datamachines/jupyter_to repository overview

latest points to the latest release, here: 20220815 with support for TensorFlow 2.9.1, OpenCV 4.6.0 and PyTorch 1.12.1

Built from datamachines/tensorflow_opencv containers (to). Tag naming mirrors the tensorflow_opencv convention: 2.8.0_4.5.5-20220318 refers to TensorFlow 2.8.0, OpenCV 4.5.5 and the base container build dating March 18th, 2022.

Provides access to TensorFlow, Opencv, Keras, NumPy, pandas, and PyTorch from within the Jupyter Notebook. Please refer to the matching tensorflow_opencv tag's BuildInfo entries in the Github repository at https://github.com/datamachines/cuda_tensorflow_opencv⁠

The container starts and exposes Jupyter Notebook on port 8888. Start using docker run -p 8888:8888 datamachines/jupyter_to:tag Please note the http://127.0.0.1:8888/ based URL shown with its access token and use to access the running instance of Jupyter Notebook.

In order to mount weights within the container, or other persistent locations for code and such, use one or more -v /local/directory:/container/mount to mount your "local directory" inside the running instance at "container mount" location.

In particular, /dmc is the default WORKDIR of the base container, as such, the container will start in this directory; if nothing is mounted on it, you will be working directly inside the container. It is therefore recommended to mount the directory where you have your data/code. For example with this directory being your current working directory, adding -v `pwd`:/dmc will accomplish this. Note that Jupyter Notebook runs as root, so keep this in mind when writing files in /dmc.

To exit Jupyter Notebook, you can either docker kill the running instance or press the Quit button on the Web UI.

For further details, including instructions on building a user-level container (using the same source as these), see: https://github.com/datamachines/cuda_tensorflow_opencv#UsingJupyter-NotebookAnoteonexposingports⁠