elswork/tf-juplab-ocv repository overview

⁠OpenCV + JupyterLab + Tensorflow

A Docker⁠ file to build images for AMD & ARM devices over a base image based with a minimal installation of Tensorflow⁠ an open source software library for numerical computation using data flow graphs. Also included in base image OpenCV⁠ is a library of programming functions mainly aimed at real-time computer vision. Over this base will be installed JupyterLab⁠ 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 Python3 language.

⁠Details

• GitHub⁠
• Deft.Work my personal blog⁠

Docker Hub	Docker Pulls	Docker Stars	Docker Build	Size/Layers
tf-juplab-ocv⁠

⁠Build Instructions

Build for amd64 or arm32v7 architecture (thanks to its Multi-Arch⁠ base image)

docker build -t elswork/tf-juplab-ocv:latest .

⁠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/tf-juplab-ocv:latest

A more complex sample:

docker run -d -p 8888:8888 -p 0.0.0.0:6006:6006 \
 --restart=unless-stopped elswork/tf-juplab-ocv:latest

If you want to add access to USB Cam attached to host (--device=/dev/video0).

docker run -d -p 8888:8888 -p 0.0.0.0:6006:6006 --device=/dev/video0  \
 --restart=unless-stopped elswork/tf-juplab-ocv: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