A lightweight training framework, making distributed training easier and faster.

OneTrainer is a streamlined, open-source framework designed to simplify distributed training of machine learning models. Built with a focus on ease of use and speed, it abstracts away much of the complexity associated with setting up and managing distributed training environments. The framework supports various training paradigms, including data parallelism and model parallelism. It features a modular architecture, allowing developers to customize components like data loaders, optimizers, and communication protocols. OneTrainer leverages efficient communication strategies to minimize network overhead and maximize training throughput. Ideal for researchers and practitioners who need to scale their training workloads without significant engineering overhead, OneTrainer enables faster experimentation and model development cycles.
OneTrainer is a streamlined, open-source framework designed to simplify distributed training of machine learning models.
Explore all tools that specialize in launch and monitor distributed training jobs. This domain focus ensures OneTrainer delivers optimized results for this specific requirement.
Explore all tools that specialize in implement data and model parallelism. This domain focus ensures OneTrainer delivers optimized results for this specific requirement.
Explore all tools that specialize in customize data loaders, optimizers and communication protocols. This domain focus ensures OneTrainer delivers optimized results for this specific requirement.
Distributes training data across multiple workers, allowing for faster training on large datasets.
Partitions the model across multiple workers, enabling training of very large models.
Automatically recovers from worker failures during training, ensuring training completion.
Accumulates gradients over multiple mini-batches to simulate larger batch sizes, improving training stability.
Allows workers to train independently and asynchronously, maximizing resource utilization.
Install OneTrainer using pip: `pip install onetrainer`
Define your model architecture in a Python file.
Create a configuration file specifying training parameters, data paths, and distributed training settings.
Instantiate the `Trainer` class with your model and configuration.
Call the `train()` method to start distributed training.
Monitor training progress using the provided logging tools.
Evaluate the trained model on a validation dataset.
All Set
Ready to go
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