* The dynamics of open source projects

Explore Better AI
The one-stop collaborative development environment for AI field provides AI development pipeline integrating code development, data management, model debugging, reasoning and evaluation
China Computing NET(C²NET)
Extensive access to intelligent computing centers, supercomputing centers and big data centers across the country to provide users with free computing resources.
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Recommended Organizations

These excellent organizations are using the OpenI AI Collaboration Platform for collaborative development of projects. To show your organization here, Click here to submit. See More Organizations

Recommended Projects

Excellent AI projects recommendation. To show your project here, Click here to submit. Click here to explore more projects.

DataSet

Open source dataset base, seamlessly integrated with your project. View all DataSet

Experience Officer

OpenI AI experience officer growth plan, More benefits

Community Activities

The community has prepared a wealth of activities, waiting for you to participate!

China Computing NET(C²NET)

Extensive access to intelligent computing centers, supercomputing centers and big data centers across the country to provide users with free computing resources.

Collaborative Development Environment

Provide a collaborative development environment for AI development, which is the biggest highlight that distinguishes the OpenI AI Collaboration Platform from other traditional Git platforms.

Unified Management of Development Elements

The platform provides four elements of AI development: unified management of model code, data set, model and execution environment.

Data Collaboration and Sharing

By uploading data sets in the project, many project members cooperate to complete data preprocessing. You can also establish a better model with community developers by setting the data as a public dataset.

Model Management and Sharing

Associate the model with the code version, you can adjust the model in different ways based on the historical version of the code and save the results. The trained model can be open and shared, so that more people can use the model to test and give feedback.

Once Configuration, Multiple Reuse

Provide execution environment sharing, Once Configuration, Multiple Reuse. Lower the threshold of model development, and avoid spending repetitive time configuring complex environments.