Model service documentation

.github/workflows/build-docs.yml

@@ -0,0 +1,17 @@
+name: Build Documentation
+
+on:
+  push:
+    branches: ["main"]
+    paths:
+      - "docs/**"
+      - ".github/workflows/build-docs.yml"
+  pull_request:
+    branches: ["main"]
+    paths:
+      - "docs/**"
+      - ".github/workflows/build-docs.yml"
+
+jobs:
+  run:
+    uses: RationAI/.github/.github/workflows/mkdocs-build.yml@main

README.md

@@ -1,93 +1,110 @@
 # Model Service
 
+Model deployment infrastructure for RationAI using Ray Serve on Kubernetes.
 
+This repository contains:
 
-## Getting started
+- A KubeRay `RayService` manifest (`ray-service.yaml`) for deploying Ray Serve on Kubernetes.
+- Model implementations under `models/` (reference: `models/binary_classifier.py`).
+- Documentation under `docs/` (MkDocs).
 
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
+## Documentation
 
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
+- MkDocs content: `docs/`
+- Key pages:
+  - `docs/get-started/quick-start.md`
+  - `docs/guides/deployment-guide.md`
+  - `docs/guides/adding-models.md`
+  - `docs/guides/configuration-reference.md`
+  - `docs/guides/troubleshooting.md`
+  - `docs/architecture/overview.md`
+  - `docs/architecture/request-lifecycle.md`
+  - `docs/architecture/queues-and-backpressure.md`
+  - `docs/architecture/batching.md`
 
-## Add your files
+## Quick Start (Kubernetes)
 
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/topics/git/add_files/#add-files-to-a-git-repository) or push an existing Git repository with the following command:
+Full walkthrough: `docs/get-started/quick-start.md`.
 
-```
-cd existing_repo
-git remote add origin https://gitlab.ics.muni.cz/rationai/infrastructure/model-service2.git
-git branch -M master
-git push -uf origin master
-```
+### Prerequisites
 
-## Integrate with your tools
+- Kubernetes cluster with KubeRay operator installed
+- `kubectl` configured for the cluster
 
-- [ ] [Set up project integrations](https://gitlab.ics.muni.cz/rationai/infrastructure/model-service2/-/settings/integrations)
+### Deploy
 
-## Collaborate with your team
+```bash
+kubectl apply -f ray-service.yaml -n [namespace]
+kubectl get rayservice rayservice-models -n [namespace]
+```
 
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/user/project/merge_requests/auto_merge/)
+### Access locally
 
-## Test and Deploy
+```bash
+kubectl port-forward -n [namespace] svc/rayservice-models-serve-svc 8000:8000
+```
 
-Use the built-in continuous integration in GitLab.
+### Test the reference model (`BinaryClassifier`)
 
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
+The reference deployment in `ray-service.yaml` exposes an app at the route prefix:
 
-***
+- `/prostate-classifier-1`
 
-# Editing this README
+`models/binary_classifier.py` expects a **request body that is LZ4-compressed raw bytes** of a single RGB tile:
 
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
+- dtype: `uint8`
+- shape: `(tile_size, tile_size, 3)`
+- byte order: row-major (NumPy default)
 
-## Suggestions for a good README
+Example (Python):
 
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
+```bash
+pip install numpy lz4 requests
+```
 
-## Name
-Choose a self-explaining name for your project.
+```python
+import lz4.frame
+import numpy as np
+import requests
 
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
+tile_size = 512  # must match RayService user_config.tile_size
+tile = np.zeros((tile_size, tile_size, 3), dtype=np.uint8)
 
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
+payload = lz4.frame.compress(tile.tobytes())
 
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
+resp = requests.post(
+		"http://localhost:8000/prostate-classifier-1/",
+		data=payload,
+		headers={"Content-Type": "application/octet-stream"},
+		timeout=60,
+)
+resp.raise_for_status()
+print(resp.json() if resp.headers.get("content-type", "").startswith("application/json") else resp.text)
+```
 
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
+## Repository Structure
 
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
+```
+model-service/
+├── models/              # Model implementations
+│   └── binary_classifier.py
+├── providers/           # Model loading providers
+│   └── model_provider.py
+├── docs/               # Documentation
+├── ray-service.yaml    # Kubernetes RayService configuration
+├── pyproject.toml      # Python dependencies
+└── README.md
+```
 
 ## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
 
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
+- **Issues:** Report bugs or request features via [GitHub Issues](https://github.com/RationAI/model-service/issues)
+- **Contact:** RationAI team at Masaryk University
 
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
+## License
 
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
+This project is part of the RationAI infrastructure and is available for use by authorized members of the RationAI group.
 
-## License
-For open source projects, say how it is licensed.
+## Authors
 
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+Developed and maintained by the RationAI team at Masaryk University, Faculty of Informatics.

docs/architecture/batching.md

@@ -0,0 +1,82 @@
+# Batching (How It Works Under the Hood)
+
+Batching in Ray Serve is a **replica-local request coalescing** mechanism.
+
+It improves throughput when your model can process multiple inputs more efficiently together (common for GPU inference).
+
+## Where batching happens
+
+Batching happens **inside each replica process**.
+
+Requests only become eligible for batching after they:
+
+1. enter through the proxy and handle queueing/backpressure, and
+2. get routed to a specific replica
+
+See also: **[Request lifecycle](request-lifecycle.md)**.
+
+## The API surface (what you configure)
+
+In user code, batching is enabled by decorating an **async** method with `@serve.batch`:
+
+- `max_batch_size`: upper bound for how many requests are grouped into one batch execution
+- `batch_wait_timeout_s`: maximum time to wait (since the first queued item) before flushing a smaller batch
+
+Serve expects the batched handler to return **one result per input** (same batch length, same order).
+
+## What Serve actually does internally
+
+Conceptually, each replica maintains an internal structure like:
+
+- an in-memory buffer of pending calls
+- a background “flush” loop that decides when to execute a batch
+- per-request futures/promises that get completed when the batch finishes
+
+### 1. Collection phase (buffering)
+
+Incoming requests that hit the batched method are appended to a replica-local buffer.
+
+Each buffered entry stores:
+
+- the request arguments (or decoded payload)
+- a future representing that request’s eventual response
+
+### 2. Flush conditions (size or time)
+
+The buffer is flushed when either condition becomes true:
+
+- **Size trigger**: buffer length reaches `max_batch_size`
+- **Time trigger**: `batch_wait_timeout_s` elapses since the **first** item currently in the buffer
+
+This is why batching can increase latency at low QPS: a request may wait up to `batch_wait_timeout_s` for more arrivals.
+
+### 3. Execution phase (single call)
+
+Serve invokes your batched handler **once** with a list of inputs.
+
+This is where you typically vectorize:
+
+- stack/concat tensors
+- run one forward pass
+- split/scatter outputs back
+
+### 4. Scatter phase (complete futures)
+
+When the batched handler returns a list of outputs, Serve resolves the stored futures in order.
+
+Each original HTTP request then completes independently with its corresponding output.
+
+## Configuration & Tuning
+
+For a deep dive into how batching interacts with concurrency limits (specifically why `max_ongoing_requests` must be larger than `max_batch_size`), see **[Queues and backpressure](queues-and-backpressure.md)**.
+
+Quick tips:
+
+- Increase `max_batch_size` if the model benefits from larger batches and you have headroom.
+- Increase `batch_wait_timeout_s` to favor fuller batches; decrease it to favor latency.
+
+## Next
+
+- Request flow including queue points: [Request lifecycle](request-lifecycle.md)
+- Queueing and rejection controls: [Queues and backpressure](queues-and-backpressure.md)
+- “Knobs” reference and meanings: [Configuration reference](../guides/configuration-reference.md)