# AI Handler
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This is a simple framework for running AI models. It makes use of the huggingface API
which gives you a queue, threading, a simple API, and the ability to run Stable Diffusion and LLMs seamlessly
from your local hardware.
This is not intended to be used as a standalone application.
It can easily be extended and used to power interfaces or it can be run from the command line.
AI Handler is a work in progress. It powers two projects at the moment, but may not be ready for general use.
## Installation
This is a work in progress.
## Pre-requisites
System requirements
- Windows 10+
- Python 3.10.8
- pip 23.0.1
- CUDA toolkit 11.7
- CUDNN 8.6.0.163
- Cuda capable GPU
- 16gb+ ram
Install
```
pip install torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 --index-url https://download.pytorch.org/whl/cu117
pip install https://github.com/w4ffl35/diffusers/archive/refs/tags/v0.14.0.ckpt_fix.tar.gz
pip install https://github.com/w4ffl35/transformers/archive/refs/tags/tensor_fix-v1.0.2.tar.gz
pip install https://github.com/acpopescu/bitsandbytes/releases/download/v0.37.2-win.0/bitsandbytes-0.37.2-py3-none-any.whl
pip install aihandlerwindows
```
#### Optional
These are optional instructions for installing TensorRT and Deepspeed for Windows
##### Install Tensor RT:
1. Download TensorRT-8.4.3.1.Windows10.x86_64.cuda-11.6.cudnn8.4
2. Git clone TensorRT 8.4.3.1
3. Follow their instructions to build TensorRT-8.4.3.1 python wheel
4. Install TensorRT `pip install tensorrt-*.whl`
##### Install Deepspeed:
1. Git clone Deepspeed 0.8.1
2. Follow their instructions to build Deepspeed python wheel
3. Install Deepspeed `pip install deepspeed-*.whl
---
## Environment variables
- `AIRUNNER_ENVIRONMENT` - `dev` or `prod`. Defaults to `dev`. This controls the LOG_LEVEL
- `LOG_LEVEL` - `FATAL` for production, `DEBUG` for development. Override this to force a log level
### Huggingface variables
#### Offline mode
These environment variables keep you offline until you need to download a model. This prevents unwanted online access and speeds up usage of huggingface libraries.
- `DISABLE_TELEMETRY` Keep this set to 1 at all times. Huggingface collects minimal telemetry when downloading a model from their repository but this will keep it disabled. [See more info in this github thread](https://github.com/huggingface/diffusers/pull/1833#issuecomment-1368484414)
- `HF_HUB_OFFLINE` When loading a diffusers model, huggingface libraries will attempt to download an updated cache before running the model. This prevents that check from happening (long with a boolean passed to `load_pretrained` see the runner.py file for examples)
- `TRANSFORMERS_OFFLINE` Similar to `HF_HUB_OFFLINE` but for transformers models
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"description": "# AI Handler\n[![Upload Python Package](https://github.com/Capsize-Games/aihandler/actions/workflows/python-publish.yml/badge.svg)](https://github.com/Capsize-Games/aihandler/actions/workflows/python-publish.yml)\n[![Discord](https://img.shields.io/discord/839511291466219541?color=5865F2&logo=discord&logoColor=white)](https://discord.gg/PUVDDCJ7gz)\n![GitHub](https://img.shields.io/github/license/Capsize-Games/aihandler)\n![GitHub last commit](https://img.shields.io/github/last-commit/Capsize-Games/aihandler)\n![GitHub issues](https://img.shields.io/github/issues/Capsize-Games/aihandler)\n![GitHub closed issues](https://img.shields.io/github/issues-closed/Capsize-Games/aihandler)\n![GitHub pull requests](https://img.shields.io/github/issues-pr/Capsize-Games/aihandler)\n![GitHub closed pull requests](https://img.shields.io/github/issues-pr-closed/Capsize-Games/aihandler)\n\nThis is a simple framework for running AI models. It makes use of the huggingface API\nwhich gives you a queue, threading, a simple API, and the ability to run Stable Diffusion and LLMs seamlessly\nfrom your local hardware.\n\nThis is not intended to be used as a standalone application.\n\nIt can easily be extended and used to power interfaces or it can be run from the command line.\n\nAI Handler is a work in progress. It powers two projects at the moment, but may not be ready for general use.\n\n## Installation\n\nThis is a work in progress.\n\n## Pre-requisites\n\nSystem requirements\n\n- Windows 10+\n- Python 3.10.8\n- pip 23.0.1\n- CUDA toolkit 11.7\n- CUDNN 8.6.0.163\n- Cuda capable GPU\n- 16gb+ ram\n\nInstall\n```\npip install torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 --index-url https://download.pytorch.org/whl/cu117\npip install https://github.com/w4ffl35/diffusers/archive/refs/tags/v0.14.0.ckpt_fix.tar.gz\npip install https://github.com/w4ffl35/transformers/archive/refs/tags/tensor_fix-v1.0.2.tar.gz\npip install https://github.com/acpopescu/bitsandbytes/releases/download/v0.37.2-win.0/bitsandbytes-0.37.2-py3-none-any.whl\npip install aihandlerwindows\n```\n\n#### Optional\n\nThese are optional instructions for installing TensorRT and Deepspeed for Windows\n\n##### Install Tensor RT:\n\n1. Download TensorRT-8.4.3.1.Windows10.x86_64.cuda-11.6.cudnn8.4\n2. Git clone TensorRT 8.4.3.1\n3. Follow their instructions to build TensorRT-8.4.3.1 python wheel\n4. Install TensorRT `pip install tensorrt-*.whl`\n \n##### Install Deepspeed:\n\n1. Git clone Deepspeed 0.8.1\n2. Follow their instructions to build Deepspeed python wheel\n3. Install Deepspeed `pip install deepspeed-*.whl\n\n---\n\n## Environment variables\n\n- `AIRUNNER_ENVIRONMENT` - `dev` or `prod`. Defaults to `dev`. This controls the LOG_LEVEL\n- `LOG_LEVEL` - `FATAL` for production, `DEBUG` for development. Override this to force a log level\n\n### Huggingface variables\n\n#### Offline mode\n\nThese environment variables keep you offline until you need to download a model. This prevents unwanted online access and speeds up usage of huggingface libraries.\n\n- `DISABLE_TELEMETRY` Keep this set to 1 at all times. Huggingface collects minimal telemetry when downloading a model from their repository but this will keep it disabled. [See more info in this github thread](https://github.com/huggingface/diffusers/pull/1833#issuecomment-1368484414)\n- `HF_HUB_OFFLINE` When loading a diffusers model, huggingface libraries will attempt to download an updated cache before running the model. This prevents that check from happening (long with a boolean passed to `load_pretrained` see the runner.py file for examples)\n- `TRANSFORMERS_OFFLINE` Similar to `HF_HUB_OFFLINE` but for transformers models\n",
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