inference-core 0.8.4

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 Roboflow Inference.

👋 hello

Roboflow Inference is an opinionated tool for running inference on state-of-the-art computer vision models. 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. Inference supports object detection, classification, and instance segmentation models, and running foundation models (CLIP and SAM).

🎥 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

👩‍🏫 Examples

The /examples directory contains example code for working with and extending inference, including HTTP and UDP client code and an insights dashboard, along with community examples (PRs welcome)!

💻 Why Inference?

Inference provides a scalable method through which you can manage inferences for your vision projects.

Inference is backed by:

• A server, so you don’t have to reimplement things like image processing and prediction visualization on every project.

• Standardized APIs for computer vision tasks, so switching out the model weights and architecture can be done independently of your application code.

• Model architecture implementations, which implement the tensor parsing glue between images and predictions for supervised models that you've fine-tuned to perform custom tasks.

• 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.

• Data management integrations, so you can collect more images of edge cases to improve your dataset & model the more it sees in the wild.

And more!

📌 Install pip vs Docker:

• pip: Installs inference into your Python environment. Lightweight, good for Python-centric projects.
• Docker: Packages inference with its environment. Ensures consistency across setups; ideal for scalable deployments.

💻 install

With ONNX CPU Runtime:

For CPU powered inference:

pip install inference

or

pip install inference-cpu

With ONNX GPU Runtime:

If you have an NVIDIA GPU, you can accelerate your inference with:

pip install inference-gpu

Without ONNX Runtime:

Roboflow Inference uses Onnxruntime as its core inference engine. Onnxruntime provides an array of different execution providers that can optimize inference on differnt target devices. If you decide to install onnxruntime on your own, install inference with:

pip install inference-core

Alternatively, you can take advantage of some advanced execution providers using one of our published docker images.

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 for instructions specific to your enviornment.

Example install with http dependencies:

pip install inference[http]

🐋 docker

You can learn more about Roboflow Inference Docker Image build, pull and run in our documentation.

• Run on x86 CPU:

docker run --net=host roboflow/roboflow-inference-server-cpu:latest

• Run on NVIDIA GPU:

docker run --network=host --gpus=all roboflow/roboflow-inference-server-gpu:latest

👉 more docker run options

• Run on arm64 CPU:

docker run -p 9001:9001 roboflow/roboflow-inference-server-arm-cpu:latest

• Run on NVIDIA GPU with TensorRT Runtime:

docker run --network=host --gpus=all roboflow/roboflow-inference-server-trt:latest

• Run on NVIDIA Jetson with JetPack 4.x:

docker run --privileged --net=host --runtime=nvidia roboflow/roboflow-inference-server-jetson:latest

• Run on NVIDIA Jetson with JetPack 5.x:

docker run --privileged --net=host --runtime=nvidia roboflow/roboflow-inference-server-jetson-5.1.1:latest

🔥 quickstart

Docker Quickstart:

import requests

dataset_id = "soccer-players-5fuqs"
version_id = "1"
image_url = "https://source.roboflow.com/pwYAXv9BTpqLyFfgQoPZ/u48G0UpWfk8giSw7wrU8/original.jpg"
#Replace ROBOFLOW_API_KEY with your Roboflow API Key
api_key = "ROBOFLOW_API_KEY"
confidence = 0.5

url = f"http://localhost:9001/{dataset_id}/{version_id}"

params = {
    "api_key": api_key,
    "confidence": confidence,
    "image": image_url,
}

res = requests.post(url, params=params)
print(res.json())

pip Quickstart:

After installing via pip, you can run a simple inference using:

from inference.models.utils import get_roboflow_model

model = get_roboflow_model(
    model_id="soccer-players-5fuqs/1", 
    #Replace ROBOFLOW_API_KEY with your Roboflow API Key
    api_key="ROBOFLOW_API_KEY"
)

results = model.infer(image="https://source.roboflow.com/pwYAXv9BTpqLyFfgQoPZ/u48G0UpWfk8giSw7wrU8/original.jpg", confidence=0.5, iou_threshold=0.5)

print(results)

CLIP Quickstart:

You can run inference with OpenAI's CLIP model using:

from inference.models import Clip

model = Clip(
    #Replace ROBOFLOW_API_KEY with your Roboflow API Key
    api_key = "ROBOFLOW_API_KEY"
)

image_url = "https://source.roboflow.com/7fLqS2r1SV8mm0YzyI0c/yy6hjtPUFFkq4yAvhkvs/original.jpg"

embeddings = model.embed_image(image_url)

print(embeddings)

SAM Quickstart:

You can run inference with Meta's Segment Anything model using:

from inference.models import SegmentAnything

model = SegmentAnything(
    #Replace ROBOFLOW_API_KEY with your Roboflow API Key
    api_key = "ROBOFLOW_API_KEY"
)

image_url = "https://source.roboflow.com/7fLqS2r1SV8mm0YzyI0c/yy6hjtPUFFkq4yAvhkvs/original.jpg"

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:

📝 license

The Roboflow Inference code is distributed under an Apache 2.0 license. The models supported by Roboflow Inference have their own licenses. View the licenses for supported models below.

model	license
inference/models/clip	MIT
inference/models/gaze	MIT, Apache 2.0
inference/models/sam	Apache 2.0
inference/models/vit	Apache 2.0
inference/models/yolact	MIT
inference/models/yolov5	AGPL-3.0
inference/models/yolov7	GPL-3.0
inference/models/yolov8	AGPL-3.0

🚀 enterprise

With a Roboflow Inference Enterprise License, you can access additional Inference features, including:

• Server cluster deployment
• Device management
• Active learning
• YOLOv5 and YOLOv8 model sub-license

To learn more, contact the Roboflow team.

📚 documentation

Visit our documentation for usage examples and reference for Roboflow Inference.

🏆 contribution

We would love your input to improve Roboflow Inference! Please see our contributing guide to get started. Thank you to all of our contributors! 🙏

💻 explore more Roboflow open source projects

Project	Description
supervision	General-purpose utilities for use in computer vision projects, from predictions filtering and display to object tracking to model evaluation.
Autodistill	Automatically label images for use in training computer vision models.
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	Tutorials for computer vision tasks, from training state-of-the-art models to tracking objects to counting objects in a zone.
Collect	Automated, intelligent data collection powered by CLIP.