(result)
+ case "deberta_gliner2":
+ normalized = normalize_gliner2(result)
+ case _:
+ assert_never(plugin)
+ for entity in normalized:
+ if entity.start < 0 or entity.end < entity.start or entity.end > len(chunk.text):
+ raise ValueError("vLLM Factory returned an invalid entity span")
+ entities.append(
+ Entity(
+ text=entity.text,
+ label=entity.label,
+ start=entity.start + chunk.offset,
+ end=entity.end + chunk.offset,
+ score=entity.score,
+ )
+ )
+ if not flat_ner:
+ entities = remove_subset_entities(entities)
+ unique: dict[tuple[str, str, int, int], Entity] = {}
+ for entity in entities:
+ key = (entity.text.strip().casefold(), entity.label, entity.start, entity.end)
+ previous = unique.get(key)
+ if previous is None or entity.score > previous.score:
+ unique[key] = entity
+ return sorted(unique.values(), key=lambda item: (item.start, item.end, item.label))
+
+
+def normalize_gliner(value: object) -> list[Entity]:
+ """Normalize the vLLM Factory DeBERTa GLiNER IOProcessor output."""
+ if not isinstance(value, list):
+ raise ValueError("GLiNER pooling data must be a list")
+ entities: list[Entity] = []
+ for item in value:
+ record = require_mapping(item, "GLiNER entity")
+ entities.append(
+ Entity(
+ text=require_string(record.get("text"), "entity text"),
+ label=require_string(record.get("label"), "entity label"),
+ start=require_integer(record.get("start"), "entity start"),
+ end=require_integer(record.get("end"), "entity end"),
+ score=require_number(record.get("score"), "entity score"),
+ )
+ )
+ return entities
+
+
+def normalize_gliner2(value: object) -> list[Entity]:
+ """Normalize the vLLM Factory DeBERTa GLiNER2 IOProcessor output."""
+ payload = require_mapping(value, "GLiNER2 pooling data")
+ by_label = require_mapping(payload.get("entities"), "GLiNER2 entities")
+ entities: list[Entity] = []
+ for label, records in by_label.items():
+ if not isinstance(records, list):
+ raise ValueError("GLiNER2 entity values must be lists")
+ for item in records:
+ record = require_mapping(item, "GLiNER2 entity")
+ entities.append(
+ Entity(
+ text=require_string(record.get("text"), "entity text"),
+ label=label,
+ start=require_integer(record.get("start"), "entity start"),
+ end=require_integer(record.get("end"), "entity end"),
+ score=require_number(record.get("confidence"), "entity confidence"),
+ )
+ )
+ return entities
+
+
+def remove_subset_entities(entities: list[Entity]) -> list[Entity]:
+ """Remove spans strictly contained by another detected span."""
+ return [
+ entity
+ for entity in entities
+ if not any(
+ other is not entity
+ and other.start <= entity.start
+ and other.end >= entity.end
+ and (other.start < entity.start or other.end > entity.end)
+ for other in entities
+ )
+ ]
+
+
+def require_mapping(value: object, name: str) -> Mapping[str, object]:
+ if not isinstance(value, Mapping) or any(not isinstance(key, str) for key in value):
+ raise ValueError(f"unexpected {name} shape")
+ return cast(Mapping[str, object], value)
+
+
+def require_string(value: object, name: str) -> str:
+ if not isinstance(value, str):
+ raise ValueError(f"{name} must be a string")
+ return value
+
+
+def require_integer(value: object, name: str) -> int:
+ if isinstance(value, bool) or not isinstance(value, int):
+ raise ValueError(f"{name} must be an integer")
+ return value
+
+
+def require_number(value: object, name: str) -> float:
+ if isinstance(value, bool) or not isinstance(value, int | float):
+ raise ValueError(f"{name} must be a number")
+ number = float(value)
+ if not math.isfinite(number):
+ raise ValueError(f"{name} must be finite")
+ return number
diff --git a/tools/inference_service_compiler/vllm_factory_integration.py b/tools/inference_service_compiler/vllm_factory_integration.py
new file mode 100644
index 00000000..4ee28692
--- /dev/null
+++ b/tools/inference_service_compiler/vllm_factory_integration.py
@@ -0,0 +1,145 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Pinned integration with the external vLLM Factory project."""
+
+from __future__ import annotations
+
+import importlib
+import json
+import re
+from contextlib import contextmanager
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Iterator, Literal, assert_never
+
+from inference_service_compiler.models import FactoryPlugin
+
+VLLM_FACTORY_SOURCE_REVISION = "7d6ff68ce68f9f7c0a9d72f9645bcf6d335d02f0"
+VLLM_FACTORY_SOURCE_URL = "https://github.com/latenceainew/vllm-factory.git"
+VLLM_FACTORY_DEPENDENCY = f"vllm-factory[gliner] @ git+{VLLM_FACTORY_SOURCE_URL}@{VLLM_FACTORY_SOURCE_REVISION}"
+
+FactoryIoProcessor = Literal["deberta_gliner_io", "deberta_gliner2_io"]
+
+
+@dataclass(frozen=True, slots=True)
+class FactoryPluginSpec:
+ """Immutable, characterized contract for one supported Factory plugin."""
+
+ io_processor: FactoryIoProcessor
+ characterized_models: frozenset[str]
+
+
+def factory_plugin_spec(plugin: FactoryPlugin) -> FactoryPluginSpec:
+ """Return the exhaustive immutable specification for a Factory plugin."""
+ match plugin:
+ case "deberta_gliner":
+ return FactoryPluginSpec("deberta_gliner_io", frozenset({"nvidia/gliner-pii"}))
+ case "deberta_gliner2":
+ return FactoryPluginSpec(
+ "deberta_gliner2_io",
+ frozenset({"fastino/gliner2-privacy-filter-PII-multi"}),
+ )
+ case _:
+ assert_never(plugin)
+
+
+def prepare_model(
+ *,
+ model_id: str,
+ revision: str | None,
+ plugin: FactoryPlugin,
+ prepared_model_root: str,
+) -> str:
+ """Prepare one pinned model through vLLM Factory's Python API."""
+ if revision is None:
+ raise ValueError("vLLM Factory models require a pinned Hugging Face revision")
+
+ output = _prepared_model_path(
+ root=Path(prepared_model_root),
+ model_id=model_id,
+ revision=revision,
+ plugin=plugin,
+ )
+ provenance_path = output / ".anonymizer-vllm-factory.json"
+ provenance = {
+ "model_id": model_id,
+ "model_revision": revision,
+ "plugin": plugin,
+ "vllm_factory_source": VLLM_FACTORY_SOURCE_URL,
+ "vllm_factory_revision": VLLM_FACTORY_SOURCE_REVISION,
+ }
+ force = not _matches_provenance(provenance_path, provenance)
+
+ model_prep = importlib.import_module("forge.model_prep")
+ transformers = importlib.import_module("transformers")
+ with _pin_hugging_face_revision(model_prep, transformers, model_id, revision):
+ prepared = model_prep.prepare_model_for_vllm_if_needed(
+ model_ref=model_id,
+ plugin=plugin,
+ output_dir=str(output),
+ force=force,
+ )
+ if prepared == model_id:
+ raise RuntimeError(f"vLLM Factory did not prepare GLiNER model {model_id!r}")
+ output.mkdir(parents=True, exist_ok=True)
+ provenance_path.write_text(json.dumps(provenance, indent=2, sort_keys=True) + "\n", encoding="utf-8")
+ return str(output)
+
+
+def io_processor_for(plugin: FactoryPlugin) -> FactoryIoProcessor:
+ """Resolve the vLLM IOProcessor entry point for a supported factory plugin."""
+ return factory_plugin_spec(plugin).io_processor
+
+
+def supports_model(plugin: FactoryPlugin, model_id: str) -> bool:
+ """Return whether this source revision was characterized for the pair."""
+ return model_id in factory_plugin_spec(plugin).characterized_models
+
+
+def _prepared_model_path(*, root: Path, model_id: str, revision: str, plugin: FactoryPlugin) -> Path:
+ safe_model = re.sub(r"[^A-Za-z0-9_.-]+", "--", model_id).strip("-")
+ return root / safe_model / revision / plugin
+
+
+def _matches_provenance(path: Path, expected: dict[str, str]) -> bool:
+ try:
+ value = json.loads(path.read_text(encoding="utf-8"))
+ except (FileNotFoundError, json.JSONDecodeError, OSError):
+ return False
+ return value == expected
+
+
+@contextmanager
+def _pin_hugging_face_revision(
+ model_prep: Any,
+ transformers: Any,
+ model_id: str,
+ revision: str,
+) -> Iterator[None]:
+ """Supply the missing revision argument at vLLM Factory's hub boundary."""
+ original_download = model_prep.hf_hub_download
+ original_list = model_prep.list_repo_files
+ original_tokenizer = transformers.AutoTokenizer.from_pretrained
+
+ def pinned_download(*args: Any, **kwargs: Any) -> Any:
+ kwargs["revision"] = revision
+ return original_download(*args, **kwargs)
+
+ def pinned_list(*args: Any, **kwargs: Any) -> Any:
+ kwargs["revision"] = revision
+ return original_list(*args, **kwargs)
+
+ def pinned_tokenizer(source: str, *args: Any, **kwargs: Any) -> Any:
+ if source == model_id:
+ kwargs["revision"] = revision
+ return original_tokenizer(source, *args, **kwargs)
+
+ model_prep.hf_hub_download = pinned_download
+ model_prep.list_repo_files = pinned_list
+ transformers.AutoTokenizer.from_pretrained = pinned_tokenizer
+ try:
+ yield
+ finally:
+ model_prep.hf_hub_download = original_download
+ model_prep.list_repo_files = original_list
+ transformers.AutoTokenizer.from_pretrained = original_tokenizer
diff --git a/tools/inference_service_compiler/vllm_runtime.py b/tools/inference_service_compiler/vllm_runtime.py
new file mode 100644
index 00000000..f4c29711
--- /dev/null
+++ b/tools/inference_service_compiler/vllm_runtime.py
@@ -0,0 +1,283 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Programmatic construction of stock vLLM and vLLM Factory servers."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import importlib
+import importlib.util
+import os
+import sys
+from argparse import Namespace
+from collections.abc import Sequence
+from dataclasses import dataclass, replace
+from pathlib import Path
+from typing import Literal
+
+from inference_service_compiler.lifecycle import LAUNCH_OWNERSHIP_MIDDLEWARE
+from inference_service_compiler.models import FactoryPlugin, parse_factory_plugin
+from inference_service_compiler.vllm_factory_integration import (
+ io_processor_for,
+ prepare_model,
+)
+
+ANONYMIZER_CHAT_MIDDLEWARE = "inference_service_compiler.vllm_factory_adapter.anonymizer_chat_compatibility"
+MINIMUM_VLLM_PYTHON = (3, 12)
+
+
+@dataclass(frozen=True, slots=True)
+class VllmServerParameters:
+ """Bounded settings accepted by the source-owned vLLM server process."""
+
+ model: str
+ host: str
+ port: int
+ revision: str | None = None
+ tokenizer_revision: str | None = None
+ served_model_name: str | None = None
+ tensor_parallel_size: int | None = None
+ gpu_memory_utilization: float | None = None
+ max_model_len: int | None = None
+ max_num_seqs: int | None = None
+ enforce_eager: bool = False
+ enable_prefix_caching: bool = False
+ async_scheduling: bool = False
+ mamba_backend: Literal["triton", "flashinfer"] | None = None
+ mamba_ssm_cache_dtype: Literal["auto", "float32", "float16", "bfloat16"] = "auto"
+ enable_mamba_cache_stochastic_rounding: bool = False
+ mamba_cache_philox_rounds: int = 0
+ lora_module: str | None = None
+ vllm_factory_plugin: FactoryPlugin | None = None
+ prepared_model_root: str = "/tmp/anonymizer-vllm-factory"
+
+
+def parse_server_parameters(argv: Sequence[str]) -> VllmServerParameters:
+ """Parse the compiler's bounded process contract without using vLLM's CLI."""
+ parser = argparse.ArgumentParser(description="Anonymizer-managed vLLM server")
+ parser.add_argument("model")
+ parser.add_argument("--host", required=True)
+ parser.add_argument("--port", required=True, type=int)
+ parser.add_argument("--revision")
+ parser.add_argument("--tokenizer-revision")
+ parser.add_argument("--served-model-name")
+ parser.add_argument("--tensor-parallel-size", type=int)
+ parser.add_argument("--gpu-memory-utilization", type=float)
+ parser.add_argument("--max-model-len", type=int)
+ parser.add_argument("--max-num-seqs", type=int)
+ parser.add_argument("--enforce-eager", action="store_true")
+ parser.add_argument("--enable-prefix-caching", action="store_true")
+ parser.add_argument("--async-scheduling", action="store_true")
+ parser.add_argument("--mamba-backend", choices=("triton", "flashinfer"))
+ parser.add_argument(
+ "--mamba-ssm-cache-dtype",
+ choices=("auto", "float32", "float16", "bfloat16"),
+ default="auto",
+ )
+ parser.add_argument("--enable-mamba-cache-stochastic-rounding", action="store_true")
+ parser.add_argument("--mamba-cache-philox-rounds", type=int, default=0)
+ parser.add_argument("--enable-lora", action="store_true")
+ parser.add_argument("--lora-modules")
+ parser.add_argument(
+ "--vllm-factory-plugin",
+ choices=("deberta_gliner", "deberta_gliner2"),
+ )
+ parser.add_argument("--prepared-model-root", default="/tmp/anonymizer-vllm-factory")
+ parsed = parser.parse_args(list(argv))
+ if parsed.enable_lora != (parsed.lora_modules is not None):
+ parser.error("--enable-lora and --lora-modules must be used together")
+ return VllmServerParameters(
+ model=parsed.model,
+ host=parsed.host,
+ port=parsed.port,
+ revision=parsed.revision,
+ tokenizer_revision=parsed.tokenizer_revision,
+ served_model_name=parsed.served_model_name,
+ tensor_parallel_size=parsed.tensor_parallel_size,
+ gpu_memory_utilization=parsed.gpu_memory_utilization,
+ max_model_len=parsed.max_model_len,
+ max_num_seqs=parsed.max_num_seqs,
+ enforce_eager=parsed.enforce_eager,
+ enable_prefix_caching=parsed.enable_prefix_caching,
+ async_scheduling=parsed.async_scheduling,
+ mamba_backend=parsed.mamba_backend,
+ mamba_ssm_cache_dtype=parsed.mamba_ssm_cache_dtype,
+ enable_mamba_cache_stochastic_rounding=parsed.enable_mamba_cache_stochastic_rounding,
+ mamba_cache_philox_rounds=parsed.mamba_cache_philox_rounds,
+ lora_module=parsed.lora_modules,
+ vllm_factory_plugin=(
+ parse_factory_plugin(parsed.vllm_factory_plugin) if parsed.vllm_factory_plugin is not None else None
+ ),
+ prepared_model_root=parsed.prepared_model_root,
+ )
+
+
+def build_server_arguments(parameters: VllmServerParameters) -> Namespace:
+ """Construct vLLM frontend and async-engine configs through its Python API."""
+ arg_utils = importlib.import_module("vllm.engine.arg_utils")
+ mamba_config = importlib.import_module("vllm.config.mamba")
+ cli_args = importlib.import_module("vllm.entrypoints.openai.cli_args")
+ model_protocol = importlib.import_module("vllm.entrypoints.openai.models.protocol")
+
+ lora_modules = None
+ if parameters.lora_module is not None:
+ name, separator, path = parameters.lora_module.partition("=")
+ if not separator or not name or not path:
+ raise ValueError("LoRA module must use the form NAME=PATH")
+ lora_modules = [model_protocol.LoRAModulePath(name=name, path=path)]
+
+ factory_plugin = parameters.vllm_factory_plugin
+ engine = arg_utils.AsyncEngineArgs(
+ model=parameters.model,
+ revision=parameters.revision,
+ tokenizer_revision=parameters.tokenizer_revision,
+ served_model_name=[parameters.served_model_name] if parameters.served_model_name is not None else None,
+ tensor_parallel_size=parameters.tensor_parallel_size or 1,
+ gpu_memory_utilization=parameters.gpu_memory_utilization or 0.9,
+ max_num_seqs=parameters.max_num_seqs,
+ enforce_eager=parameters.enforce_eager,
+ enable_lora=lora_modules is not None,
+ runner="pooling" if factory_plugin is not None else "auto",
+ trust_remote_code=factory_plugin is not None,
+ dtype="bfloat16" if factory_plugin is not None else "auto",
+ enable_prefix_caching=False if factory_plugin is not None else parameters.enable_prefix_caching,
+ enable_chunked_prefill=False if factory_plugin is not None else None,
+ io_processor_plugin=io_processor_for(factory_plugin) if factory_plugin is not None else None,
+ async_scheduling=parameters.async_scheduling,
+ mamba_backend=mamba_config.MambaBackendEnum[(parameters.mamba_backend or "triton").upper()],
+ mamba_ssm_cache_dtype=parameters.mamba_ssm_cache_dtype,
+ enable_mamba_cache_stochastic_rounding=parameters.enable_mamba_cache_stochastic_rounding,
+ mamba_cache_philox_rounds=parameters.mamba_cache_philox_rounds,
+ )
+ if parameters.max_model_len is not None:
+ engine.max_model_len = parameters.max_model_len
+ middleware = [LAUNCH_OWNERSHIP_MIDDLEWARE]
+ if factory_plugin is not None:
+ middleware.append(ANONYMIZER_CHAT_MIDDLEWARE)
+ frontend = cli_args.FrontendArgs(
+ host=parameters.host,
+ port=parameters.port,
+ lora_modules=lora_modules,
+ middleware=middleware,
+ )
+ values = vars(engine) | vars(frontend)
+ values.update(
+ model_tag=None,
+ headless=False,
+ api_server_count=1,
+ config=None,
+ grpc=False,
+ )
+ arguments = Namespace(**values)
+ cli_args.validate_parsed_serve_args(arguments)
+ return arguments
+
+
+def expose_interpreter_tools() -> None:
+ """Expose console tools installed beside the selected Python interpreter."""
+ interpreter_bin = str(Path(sys.prefix) / "bin")
+ current_path = os.environ.get("PATH", "")
+ path_entries = [entry for entry in current_path.split(os.pathsep) if entry != interpreter_bin]
+ os.environ["PATH"] = os.pathsep.join([interpreter_bin, *path_entries])
+
+
+def _prepend_environment_path(name: str, path: Path) -> None:
+ current = os.environ.get(name, "")
+ entries = [entry for entry in current.split(os.pathsep) if entry and entry != str(path)]
+ os.environ[name] = os.pathsep.join([str(path), *entries])
+
+
+def _packaged_cuda_root() -> Path | None:
+ """Find the CUDA toolkit installed beside the selected Python interpreter."""
+ package = importlib.util.find_spec("nvidia")
+ if package is None or package.submodule_search_locations is None:
+ return None
+ for location in package.submodule_search_locations:
+ candidate = Path(location) / "cu13"
+ if (candidate / "bin" / "nvcc").is_file() and (candidate / "lib").is_dir():
+ return candidate
+ return None
+
+
+def _cudart_link_directory(cuda_root: Path) -> Path:
+ """Expose the versioned CUDA wheel runtime under the linker name FlashInfer expects."""
+ library_directory = cuda_root / "lib"
+ unversioned = library_directory / "libcudart.so"
+ if unversioned.exists():
+ return library_directory
+ candidates = sorted(library_directory.glob("libcudart.so.*"))
+ if not candidates:
+ raise RuntimeError(f"CUDA toolkit at {cuda_root} does not contain libcudart")
+ root_digest = hashlib.sha256(os.fsencode(cuda_root.resolve())).hexdigest()[:16]
+ link_directory = Path.home() / ".cache" / "nemo-anonymizer" / "cuda-link" / root_digest
+ link_directory.mkdir(parents=True, exist_ok=True)
+ link = link_directory / "libcudart.so"
+ target = candidates[-1].resolve()
+ if link.is_symlink() and link.resolve() == target:
+ return link_directory
+ if link.exists() or link.is_symlink():
+ link.unlink()
+ try:
+ link.symlink_to(target)
+ except FileExistsError:
+ if not link.is_symlink() or link.resolve() != target:
+ raise
+ return link_directory
+
+
+def configure_flashinfer_toolchain() -> None:
+ """Configure FlashInfer JIT from the CUDA wheels pinned with vLLM."""
+ configured = os.environ.get("CUDA_HOME")
+ if configured:
+ compiler = Path(configured) / "bin" / "nvcc"
+ if not compiler.is_file():
+ raise RuntimeError(f"CUDA_HOME does not contain bin/nvcc: {configured}")
+ return
+ cuda_root = _packaged_cuda_root()
+ if cuda_root is None:
+ raise RuntimeError("FlashInfer Mamba requires a CUDA compiler; install the local-models group or set CUDA_HOME")
+ os.environ["CUDA_HOME"] = str(cuda_root)
+ _prepend_environment_path("LIBRARY_PATH", _cudart_link_directory(cuda_root))
+ _prepend_environment_path("LD_LIBRARY_PATH", cuda_root / "lib")
+
+
+def prepare_runtime_environment(parameters: VllmServerParameters) -> VllmServerParameters:
+ """Prepare the selected stock vLLM or vLLM Factory runtime."""
+ if sys.version_info < MINIMUM_VLLM_PYTHON:
+ raise RuntimeError("vLLM 0.27.1 local serving requires Python 3.12 or later")
+ expose_interpreter_tools()
+ os.environ.setdefault("VLLM_USE_FLASHINFER_SAMPLER", "0")
+ if parameters.mamba_backend == "flashinfer":
+ configure_flashinfer_toolchain()
+ plugin = parameters.vllm_factory_plugin
+ if plugin is None:
+ return parameters
+ os.environ["VLLM_PLUGINS"] = f"{plugin},{io_processor_for(plugin)}"
+ os.environ["ANONYMIZER_VLLM_FACTORY_PLUGIN"] = plugin
+ prepared_model = prepare_model(
+ model_id=parameters.model,
+ revision=parameters.revision,
+ plugin=plugin,
+ prepared_model_root=parameters.prepared_model_root,
+ )
+ public_name = parameters.served_model_name or parameters.model
+ return replace(
+ parameters,
+ model=prepared_model,
+ revision=None,
+ tokenizer_revision=None,
+ served_model_name=public_name,
+ )
+
+
+def run_server(argv: Sequence[str]) -> None:
+ """Construct and run vLLM's Python-owned server lifecycle."""
+ parameters = prepare_runtime_environment(parse_server_parameters(argv))
+ uvloop = importlib.import_module("uvloop")
+ api_server = importlib.import_module("vllm.entrypoints.openai.api_server")
+ api_utils = importlib.import_module("vllm.entrypoints.serve.utils.api_utils")
+
+ api_utils.cli_env_setup()
+ arguments = build_server_arguments(parameters)
+ uvloop.run(api_server.run_server(arguments))
diff --git a/tools/inference_service_compiler/vllm_server.py b/tools/inference_service_compiler/vllm_server.py
new file mode 100644
index 00000000..b5ddb5b5
--- /dev/null
+++ b/tools/inference_service_compiler/vllm_server.py
@@ -0,0 +1,16 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+"""Internal process entry point for the programmatic vLLM runtime."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+TOOLS_ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(TOOLS_ROOT))
+
+from inference_service_compiler.vllm_runtime import run_server # noqa: E402
+
+if __name__ == "__main__":
+ run_server(sys.argv[1:])
diff --git a/tools/inference_service_profiles/gemma-4-12b-it.toml b/tools/inference_service_profiles/gemma-4-12b-it.toml
new file mode 100644
index 00000000..ab92de3f
--- /dev/null
+++ b/tools/inference_service_profiles/gemma-4-12b-it.toml
@@ -0,0 +1,24 @@
+schema_version = "inference-service.local-spec/v2"
+
+[task]
+kind = "generation"
+
+[model]
+model_id = "google/gemma-4-12B-it"
+revision = "707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7"
+
+[vllm]
+python_executable = ".venv/bin/python"
+served_model_name = "gemma-4-12b-it-local"
+gpu_memory_utilization = 0.90
+max_model_len = 8192
+max_num_seqs = 16
+enable_prefix_caching = true
+async_scheduling = true
+
+[local]
+host = "127.0.0.1"
+port = 8000
+
+startup_timeout_seconds = 1800
+shutdown_timeout_seconds = 60
diff --git a/tools/inference_service_profiles/gliner2.toml b/tools/inference_service_profiles/gliner2.toml
new file mode 100644
index 00000000..30b6868e
--- /dev/null
+++ b/tools/inference_service_profiles/gliner2.toml
@@ -0,0 +1,27 @@
+schema_version = "inference-service.local-spec/v2"
+
+[task]
+kind = "entity-detection"
+dynamic_labels = true
+offsets = true
+scores = true
+
+[model]
+model_id = "fastino/gliner2-privacy-filter-PII-multi"
+revision = "59894c087cb2923b01f337d4ee72f6ff84d5bdd6"
+
+[vllm]
+python_executable = ".venv/bin/python"
+gpu_memory_utilization = 0.85
+max_model_len = 512
+
+[vllm.factory]
+plugin = "deberta_gliner2"
+prepared_model_root = "/tmp/anonymizer-vllm-factory"
+
+[local]
+host = "127.0.0.1"
+port = 8002
+
+startup_timeout_seconds = 300
+shutdown_timeout_seconds = 30
diff --git a/tools/inference_service_profiles/gpt-oss-120b.toml b/tools/inference_service_profiles/gpt-oss-120b.toml
new file mode 100644
index 00000000..3b6d7676
--- /dev/null
+++ b/tools/inference_service_profiles/gpt-oss-120b.toml
@@ -0,0 +1,24 @@
+schema_version = "inference-service.local-spec/v2"
+
+[task]
+kind = "generation"
+
+[model]
+model_id = "openai/gpt-oss-120b"
+revision = "b5c939de8f754692c1647ca79fbf85e8c1e70f8a"
+
+[vllm]
+python_executable = ".venv/bin/python"
+served_model_name = "gpt-oss-120b-local"
+gpu_memory_utilization = 0.95
+max_model_len = 8192
+max_num_seqs = 8
+enable_prefix_caching = true
+async_scheduling = true
+
+[local]
+host = "127.0.0.1"
+port = 8000
+
+startup_timeout_seconds = 1800
+shutdown_timeout_seconds = 60
diff --git a/tools/inference_service_profiles/gpt-oss-20b.toml b/tools/inference_service_profiles/gpt-oss-20b.toml
new file mode 100644
index 00000000..e33dfdbc
--- /dev/null
+++ b/tools/inference_service_profiles/gpt-oss-20b.toml
@@ -0,0 +1,24 @@
+schema_version = "inference-service.local-spec/v2"
+
+[task]
+kind = "generation"
+
+[model]
+model_id = "openai/gpt-oss-20b"
+revision = "6cee5e81ee83917806bbde320786a8fb61efebee"
+
+[vllm]
+python_executable = ".venv/bin/python"
+served_model_name = "gpt-oss-20b-local"
+gpu_memory_utilization = 0.85
+max_model_len = 8192
+max_num_seqs = 16
+enable_prefix_caching = true
+async_scheduling = true
+
+[local]
+host = "127.0.0.1"
+port = 8000
+
+startup_timeout_seconds = 1200
+shutdown_timeout_seconds = 60
diff --git a/tools/inference_service_profiles/nemotron-3.5-lightning-nvfp4.toml b/tools/inference_service_profiles/nemotron-3.5-lightning-nvfp4.toml
new file mode 100644
index 00000000..09281d9a
--- /dev/null
+++ b/tools/inference_service_profiles/nemotron-3.5-lightning-nvfp4.toml
@@ -0,0 +1,28 @@
+schema_version = "inference-service.local-spec/v2"
+
+[task]
+kind = "generation"
+
+[model]
+model_id = "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4"
+revision = "cc84af2fe71647d87f4486c064f320e1e7535243"
+
+[vllm]
+python_executable = ".venv/bin/python"
+served_model_name = "nemotron-3.5-lightning-nvfp4-local"
+gpu_memory_utilization = 0.88
+max_model_len = 8192
+max_num_seqs = 16
+enable_prefix_caching = true
+async_scheduling = true
+mamba_backend = "flashinfer"
+mamba_ssm_cache_dtype = "float16"
+enable_mamba_cache_stochastic_rounding = true
+mamba_cache_philox_rounds = 5
+
+[local]
+host = "127.0.0.1"
+port = 8000
+
+startup_timeout_seconds = 1800
+shutdown_timeout_seconds = 60
diff --git a/tools/inference_service_profiles/nemotron-3.5-lightning.toml b/tools/inference_service_profiles/nemotron-3.5-lightning.toml
new file mode 100644
index 00000000..6131711d
--- /dev/null
+++ b/tools/inference_service_profiles/nemotron-3.5-lightning.toml
@@ -0,0 +1,28 @@
+schema_version = "inference-service.local-spec/v2"
+
+[task]
+kind = "generation"
+
+[model]
+model_id = "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16"
+revision = "33268dc8a6da85a56be2b12241453e4e1237bbe1"
+
+[vllm]
+python_executable = ".venv/bin/python"
+served_model_name = "nemotron-3.5-lightning-local"
+gpu_memory_utilization = 0.88
+max_model_len = 8192
+max_num_seqs = 16
+enable_prefix_caching = true
+async_scheduling = true
+mamba_backend = "flashinfer"
+mamba_ssm_cache_dtype = "float16"
+enable_mamba_cache_stochastic_rounding = true
+mamba_cache_philox_rounds = 5
+
+[local]
+host = "127.0.0.1"
+port = 8000
+
+startup_timeout_seconds = 1800
+shutdown_timeout_seconds = 60
diff --git a/tools/inference_service_profiles/nvidia-gliner.toml b/tools/inference_service_profiles/nvidia-gliner.toml
new file mode 100644
index 00000000..f7024aac
--- /dev/null
+++ b/tools/inference_service_profiles/nvidia-gliner.toml
@@ -0,0 +1,27 @@
+schema_version = "inference-service.local-spec/v2"
+
+[task]
+kind = "entity-detection"
+dynamic_labels = true
+offsets = true
+scores = true
+
+[model]
+model_id = "nvidia/gliner-pii"
+revision = "bd23e8ef4425fd04e34c5204ab49ffaa706eae79"
+
+[vllm]
+python_executable = ".venv/bin/python"
+gpu_memory_utilization = 0.85
+max_model_len = 512
+
+[vllm.factory]
+plugin = "deberta_gliner"
+prepared_model_root = "/tmp/anonymizer-vllm-factory"
+
+[local]
+host = "127.0.0.1"
+port = 8001
+
+startup_timeout_seconds = 300
+shutdown_timeout_seconds = 30
diff --git a/tools/inference_service_profiles/qwen3-30b-a3b-instruct.toml b/tools/inference_service_profiles/qwen3-30b-a3b-instruct.toml
new file mode 100644
index 00000000..399c60d4
--- /dev/null
+++ b/tools/inference_service_profiles/qwen3-30b-a3b-instruct.toml
@@ -0,0 +1,24 @@
+schema_version = "inference-service.local-spec/v2"
+
+[task]
+kind = "generation"
+
+[model]
+model_id = "Qwen/Qwen3-30B-A3B-Instruct-2507"
+revision = "0d7cf23991f47feeb3a57ecb4c9cee8ea4a17bfe"
+
+[vllm]
+python_executable = ".venv/bin/python"
+served_model_name = "qwen3-30b-a3b-instruct-local"
+gpu_memory_utilization = 0.90
+max_model_len = 8192
+max_num_seqs = 16
+enable_prefix_caching = true
+async_scheduling = true
+
+[local]
+host = "127.0.0.1"
+port = 8000
+
+startup_timeout_seconds = 1800
+shutdown_timeout_seconds = 60
diff --git a/tools/inference_service_profiles/vllm-local.toml b/tools/inference_service_profiles/vllm-local.toml
new file mode 100644
index 00000000..bfbb5650
--- /dev/null
+++ b/tools/inference_service_profiles/vllm-local.toml
@@ -0,0 +1,21 @@
+schema_version = "inference-service.local-spec/v2"
+
+[task]
+kind = "generation"
+
+[model]
+model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+revision = "fe8a4ea1ffedaf415f4da2f062534de366a451e6"
+
+[vllm]
+python_executable = ".venv/bin/python"
+served_model_name = "anonymizer-local"
+gpu_memory_utilization = 0.85
+max_model_len = 2048
+
+[local]
+host = "127.0.0.1"
+port = 8000
+
+startup_timeout_seconds = 600
+shutdown_timeout_seconds = 30
diff --git a/tools/measurement/README.md b/tools/measurement/README.md
index 08922da5..71e5a528 100644
--- a/tools/measurement/README.md
+++ b/tools/measurement/README.md
@@ -475,6 +475,68 @@ Strict online imports require `--wandb-entity` (or
`ANONYMIZER_MEASUREMENT_WANDB_ENTITY`) so the result always identifies the
destination and includes a W&B run URL. Offline imports may omit the entity.
+### NVIDIA PR vs. Main Scorecard
+
+The NVIDIA benchmark project has a saved
+[PR vs. main scorecard](https://wandb.ai/nemo-llm-service/nemo-anonymizer-benchmarks/reports/PR-vs-main-scorecard--VmlldzoxNzY4NTMzMA).
+The report does not query every benchmark run directly. It displays one active
+dashboard snapshot generated from eligible imported runs.
+
+A benchmark run enters the scorecard snapshot only when all of these conditions
+hold:
+
+- The W&B run state is `finished`.
+- `benchmark_role` is `main-baseline` or `candidate`.
+- `anonymizer_mode` is `replace` or `rewrite`.
+- The run config contains `anonymizer_config_id`, and `benchmark_config_ids`
+ resolves to at least one config ID.
+- The run summary contains `publication/complete=true`.
+
+The scorecard matches main and candidate runs by `anonymizer_config_id` and
+`benchmark_config_ids`. It separates datasets by `benchmark_workload_ids`.
+Use the same values for a comparison pair; PR candidates also require a PR
+number. The sealed case supplies `benchmark_config_ids` and
+`benchmark_workload_ids`. The importer command supplies the remaining identity
+fields.
+
+Append fields like these to the strict importer command above for a main
+baseline:
+
+```bash
+ --benchmark-role main-baseline \
+ --benchmark-kind main \
+ --branch main \
+ --commit-sha 0123456789abcdef0123456789abcdef01234567 \
+ --anonymizer-config-id rat-replace-throughput \
+ --anonymizer-mode replace
+```
+
+Use matching comparison identifiers for the candidate:
+
+```bash
+ --benchmark-role candidate \
+ --benchmark-kind pr \
+ --branch contributor/feat/local-models \
+ --commit-sha 89abcdef0123456789abcdef0123456789abcdef \
+ --pr-number 212 \
+ --anonymizer-config-id rat-replace-throughput \
+ --anonymizer-mode replace
+```
+
+The strict sealed importer is the only repository command that currently emits
+the completion marker and accepts the full scorecard identity. Native runs from
+`run_benchmarks.py --wandb-mode online` do not set those fields and will not
+appear in this scorecard. They remain available in the W&B runs table and in
+workspaces created by `create_wandb_report.py`.
+
+After publishing both sides, rerun the scorecard snapshot publisher. That
+publisher is not shipped in this repository. The active W&B dashboard run
+retains its source as `code/publish_pr_scorecard.py`, but the publisher also
+depends on a team-owned HTML template and saved-view state. Until those assets
+move into the repository, coordinate the refresh with the dashboard publisher
+owner. A successful benchmark import does not refresh the saved scorecard by
+itself.
+
The goal of W&B support is to get sanitized benchmark data into W&B. Workspaces,
reports, views, and panels are presentation layers on top of that data. They
can be edited in W&B, regenerated with the tooling below, or replaced as the
diff --git a/tools/serve_gliner.py b/tools/serve_gliner.py
deleted file mode 100644
index 6f8ecaf2..00000000
--- a/tools/serve_gliner.py
+++ /dev/null
@@ -1,486 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-"""Minimal OpenAI-compatible GLiNER server for Anonymizer detection.
-
-Fakes /v1/chat/completions so Anonymizer routes its `entity_detector` role
-here instead of hitting build.nvidia.com. The response's message.content is
-a JSON string of shape ``{"entities": [...]}`` as expected by
-``anonymizer.engine.detection.postprocess.parse_raw_entities``.
-
-Run:
- python tools/serve_gliner.py # binds 127.0.0.1:8001 (default)
- python tools/serve_gliner.py --port 9000 # override listen port
- python tools/serve_gliner.py --host 0.0.0.0 # all interfaces (no auth)
-
-Chunk batching and entity deduplication are implemented for robust local
-inference. This file adds the Anonymizer chat-completion adapter and optional request
-coalescing when DataDesigner runs with ``max_parallel_requests`` > 1.
-
-See ``docs/concepts/self-hosting-gliner.md`` for full usage.
-"""
-
-from __future__ import annotations
-
-import argparse
-import asyncio
-import json
-import logging
-import os
-import time
-import uuid
-from concurrent.futures import ThreadPoolExecutor
-from dataclasses import dataclass
-from typing import Any
-
-import torch # ty: ignore[unresolved-import] -- optional server dependency
-import uvicorn
-from fastapi import FastAPI, HTTPException, Request # ty: ignore[unresolved-import] -- optional server dependency
-from gliner import GLiNER # ty: ignore[unresolved-import] -- optional server dependency
-
-MODEL_NAME = "nvidia/gliner-pii"
-DEFAULT_HOST = "127.0.0.1"
-DEFAULT_PORT = 8001
-DEFAULT_CHUNK_LENGTH = 384
-DEFAULT_OVERLAP = 128
-DEFAULT_FLAT_NER = False
-DEFAULT_INFERENCE_BATCH_SIZE = 8
-
-BATCH_MODE = os.getenv("GLINER_BATCH_MODE", "true").lower() not in {"0", "false", "no"}
-MAX_BATCH_REQUESTS = int(os.getenv("GLINER_MAX_BATCH_REQUESTS", "32"))
-BATCH_WAIT_MS = float(os.getenv("GLINER_BATCH_WAIT_MS", "10")) / 1000.0
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
-log = logging.getLogger("gliner-server")
-
-app = FastAPI()
-model: GLiNER | None = None
-_device = "cpu"
-
-
-def _resolve_device() -> str:
- device_env = os.getenv("DEVICE", "auto")
- if device_env != "auto":
- return device_env
- if torch.backends.mps.is_available():
- return "mps"
- if torch.cuda.is_available():
- return "cuda"
- return "cpu"
-
-
-def _load_model() -> None:
- global model, _device
- _device = _resolve_device()
- log.info("loading %s on %s", MODEL_NAME, _device)
- model = GLiNER.from_pretrained(MODEL_NAME, map_location=_device)
-
-
-@dataclass(frozen=True)
-class DetectParams:
- labels: tuple[str, ...]
- threshold: float
- chunk_length: int
- overlap: int
- flat_ner: bool
- inference_batch_size: int
-
-
-@dataclass
-class DetectJob:
- text: str
- params: DetectParams
- future: asyncio.Future[list[dict[str, Any]]]
-
-
-def _extract_text(messages: list[dict[str, Any]]) -> str:
- """Pull the user message text. Handles both string and multi-part content."""
- if not messages:
- return ""
- content = messages[-1].get("content", "")
- if isinstance(content, str):
- return content
- if isinstance(content, list):
- return "".join(part.get("text", "") for part in content if isinstance(part, dict))
- return str(content)
-
-
-def _validate_chunk_params(chunk_length: int, overlap: int) -> None:
- if chunk_length < 1:
- raise HTTPException(status_code=422, detail="chunk_length must be >= 1")
- if overlap < 0:
- raise HTTPException(status_code=422, detail="overlap must be >= 0")
- if overlap >= chunk_length:
- raise HTTPException(status_code=422, detail="overlap must be less than chunk_length")
-
-
-def _create_text_chunks(text: str, chunk_length: int, overlap: int) -> tuple[list[str], list[int]]:
- chunks: list[str] = []
- offsets: list[int] = []
- start = 0
- while start < len(text):
- chunks.append(text[start : start + chunk_length])
- offsets.append(start)
- if start + chunk_length >= len(text):
- break
- start += chunk_length - overlap
- return chunks, offsets
-
-
-def _shift_offsets(entities: list[dict[str, Any]], offset: int) -> None:
- for entity in entities:
- entity["start"] = int(entity["start"]) + offset
- entity["end"] = int(entity["end"]) + offset
-
-
-def _remove_subset_entities(entities: list[dict[str, Any]]) -> list[dict[str, Any]]:
- """Drop spans fully contained in another span (dedup step for nested NER)."""
- if not entities:
- return []
-
- kept = list(entities)
- to_delete: list[int] = []
- for idx, ent in enumerate(kept):
- has_superset = any(
- i != idx
- and i not in to_delete
- and other["start"] <= ent["start"]
- and other["end"] >= ent["end"]
- and (other["start"] < ent["start"] or other["end"] > ent["end"])
- for i, other in enumerate(kept)
- )
- if has_superset:
- to_delete.append(idx)
-
- for idx in sorted(to_delete, reverse=True):
- del kept[idx]
- return kept
-
-
-def _dedupe_entities(entities: list[dict[str, Any]]) -> list[dict[str, Any]]:
- """Collapse duplicate spans from overlapping chunks, not repeated text elsewhere."""
- best: dict[tuple[str, str, int, int], dict[str, Any]] = {}
- for entity in entities:
- label = str(entity.get("label", ""))
- text = str(entity.get("text", ""))
- start = int(entity.get("start", 0))
- end = int(entity.get("end", 0))
- key = (label, text.strip().lower(), start, end)
- score = float(entity.get("score", 0.0))
- if key not in best or score > float(best[key].get("score", 0.0)):
- best[key] = entity
- return list(best.values())
-
-
-def _format_entities(raw: list[dict[str, Any]]) -> list[dict[str, Any]]:
- return [
- {
- "text": e["text"],
- "label": e["label"],
- "start": e["start"],
- "end": e["end"],
- "score": e["score"],
- }
- for e in raw
- ]
-
-
-def _finalize_entities(entities: list[dict[str, Any]], *, flat_ner: bool) -> list[dict[str, Any]]:
- if not entities:
- return []
- if flat_ner:
- processed = entities
- else:
- processed = _remove_subset_entities([dict(entity) for entity in entities])
- return _format_entities(_dedupe_entities(processed))
-
-
-def _run_chunk_inference(
- chunks: list[str],
- *,
- labels: list[str],
- threshold: float,
- flat_ner: bool,
- inference_batch_size: int,
-) -> list[list[dict[str, Any]]]:
- if model is None:
- raise RuntimeError("GLiNER model is not loaded")
- if not chunks:
- return []
-
- batch_entities = model.inference(
- texts=chunks,
- labels=labels,
- threshold=threshold,
- flat_ner=flat_ner,
- relations=[],
- batch_size=inference_batch_size,
- )
- if not isinstance(batch_entities, list) or (batch_entities and not isinstance(batch_entities[0], list)):
- raise ValueError("unexpected GLiNER inference batch shape")
- return batch_entities
-
-
-def _detect_entities_for_text(
- text: str,
- labels: list[str],
- *,
- threshold: float,
- chunk_length: int,
- overlap: int,
- flat_ner: bool,
- inference_batch_size: int,
-) -> list[dict[str, Any]]:
- if not labels or not text:
- return []
-
- chunks, offsets = _create_text_chunks(text, chunk_length, overlap)
- batch = _run_chunk_inference(
- chunks,
- labels=labels,
- threshold=threshold,
- flat_ner=flat_ner,
- inference_batch_size=inference_batch_size,
- )
-
- merged: list[dict[str, Any]] = []
- for chunk_entities, offset in zip(batch, offsets, strict=True):
- adjusted = [dict(entity) for entity in chunk_entities]
- _shift_offsets(adjusted, offset)
- merged.extend(adjusted)
-
- return _finalize_entities(merged, flat_ner=flat_ner)
-
-
-def _detect_entities_for_texts(
- texts: list[str],
- labels: list[str],
- *,
- threshold: float,
- chunk_length: int,
- overlap: int,
- flat_ner: bool,
- inference_batch_size: int,
-) -> list[list[dict[str, Any]]]:
- if not labels:
- return [[] for _ in texts]
-
- chunk_records: list[tuple[int, int, str]] = []
- for text_idx, text in enumerate(texts):
- if not text:
- continue
- chunks, offsets = _create_text_chunks(text, chunk_length, overlap)
- for chunk, offset in zip(chunks, offsets, strict=True):
- chunk_records.append((text_idx, offset, chunk))
-
- per_text: list[list[dict[str, Any]]] = [[] for _ in texts]
- if not chunk_records:
- return per_text
-
- flat_chunks = [chunk for _, _, chunk in chunk_records]
- batch = _run_chunk_inference(
- flat_chunks,
- labels=labels,
- threshold=threshold,
- flat_ner=flat_ner,
- inference_batch_size=inference_batch_size,
- )
-
- for (text_idx, offset, _), chunk_entities in zip(chunk_records, batch, strict=True):
- adjusted = [dict(entity) for entity in chunk_entities]
- _shift_offsets(adjusted, offset)
- per_text[text_idx].extend(adjusted)
-
- return [_finalize_entities(entities, flat_ner=flat_ner) for entities in per_text]
-
-
-class BatchDetector:
- """Coalesce concurrent detect requests into shared GLiNER inference calls."""
-
- def __init__(self) -> None:
- self._queue: asyncio.Queue[DetectJob | None] = asyncio.Queue()
- self._executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="gliner-infer")
- self._worker_task: asyncio.Task[None] | None = None
-
- def start(self) -> None:
- self._worker_task = asyncio.create_task(self._worker())
-
- async def stop(self) -> None:
- if self._worker_task is None:
- return
- await self._queue.put(None)
- await self._worker_task
- self._executor.shutdown(wait=True)
-
- async def detect(self, text: str, params: DetectParams) -> list[dict[str, Any]]:
- loop = asyncio.get_running_loop()
- if not BATCH_MODE:
- return await loop.run_in_executor(
- self._executor,
- lambda: _detect_entities_for_text(
- text,
- list(params.labels),
- threshold=params.threshold,
- chunk_length=params.chunk_length,
- overlap=params.overlap,
- flat_ner=params.flat_ner,
- inference_batch_size=params.inference_batch_size,
- ),
- )
-
- future: asyncio.Future[list[dict[str, Any]]] = loop.create_future()
- await self._queue.put(DetectJob(text=text, params=params, future=future))
- return await future
-
- async def _worker(self) -> None:
- while True:
- first = await self._queue.get()
- if first is None:
- break
-
- batch = [first]
- deadline = asyncio.get_running_loop().time() + BATCH_WAIT_MS
- while len(batch) < MAX_BATCH_REQUESTS:
- timeout = deadline - asyncio.get_running_loop().time()
- if timeout <= 0:
- break
- try:
- job = await asyncio.wait_for(self._queue.get(), timeout=timeout)
- except TimeoutError:
- break
- if job is None:
- await self._queue.put(None)
- break
- batch.append(job)
-
- await self._dispatch(batch)
-
- async def _dispatch(self, jobs: list[DetectJob]) -> None:
- grouped: dict[DetectParams, list[DetectJob]] = {}
- for job in jobs:
- grouped.setdefault(job.params, []).append(job)
-
- loop = asyncio.get_running_loop()
- for params, group in grouped.items():
- texts = [job.text for job in group]
- try:
- results = await loop.run_in_executor(
- self._executor,
- lambda p=params, t=texts: _detect_entities_for_texts(
- t,
- list(p.labels),
- threshold=p.threshold,
- chunk_length=p.chunk_length,
- overlap=p.overlap,
- flat_ner=p.flat_ner,
- inference_batch_size=p.inference_batch_size,
- ),
- )
- except Exception as exc:
- for job in group:
- if not job.future.done():
- job.future.set_exception(exc)
- continue
-
- for job, entities in zip(group, results, strict=True):
- if not job.future.done():
- job.future.set_result(entities)
-
-
-detector = BatchDetector()
-
-
-@app.on_event("startup")
-async def startup() -> None:
- await asyncio.to_thread(_load_model)
- log.info(
- "device=%s batch_mode=%s max_requests=%d wait_ms=%.0f inference_batch_size=%d",
- _device,
- BATCH_MODE,
- MAX_BATCH_REQUESTS,
- BATCH_WAIT_MS * 1000,
- DEFAULT_INFERENCE_BATCH_SIZE,
- )
- detector.start()
-
-
-@app.on_event("shutdown")
-async def shutdown() -> None:
- await detector.stop()
-
-
-@app.get("/v1/models")
-def list_models() -> dict[str, Any]:
- return {"object": "list", "data": [{"id": MODEL_NAME, "object": "model"}]}
-
-
-@app.post("/v1/chat/completions")
-async def chat_completions(request: Request) -> dict[str, Any]:
- if model is None:
- raise HTTPException(status_code=503, detail="GLiNER model is not loaded")
-
- body = await request.json()
- text = _extract_text(body.get("messages", []))
- labels = body.get("labels") or []
- threshold = float(body.get("threshold", 0.3))
- chunk_length = int(body.get("chunk_length", DEFAULT_CHUNK_LENGTH))
- overlap = int(body.get("overlap", DEFAULT_OVERLAP))
- flat_ner = bool(body.get("flat_ner", DEFAULT_FLAT_NER))
- inference_batch_size = int(body.get("batch_size", DEFAULT_INFERENCE_BATCH_SIZE))
- _validate_chunk_params(chunk_length, overlap)
-
- params = DetectParams(
- labels=tuple(labels),
- threshold=threshold,
- chunk_length=chunk_length,
- overlap=overlap,
- flat_ner=flat_ner,
- inference_batch_size=inference_batch_size,
- )
-
- log.info(
- "detect: labels=%d threshold=%.2f chunk=%d overlap=%d flat_ner=%s batch_size=%d text_len=%d",
- len(labels),
- threshold,
- chunk_length,
- overlap,
- flat_ner,
- inference_batch_size,
- len(text),
- )
- entities = await detector.detect(text, params)
- content = json.dumps({"entities": entities})
- return {
- "id": f"chatcmpl-{uuid.uuid4().hex[:12]}",
- "object": "chat.completion",
- "created": int(time.time()),
- "model": body.get("model", MODEL_NAME),
- "choices": [
- {
- "index": 0,
- "message": {"role": "assistant", "content": content},
- "finish_reason": "stop",
- }
- ],
- "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
- }
-
-
-def _parse_args() -> argparse.Namespace:
- parser = argparse.ArgumentParser(description="OpenAI-compatible GLiNER server for Anonymizer.")
- parser.add_argument(
- "--host",
- default=DEFAULT_HOST,
- help=f"Bind address (default: {DEFAULT_HOST})",
- )
- parser.add_argument(
- "--port",
- type=int,
- default=DEFAULT_PORT,
- help=f"Listen port (default: {DEFAULT_PORT})",
- )
- return parser.parse_args()
-
-
-if __name__ == "__main__":
- cli = _parse_args()
- uvicorn.run(app, host=cli.host, port=cli.port)
diff --git a/uv.lock b/uv.lock
index 1cdea812..3bc6d52d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,10 +1,15 @@
version = 1
-revision = 2
+revision = 3
requires-python = ">=3.11"
resolution-markers = [
- "python_full_version >= '3.14'",
- "python_full_version >= '3.12' and python_full_version < '3.14'",
- "python_full_version < '3.12'",
+ "python_full_version >= '3.14' and sys_platform == 'darwin'",
+ "python_full_version >= '3.14' and sys_platform != 'darwin'",
+ "python_full_version == '3.13.*' and sys_platform == 'darwin'",
+ "python_full_version == '3.12.*' and sys_platform == 'darwin'",
+ "python_full_version == '3.13.*' and sys_platform != 'darwin'",
+ "python_full_version == '3.12.*' and sys_platform != 'darwin'",
+ "python_full_version < '3.12' and sys_platform == 'darwin'",
+ "python_full_version < '3.12' and sys_platform != 'darwin'",
]
[manifest]
@@ -157,6 +162,25 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
]
+[[package]]
+name = "anthropic"
+version = "0.121.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "anyio" },
+ { name = "distro" },
+ { name = "docstring-parser" },
+ { name = "httpx" },
+ { name = "jiter" },
+ { name = "pydantic" },
+ { name = "sniffio" },
+ { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0f/ca/3cb2c20ee729736fbd4546d5d8b67e818288529fe70cb7a80dbf80aef70b/anthropic-0.121.0.tar.gz", hash = "sha256:e79d6e08ab3376602fc9a70d4d5ea3540817c76cf7e16658bed790834e1833d6", size = 1013292, upload-time = "2026-08-07T17:11:07.241Z" }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/fa/91/b3d41643f1f639927e8c5fb02c3bd8bffe6f1f29e219b3bd4c61e267b15c/anthropic-0.121.0-py3-none-any.whl", hash = "sha256:6048713fa441e59e1cba8363171cd2a86273b25bd213e9c7ac70a523af88b011", size = 1035493, upload-time = "2026-08-07T17:11:08.508Z" },
+]
+
[[package]]
name = "anyascii"
version = "0.3.3"
@@ -179,6 +203,29 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" },
]
+[[package]]
+name = "apache-tvm-ffi"
+version = "0.1.11"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "typing-extensions" },
+]
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