ImJoy: an open-source computational platform for the deep learning era

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Fig. 1: Overview of ImJoy.

Code Availability

Source code for ImJoy and the example plugins is available at https://imjoy.io and https://github.com/imjoy-team/example-plugins, respectively.

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Acknowledgements

This work was funded by the Institut Pasteur. W.O. was a scholar in the Pasteur–Paris University (PPU) International PhD program and was partly funded by a Fondation de la Recherche Médicale (FRM) grant to C.Z. (DEQ 20150331762). W.O. is a postdoctoral researcher supported by the Knut and Alice Wallenberg Foundation (2016.0204) and Erling-Persson Foundation (20180316) grants to E.L. We also acknowledge Investissement d’Avenir grant ANR-16-CONV-0005 for funding a GPU farm used for testing ImJoy. We thank the IT department of Institut Pasteur, in particular S. Fournier and T. Menard, for providing access to the kubernetes cluster and DGX-1 server for running and testing the ImJoy plugin engine and for technical support. We thank Q.T. Huynh for maintaining the GPU farm and for advice and assistance during the development of ImJoy. We also thank A. Martinez Casals, P. Thul, H. Xu, A. Aristov, A. Cesnik, C. Gnann, J. Parmar, K.M. Douglass, N. Stuurman, X. Hao, S. Dai, A. Hu, D. Guo, K. Zhou for testing and helping with ImJoy plugin development. We thank E. Rensen for proofreading the manuscript. We thank J. Nunez-Iglesias, S. Mehta, B. Chhun, J. Batson, L. Royer, N. Sofroniew and M. Woringer for useful advice and discussion.

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Correspondence to Wei Ouyang or Florian Mueller.

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Supplementary Information

Supplementary Notes 1 and 2

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Ouyang, W., Mueller, F., Hjelmare, M. et al. ImJoy: an open-source computational platform for the deep learning era. Nat Methods 16, 1199–1200 (2019) doi:10.1038/s41592-019-0627-0

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