Tensorflow Keras Jupyter Notebooks Docker Install For Mac Pc

Installing Keras on Docker One of the easiest ways to get started with TensorFlow and Keras is running in a Docker container. A convenient solution is to use a predefined Docker image for deep learning created by the community that contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, and so on).

Docker name: Keras Tensorflow Python3 Jupyter

Author: George Shih / hello@georgeai.xyz

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Original upload date: 5/12/2017

Purpose:

This docker provides a development environment for using the latest Keras, Tensorflow, Python3.x, Jupyter notebook, andJupyterlab for use on a desktop or laptop.

Hardware Requirements:

Recommend at least 4GB RAM, although more is always better. Tensorflow is usingCPU for this Docker

Software Requirements:

Please install latest docker from docker.com

Should work with Linux, Windows, and Mac computers (using Intel processors). If you need help with thisrefer to https://github.com/ImagingInformatics/machine-learning/blob/master/Documentation/HOWTO-1.md

Dataset recommendations:

Does not include any datasets, but a sample Jupyter notebook is included

Instructions:

Assassin's creed brotherhood torrent mac. The source code included allows you to build the docker container from scratch, to do so clonethe repository to your local computer and enter 'make build' at the command line.However, the above is not required to run the software.

If you are on a Mac or *NIX computer

you can use the pre-built docker container at (https://cloud.docker.com/repository/docker/sglanger/jupyter_with_keras )You will also need to create a directory called ~/src/ALL_NOTEBOOKS on your local machine. This is where you should keep all your files, so that even if you stop the Docker container, your files will persist there. Then follow the instructions below from your computer's terminal program (eg, using xterm):

Jupyter

Change password to something else

JupyterLab (Alpha)

Change password to something else. After building the Docker via either the above methods, startit by typing './run_jupyter.sh'.

For Windows users

After installing Docker Desktop for Windows, open PowerShell and

1. In your personal folder (e.g. C:Users{your_username}), type “md srcALL_NOTEBOOKS” to create the srcALL_NOTEBOOKS folder
2. Unzip the Machine Learning notebooks you downloaded from github into this folder
3. Run the “docker run” instruction as indicated above

Caveats: you may find saving files under Windows problematic. Consider setting use_multiprocessing to 'False'

Once started, access the DOcker by pointing your browser (chrome works best) to the IP address ofthe host machine and append the port number. For example if your DOcker host computer is IP address = 192.168.100.10,then point a browser to''http://192.168.100.10:8888''