{"id":14977343,"url":"https://github.com/sflender/deep-learning-test","last_synced_at":"2026-03-07T05:03:25.774Z","repository":{"id":71735103,"uuid":"109062607","full_name":"sflender/deep-learning-test","owner":"sflender","description":"Exploring deep learning on Cooley","archived":false,"fork":false,"pushed_at":"2017-12-01T18:02:05.000Z","size":326,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-02T04:29:44.427Z","etag":null,"topics":["augmentation","convnet","cooley","data-augmentation","gpu","ipynb-notebook","learning-curve","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Content\n\n- deep-digits-local.py : explore a simple convnet on your laptop\n\n- keras-test.py : code for running a convnet on Cooley and save the learning curve\n\n- deep-digits-history.ipynb : notebook for plotting learning curves\n\n- digit_augmentation_exploration.ipynb : notebook for exploring data augmentation on your laptop\n\n- keras_with_augmentation.py : code for running a convnet with data augmentation on Cooley and save the learning curve.  \n  \n- parallel-keras-test.py : use both Cooley GPU's\n  \n## Installing tensorflow and Keras on Cooley\n\nI followed instructipon from https://gist.github.com/wscullin/70409948a5a812e0e874339a8a1a256c with the difference that I used the pre-build wheel at /soft/libraries/unsupported/tensorflow-whl-1.3.0/  \n\nMy soft environment is set up like this:\n```\n+mvapich2\n+gcc-4.9.3\n+cuda-7.5.18\n+git-2.10.0\n+java-1.8.0.60\nLD_LIBRARY_PATH+=/soft/libraries/unsupported/cudnn-7.5.1/cuda/lib64\n@default\n```\n  \nFirst create a new conda environment:\n```\nconda create -n \"test_env\" python=2.7 anaconda\n```\n  \nactivate the environment: \n```\nsource activate test_env\n```\n\npip install of the tensorflow wheel:  \n```\npip install /soft/libraries/unsupported/tensorflow-whl-1.3.0/tensorflow-1.3.0-cp27-cp27mu-linux_x86_64.whl\n``` \n  \nalso install keras to run the exaple code:  \n```\npip install keras\n``` \n  \nnow get an interactive node:\n```\nqsub -I -A datascience -t 00:30:00 -n 1 -q debug\n```\n\nactivate the environment:\n```\nsource activate test_env\n```\n\nTo see if your tensorflow installation sees both of the GPUs on one Cooley node, type this into a python shell: \n  \n```\nfrom tensorflow.python.client import device_lib\ndevice_lib.list_local_devices()\n```\n\nnow you can run the example:  \n```\npython keras-test.py\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsflender%2Fdeep-learning-test","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsflender%2Fdeep-learning-test","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsflender%2Fdeep-learning-test/lists"}