OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Evaluation & Observability

Deepkit

Deepkit is an open-source machine-learning development tool with desktop and web interfaces, CLI tools and a Python SDK for executing, tracking and debugging experiments. It supports local or Linux-server execution, experiment logging and comparison, and project management.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Deepkit — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Deepkit when you need a workflow that connects experiment execution with result tracking, debugging and comparison, or when you want to schedule experiments on Linux servers.

Who it is for

  • Machine-learning developers who need to run and organize experiments.
  • Teams that want shared experiment tracking and project-management tools.

Documented uses

  • Execute experiments locally or on managed Linux servers.
  • Log metrics, text, images, NumPy arrays and artifacts such as weights and snapshots.
  • Compare experiment metrics and files side by side.
  • Debug model layers in Keras 2 and PyTorch.
  • Schedule server jobs with resource limits and assignments.
Before you choose

Requirements & limits

The project README links to a download page and describes Deepkit as open source under the MIT license.

  • Execution is documented locally and on Linux servers, with Docker-based runs and automatic builds.
  • Server scheduling includes resource limits and assignment; NVIDIA Docker GPU support is also documented.
  • Layer debugging is specified for Keras 2 and PyTorch.
  • The server component stores data using the filesystem and MongoDB.
  • An optional centralized server is available for teams.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up Deepkit's development app

Prepare the Deepkit development app and start its documented development watchers.

Before you start

  • A local Deepkit repository checkout.
  • Node.js/npm tooling and the other project dependencies needed by the documented npm, make, and server commands.

Steps

  1. From the repository directory, run the command below.

    Documented command
    npm run bootstrap

  2. Run npm-local-development from the repository directory.

  3. Run the command below and server-watch from the repository directory.

    Documented command
    make angular-watch

Expected outcome

The documented development bootstrap and watchers are started for the app's Angular interface and server.

Guide limitations

  • These steps cover development setup and watchers, not running or tracking a machine-learning experiment.
  • The README does not specify dependency versions or explain the prerequisites for the watcher commands.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
voided-org/deepkit-ml · Official homepage title · retained in its source language
How we review sourcesSuggest a correction