This repository serves a dual purpose.

• Firstly, it facilitates the automated setup and execution of two Docker containers, streamlining the process for users interested in TextGen.
• Additionally, it provides an efficient solution for those working with ImageGen, enabling image generation, post-processing, or analysis with ease through Docker containers.

• TextGen Automation section
• ImageGen Automation section

Feel free to jump to the section that interests you the most!

AI-Grid-Worker
This component enables the setup of an AI Grid Worker, designed to complete text inference jobs on The Grid.

aphrodite-engine
Aphrodite serves as the official backend engine for Text Inference.

• Install and configure
  • Docker: https://docs.docker.com/engine/install
  • Python
  • CUDA Driver, CUDA Toolkit and NVIDIA Container Toolkit on your system: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
  • On Ubuntu you will also need to run: sudo apt install python3.10-venv

To use TextGen, follow these steps:

1. Clone the repository AIPG-GenHub.

git clone https://github.com/gonner22/AIPG-GenHub

2. Navigate into the cloned directory.

cd AIPG-GenHub

3. Create a Python virtual environment.

python3 -m venv venv

4. Activate the virtual environment.

source venv/bin/activate

5. Install PyYAML.

pip install pyyaml

To customize your TextGen Worker, follow these steps:

1. Create a copy of bridgeData_textgen_template.yaml in the root directory of the cloned repository and rename it to bridgeData.yaml. Update the following mandatory fields in bridgeData.yaml:

   • api_key (required): Enter your Grid API key. If you don't have one, register here to get an API key.
   • scribe_name (required): Choose a custom name for your worker.

   Optionally, you can also modify these fields:

   • max_threads: Set the number of concurrent requests your worker should handle. Higher values require more VRAM. Default is 1.
   • kai_url: Specify the Aphrodite URL. By default, it should be http:https://<container_name>:7860. In the template, it's set to http:https://aphrodite-engine:7860. Note that <container_name> refers to the name of the Aphrodite container.
   • max_length: Define the maximum number of tokens per request. A recommended value is 512.
   • max_context_length: Set the maximum context length of the Grid worker. This should match your model's default max length or the value you provided for --max-model-len when starting the engine.

2. Edit config.yaml in the root directory of the cloned repository to configure container settings:

   • MODEL_NAME (required): Specify the name of the model you want to use. Refer to the [Model List](to be added) for supported models and their VRAM requirements.
   • HF_TOKEN (required for gated models): If you're using a gated model like llama 3, provide your Hugging Face token.
   • GPU_MEMORY_UTILIZATION (optional): If you encounter memory issues, consider reducing this value.

Please ensure that you provide the mandatory fields MODEL_NAME, HF_TOKEN (if using a gated model), and api_key in the respective configuration files. The other fields are optional and can be left at their default values if desired.

For the complete list of environment variables, please refer to here. These represent the default configuration, which can be further customized based on individual user requirements and hardware specifications.

sudo python3 worker_texgen.py

Run to clean up containers when you are done

sudo ./cleanup.sh

After both containers are up and running, Aphrodite-engine will begin downloading additional models, which total around 14-48 GB in size depending on the model. Please allow a few extra minutes for this process to finish. Once completed, you will observe both containers starting to communicate with each other.

Note: To interact with the Docker container, you can follow these steps:

• To list your running containers, use the command docker ps -a
• To enter the running container, use the command docker attach <container_name>
• To exit the container without stopping it, press Ctrl + P, followed by Ctrl + Q
• If your Texgen application utilizes ports other than 7860 for internal Docker-to-Docker communication and 2242 for internal Docker-to-host communication, you will need to adjust them accordingly. After modifying these ports, ensure to update them both in the Dockerfile and the bridgeData.yaml configuration file.

AI Power Grid Image Worker This module facilitates the setup of an AI Power Grid Image Worker through the creation and execution of a Docker container, offering capabilities for image generation, post-processing, or analysis for diverse applications.

• Install and configure
  • Docker: https://docs.docker.com/engine/install
  • Python
  • CUDA Driver 12.1, 12.2 or 12.3 only. Ensure that one of these three versions is installed on your system, as you will later need to select the container image that matches the installed version.
  • CUDA Toolkit and NVIDIA Container Toolkit on your system: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
  • On Ubuntu you will also need to run: sudo apt install python3.10-venv

To use ImageGen, follow these steps:

1. Clone the repository AIPG-GenHub.

git clone https://github.com/gonner22/AIPG-GenHub

2. Navigate into the cloned directory.

cd AIPG-GenHub

3. Create a Python virtual environment.

python3 -m venv venv-imagegen

4. Activate the virtual environment.

source venv-imagegen/bin/activate

5. Install Dependencies.

pip install pyyaml python-dotenv loguru ruamel.yaml horde-model-reference horde-sdk

To customize your ImageGen Worker, follow these steps:

1. Create a copy of bridgeData_imagegen_template.yaml in the root directory of the cloned repository and rename it to bridgeData.yaml. Update the following mandatory fields in bridgeData.yaml:

   • horde_url (required): Enter horde url ("https://api.aipowergrid.io")
   • api_key (required): Enter your Grid API key. If you don't have one, register here to get an API key.
   • dreamer_name (required): Choose a custom name for your worker.
   • cache_home (required): The location in which stable diffusion ckpt models are stored

   Optionally, you can also modify these fields:

   • max_threads: Set the number of concurrent requests your worker should handle. Higher values require more VRAM. Default is 1.
   • models_to_load: The models to use
   • Please ensure to review and adjust other parameters as needed for specific configurations.

2. Edit config-imagengen.yaml in the root directory of the cloned repository to configure container settings:

• exec_type: Define the exec type of the container
• ports: Ports enabled
• container_name: Define the container name
• gpus: All the gpus with "all", or using the gpu number
• env-file: the file where the container env variables are stored, default file is bridgeData.env
• image_name: Define the image name

sudo python3 worker_imagegen.py

After executing this command, the container will be loaded. Please wait a few minutes as it downloads the models.

Note: To interact with the Docker container, you can follow these steps:

• To list your running containers, use the command docker ps -a
• To enter the running container, use the command docker attach <container_name>
• To exit the container without stopping it, press Ctrl + P, followed by Ctrl + Q