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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. H2O vs Trax

H2O vs Trax

OverviewComparisonAlternatives

Overview

H2O
H2O
Stacks122
Followers211
Votes8
GitHub Stars7.3K
Forks2.0K
Trax
Trax
Stacks8
Followers49
Votes0
GitHub Stars8.3K
Forks827

Trax vs H2O: What are the differences?

Trax: Your path to advanced deep learning (By Google). It helps you understand and explore advanced deep learning. It is actively used and maintained in the Google Brain team You can use It either as a library from your own python scripts and notebooks or as a binary from the shell, which can be more convenient for training large models. It includes a number of deep learning models (ResNet, Transformer, RNNs, ...) and has bindings to a large number of deep learning datasets, including Tensor2Tensor and TensorFlow datasets. It runs without any changes on CPUs, GPUs and TPUs.; H2O: H2O.ai AI for Business Transformation. 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.

Trax and H2O can be categorized as "Machine Learning" tools.

H2O is an open source tool with 4.64K GitHub stars and 1.69K GitHub forks. Here's a link to H2O's open source repository on GitHub.

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Detailed Comparison

H2O
H2O
Trax
Trax

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.

It helps you understand and explore advanced deep learning. It is actively used and maintained in the Google Brain team. You can use It either as a library from your own python scripts and notebooks or as a binary from the shell, which can be more convenient for training large models. It includes a number of deep learning models (ResNet, Transformer, RNNs, ...) and has bindings to a large number of deep learning datasets, including Tensor2Tensor and TensorFlow datasets. It runs without any changes on CPUs, GPUs and TPUs.

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Advanced deep learning; Actively used and maintained in the Google Brain team; Runs without any changes on CPUs, GPUs and TPUs
Statistics
GitHub Stars
7.3K
GitHub Stars
8.3K
GitHub Forks
2.0K
GitHub Forks
827
Stacks
122
Stacks
8
Followers
211
Followers
49
Votes
8
Votes
0
Pros & Cons
Pros
  • 2
    Very fast and powerful
  • 2
    Auto ML is amazing
  • 2
    Highly customizable
  • 2
    Super easy to use
Cons
  • 1
    Not very popular
No community feedback yet

What are some alternatives to H2O, Trax?

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.

PredictionIO

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

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