Supercharged Machine Learning ToolBox for ARM
A Docker image for ARM devices with Tensorflow 1.4.0 an open source software library for numerical computation using data flow graphs that will let you play and learn distinct Machine Learning techniques over Jupyter Notebook an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Computational Narratives as the Engine of Collaborative Data Science. All this under Python 2.7 language.
There is very similar image based on Python 3.4 instead of 2.7 elswork/rpi-tensorflow-py3.
Be aware! You should read carefully the usage documentation of every tool!
- Upgraded from Tensorflow 1.3.0 to 1.4.0
- Replaced Tensorflow local binary to tensorflow-1.4.0-cp27-none-any.whl
- Upgraded from Tensorflow 1.2.1 to 1.3.0
- Replaced base image by a Docker Official Image
- Upgraded from Tensorflow 1.1.0 to 1.2.1
- Romilly Cocking for the idea
- Pi tensorflow whl file that i builded thanks to Sam Abrahm's Step-By-Step Guide for build Tensorflow for Raspberry Pi
My Real Usage Example
In order everyone could take full advantages of the usage of this docker container, I'll describe my own real usage setup.
$ docker run -d -p 8888:8888 elswork/rpi-tensorflow:latest
A more complex sample:
$ docker run -d -p 8888:8888 \ -p 0.0.0.0:6006:6006 \ -v ~/myNotebooks:/notebooks/myNotebooks \ --restart=unless-stopped \ elswork/rpi-tensorflow:latest
Point your browser to
First time you open it, you should provide a Token to log on you cand find it with this command:
$ docker logs container_name
With the second example you can run TensorBoard executing this command in the container:
$ tensorboard --logdir=path/to/log-directory --host=0.0.0.0
And pointing your browser to