I'm currently using gcr.io/kubeflow-images-public/tensorflow-1.15.2-notebook-gpu:1.0.0 deployed in a kubernete cluster. But it's by default no root permission in the notebook server. I have tried to use other images such as jupyter/datascience-notebook where you can allow sudo by setting the parameters. The docker command is
docker run -it --rm -p 8888:8888 --user root -e NB_GID=100 -e GEN_CERT=yes -e GRANT_SUDO=yes jupyter/datascience-notebook
But I have to use yaml file to deploy the server in a cluster. The question is how to convert this docker command to a StatefulSet yaml file? Or are there other suggestions which allows sudo in a jupyter notebook server in Kubernetes?
Related
I'm trying to work in Jupyter Lab run via Docker on a remote machine, but can't save any of the files I open.
I'm working with a Jupyter Docker Stack. I've installed docker on my remote machine and successfully pulled the image.
I set up port forwarding in my ~/.ssh/config file:
Host mytunnel
HostName <remote ip>
User root
ForwardAgent yes
LocalForward 8888 localhost:8888
When I fire up the container, I use the following script:
docker run \
-p 8888:8888 \
-e JUPYTER_ENABLE_LAB=yes \
-v "${PWD}":/home/jovyan/work jupyter/tensorflow-notebook
The container is running:
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
c8fc3c720af1 jupyter/tensorflow-notebook "tini -g -- start-no…" 8 minutes ago Up 8 minutes 0.0.0.0:8888->8888/tcp, :::8888->8888/tcp adoring_khorana
I get the regular Jupyter url back:
http://127.0.0.1:8888/lab?token=<token>
But when I access the server in my browser, the Save option is disabled.
I've tried some of the solutions proposed elsewhere in SO, but no luck.
Is this something about connecting over SSH? The Jupyter server thinks it's not a secure connection?
It is possible that the problem is related to the SSH configuration, but I think is more probably related to a permission issue with your volume mount.
Please, try reviewing your docker container logs looking for permissions related errors. You can do that using the following:
docker container logs <container id>
See the output provided by your docker run command too.
In addition, try opening a shell in the container:
docker exec -it <container id> /bin/bash
and see if you are able to create a file in the default work directory:
touch /home/jovyan/work/test_file
Finally, the Jupyter docker stacks repository has a troubleshooting page almost entirely devoted to permissions issues.
Consider especially the solutions provided in the Additional tips and troubleshooting commands for permission-related errors and, as suggested, try providing launching the container with your OS user:
docker run \
-p 8888:8888 \
-e JUPYTER_ENABLE_LAB=yes \
--user "$(id -u)" --group-add users \
-v "${PWD}":/home/jovyan/work jupyter/tensorflow-notebook
After that, as suggested in the mentioned documentation as well, see if the container is properly mounted using the following command:
docker inspect <container_id>
In the obtained result note the value of the RW field which indicates whether the volume is writable (true) or not (false).
I have set up jupyter notebook in the correct port in docker , everytime I need to upload data into the notebook and do analysis , is there anyway I can get up my jupyter file location to a particular file ,keeping in mind I'm using docker.
You need to volume your folder to the Docker container. An example that you use jupyter/all-spark-notebook image, so you can run:
docker run -it --rm -p 8888:8888 -p 4040:4040 -v your-path:/home/jovyan/workspace jupyter/all-spark-notebook
Update your-path to the path contains your notebooks
I installed and run nginx on my linux machine to understand the configurations etc. After a while i decided to remove it safely by following this thread in order to use it in docker
By following this documentaion i run this command
sudo docker run --name ngix -d -p 8080:80 pillalexakis/myrestapi:01
And i saw ngix's homepage at localhost
Then i deleted all ngix images & stopped all containers and i also run this command
sudo docker system prune -a
But now restarted my service by this command
sudo docker run -p 192.168.2.9:7777:8085 phillalexakis/myfirstapi:01 and i keep seeing at localhost ngix index.html
How can i totally remove it ?
Note: I'm new with docker and i might have missed a lot of things. Let me know what extra docker commands should i run in order provide better information.
Assuming your host have been preparing as below
your files (index.html, js, etc) under folder - /myhost/nginx/html
your nginx configuration - /myhost/nginx/nginx.conf
Solution
map your files (call volume) on the fly from outside docker image via docker cli
This is the command
docker run -it --rm -d -p 8080:80 --name web \
-v /myhost/nginx/html:/usr/share/nginx/html \
-v /myhost/nginx/nginx.conf:/etc/nginx/nginx.conf \
nginx
copy your files into docker image by build your own docker image via Dockerfile
This is your Dockerfile under /myhost/nginx
FROM nginx:latest
COPY ./html/index.html /usr/share/nginx/html/index.html
This is the command to build your docker image
cd /myhost/nginx
docker build -t pillalexakis/nginx .
This is the command to run your docker image
docker run -it --rm -d -p 8080:80 --name web \
pillalexakis/nginx
I want to use the official Tensorflow docker images to create and edit a Jupyter notebook stored on the host.
I'm a little confused with what switches I need to provide. To run a Tensorflow script on the host the docs suggest:
docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/tensorflow python ./script.py
..and to run the Jupyter service:
docker run -it -p 8888:8888 tensorflow/tensorflow:nightly-py3-jupyter
When I try merging the switches to run Jupyter + mount the host volume:
docker run -it --rm -v $PWD:/tmp -w /tmp -p 8888:8888 tensorflow/tensorflow:nightly-py3-jupyter
...its still accessing notebooks stored in the container, not the host.
Notebooks are stored inside the container /tf folder, so copying your files there will do the trick:
docker run -it --rm -v $PWD:/tf -p 8888:8888 tensorflow/tensorflow:nightly-py3-jupyter
The first command you mentioned is used to run a TensorFlow program developed on the host machine, not a notebook.
I am running a python interactive docker container on Ubuntu 14.04 using docker 17.03.1. I want to share files between local host and docker container so that files I create in the container are visible in the local directory and vice-versa. However when I run the following command I see an empty working directory in the container with no files.
docker run -e USER=$USER -e USERID=$UID -v /home/watts/python:/home/watts/python -w=/home/watts/python -p 8888:8888 --rm -it watts/python jupyter notebook --no-browser --notebook-dir=/home/watts/python --allow-root
I just run this command:
docker run -v `pwd`/home/watts/python:/home/watts/python -it kaggle/python /bin/bash
And after that, I started creating a few files both host and container. All files are visible in both sides.
Hopefully this will help.