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OpenI AI Development Cooperation Platform

The one-stop collaborative development environment for AI field provides AI development pipeline integrating code development, data management, model debugging, reasoning and evaluation

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* Only show the dynamics of open source projects

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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.

PengCheng Cloudbrain Open Source Collaboration

The platform has been connected with Pengcheng Cloudbrain and can use the rich computing resources of Pengcheng Cloudbrain to complete AI development tasks.
Pengcheng Cloudbrain's existing AI computing power is 100p FLOPS@FP16 (billions of half precision floating-point calculations per second), the main hardware infrastructure is composed of GPU server equipped with NVIDIA Tesla V100 and Atlas 900 AI cluster equipped with Kunpeng and Ascend processors.
Developers can freely choose the corresponding computing resources according to their needs, and can test the adaptability, performance, stability of the model in different hardware environments.
If your model requires more computing resources, you can also apply for it separately.

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