Instill Model (Deprecated)

⚗️ Instill Model, or simply Model, is an integral component of the Instill Core project. It serves as an advanced ModelOps/LLMOps platform focused on empowering users to seamlessly import, serve, fine-tune, and monitor Machine Learning (ML) models for continuous optimization.

Prerequisites

• macOS or Linux - Instill Model works on macOS or Linux, but does not support Windows yet.

• Docker and Docker Compose - Instill Model uses Docker Compose (specifically, Compose V2 and Compose specification) to run all services at local. Please install the latest stable Docker and Docker Compose before using Instill Model.

• yq > v4.x. Please follow the installation guide.

• (Optional) NVIDIA Container Toolkit - To enable GPU support in Instill Model, please refer to NVIDIA Cloud Native Documentation to install NVIDIA Container Toolkit. If you'd like to specifically allot GPUs to Instill Model, you can set the environment variable NVIDIA_VISIBLE_DEVICES. For example, NVIDIA_VISIBLE_DEVICES=0,1 will make the triton-server consume GPU device id 0 and 1 specifically. By default NVIDIA_VISIBLE_DEVICES is set to all to use all available GPUs on the machine.

Quick start

Note The image of model-backend (~2GB) and Triton Inference Server (~23GB) can take a while to pull, but this should be an one-time effort at the first setup.

Use stable release version

Execute the following commands to pull pre-built images with all the dependencies to launch:

$ git clone -b v0.10.0-alpha https://github.com/instill-ai/deprecated-model.git && cd deprecated-model

# Launch all services
$ make all

🚀 That's it! Once all the services are up with health status, the UI is ready to go at http://localhost:3000. Please find the default login credentials in the documentation.

To shut down all running services:

$ make down

Explore the documentation to discover all available deployment options.

Officially supported models

We curate a list of ready-to-use models. These pre-trained models are from different sources and have been trained and deployed by our team. Want to contribute a new model? Please create an issue, we are happy to add it to the list 👐.

Model	Task	Sources	Framework	CPU	GPU
MobileNet v2	Image Classification	GitHub-DVC	ONNX	✅	✅
Vision Transformer (ViT)	Image Classification	Hugging Face	ONNX	✅	❌
YOLOv4	Object Detection	GitHub-DVC	ONNX	✅	✅
YOLOv7	Object Detection	GitHub-DVC	ONNX	✅	✅
YOLOv7 W6 Pose	Keypoint Detection	GitHub-DVC	ONNX	✅	✅
PSNet + EasyOCR	Optical Character Recognition (OCR)	GitHub-DVC	ONNX	✅	✅
Mask RCNN	Instance Segmentation	GitHub-DVC	PyTorch	✅	✅
Lite R-ASPP based on MobileNetV3	Semantic Segmentation	GitHub-DVC	ONNX	✅	✅
Stable Diffusion	Text to Image	GitHub-DVC, Local-CPU, Local-GPU	ONNX	✅	✅
Stable Diffusion XL	Text to Image	GitHub-DVC	PyTorch	❌	✅
Control Net - Canny	Image to Image	GitHub-DVC	PyTorch	❌	✅
Megatron GPT2	Text Generation	GitHub-DVC	FasterTransformer	❌	✅
Llama2	Text Generation	GitHub-DVC	vLLM, PyTorch	✅	✅
Code Llama	Text Generation	GitHub-DVC	vLLM	❌	✅
Llama2 Chat	Text Generation Chat	GitHub-DVC	vLLM	❌	✅
MosaicML MPT	Text Generation Chat	GitHub-DVC	vLLM	❌	✅
Mistral	Text Generation Chat	GitHub-DVC	vLLM	❌	✅
Zephyr-7b	Text Generation Chat	GitHub-DVC	PyTorch	✅	✅
Llava	Visual Question Answering	GitHub-DVC	PyTorch	❌	✅

Note: The GitHub-DVC source in the table means importing a model into Instill Model from a GitHub repository that uses DVC to manage large files.

License

See the LICENSE file for licensing information.