The command below has a serie of ENV variables that provide these features:
- User in the container is mapped to your user in the host machine
-w /home/jupyter
&-e HOME=/home/jupyter
are required so that notebooks are placed at the home path
docker run --restart always -p 8008:8888 --name jupyter-scipy --user root \
-e NB_USER=$(whoami) -e NB_GROUP=RnD -e NB_UID=$(id -u) -e NB_GID=$(cut -d: -f3 < <(getent group RnD)) -e JUPYTER_ENABLE_LAB=yes \
-e HOME=/home/jupyter -e CHOWN_HOME_OPTS=-R -e CHOWN_HOME=yes -e GRANT_SUDO=yes -e NB_UMASK=022 \
-w /home/jupyter -v $(pwd):/home/jupyter jupyter/scipy-notebook
NOTE: $(whoami)
= pablo
-
Plain Jupyter enviroment is accessible at URL http://192.168.20.28:8811/tree
-
Lab Jupyter enviroment is accessible at URL http://192.168.20.28:8811/lab
- This feature is provided by the ENV variable:
-e JUPYTER_ENABLE_LAB=yes
- This feature is provided by the ENV variable:
Source: Utilizing the Kaggle Python Docker Container image
Docker container will map this folder.
mkdir data
docker run --restart always -v ${PWD}/data:/tmp/working -w=/tmp/working -p 8800:8888 --name kaggle \
-d kaggle/python jupyter notebook --no-browser --ip="0.0.0.0" --notebook-dir=/tmp/working --allow-root
docker logs kaggle
CURRENT TOKEN:
40119a2f87c125c72f7603945ca6b1561e0fb9ed45929234
For example:
http://640b804c545b:8888/?token=8e28bf1201d83f3f43521fba4b0cf382107781a4955ecf93&token=8e28bf1201d83f3f43521fba4b0cf382107781a4955ecf93
- Replace 640b804c545b with
localhost
or the IP of the machine where Kaggle image is running. - Replace port 8888 (container) by 8800 (host)
Everything can be done with the bash script ./kaggle.sh
In the http line above:
token=40119a2f87c125c72f7603945ca6b1561e0fb9ed45929234
So if you want to set a password for accessing Jupyter, after launching the container go to:
http://localhost:8888
Enter your token and change the password.
docker exec -it kaggle bash
- Using raw temporal serie: AUC= 0.514
- Using standard deviation over sets of consecutive data points (AUC):
- stdev every 10 data points: 0.717
- stdev every 100 data points: 0.911
- stdev every 1000 data points: 1.000
- Replicated from ROC of PIMA dataset. ROC curve explained HERE
- Interactive plot of ROC changing the threshold value in the probability distribution, for both:
- Logistic regression
- Random forest
- Interactive plot of ROC changing the threshold value in the probability distribution, for both:
Based on the Titanic dataset, copied into this one in my Kaggle profile
Pending tests from the command line:
ludwig experiment
ludwig visualize
There are more advanced examples with this dataset in Uber Ludwig examples in its official repository