Replicate vs Tensor2Tensor
Overview
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CLI (Node.js)
Manual
Detailed Comparison
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. | It lets you run machine learning models with a few lines of code, without needing to understand how machine learning works. |
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 (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 model;
Support for multi-GPU machines and synchronous (1 master, many workers) and asynchronous (independent workers synchronizing through a parameter server) 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;
Train on Google Cloud ML and Cloud TPUs | Thousands of models, ready to use;
Automatic API;
Automatic scale;
Pay by the second |
Statistics | |
GitHub Stars 16.7K | GitHub Stars - |
GitHub Forks 3.7K | GitHub Forks - |
Stacks 4 | Stacks 53 |
Followers 12 | Followers 12 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to Tensor2Tensor, Replicate?
Git
Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.
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.
Mercurial
Mercurial is dedicated to speed and efficiency with a sane user interface. It is written in Python. Mercurial's implementation and data structures are designed to be fast. You can generate diffs between revisions, or jump back in time within seconds.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
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.
SVN (Subversion)
Subversion exists to be universally recognized and adopted as an open-source, centralized version control system characterized by its reliability as a safe haven for valuable data; the simplicity of its model and usage; and its ability to support the needs of a wide variety of users and projects, from individuals to large-scale enterprise operations.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
NanoNets
Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.
Kubeflow
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
TensorFlow.js
Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
