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# Make yaml file. $ vi environment-gpu.yml name: keras-studytf-gpu-1.15 dependencies: - python=3.6 - tensorflow-gpu=1.15 - keras=2.3.71 - scikit-learn - scipy - numpy - matplotlib - ipython - jupyter - pillow - opencv - graphviz - cython - pip: - tensorflow-gpu==1.15 - keras==2.2.4 - pydot-ng # Create conda env $ conda env create -f environment-gpu.yml # Enter karas-studytf-gpu-1.15 env $ conda activate karas-studytf-gpu-1.15 |
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If not able to activate a certain conda env, add this instruction into .bashrc
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# Check out the installed packages about cuda.
$ conda list
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cudatoolkit 10.0.130 0
cudnn 7.6.5 cuda10.0_0
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keras 2.3.1 0
keras-applications 1.0.8 py_1
keras-base 2.3.1 py36_0
keras-preprocessing 1.1.2 pyhd3eb1b0_0
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tensorflow 1.15.0 gpu_py36h2b26d6b_0
tensorflow-base 1.15.0 gpu_py36h1afeea4_0
tensorflow-estimator 1.15.1 pyh2649769_0
tensorflow-gpu 1.15.0 h0d30ee6_0
... |