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Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and compare the results. Tensor2Tensor, or T2T for short, is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. T2T was developed by researchers and engineers in the Google Brain team and a community of users. It is now deprecated, we keep it running and welcome bug-fixes, but encourage users to use the successor library Trax.
Features
- Library of deep learning models and datasets
- Designed to make deep learning more accessible and accelerate ML research
- Many state of the art and baseline models are built-in and new models can be added easily
- Many datasets across modalities, text, audio, image, available for generation and use, and new ones can be added easily
- Models can be used with any dataset and input mode
- Support for multi-GPU machines and synchronous and asynchronous distributed training
- Easily swap amongst datasets and models by command-line flag with the data generation script t2t-datagen and the training script t2t-trainer
Project Samples
