Skip to main content

A JupyterLab extension for rendering and editing xircuit files.

Project description

DocsInstallTutorialsDeveloper GuidesContributeBlogDiscord
Component LibrariesProject Templates

GitHub GitHub release Binder Documentation Python

1 10 release

Xircuits is a Jupyterlab-based extension that enables visual, low-code, training workflows. It allows anyone to easily create executable python code in seconds.

Features

Rich Xircuits Canvas Interface

Unreal Engine-like Chain Component Interface

Custom Nodes and Ports

Smart Link and Type Check Logic

Component Tooltips

Dynamic Ports

Code Generation

Xircuits generates executable python scripts from the canvas. As they're very customizable, you can perform DevOps automation like actions. Consider this Xircuits template which trains an mnist classifier.

hyperpara-codegen

You can run the code generated python script in Xircuits, but you can also take the same script to train 3 types of models in one go using bash script:

TrainModel.py --epoch 5 --model "resnet50"
TrainModel.py --epoch 5 --model "vgg16"
TrainModel.py --epoch 5 --model "mobilenet"
Famous Python Library Support Xircuits is built on top of the shoulders of giants. Perform ML and DL using Tensorflow or Pytorch, accelerate your big data processing via Spark, or perform autoML using Pycaret. We're constantly updating our Xircuits library, so stay tuned for more!

Didn't find what you're looking for? Creating Xircuits components is very easy! If it's in python - it can be made into a component. Your creativity is the limit, create components that are easily extendable!

Effortless Collaboration Created a cool Xircuits workflow? Just pass the .xircuits file to your fellow data scientist, they will be able to load your Xircuits canvas instantly.

collab

Created a cool component library? All your colleagues need to do is to drop your component library folder in theirs and they can immediately use your components.

And many more.

Installation

You will need python 3.8+ to install Xircuits. We recommend installing in a virtual environment.

$ pip install xircuits

You will also need to install the component library before using them. For example, if you would like to use the Pytorch components, install them by:

$ xircuits install pytorch

For the list of available libraries, you can check here.

Download Examples

$ xircuits examples

Launch

$ xircuits

Development

Creating workflows and components in Xircuits is easy. We've provided extensive guides for you in our documentation. Here are a few quick links to get you started:

Use Cases

GPT Agent Toolkit | BabyAGI

BabyAGI demo

Discord Bots

DiscordCVBot

PySpark

spark submit

AutoML

automl

Anomaly Detection

anomaly-detection

NLP

nlp

Developers Discord

Have any questions? Feel free to chat with the devs at our Discord!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

xircuits-1.11.0.tar.gz (2.9 MB view details)

Uploaded Source

Built Distribution

xircuits-1.11.0-py3-none-any.whl (3.8 MB view details)

Uploaded Python 3

File details

Details for the file xircuits-1.11.0.tar.gz.

File metadata

  • Download URL: xircuits-1.11.0.tar.gz
  • Upload date:
  • Size: 2.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.19

File hashes

Hashes for xircuits-1.11.0.tar.gz
Algorithm Hash digest
SHA256 371f3693bd6cf66af750f35b36c8717c59d52a5b054566e413d1acb748c156e8
MD5 ad439cf6a7439e36f2b23ddb9b20ab60
BLAKE2b-256 9dc040d2814f8f6b4f51d401882b07960dcd6fda509b80b83c4f5a4b9ec5ae57

See more details on using hashes here.

File details

Details for the file xircuits-1.11.0-py3-none-any.whl.

File metadata

  • Download URL: xircuits-1.11.0-py3-none-any.whl
  • Upload date:
  • Size: 3.8 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.19

File hashes

Hashes for xircuits-1.11.0-py3-none-any.whl
Algorithm Hash digest
SHA256 50131651630da5d8287724f307f609ef953f56bd0f439147c2374415170128ef
MD5 24969a182b127d682dcabe887da28ce3
BLAKE2b-256 c5cef4b9d5da563d00e752bc5a1a2a43622a5cd8a2e5818d0af069b20a0ba3aa

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page