Upload 11 files

.actrc

@@ -0,0 +1 @@
+-e .github/act_event.json

.dockerignore

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+inference/landing/*
+!inference/landing/out/
+
+# NodeJS (landing page)
+**/node_modules/
+**/dist
+.git
+npm-debug.log
+.coverage
+.coverage.*
+.env
+.aws
+
+# Python (inference)
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+env
+pip-log.txt
+pip-delete-this-directory.txt
+.tox
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.log
+.git
+.mypy_cache
+.pytest_cache
+.hypothesis

.gitignore

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+# IDE
+.idea/
+.vscode/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+_classes.csv
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+deps/
+profile/
+*.onnx
+*.jpg
+*.pstats
+*.jpeg
+*.png
+!icon.png
+*.mp4
+*.roboflow.txt
+dev_tools/
+*.tmp
+ASL-*
+README.dataset*
+_annotations*
+
+export_api_keys.sh
+inference_cli/version.py
+inference_sdk/version.py
+
+**/.DS_Store

CITATION.cff

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+cff-version: 1.2.0
+title: Roboflow Inference Server
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
+authors:
+  - given-names: Robfolow
+    email: support@roboflow.com
+repository-code: 'https://github.com/roboflow/inference-server'
+url: 'https://roboflow.com'
+abstract: >-
+  An opinionated, easy-to-use inference server for use with
+  computer vision models.
+keywords:
+  - computer vision
+  - inference
+license: Apache-2.0

CONTRIBUTING.md

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+# Contributing to the Roboflow Inference Server 🛠️
+
+Thank you for your interest in contributing to the Roboflow Inference Server!
+
+We welcome any contributions to help us improve the quality of `inference-server` and expand the range of supported models.
+
+## Contribution Guidelines
+
+We welcome contributions to:
+
+1. Add support for running inference on a new model.
+2. Report bugs and issues in the project.
+3. Submit a request for a new task or feature.
+4. Improve our test coverage.
+
+### Contributing Features
+
+The Inference Server provides a standard interface through which you can work with computer vision models. With Inference Server, you can use state-of-the-art models with your own weights without having to spend time installing dependencies, configuring environments, and writing inference code.
+
+We welcome contributions that add support for new models to the project. Before you begin, please make sure that another contributor has not already begun work on the model you want to add. You can check the [project README](https://github.com/roboflow/inference-server/blob/main/README.md) for our roadmap on adding more models.
+
+You will need to add documentation for your model and link to it from the `inference-server` README. You can add a new page to the `docs/models` directory that describes your model and how to use it. You can use the existing model documentation as a guide for how to structure your documentation.
+
+## How to Contribute Changes
+
+First, fork this repository to your own GitHub account. Create a new branch that describes your changes (i.e. `line-counter-docs`). Push your changes to the branch on your fork and then submit a pull request to this repository.
+
+When creating new functions, please ensure you have the following:
+
+1. Docstrings for the function and all parameters.
+2. Examples in the documentation for the function.
+3. Created an entry in our docs to autogenerate the documentation for the function.
+
+All pull requests will be reviewed by the maintainers of the project. We will provide feedback and ask for changes if necessary.
+
+PRs must pass all tests and linting requirements before they can be merged.
+
+## 🧹 Code quality 
+
+We provide two handy commands inside the `Makefile`, namely:
+
+- `make style` to format the code
+- `make check_code_quality` to check code quality (PEP8 basically)
+
+## 🧪 Tests 
+
+[`pytests`](https://docs.pytest.org/en/7.1.x/) is used to run our tests.
+
+## 📚 Documentation
+
+Roboflow Inference uses mkdocs and mike to offer versioned documentation. The project documentation is hosted on [GitHub Pages](https://inference.roboflow.com).
+
+To build the Inference documentation, first install the project development dependencies:
+
+```bash
+pip install -r requirements/requirements.docs.txt
+```
+
+To run the latest version of the documentation, run:
+
+```bash
+mike serve
+```
+
+Before a new release is published, a new version of the documentation should be built. To create a new version, run:
+
+```bash
+mike deploy <version-number>
+```

LICENSE

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+LICENSE.core (Apache 2.0) applies to all files in this repository
+except for files in or under any directory that contains a superseding
+license file (such as the models located in `inference/models/`).

LICENSE.core

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+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
+
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+5. Submission of Contributions. Unless You explicitly state otherwise,
+any Contribution intentionally submitted for inclusion in the Work
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+Notwithstanding the above, nothing herein shall supersede or modify
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+6. Trademarks. This License does not grant permission to use the trade
+names, trademarks, service marks, or product names of the Licensor,
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+negligent acts) or agreed to in writing, shall any Contributor be
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+Work (including but not limited to damages for loss of goodwill,
+work stoppage, computer failure or malfunction, or any and all
+other commercial damages or losses), even if such Contributor
+has been advised of the possibility of such damages.
+
+9. Accepting Warranty or Additional Liability. While redistributing
+the Work or Derivative Works thereof, You may choose to offer,
+and charge a fee for, acceptance of support, warranty, indemnity,
+or other liability obligations and/or rights consistent with this
+License. However, in accepting such obligations, You may act only
+on Your own behalf and on Your sole responsibility, not on behalf
+of any other Contributor, and only if You agree to indemnify,
+defend, and hold each Contributor harmless for any liability
+incurred by, or claims asserted against, such Contributor by reason
+of your accepting any such warranty or additional liability.
+
+END OF TERMS AND CONDITIONS
+
+APPENDIX: How to apply the Apache License to your work.
+
+To apply the Apache License to your work, attach the following
+boilerplate notice, with the fields enclosed by brackets "[]"
+replaced with your own identifying information. (Don't include
+the brackets!)  The text should be enclosed in the appropriate
+comment syntax for the file format. We also recommend that a
+file or class name and description of purpose be included on the
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+
+Copyright 2023, Roboflow
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
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+
+Unless required by applicable law or agreed to in writing, software
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+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

Makefile

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+.PHONY: style check_code_quality
+
+export PYTHONPATH = .
+check_dirs := inference inference_sdk
+
+style:
+	black  $(check_dirs)
+	isort --profile black $(check_dirs)
+
+check_code_quality:
+	black --check $(check_dirs)
+	isort --check-only --profile black $(check_dirs)
+	# stop the build if there are Python syntax errors or undefined names
+	flake8 $(check_dirs) --count --select=E9,F63,F7,F82 --show-source --statistics
+	# exit-zero treats all errors as warnings. E203 for black, E501 for docstring, W503 for line breaks before logical operators 
+	flake8 $(check_dirs) --count --max-line-length=88 --exit-zero  --ignore=D --extend-ignore=E203,E501,W503  --statistics
+
+start_test_docker_cpu:
+	docker run -d --rm -p $(PORT):$(PORT) -e PORT=$(PORT) -e MAX_BATCH_SIZE=17 --name inference-test roboflow/${INFERENCE_SERVER_REPO}:test
+
+start_test_docker_gpu:
+	docker run -d --rm -p $(PORT):$(PORT) -e PORT=$(PORT) -e MAX_BATCH_SIZE=17 --gpus=all --name inference-test roboflow/${INFERENCE_SERVER_REPO}:test
+
+start_test_docker_jetson:
+	docker run -d --rm -p $(PORT):$(PORT) -e PORT=$(PORT) -e MAX_ACTIVE_MODELS=1 -e MAX_BATCH_SIZE=17 --runtime=nvidia --name inference-test roboflow/${INFERENCE_SERVER_REPO}:test
+
+stop_test_docker:
+	docker rm -f inference-test
+
+create_wheels:
+	python -m pip install --upgrade pip
+	python -m pip install wheel twine requests -r requirements/_requirements.txt
+	rm -f dist/*
+	python .release/pypi/inference.core.setup.py bdist_wheel
+	python .release/pypi/inference.cpu.setup.py bdist_wheel
+	python .release/pypi/inference.gpu.setup.py bdist_wheel
+	python .release/pypi/inference.setup.py bdist_wheel
+	python .release/pypi/inference.sdk.setup.py bdist_wheel
+	python .release/pypi/inference.cli.setup.py bdist_wheel
+
+upload_wheels:
+	twine upload dist/*.whl

README.md

@@ -1,11 +1,388 @@
----
-title: Docker Ml
-emoji: 🐢
-colorFrom: indigo
-colorTo: gray
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+![Roboflow Inference banner](https://github.com/roboflow/inference/blob/main/banner.png?raw=true)
+
+## 🎬 pip install inference
+
+[Roboflow](https://roboflow.com) Inference is the easiest way to use and deploy computer vision models.
+Inference supports running object detection, classification, instance segmentation, and even foundation models (like CLIP and SAM).
+You can [train and deploy your own custom model](https://github.com/roboflow/notebooks) or use one of the 50,000+
+[fine-tuned models shared by the community](https://universe.roboflow.com).
+
+There are three primary `inference` interfaces:
+* A Python-native package (`pip install inference`)
+* A self-hosted inference server (`inference server start`)
+* A [fully-managed, auto-scaling API](https://docs.roboflow.com).
+
+## 🏃 Getting Started
+
+Get up and running with `inference` on your local machine in 3 minutes.
+
+```sh
+pip install inference # or inference-gpu if you have CUDA
+```
+
+Setup [your Roboflow Private API Key](https://app.roboflow.com/settings/api)
+by exporting a `ROBOFLOW_API_KEY` environment variable or
+adding it to a `.env` file.
+
+```sh
+export ROBOFLOW_API_KEY=your_key_here
+```
+
+Run [an open-source Rock, Paper, Scissors model](https://universe.roboflow.com/roboflow-58fyf/rock-paper-scissors-sxsw)
+on your webcam stream:
+
+```python
+import inference
+
+inference.Stream(
+    source="webcam", # or rtsp stream or camera id
+    model="rock-paper-scissors-sxsw/11", # from Universe
+
+    on_prediction=lambda predictions, image: (
+        print(predictions) # now hold up your hand: 🪨 📄 ✂️
+    )
+)
+```
+
+> [!NOTE]  
+> Currently, the stream interface only supports object detection
+
+Now let's extend the example to use [Supervision](https://roboflow.com/supervision)
+to visualize the predictions and display them on screen with OpenCV:
+
+```python
+import cv2
+import inference
+import supervision as sv
+
+annotator = sv.BoxAnnotator()
+
+inference.Stream(
+    source="webcam", # or rtsp stream or camera id
+    model="rock-paper-scissors-sxsw/11", # from Universe
+
+    output_channel_order="BGR",
+    use_main_thread=True, # for opencv display
+    
+    on_prediction=lambda predictions, image: (
+        print(predictions), # now hold up your hand: 🪨 📄 ✂️
+        
+        cv2.imshow(
+            "Prediction", 
+            annotator.annotate(
+                scene=image, 
+                detections=sv.Detections.from_roboflow(predictions)
+            )
+        ),
+        cv2.waitKey(1)
+    )
+)
+
+```
+
+## 👩‍🏫 More Examples
+
+The [`/examples`](https://github.com/roboflow/inference/tree/main/examples/) directory contains code samples for working with and extending `inference` including using foundation models like CLIP, HTTP and UDP clients, and an insights dashboard, along with community examples (PRs welcome)!
+
+## 🎥 Inference in action
+
+Check out Inference running on a video of a football game:
+
+https://github.com/roboflow/inference/assets/37276661/121ab5f4-5970-4e78-8052-4b40f2eec173
+
+## 💻 Why Inference?
+
+Inference provides a scalable method through which you can manage inferences for your vision projects.
+
+Inference is composed of:
+
+- Thousands of [pre-trained community models](https://universe.roboflow.com) that you can use as a starting point.
+
+- Foundation models like CLIP, SAM, and OCR.
+
+- A tight integration with [Supervision](https://roboflow.com/supervision).
+
+- An HTTP server, so you don’t have to reimplement things like image processing and prediction visualization on every project and you can scale your GPU infrastructure independently of your application code, and access your model from whatever language your app is written in.
+
+- Standardized APIs for computer vision tasks, so switching out the model weights and architecture can be done independently of your application code.
+
+- A model registry, so your code can be independent from your model weights & you don't have to re-build and re-deploy every time you want to iterate on your model weights.
+
+- Active Learning integrations, so you can collect more images of edge cases to improve your dataset & model the more it sees in the wild.
+
+- Seamless interoperability with [Roboflow](https://roboflow.com) for creating datasets, training & deploying custom models.
+
+And more!
+
+### 📌 Use the Inference Server
+
+You can learn more about Roboflow Inference Docker Image build, pull and run in our [documentation](https://inference.roboflow.com/quickstart/docker/).
+
+- Run on x86 CPU:
+
+```bash
+docker run --net=host roboflow/roboflow-inference-server-cpu:latest
+```
+
+- Run on NVIDIA GPU:
+
+```bash
+docker run --network=host --gpus=all roboflow/roboflow-inference-server-gpu:latest
+```
+
+<details close>
+<summary>👉 more docker run options</summary>
+
+- Run on arm64 CPU:
+
+```bash
+docker run -p 9001:9001 roboflow/roboflow-inference-server-arm-cpu:latest
+```
+
+- Run on NVIDIA GPU with TensorRT Runtime:
+
+```bash
+docker run --network=host --gpus=all roboflow/roboflow-inference-server-trt:latest
+```
+
+- Run on NVIDIA Jetson with JetPack `4.x`:
+
+```bash
+docker run --privileged --net=host --runtime=nvidia roboflow/roboflow-inference-server-jetson:latest
+```
+
+- Run on NVIDIA Jetson with JetPack `5.x`:
+
+```bash
+docker run --privileged --net=host --runtime=nvidia roboflow/roboflow-inference-server-jetson-5.1.1:latest
+```
+
+</details>
+
+### Extras:
+
+Some functionality requires extra dependencies. These can be installed by specifying the desired extras during installation of Roboflow Inference.
+| extra | description |
+|:-------|:-------------------------------------------------|
+| `clip` | Ability to use the core `CLIP` model (by OpenAI) |
+| `gaze` | Ability to use the core `Gaze` model |
+| `http` | Ability to run the http interface |
+| `sam` | Ability to run the core `Segment Anything` model (by Meta AI) |
+
+**_Note:_** Both CLIP and Segment Anything require pytorch to run. These are included in their respective dependencies however pytorch installs can be highly environment dependent. See the [official pytorch install page](https://pytorch.org/get-started/locally/) for instructions specific to your enviornment.
+
+Example install with CLIP dependencies:
+
+```bash
+pip install "inference[clip]"
+```
+
+## Inference Client
+
+To consume predictions from inference server in Python you can
+use the `inference-sdk` package.
+
+```bash
+pip install inference-sdk
+```
+
+```python
+from inference_sdk import InferenceHTTPClient
+
+image_url = "https://media.roboflow.com/inference/soccer.jpg"
+
+# Replace ROBOFLOW_API_KEY with your Roboflow API Key
+client = InferenceHTTPClient(
+    api_url="http://localhost:9001", # or https://detect.roboflow.com for Hosted API
+    api_key="ROBOFLOW_API_KEY"
+)
+with client.use_model("soccer-players-5fuqs/1"):
+    predictions = client.infer(image_url)
+
+print(predictions)
+```
+
+Visit our [documentation](https://inference.roboflow.com/) to discover capabilities of `inference-clients` library.
+
+## Single Image Inference
+
+After installing `inference` via pip, you can run a simple inference
+on a single image (vs the video stream example above) by instantiating
+a `model` and using the `infer` method (don't forget to setup your
+`ROBOFLOW_API_KEY` environment variable or `.env` file):
+
+```python
+from inference.models.utils import get_roboflow_model
+
+model = get_roboflow_model(
+    model_id="soccer-players-5fuqs/1"
+)
+
+# you can also infer on local images by passing a file path,
+# a PIL image, or a numpy array
+results = model.infer(
+  image="https://media.roboflow.com/inference/soccer.jpg",
+  confidence=0.5,
+  iou_threshold=0.5
+)
+
+print(results)
+```
+
+## Getting CLIP Embeddings
+
+You can run inference with [OpenAI's CLIP model](https://blog.roboflow.com/openai-clip) using:
+
+```python
+from inference.models import Clip
+
+image_url = "https://media.roboflow.com/inference/soccer.jpg"
+
+model = Clip()
+embeddings = model.embed_image(image_url)
+
+print(embeddings)
+```
+
+## Using SAM
+
+You can run inference with [Meta's Segment Anything model](https://blog.roboflow.com/segment-anything-breakdown/) using:
+
+```python
+from inference.models import SegmentAnything
+
+image_url = "https://media.roboflow.com/inference/soccer.jpg"
+
+model = SegmentAnything()
+embeddings = model.embed_image(image_url)
+
+print(embeddings)
+```
+
+## 🏗️ inference Process
+
+To standardize the inference process throughout all our models, Roboflow Inference has a structure for processing inference requests. The specifics can be found on each model's respective page, but overall it works like this for most models:
+
+<img width="900" alt="inference structure" src="https://github.com/stellasphere/inference/assets/29011058/abf69717-f852-4655-9e6e-dae19fc263dc">
+
+## ✅ Supported Models
+
+### Load from Roboflow
+
+You can use models hosted on Roboflow with the following architectures through Inference:
+
+- YOLOv5 Object Detection
+- YOLOv5 Instance Segmentation
+- YOLOv8 Object Detection
+- YOLOv8 Classification
+- YOLOv8 Segmentation
+- YOLACT Segmentation
+- ViT Classification
+
+### Core Models
+
+Core Models are foundation models and models that have not been fine-tuned on a specific dataset.
+
+The following core models are supported:
+
+1. CLIP
+2. L2CS (Gaze Detection)
+3. Segment Anything (SAM)
+
+## 📝 License
+
+The Roboflow Inference code is distributed under an [Apache 2.0 license](https://github.com/roboflow/inference/blob/master/LICENSE.md). The models supported by Roboflow Inference have their own licenses. View the licenses for supported models below.
+
+| model                     |                                                                license                                                                |
+| :------------------------ | :-----------------------------------------------------------------------------------------------------------------------------------: |
+| `inference/models/clip`   |                                        [MIT](https://github.com/openai/CLIP/blob/main/LICENSE)                                        |
+| `inference/models/gaze`   | [MIT](https://github.com/Ahmednull/L2CS-Net/blob/main/LICENSE), [Apache 2.0](https://github.com/google/mediapipe/blob/master/LICENSE) |
+| `inference/models/sam`    |                         [Apache 2.0](https://github.com/facebookresearch/segment-anything/blob/main/LICENSE)                          |
+| `inference/models/vit`    |                         [Apache 2.0](https://github.com/roboflow/inference/main/inference/models/vit/LICENSE)                         |
+| `inference/models/yolact` |                                     [MIT](https://github.com/dbolya/yolact/blob/master/README.md)                                     |
+| `inference/models/yolov5` |                                 [AGPL-3.0](https://github.com/ultralytics/yolov5/blob/master/LICENSE)                                 |
+| `inference/models/yolov7` |                                  [GPL-3.0](https://github.com/WongKinYiu/yolov7/blob/main/README.md)                                  |
+| `inference/models/yolov8` |                              [AGPL-3.0](https://github.com/ultralytics/ultralytics/blob/master/LICENSE)                               |
+
+## Inference CLI
+We've created a CLI tool with useful commands to make the `inference` usage easier. Check out [docs](./inference_cli/README.md).
+
+## 🚀 Enterprise
+
+With a Roboflow Inference Enterprise License, you can access additional Inference features, including:
+
+- Server cluster deployment
+- Device management
+- Active learning
+- YOLOv5 and YOLOv8 commercial license
+
+To learn more, [contact the Roboflow team](https://roboflow.com/sales).
+
+## 📚 documentation
+
+Visit our [documentation](https://inference.roboflow.com) for usage examples and reference for Roboflow Inference.
+
+## 🏆 contribution
+
+We would love your input to improve Roboflow Inference! Please see our [contributing guide](https://github.com/roboflow/inference/blob/master/CONTRIBUTING.md) to get started. Thank you to all of our contributors! 🙏
+
+## 💻 explore more Roboflow open source projects
+
+| Project                                                           | Description                                                                                                                                            |
+| :---------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [supervision](https://roboflow.com/supervision)                   | General-purpose utilities for use in computer vision projects, from predictions filtering and display to object tracking to model evaluation.          |
+| [Autodistill](https://github.com/autodistill/autodistill)         | Automatically label images for use in training computer vision models.                                                                                 |
+| [Inference](https://github.com/roboflow/inference) (this project) | An easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models. |
+| [Notebooks](https://roboflow.com/notebooks)                       | Tutorials for computer vision tasks, from training state-of-the-art models to tracking objects to counting objects in a zone.                          |
+| [Collect](https://github.com/roboflow/roboflow-collect)           | Automated, intelligent data collection powered by CLIP.                                                                                                |
+
+<br>
+
+<div align="center">
+
+  <div align="center">
+      <a href="https://youtube.com/roboflow">
+          <img
+            src="https://media.roboflow.com/notebooks/template/icons/purple/youtube.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949634652"
+            width="3%"
+          />
+      </a>
+      <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
+      <a href="https://roboflow.com">
+          <img
+            src="https://media.roboflow.com/notebooks/template/icons/purple/roboflow-app.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949746649"
+            width="3%"
+          />
+      </a>
+      <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
+      <a href="https://www.linkedin.com/company/roboflow-ai/">
+          <img
+            src="https://media.roboflow.com/notebooks/template/icons/purple/linkedin.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633691"
+            width="3%"
+          />
+      </a>
+      <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
+      <a href="https://docs.roboflow.com">
+          <img
+            src="https://media.roboflow.com/notebooks/template/icons/purple/knowledge.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949634511"
+            width="3%"
+          />
+      </a>
+      <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
+      <a href="https://disuss.roboflow.com">
+          <img
+            src="https://media.roboflow.com/notebooks/template/icons/purple/forum.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633584"
+            width="3%"
+          />
+      <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
+      <a href="https://blog.roboflow.com">
+          <img
+            src="https://media.roboflow.com/notebooks/template/icons/purple/blog.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633605"
+            width="3%"
+          />
+      </a>
+      </a>
+  </div>
+
+</div>
+  </div>

mkdocs.yml

@@ -0,0 +1,103 @@
+site_name: Roboflow Inference
+site_url: https://inference.roboflow.com/
+site_author: Roboflow
+site_description: With no prior knowledge of machine learning or device-specific deployment, you can deploy a computer vision model to a range of devices and environments using the Roboflow Inference Server.
+repo_name: roboflow/inference
+repo_url: https://github.com/roboflow/inference
+edit_uri: https://github.com/roboflow/inference/tree/main/docs
+copyright: Roboflow 2023. All rights reserved.
+
+extra:
+  social:
+    - icon: fontawesome/brands/github
+      link: https://github.com/roboflow
+    - icon: fontawesome/brands/youtube
+      link: https://www.youtube.com/roboflow
+    - icon: fontawesome/brands/linkedin
+      link: https://www.linkedin.com/company/roboflow-ai/mycompany/
+    - icon: fontawesome/brands/twitter
+      link: https://twitter.com/roboflow
+  analytics:
+    provider: google
+    property: G-T0CED2YY8K
+  version:
+    default: 1.0
+
+extra_css:
+  - styles.css
+
+nav:
+  - Home:
+    - Home: index.md
+    - Get Started:
+      - What is Inference?: quickstart/what_is_inference.md
+      - What Devices Can I Use?: quickstart/devices.md
+      - Run Your First Model: quickstart/run_a_model.md
+      - Run a Fine-Tuned Model: quickstart/explore_models.md
+    - Run a Model:
+      - On an Image (Using HTTP): quickstart/http_inference.md
+      - On an Image (Using SDK): quickstart/run_model_on_image.md
+      - On a Video, Webcam or RTSP Stream: quickstart/run_model_on_rtsp_webcam.md
+      - Over UDP: quickstart/run_model_over_udp.md
+    - Use a Foundation Model:
+      - What is a Foundation Model?: foundation/about.md
+      - CLIP (Classification, Embeddings): foundation/clip.md
+      - DocTR (OCR): foundation/doctr.md
+      - Grounding DINO (Object Detection): foundation/grounding_dino.md
+      - L2CS-Net (Gaze Detection): foundation/gaze.md
+      - Segment Anything (Segmentation): foundation/sam.md
+    - Integrate with Inference:
+        - SDK Reference: inference_sdk/http_client.md
+        - Collecting data without model: quickstart/stubs.md
+    - Reference:
+      - Model Licensing: quickstart/licensing.md
+      - Model Device Compatability: quickstart/compatability_matrix.md
+      - Install Inference with Docker: quickstart/docker.md
+      - Docker Configuration Options: quickstart/docker_configuration_options.md
+      - HTTP Inference: quickstart/http_inference.md
+  - HTTP API Reference:
+    - API Reference: api.md
+    - Back to Quickstart: /quickstart/what_is_inference/
+  - Contribute:
+    - Contribute to Inference: contributing.md
+  - Changelog: https://github.com/roboflow/inference/releases
+
+theme:
+  name: 'material'
+  logo: https://media.roboflow.com/inference-icon.png
+  favicon: https://media.roboflow.com/inference-icon.png
+  font:
+    text: Roboto
+    code: Roboto Mono
+  custom_dir: 'custom_theme'
+  features:
+    - navigation.top
+    - navigation.tabs
+    - navigation.tabs.sticky
+    - navigation.prune
+    - navigation.footer
+    - navigation.tracking
+    - navigation.instant
+    - navigation.instant.progress
+    - navigation.indexes
+    - navigation.sections
+
+plugins:
+  - mkdocstrings
+  - search
+  - swagger-ui-tag
+
+markdown_extensions:
+  - admonition
+  - pymdownx.details
+  - pymdownx.superfences
+  - attr_list
+  - md_in_html
+  - pymdownx.tabbed:
+      alternate_style: true
+  - toc:
+      permalink: true
+
+extra_javascript:
+  - "https://widget.kapa.ai/kapa-widget.bundle.js"
+  - "javascript/init_kapa_widget.js"