💻 programming

prime

Efficient global distributed AI model training framework

#AI
#Model training
#Distributed training
#Computational optimization
#Globally distributed
prime

Product Details

PrimeIntellect-ai/prime is a framework for efficient, globally distributed training of AI models on the Internet. Through technological innovation, it realizes cross-regional AI model training, improves the utilization of computing resources, and reduces training costs. It is of great significance to AI research and application development that require large-scale computing resources.

Main Features

1
ElasticDeviceMesh: supports fault-tolerant training and dynamically manages global process groups.
2
Asynchronous distributed checkpoints: Reduce model saving time and improve computing utilization.
3
Real-time checkpoint recovery: allows nodes to join mid-training to quickly obtain model status.
4
Customized Int8 All-Reduce Kernel: Reduce communication load and improve bandwidth utilization.
5
Maximize bandwidth utilization: Improve network bandwidth utilization through sharding technology.
6
PyTorch FSDP2/DTensor ZeRO-3 implementation: supports sharding of model weights, gradients, and optimizer state.
7
CPU Off-Loading: Offloads all tensors required by the Diloco optimizer to CPU memory to reduce the burden on the GPU.

How to Use

1
1. Clone the repository: Use the git clone command to clone the PrimeIntellect-ai/prime project locally.
2
2. Install uv: Follow the instructions provided on the project page to install the uv tool.
3
3. Set up the environment: install the iperf tool, create and activate a virtual environment, and synchronize dependencies.
4
4. Log in to Hugging Face: Use the huggingface-cli command to log in to the Hugging Face platform.
5
5. Run the test: Run the test using the provided command to verify that the settings are correct.
6
6. Run DiLoCo: Use the helper script to test DiLoCo locally.
7
7. Run the full test suite: Make sure you have at least two GPUs and run the pytest command.
8
8. Export checkpoints: Use the provided export_dcp.py script to convert the checkpoints saved by the training script into a Hugging Face compatible model.

Target Users

The target audience is AI researchers and developers, especially professionals who need large-scale distributed training models. This framework makes large-scale AI model training more efficient by optimizing the distributed training process, and is suitable for scenarios that require processing large-scale data and complex models.

Examples

Used to train large-scale language models such as BERT or GPT.

In medical image analysis, used to train deep learning models across multiple data centers.

In finance, globally distributed training of risk assessment models.

Quick Access

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Categories

💻 programming
› Development and Tools
› Model training and deployment

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