Replicate vs TensorFlow.js

Overview

TensorFlow.js

Stacks184

Followers378

Votes18

GitHub Stars19.0K

Forks2.0K

Replicate

Stacks53

Followers12

Votes0

🔥 Trending in Development & Training Tools on StackShare

Tinker AI Development Training Tools

Tinker

Try it View Docs Alternatives

Try

Detailed Comparison

TensorFlow.js	Replicate
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	It lets you run machine learning models with a few lines of code, without needing to understand how machine learning works.
-	Thousands of models, ready to use; Automatic API; Automatic scale; Pay by the second
Statistics
GitHub Stars 19.0K	GitHub Stars -
GitHub Forks 2.0K	GitHub Forks -
Stacks 184	Stacks 53
Followers 378	Followers 12
Votes 18	Votes 0
Pros & Cons
Pros 6 Open Source 5 NodeJS Powered 2 Deploy python ML model directly into javascript 1 Cost - no server needed for inference 1 Easy to share and use - get more eyes on your research	No community feedback yet
Integrations
JavaScript TensorFlow	Python Cog Next.js JavaScript Vercel CUDA

What are some alternatives to TensorFlow.js, 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.

Plastic SCM

Plastic SCM is a distributed version control designed for big projects. It excels on branching and merging, graphical user interfaces, and can also deal with large files and even file-locking (great for game devs). It includes "semantic" features like refactor detection to ease diffing complex refactors.