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The collaborative and analytical AI training suite
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What is Deepkit?

It is the collaborative and analytical training suite for insightful, fast, and reproducible modern machine learning. All in one cross-platform desktop app for you alone, corporate or open-source teams.
Deepkit is a tool in the Machine Learning Tools category of a tech stack.

Who uses Deepkit?


Deepkit Integrations

Python, Git, Docker, TensorFlow, and PyTorch are some of the popular tools that integrate with Deepkit. Here's a list of all 6 tools that integrate with Deepkit.

Deepkit's Features

  • Real-time UI and collaboration
  • Unified experiments
  • Model debugger
  • Any framework, all languages
  • Job scheduling
  • Pipeling
  • Docker and GPU support
  • Docker and GPU support
  • Offline first
  • Git integration / CI

Deepkit Alternatives & Comparisons

What are some alternatives to Deepkit?
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 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.
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano.
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
See all alternatives

Deepkit's Followers
4 developers follow Deepkit to keep up with related blogs and decisions.