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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Comet.ml vs MXNet

Comet.ml vs MXNet

OverviewComparisonAlternatives

Overview

MXNet
MXNet
Stacks49
Followers81
Votes2
Comet.ml
Comet.ml
Stacks12
Followers50
Votes3

Comet.ml vs MXNet: 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; MXNet: A flexible and efficient library for deep learning. A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.

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

MXNet is an open source tool with 17.5K GitHub stars and 6.21K GitHub forks. Here's a link to MXNet's open source repository on GitHub.

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Detailed Comparison

MXNet
MXNet
Comet.ml
Comet.ml

A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.

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.

Lightweight;Portable;Flexible distributed/Mobile deep learning;
-
Statistics
Stacks
49
Stacks
12
Followers
81
Followers
50
Votes
2
Votes
3
Pros & Cons
Pros
  • 2
    User friendly
Pros
  • 3
    Best tool for comparing experiments
Integrations
Clojure
Clojure
Python
Python
Java
Java
JavaScript
JavaScript
Scala
Scala
Julia
Julia
TensorFlow
TensorFlow
Theano
Theano
scikit-learn
scikit-learn
PyTorch
PyTorch
Keras
Keras

What are some alternatives to MXNet, Comet.ml?

TensorFlow

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.

scikit-learn

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

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.

Keras

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

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

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

Polyaxon

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

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