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CityUHK-CompBio / DeepCC

DeepCC: a novel deep learning-based framework for cancer molecular subtype classification
https://CityUHK-CompBio.github.io/DeepCC/
MIT License
20 stars 16 forks source link

Rocker/ml Docker container with DeepCC #5

Closed hypotheses closed 4 years ago

hypotheses commented 4 years ago

Build a container based on rocker/ml:3.6.0 with DeepCC installed to facilitate local installation of mxnet (branch version 1.3.1)

gaofeng21cn commented 4 years ago

Thanks a lot and it’s really great! I will see how to merge it. In addition, we just updated DeepCC with Keras backend and will switch the keras version to master.

Best regards, Feng GAO, Ph.D., Associate Professor The Sixth Affiliated Hospital, Sun Yat-sen University Guangzhou, China 2020年6月10日 +0800 14:07 Bhoom Suktitipat notifications@github.com，写道： Build a container based on rocker/ml:3.6.0 with DeepCC installed to facilitate local installation of mxnet (branch version 1.3.1) You can view, comment on, or merge this pull request online at: https://github.com/CityUHK-CompBio/DeepCC/pull/5 Commit Summary

• create dockerfile • Create README.md • Update Dockerfile

File Changes

• A docker/Dockerfile (31) • A docker/README.md (30)

Patch Links:

• https://github.com/CityUHK-CompBio/DeepCC/pull/5.patch • https://github.com/CityUHK-CompBio/DeepCC/pull/5.diff

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hypotheses commented 4 years ago

I agree. Sticking with Keras might have a better long term future. Mxnet seems rather inactive in the past few years, and with the difficulty installing mxnet, our group had almost given up.

On Wed, Jun 10, 2020 at 4:13 PM zero19970 notifications@github.com wrote:

Merged #5 https://github.com/CityUHK-CompBio/DeepCC/pull/5 into master.

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zero19970 commented 4 years ago

Thanks for understanding. We will keep a mxnet branch for use, but we will stop updating this branch.

gaofeng21cn commented 4 years ago

Yes, mxnet was the only choice when we started developing DeepCC in R with GPU support. Actually, the prototype of DeepCC is running on H2O with only CPU support.

Now RStudio team officially maintains the R interface of Keras, making it very easy to use. In addition, with an optimized deep network and Keras. The training process takes only a few minutes with only CPUs.