A Docker image for ARM devices with TFlearn, a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. Latest Tensorflow 1.3.0 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.
Be aware! You should read carefully the usage documentation of every tool!
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-tflearn: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-tflearn:latest
Point your browser to http://localhost:8888
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 http://localhost:6006
Content type
Image
Digest
Size
337.4 MB
Last updated
about 9 years ago
docker pull elswork/rpi-tflearn