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Comet.ml

12
48
+ 1
3
Gluon

29
77
+ 1
3
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Comet.ml vs Gluon: What are the differences?

Comet.ml: Track, compare and collaborate on Machine Learning experiments. Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility; Gluon: Deep Learning API from AWS and Microsoft. A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

Comet.ml and Gluon belong to "Machine Learning Tools" category of the tech stack.

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Pros of Comet.ml
Pros of Gluon
  • 3
    Best tool for comparing experiments
  • 3
    Good learning materials

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What is Comet.ml?

Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.

What is Gluon?

A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

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What companies use Comet.ml?
What companies use Gluon?
See which teams inside your own company are using Comet.ml or Gluon.
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What tools integrate with Comet.ml?
What tools integrate with Gluon?

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What are some alternatives to Comet.ml and Gluon?
MLflow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
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.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
See all alternatives