Hyperdash - Machine Learning 1.8

Andrew Schreiber ❘ Freeware ❘
iOS
out of 8 votes

Hyperdash is a machine learning monitoring platform designed with data scientists in mind. It allows you to monitor the progress of your training sessions with live alerts and conveniently stream console logs from your machine to any device.

Monitor the status of your training sessions in real-time.
Access complete console logs for each training run, both current and previous.
View stack traces in case of a failed run.
Contact developers via text easily to address bugs or incorporate new features.

For more information, visit hyperdash.io

Hyperdash's open-source Python SDK:

Compatible with all Python 2.7 and 3.x machine learning libraries, such as Keras, Tensorflow, Theano, PyTorch, and more.
Works seamlessly on cloud instances and local machines, allowing simultaneous use of both.
Emphasizes security and privacy – your dataset remains on your local environment and is never transferred to external servers.

How To Guide

Download our Python SDK (supports 2.7 & 3.x) via pip and sign up through the command line:

$ pip install hyperdash && hyperdash signup

To test the setup, run:

$ hyperdash demo

Proceed to utilize the same credentials to access the iPhone app and monitor your test run progress in real-time. Detailed instructions and additional resources are available on our website as well as our GitHub repository to help you integrate Hyperdash seamlessly into your machine learning projects.

Overview

Hyperdash - Machine Learning is a Freeware software in the category Business developed by Andrew Schreiber.

The latest version of Hyperdash - Machine Learning is 1.8, released on 06/26/2024. It was initially added to our database on 06/26/2024.

Hyperdash - Machine Learning runs on the following operating systems: iOS.

Users of Hyperdash - Machine Learning gave it a rating of 5 out of 5 stars.

Pros

User-friendly interface makes it easy to build and train machine learning models
Provides a variety of pre-built models and tools to simplify the machine learning process
Supports various platforms and frameworks, offering flexibility in development
Offers a collaborative workspace for team projects
Automated hyperparameter tuning saves time and improves model performance

Cons

Can be more expensive compared to other machine learning platforms
May have limitations in customization for advanced users who require extensive control over their models
Less documentation and community support compared to more popular machine learning tools

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