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docs: add aikit to integrations (#1412)
* docs: add aikit to integrations Signed-off-by: Sertac Ozercan <sozercan@gmail.com> * docs: add to readme Signed-off-by: Sertac Ozercan <sozercan@gmail.com> --------- Signed-off-by: Sertac Ozercan <sozercan@gmail.com> Co-authored-by: lunamidori5 <118759930+lunamidori5@users.noreply.github.com>
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Sertaç Özercan
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@ -112,6 +112,9 @@ Check out the [Getting started](https://localai.io/basics/getting_started/index.
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### 🔗 Community and integrations
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Build and deploy custom containers:
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- https://github.com/sozercan/aikit
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WebUIs:
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- https://github.com/Jirubizu/localai-admin
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- https://github.com/go-skynet/LocalAI-frontend
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178
docs/content/integrations/AIKit.md
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178
docs/content/integrations/AIKit.md
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@ -0,0 +1,178 @@
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+++
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disableToc = false
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title = "AIKit"
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description="AI + BuildKit = AIKit: Build and deploy large language models easily"
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weight = 2
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+++
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GitHub Link - https://github.com/sozercan/aikit
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[AIKit](https://github.com/sozercan/aikit) is a quick, easy, and local or cloud-agnostic way to get started to host and deploy large language models (LLMs) for inference. No GPU, internet access or additional tools are needed to get started except for [Docker](https://docs.docker.com/desktop/install/linux-install/)!
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AIKit uses [LocalAI](https://localai.io/) under-the-hood to run inference. LocalAI provides a drop-in replacement REST API that is OpenAI API compatible, so you can use any OpenAI API compatible client, such as [Kubectl AI](https://github.com/sozercan/kubectl-ai), [Chatbot-UI](https://github.com/sozercan/chatbot-ui) and many more, to send requests to open-source LLMs powered by AIKit!
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> At this time, AIKit is tested with LocalAI `llama` backend. Other backends may work but are not tested. Please open an issue if you'd like to see support for other backends.
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## Features
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- 🐳 No GPU, Internet access or additional tools needed except for [Docker](https://docs.docker.com/desktop/install/linux-install/)!
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- 🤏 Minimal image size, resulting in less vulnerabilities and smaller attack surface with a custom [distroless](https://github.com/GoogleContainerTools/distroless)-based image
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- 🚀 Easy to use declarative configuration
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- ✨ OpenAI API compatible to use with any OpenAI API compatible client
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- 🚢 Kubernetes deployment ready
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- 📦 Supports multiple models with a single image
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- 🖥️ Supports GPU-accelerated inferencing with NVIDIA GPUs
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- 🔐 Signed images for `aikit` and pre-made models
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## Pre-made Models
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AIKit comes with pre-made models that you can use out-of-the-box!
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### CPU
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- 🦙 Llama 2 7B Chat: `ghcr.io/sozercan/llama2:7b`
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- 🦙 Llama 2 13B Chat: `ghcr.io/sozercan/llama2:13b`
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- 🐬 Orca 2 13B: `ghcr.io/sozercan/orca2:13b`
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### NVIDIA CUDA
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- 🦙 Llama 2 7B Chat (CUDA): `ghcr.io/sozercan/llama2:7b-cuda`
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- 🦙 Llama 2 13B Chat (CUDA): `ghcr.io/sozercan/llama2:13b-cuda`
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- 🐬 Orca 2 13B (CUDA): `ghcr.io/sozercan/orca2:13b-cuda`
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> CUDA models includes CUDA v12. They are used with [NVIDIA GPU acceleration](#gpu-acceleration-support).
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## Quick Start
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### Creating an image
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> This section shows how to create a custom image with models of your choosing. If you want to use one of the pre-made models, skip to [running models](#running-models).
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>
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> Please see [models folder](./models/) for pre-made model definitions. You can find more model examples at [go-skynet/model-gallery](https://github.com/go-skynet/model-gallery).
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Create an `aikitfile.yaml` with the following structure:
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```yaml
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#syntax=ghcr.io/sozercan/aikit:latest
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apiVersion: v1alpha1
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models:
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- name: llama-2-7b-chat
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source: https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/resolve/main/llama-2-7b-chat.Q4_K_M.gguf
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```
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> This is the simplest way to get started to build an image. For full `aikitfile` specification, see [specs](docs/specs.md).
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First, create a buildx buildkit instance. Alternatively, if you are using Docker v24 with [containerd image store](https://docs.docker.com/storage/containerd/) enabled, you can skip this step.
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```bash
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docker buildx create --use --name aikit-builder
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```
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Then build your image with:
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```bash
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docker buildx build . -t my-model -f aikitfile.yaml --load
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```
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This will build a local container image with your model(s). You can see the image with:
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```bash
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docker images
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REPOSITORY TAG IMAGE ID CREATED SIZE
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my-model latest e7b7c5a4a2cb About an hour ago 5.51GB
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```
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### Running models
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You can start the inferencing server for your models with:
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```bash
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# for pre-made models, replace "my-model" with the image name
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docker run -d --rm -p 8080:8080 my-model
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```
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You can then send requests to `localhost:8080` to run inference from your models. For example:
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```bash
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curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
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"model": "llama-2-7b-chat",
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"messages": [{"role": "user", "content": "explain kubernetes in a sentence"}]
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}'
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{"created":1701236489,"object":"chat.completion","id":"dd1ff40b-31a7-4418-9e32-42151ab6875a","model":"llama-2-7b-chat","choices":[{"index":0,"finish_reason":"stop","message":{"role":"assistant","content":"\nKubernetes is a container orchestration system that automates the deployment, scaling, and management of containerized applications in a microservices architecture."}}],"usage":{"prompt_tokens":0,"completion_tokens":0,"total_tokens":0}}
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```
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## Kubernetes Deployment
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It is easy to get started to deploy your models to Kubernetes!
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Make sure you have a Kubernetes cluster running and `kubectl` is configured to talk to it, and your model images are accessible from the cluster.
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> You can use [kind](https://kind.sigs.k8s.io/) to create a local Kubernetes cluster for testing purposes.
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```bash
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# create a deployment
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# for pre-made models, replace "my-model" with the image name
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kubectl create deployment my-llm-deployment --image=my-model
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# expose it as a service
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kubectl expose deployment my-llm-deployment --port=8080 --target-port=8080 --name=my-llm-service
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# easy to scale up and down as needed
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kubectl scale deployment my-llm-deployment --replicas=3
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# port-forward for testing locally
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kubectl port-forward service/my-llm-service 8080:8080
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# send requests to your model
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curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
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"model": "llama-2-7b-chat",
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"messages": [{"role": "user", "content": "explain kubernetes in a sentence"}]
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}'
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{"created":1701236489,"object":"chat.completion","id":"dd1ff40b-31a7-4418-9e32-42151ab6875a","model":"llama-2-7b-chat","choices":[{"index":0,"finish_reason":"stop","message":{"role":"assistant","content":"\nKubernetes is a container orchestration system that automates the deployment, scaling, and management of containerized applications in a microservices architecture."}}],"usage":{"prompt_tokens":0,"completion_tokens":0,"total_tokens":0}}
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```
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> For an example Kubernetes deployment and service YAML, see [kubernetes folder](./kubernetes/). Please note that these are examples, you may need to customize them (such as properly configured resource requests and limits) based on your needs.
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## GPU Acceleration Support
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> At this time, only NVIDIA GPU acceleration is supported. Please open an issue if you'd like to see support for other GPU vendors.
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### NVIDIA
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AIKit supports GPU accelerated inferencing with [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-container-toolkit). You must also have [NVIDIA Drivers](https://www.nvidia.com/en-us/drivers/unix/) installed on your host machine.
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For Kubernetes, [NVIDIA GPU Operator](https://github.com/NVIDIA/gpu-operator) provides a streamlined way to install the NVIDIA drivers and container toolkit to configure your cluster to use GPUs.
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To get started with GPU-accelerated inferencing, make sure to set the following in your `aikitfile` and build your model.
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```yaml
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runtime: cuda # use NVIDIA CUDA runtime
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f16: true # use float16 precision
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gpu_layers: 35 # number of layers to offload to GPU
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low_vram: true # for devices with low VRAM
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```
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> Make sure to customize these values based on your model and GPU specs.
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After building the model, you can run it with [`--gpus all`](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/docker-specialized.html#gpu-enumeration) flag to enable GPU support:
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```bash
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# for pre-made models, replace "my-model" with the image name
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docker run --rm --gpus all -p 8080:8080 my-model
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```
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If GPU acceleration is working, you'll see output that is similar to following in the debug logs:
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```bash
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5:32AM DBG GRPC(llama-2-7b-chat.Q4_K_M.gguf-127.0.0.1:43735): stderr ggml_init_cublas: found 1 CUDA devices:
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5:32AM DBG GRPC(llama-2-7b-chat.Q4_K_M.gguf-127.0.0.1:43735): stderr Device 0: Tesla T4, compute capability 7.5
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...
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5:32AM DBG GRPC(llama-2-7b-chat.Q4_K_M.gguf-127.0.0.1:43735): stderr llm_load_tensors: using CUDA for GPU acceleration
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5:32AM DBG GRPC(llama-2-7b-chat.Q4_K_M.gguf-127.0.0.1:43735): stderr llm_load_tensors: mem required = 70.41 MB (+ 2048.00 MB per state)
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5:32AM DBG GRPC(llama-2-7b-chat.Q4_K_M.gguf-127.0.0.1:43735): stderr llm_load_tensors: offloading 32 repeating layers to GPU
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5:32AM DBG GRPC(llama-2-7b-chat.Q4_K_M.gguf-127.0.0.1:43735): stderr llm_load_tensors: offloading non-repeating layers to GPU
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5:32AM DBG GRPC(llama-2-7b-chat.Q4_K_M.gguf-127.0.0.1:43735): stderr llm_load_tensors: offloading v cache to GPU
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5:32AM DBG GRPC(llama-2-7b-chat.Q4_K_M.gguf-127.0.0.1:43735): stderr llm_load_tensors: offloading k cache to GPU
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5:32AM DBG GRPC(llama-2-7b-chat.Q4_K_M.gguf-127.0.0.1:43735): stderr llm_load_tensors: offloaded 35/35 layers to GPU
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5:32AM DBG GRPC(llama-2-7b-chat.Q4_K_M.gguf-127.0.0.1:43735): stderr llm_load_tensors: VRAM used: 5869 MB
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```
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