okwrtdsh / anaconda3

Anaconda3, Jupyter Notebook, OpenCV3, TensorFlow and Keras2 for Deep Learning🐳

anaconda3's Issues

Best way to enrich with my own requirements?

I'm in love with your level-based approach for these Dockers! Great job and thanks for sharing!
However, what would you recommend as the favorable approach to enrich them with my own requirements/dependencies?

For example, if I'd like to run Keras on my GPU, with some new dependencies which are not currently present in your base image..
Would you recommend to create a new Dockerfile, build FROM keras-10.0-cudnn7 as base image, add my dependencies here?

Or, should I change your base 10.0-cudnn7 Dockerfile and insert my own dependencies, then build everything from scratch?

Config file location in Unraid

When running $ jupyter notebook --generate-config the file is "generated" according to the response in /root/.jupyter/ folder. I can navigate to that folder in a container terminal and see the file listed, but cannot open or access it. And when looking for that folder with any other option on my server, it just doesn't exist.

tensorflow gpu version not enabled in okwrtdsh/anaconda3:keras-9.2-cudnn7

Running the container:
sudo nvidia-docker run -it okwrtdsh/anaconda3:keras-9.2-cudnn7 bash

"attaching" to container:
sudo docker exec -it container_name bash

Running python repl and importing tensorflow to check whether gpu is enabled in tensorflow:

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

which prints:

2018-07-05 14:56:47.988072: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 3815053792300585653
]

That shows device_type as cpu. Can this be a bug?