This software is for sale
please contact: https://discordapp.com/users/814589682830016602/ on discord
it is a distributed GPU trainer for machine learning
2 GPUs in one machine and 2 GPUs in another will use all the GPUs you can have as many GPUs as you want
... right now configured for diffusion models, but you could do it for text or video as well
GPUMultilearner
working on cuda 12.6 pytorch 2.6.0 and python 3.12
Introduction:
The rapid advancements in generative modeling, particularly within the realm of diffusion models, have unlocked unprecedented capabilities in image synthesis and manipulation. However, this progress comes at a significant computational cost. Training these models, especially at high resolutions, demands substantial processing power, often exceeding the capabilities of a single GPU. To address this challenge, we have developed a distributed training system designed to leverage the parallel processing power of multiple CUDA-enabled devices. This system facilitates the efficient training of diffusion models by distributing the computational workload across a network of client machines, each equipped with one or more NVIDIA GPUs.
Our approach centers around a client-server architecture, where a central server orchestrates the training process by distributing image data and training tasks to connected clients. These clients, equipped with CUDA-capable GPUs, execute the computationally intensive training loops, leveraging the inherent parallelism of these devices to accelerate model convergence. By distributing the workload, we significantly reduce the training time required for large-scale diffusion models, enabling researchers and developers to iterate more rapidly and explore new creative possibilities.
The use of CUDA devices is paramount to the efficiency of our system. NVIDIA's CUDA platform provides a robust framework for harnessing the parallel processing power of GPUs, enabling significant speedups in tasks involving large matrix operations and complex computations, which are fundamental to diffusion model training. Our system is designed to seamlessly integrate with CUDA, ensuring optimal utilization of GPU resources.
This paper details the architecture and implementation of our distributed training system, highlighting the key components that contribute to its efficiency and scalability. We delve into the network communication protocols, data distribution strategies, and local checkpointing mechanisms that enable robust and fault-tolerant training. Furthermore, we present performance evaluations that demonstrate the effectiveness of our approach, showcasing significant reductions in training time compared to single-GPU training. By providing a scalable and efficient solution for training diffusion models, we aim to democratize access to these powerful tools and accelerate innovation in the field of generative AI.
Installation Guide: Distributed Diffusion Model Training System
This guide provides step-by-step instructions for installing and configuring the distributed diffusion model training system.
Prerequisites:
Hardware: Multiple machines with NVIDIA GPUs (CUDA-capable). A stable network connection between machines. Software: Linux operating system (Ubuntu recommended). Python 3.8 or higher. CUDA Toolkit (matching your GPU capabilities). NVIDIA GPU drivers. Step 1: Install NVIDIA Drivers and CUDA Toolkit
Install NVIDIA Drivers: Visit the NVIDIA website to download the appropriate drivers for your GPUs and operating system. Follow the installation instructions provided by NVIDIA. Verify the installation using nvidia-smi. Install CUDA Toolkit: Download the CUDA Toolkit from the NVIDIA website. Follow the installation instructions provided by NVIDIA. Add CUDA to your system's PATH environment variable. Verify the installation using nvcc --version. Step 2: Install Python and Required Libraries
Install Python: If Python is not already installed, use your system's package manager to install it (e.g., sudo apt-get install python3). Create a Virtual Environment (Recommended): python3 -m venv venv source venv/bin/activate Install PyTorch with CUDA Support: Visit the PyTorch website to get the correct installation command for your CUDA version. Example: pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 (replace cu118 with your CUDA version). Install Other Required Libraries: pip install diffusers albumentations matplotlib pillow Step 3: Clone or Copy the Script Files
Obtain the Server and Client Scripts: If the scripts are in a Git repository, clone it: git clone <repository_url>. If you have local files, copy them to your desired directory. Place the scripts: Place the server script on the server machine. Place the client script on each client machine. Step 4: Configure the Scripts
Server Configuration: Open the server script and modify the following variables: SERVER_HOST: Set the IP address of the server machine. SERVER_PORT: Choose a port for the server to listen on. WORLD_SIZE: Set the total number of worker GPUs. dataset_path: The location of your image dataset. Adjust other variables as needed (e.g., image size, batch size, epochs). Client Configuration: Open the client script and modify the following variables: SERVER_HOST: Set the IP address of the server machine. SERVER_PORT: Set the server port. Step 5: Prepare the Dataset
Organize your image dataset: Place all images in the directory specified by dataset_path in the server script. Ensure image compatibility: Make sure your images are in a compatible format (e.g., PNG, JPG). Step 6: Run the Server
Navigate to the server script directory: cd <server_script_directory> Run the server script: python server_script.py Step 7: Run the Clients
Navigate to the client script directory on each client machine: cd <client_script_directory> Run the client script on each client machine: python client_script.py Verification:
Monitor the server's log output for client connections and training progress. Monitor the client's log output for task recieval and training progress. Use nvidia-smi on both the server and client machines to monitor GPU usage. Troubleshooting:
Ensure that the server and client machines can communicate with each other over the network. Check for any errors in the log output of the server and client scripts. Verify that the CUDA Toolkit and drivers are installed correctly. Double check your network cables. This installation guide should help you get your distributed training system up and running. Remember to adapt the instructions to your specific environment and requirements.
to do:
add client server resume on disconnect optimise
make run faster on GPU