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Coretex.ai

Coretex is a powerful MLOps platform designed to make AI experimentation fast and efficient. With Coretex, data scientists, ML engineers, and less experienced users can easily:

  • Run their data processing experiments,
  • Build AI models,
  • Perform statistical data analysis,
  • Run computational simulations.

Check the highlight video bellow to see it in action:

Coretex Intro

Coretex.ai helps you iterate faster and with more confidence. You get reproducibility, scalability, transparency, and cost-effectiveness.

VS Code extension

Coretex VS Code extension automates working with Coretex CLI and streamlines development workflow to make your iterations faster.

Among others, there are the benefits of using the extension:

  • sytax highlighting and syntax checks for Coretex configuration files
  • automatic conversion of your projects into Coretex-compatible projects
  • installation of Coretex CLI
  • Coretex Node configuration and update
  • and many more...

Please check the list of supported commands by opening your VS Code command box [Cmd + Shift + P] and start typing "Coretex: ...".

Get started

Step 1: Sign up for a free account ->

Step 2: Install coretex:

$ pip install coretex

Step 3: Migrate your project to coretex:

from coretex import CustomDataset, ExecutingExperiment
from coretex.project import initializeProject


def main(experiment: ExecutingExperiment[CustomDataset]):
    # Remove "pass" and start project execution from here
    pass


if __name__ == "__main__":
    initializeProject(main)

Read the documentation and learn how you can migrate your project to the Coretex platform -> Migrate your project to Coretex

Key Features

Coretex offers a range of features to support users in their AI experimentation, including:

  • Project Templates: Battle-tested templates that make training ML models and processing data simple,

  • Machine Learning Model Creation: Quick and easy creation of machine learning models, with less friction and more stability,

  • Optimized Pipeline Execution: Execution optimization of any computational pipeline, including large-scale statistical analysis and various simulations,

  • Team Collaboration: The whole workflow in Coretex is centered around this concept to help centralize user management and enable transparent monitoring of storage and compute resources for administrators,

  • Dataset Management and Annotation Tools: Powerful tools for managing and annotating datasets,

  • Experiment Orchestration and Result Analysis: Detailed management of experiments, ensuring reproducibility and easy comparison of results,

  • IT Infrastructure Setup: Easy setup of IT infrastructure, whether connecting self-managed computers or using paid, dynamically scalable cloud computers,

  • Live Metrics Tracking: Real-time tracking of experiment metrics during execution,

  • Artifact Upload and Management: Easy upload and management of experiment artifacts, including models and results.

Reproducibility

One of the key benefits of Coretex is its ability to guarantee reproducibility. The platform keeps track of all experiment configurations and parameters between runs, ensuring that users never lose track of their work.

Supported Use Cases

Coretex is a versatile platform that can be used for a variety of use cases, including:

  • Training ML models,
  • Large-scale statistical analysis,
  • Simulations (physics, molecular dynamics, population dynamics, econometrics, and more).

Compatibility with other libraries

Coretex is compatible with all ML libraries such as Wandb, Tensorboard, PyTorch, and etc. There are no limits when it comes to Coretex integration with other libraries.

Support

If you require any assistance or have any questions, our support team is available to help. Please feel free to reach out to us through our contact page or via email [email protected]. We will be happy to assist you with any inquiries or issues you may have. Check out the Coretex platform overview at coretex.ai for more information, tutorials, and documentation.