Deepo vs Lobe.ai

Overview

Deepo

Stacks0

Followers14

Votes0

GitHub Stars6.3K

Forks748

Lobe.ai

Stacks7

Followers21

Votes0

Lobe.ai vs Deepo: What are the differences?

Developers describe Lobe.ai as "A simple tool for training machine learning models". It helps you train machine learning models with a free, easy to use tool. It has everything you need to bring your machine learning ideas to life. Just show it examples of what you want it to learn, and it automatically trains a custom machine learning model that can be shipped in your app. On the other hand, Deepo is detailed as "A Docker image containing almost all popular deep learning frameworks". Deepo is a Docker image with a full reproducible deep learning research environment. It contains most popular deep learning frameworks: theano, tensorflow, sonnet, pytorch, keras, lasagne, mxnet, cntk, chainer, caffe, torch.

Lobe.ai and Deepo can be categorized as "Machine Learning" tools.

Deepo is an open source tool with 5.8K GitHub stars and 728 GitHub forks. Here's a link to Deepo's open source repository on GitHub.

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Detailed Comparison

Deepo	Lobe.ai
Deepo is a Docker image with a full reproducible deep learning research environment. It contains most popular deep learning frameworks: theano, tensorflow, sonnet, pytorch, keras, lasagne, mxnet, cntk, chainer, caffe, torch.	It helps you train machine learning models with a free, easy to use tool. It has everything you need to bring your machine learning ideas to life. Just show it examples of what you want it to learn, and it automatically trains a custom machine learning model that can be shipped in your app.
-	Machine learning made easy; Free and Private; Ship Anywhere; Label, Train, Play
Statistics
GitHub Stars 6.3K	GitHub Stars -
GitHub Forks 748	GitHub Forks -
Stacks 0	Stacks 7
Followers 14	Followers 21
Votes 0	Votes 0
Integrations
TensorFlow Docker Keras	No integrations available

What are some alternatives to Deepo, Lobe.ai?

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 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.

Keras

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

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

Polyaxon

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

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

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.