Tensor2Tensor
What is Tensor2Tensor?
It is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. It was developed by researchers and engineers in the Google Brain team and a community of users.
Tensor2Tensor is a tool in the Datasets & Benchmarks category of a tech stack.
Key Features
Many state of the art and baseline models are built-in and new models can be added easilyMany datasets across modalities - text, audio, image - available for generation and use, and new ones can be added easilyModels can be used with any dataset and input mode (or even multiple)all modality-specific processing (e.g. embedding lookups for text tokens) is done with bottom and top transformations, which are specified per-feature in the modelSupport for multi-GPU machines and synchronous (1 master, many workers) and asynchronous (independent workers synchronizing through a parameter server) distributed trainingEasily swap amongst datasets and models by command-line flag with the data generation script t2t-datagen and the training script t2t-trainerTrain on Google Cloud ML and Cloud TPUs
Tensor2Tensor Pros & Cons
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Cons of Tensor2Tensor
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Tensor2Tensor Alternatives & Comparisons
What are some alternatives to Tensor2Tensor?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
