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https://github.com/iot-salzburg/gpu-jupyter
GPU-Jupyter: Leverage the flexibility of Jupyterlab through the power of your NVIDIA GPU to run your code from Tensorflow and Pytorch in collaborative notebooks on the GPU.
https://github.com/iot-salzburg/gpu-jupyter
reproducible-research
Last synced: about 1 month ago
JSON representation
GPU-Jupyter: Leverage the flexibility of Jupyterlab through the power of your NVIDIA GPU to run your code from Tensorflow and Pytorch in collaborative notebooks on the GPU.
- Host: GitHub
- URL: https://github.com/iot-salzburg/gpu-jupyter
- Owner: iot-salzburg
- License: apache-2.0
- Created: 2019-11-12T20:10:23.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-07-18T01:34:05.000Z (5 months ago)
- Last Synced: 2024-08-02T15:54:11.669Z (4 months ago)
- Topics: reproducible-research
- Language: Jupyter Notebook
- Homepage:
- Size: 1.15 MB
- Stars: 686
- Watchers: 17
- Forks: 231
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-jupyter-resources - GitHub - 21% open · ⏱️ 21.07.2022): (Notebook环境)
- best-of-jupyter - GitHub - 4% open · ⏱️ 26.11.2024): (Notebook Environments)
README
# GPU-Jupyter
#### GPU-Jupyter: Leverage Jupyter Notebooks with the power of your NVIDIA GPU and perform GPU calculations using Tensorflow and Pytorch in collaborative notebooks.
[](https://hub.docker.com/r/cschranz/gpu-jupyter)
[](https://hub.docker.com/r/cschranz/gpu-jupyter)
[](https://GitHub.com/iot-salzburg/gpu-jupyter/network/)
[](https://GitHub.com/iot-salzburg/gpu-jupyter/stargazers/)
[](https://github.com/iot-salzburg/gpu-jupyter/pulls?q=is%3Aclosed)
[](https://GitHub.com/iot-salzburg/gpu-jupyter/commit/)
[](https://github.com/iot-salzburg/gpu-jupyter/graphs/commit-activity)
Welcome to this project, which provides a **GPU-capable environment** based on NVIDIA's CUDA Docker image and the popular [docker-stacks](https://github.com/jupyter/docker-stacks).
Our toolstack enables GPU calculations in Jupyter notebooks, while the use of containers and versioned tags ensures the reproducibility of experiments.
By utilizing version control for the source code, tagged data spaces, seeds for the random functions, and tagged environments as provided here, our solution **empowers researchers to conduct highly complex big data experiments that are fully reproducible**.
The images of this repository are available on [Dockerhub](https://hub.docker.com/r/cschranz/gpu-jupyter).
A big thank you to [docker-stacks](https://github.com/jupyter/docker-stacks)
for creating and maintaining a robust Python, R, and Julia toolstack for Data Science.
## Contents
1. [Quickstart](#quickstart)
2. [Build Your image](#build-your-image)
3. [Tracing](#tracing)
4. [Configuration](#configuration)
5. [Deployment](#deployment-in-the-docker-swarm)
6. [Issues and Contributing](#issues-and-contributing)
7. [Support](#support)
## Quickstart
1. Ensure that you have access to a computer with an NVIDIA GPU. If you don’t have your own setup, you can try [Saturn Cloud](https://saturncloud.io/?utm_source=Github+&utm_Github=TDS&utm_campaign=ChristophSchranz&utm_term=GPUJupyter) for a free GPU-powered Jupyter solution.
2. Install [Docker](https://www.docker.com/community-edition#/download) version **1.10.0+**
and [Docker Compose](https://docs.docker.com/compose/install/) version **1.28.0+**.
3. Get access to your GPU via CUDA drivers within Docker containers. For this, follow the installation steps in this
[Medium article](https://medium.com/@christoph.schranz/set-up-your-own-gpu-based-jupyterlab-e0d45fcacf43). You can confirm that you can access your GPU within Docker if the command below returns a result similar to this one:
```bash
docker run --gpus all nvidia/cuda:12.2.2-cudnn8-runtime-ubuntu22.04 nvidia-smi
```
```bash
Tue Jun 18 12:39:31 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.23.05 Driver Version: 545.84 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 3060 ... On | 00000000:01:00.0 Off | N/A |
| N/A 46C P8 10W / 60W | 202MiB / 6144MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
```
**It is important to keep your installed CUDA version in mind when you pull images.
Note that you can't run images based on `nvidia/cuda:11.2` if you have only CUDA version 10.1 installed, use `nvcc --version` to get the correct cuda version. Additionally, a NVIDIA driver version of at least 520 is suggested, as the images are built and tested using this and later versions.**
4. Pull and run the GPU-Jupyter image. This may take some time as the whole environment for data science will be downloaded:
```bash
cd your-working-directory
ll data # this path will be mounted by default
docker run --gpus all -d -it -p 8848:8888 -v $(pwd)/data:/home/jovyan/work -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes --user root cschranz/gpu-jupyter:v1.7_cuda-12.2_ubuntu-22.04
```
This starts an instance of *GPU-Jupyter* with the tag `v1.7_cuda-12.2_ubuntu-22.04` at [http://localhost:8848](http://localhost:8848) (port `8848`).
To log into Jupyterlab, you have to specify a token that you get from:
```bash
docker exec -it [container-ID/name] jupyter server list
# [JupyterServerListApp] Currently running servers:
# [JupyterServerListApp] http://791003a731e1:8888/?token=5b96bb15be315ccb24643ea368a52cc0ba13657fbc29e409 :: /home/jovyan
```
You can optionally set a password in [http://localhost:8848/login](http://localhost:8848/login) or as described [below](#change-the-password) (former default password `gpu-jupyter`).
Additionally, data within the host's `data` directory is shared with the container.
The following images of GPU-Jupyter are available on [Dockerhub](https://hub.docker.com/r/cschranz/gpu-jupyter):
- `v1.7_cuda-12.2_ubuntu-22.04` (full image)
- `v1.7_cuda-12.2_ubuntu-22.04_python-only` (only with a python interpreter and without Julia and R)
- `v1.7_cuda-12.2_ubuntu-22.04_slim` (only with a python interpreter and without additional packages)
- `v1.7_cuda-12.3_ubuntu-22.04` (full image)
- `v1.7_cuda-12.3_ubuntu-22.04_python-only` (only with a python interpreter and without Julia and R)
- `v1.7_cuda-12.3_ubuntu-22.04_slim` (only with a python interpreter and without additional packages)
- `v1.6_cuda-12.0_ubuntu-22.04` (full image)
- `v1.6_cuda-12.0_ubuntu-22.04_python-only` (only with a python interpreter and without Julia and R)
- `v1.6_cuda-12.0_ubuntu-22.04_slim` (only with a python interpreter and without additional packages)
- `v1.6_cuda-11.8_ubuntu-22.04` (full image)
- `v1.6_cuda-11.8_ubuntu-22.04_python-only` (only with a python interpreter and without Julia and R)
- `v1.6_cuda-11.8_ubuntu-22.04_slim` (only with a python interpreter and without additional packages)
Older images
- `v1.5_cuda-12.0_ubuntu-22.04` (full image)
- `v1.5_cuda-12.0_ubuntu-22.04_python-only` (only with a python interpreter and without Julia and R)
- `v1.5_cuda-12.0_ubuntu-22.04_slim` (only with a python interpreter and without additional packages)
- `v1.5_cuda-11.8_ubuntu-22.04` (full image)
- `v1.5_cuda-11.8_ubuntu-22.04_python-only` (only with a python interpreter and without Julia and R)
- `v1.5_cuda-11.8_ubuntu-22.04_slim` (only with a python interpreter and without additional packages)
- `v1.5_cuda-11.6_ubuntu-20.04` (full image)
- `v1.5_cuda-11.6_ubuntu-20.04_python-only` (only with a python interpreter and without Julia and R)
- `v1.5_cuda-11.6_ubuntu-20.04_slim` (only with a python interpreter and without additional packages)
- `v1.4_cuda-11.6_ubuntu-20.04` (full image)
- `v1.4_cuda-11.6_ubuntu-20.04_python-only` (only with a python interpreter and without Julia and R)
- `v1.4_cuda-11.6_ubuntu-20.04_slim` (only with a python interpreter and without additional packages)
- `v1.4_cuda-11.2_ubuntu-20.04` (full image)
- `v1.4_cuda-11.2_ubuntu-20.04_python-only` (only with a python interpreter and without Julia and R)
- `v1.4_cuda-11.2_ubuntu-20.04_slim` (only with a python interpreter and without additional packages)
- `v1.4_cuda-11.0_ubuntu-20.04` (full image)
- `v1.4_cuda-11.0_ubuntu-20.04_python-only` (only with a python interpreter and without Julia and R)
- `v1.4_cuda-11.0_ubuntu-20.04_slim` (only with a python interpreter and without additional packages)
- `v1.4_cuda-11.0_ubuntu-18.04` (full image)
- `v1.4_cuda-11.0_ubuntu-18.04_python-only` (only with a python interpreter and without Julia and R)
- `v1.4_cuda-11.0_ubuntu-18.04_slim` (only with a python interpreter and without additional packages)
- `v1.4_cuda-10.1_ubuntu-18.04` (full image)
- `v1.4_cuda-10.1_ubuntu-18.04_python-only` (only with a python interpreter and without Julia and R)
- `v1.4_cuda-10.1_ubuntu-18.04_slim` (only with a python interpreter and without additional packages)
The version, e.g. `v1.7`, declares the version of the generator setup.
The Cuda version, e.g. `cuda-12.2`, must match the CUDA driver version and be supported by the GPU libraries.
These and older versions of GPU-Jupyter are listed on [Dockerhub](https://hub.docker.com/r/cschranz/gpu-jupyter/tags?page=1&ordering=last_updated).
In case you are using another version or the GPU libraries don't work on your hardware, please try to build the image on your own as described below.
Note that the images built for Ubuntu 20.04 LTS work also on Ubuntu 22.04 LTS is currently not supported.
Within the Jupyterlab UI, ensure you can access your GPU by opening a new Terminal window and running `nvidia-smi`.
Here, you can also install additional packages on top of the built image.
To help you get started with using the GPU, the repository includes some sample code located in `extra/Getting_Started`.
If you're new to JupyterLab or want to learn more about its features,
we recommend checking out this [tutorial](https://www.youtube.com/watch?v=7wfPqAyYADY).
## Build Your Image
Building a custom Docker image is the recommended option if you have a different GPU architecture or if you want to customize the pre-installed packages. The Dockerfiles in `custom/` can be modified to achieve this. To use a custom base image, modify `custom/header.Dockerfile`. To install specific GPU-related libraries, modify `custom/gpulibs.Dockerfile`, and to add specific libraries, append them to `custom/usefulpackages.Dockerfile`.
After making the necessary modifications, regenerate the `Dockerfile` in `/.build`. Once you have confirmed that your GPU is accessible within Docker containers by running `docker run --gpus all nvidia/cuda:12.2.2-cudnn8-runtime-ubuntu22.04 nvidia-smi` and seeing the GPU statistics, you can generate, build, and run the Docker image.
The following commands will start *GPU-Jupyter* on [localhost:8848](http://localhost:8848) with the default password `gpu-jupyter`.
```bash
git clone https://github.com/iot-salzburg/gpu-jupyter.git
cd gpu-jupyter
git branch # Check for extisting branches
git checkout v1.7_cuda-12.2_ubuntu-22.04 # select or create a new version
# generate the Dockerfile with python and without Julia and R (see options: --help)
./generate-Dockerfile.sh --python-only
docker build -t gpu-jupyter .build/ # will take a while
docker run --gpus all -d -it -p 8848:8888 -v $(pwd)/data:/home/jovyan/work -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes -e NB_UID="$(id -u)" -e NB_GID="$(id -g)" --user root --restart always --name gpu-jupyter_1 gpu-jupyter
```
This command starts a container with GPU support and a shared local data volume `/data`, along with other necessary configurations, such as root permissions to install packages within the container. For more configuration options, see [Configuration of the Dockerfile-Generation](#configuration-of-the-dockerfile-generation) below.
### Start via Docker Compose
To start *GPU-Jupyter* using `docker-compose.yml`, run the following command:
```bash
docker-compose up --build -d # build and run in detached mode
docker-compose ps # check if was successful
docker-compose logs -f # view the logs
docker-compose down # stop the container
```
This step requires a `docker-compose` version of at least `1.28.0`,
as the Dockerfile requests GPU resources (see this [changelog](https://docs.docker.com/compose/release-notes/#1280)).
To update `docker-compose`, this [discussion](https://stackoverflow.com/a/50454860) may be useful.
## Tracing
With these commands we can investigate the container:
```bash
docker ps # use the flat '-a' to view all
docker stats
docker logs [service-name | UID] -f # view the logs
docker exec -it [service-name | UID] bash # open bash in the container
```
To stop the local deployment, run:
```bash
docker rm -f [service-name | UID] # or
```
## Configuration
### Configuration of the Dockerfile-Generation
To generate a Dockerfile for *GPU-Jupyter*, you can use the script `generate-Dockerfile.sh`. It generates a Dockerfile in the `.build/` directory, which is overwritten with each new generation and entries within this directory should not be modified manually.
The script has the following parameters:
* `-h|--help`: Show a help message.
* `-p|--pw|--password`: Set the password for *GPU-Jupyter* by updating
the salted hashed token in `custom/jupyter_notebook_config.json`.
* `-c|--commit`: specify a commit or `"latest"` for the `docker-stacks`,
the default commit is a working one.
* `-s|--slim`: Generate a slim Dockerfile.
As some installations are not needed by everyone, there is the possibility to skip some
installations to reduce the size of the image.
Here the `docker-stack` `scipy-notebook` is used instead of `datascience-notebook`
that comes with Julia and R.
Moreover, none of the packages within `custom/usefulpackages.Dockerfile` is installed.
* `--python-only|--no-datascience-notebook`: As the name suggests, the `docker-stack` `datascience-notebook`
is not installed
on top of the `scipy-notebook`, but the packages within `custom/usefulpackages.Dockerfile` are.
* `--no-useful-packages`: On top of the `docker-stack` `datascience-notebook` (Julia and R),
the essential `gpulibs` are installed, but not the packages within `custom/usefulpackages.Dockerfile`.
Note that only one of the parameters `--slim`, `--python-only`, and `--no-useful-packages` can be used at the same time:
### Custom Installations
If you need to install custom packages within the container, you can modify the `custom/usefulpackages.Dockerfile` file or do it directly within the container.
**Keep in mind that every time a Dockerfile is generated, the file `.build/Dockerfile` is overwritten, so it's best to append custom installations in `custom/usefulpackages.Dockerfile` or `generate-Dockerfile.sh`.**
Some useful packages are suggested in the [Extension docs](https://jupyterlab.readthedocs.io/en/stable/user/extensions.html) and in this blog article from [neptune.ai](https://neptune.ai/blog/jupyterlab-extensions-for-machine-learning).
If you notice that an important package is missing in the default stack, please let us know so we can update it.
### Authorization
#### Set a Static Token
Jupyter by default regenerates a new token on each new start.
GPU-Jupyter provides the environment variable `JUPYTER_TOKEN` to set a customized static token.
This option is practicable if the host machine is periodically restartet.
It is suggested to use a long token such as a UUID:
```bash
export JUPYTER_TOKEN=$(uuidgen)
echo $JUPYTER_TOKEN
```
- For Docker add the parameter `-e JUPYTER_TOKEN=${JUPYTER_TOKEN}`, e.g.:
```bash
docker run --gpus all -d -it -p 8848:8888 -v $(pwd)/data:/home/jovyan/work -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes -e NB_UID="$(id -u)" -e NB_GID="$(id -g)" -e JUPYTER_TOKEN=${JUPYTER_TOKEN} --user root --restart always --name gpu-jupyter_1 gpu-jupyter
```
- In `docker-compose.yml`, the environment variable can be set under
`ENVIRONMENT`:
`JUPYTER_TOKEN: ${JUPYTER_TOKEN}`
Please note that environment variables might not be accessable for all docker-compose versions. Consider to setting `JUPYTER_TOKEN` in a separate `.env`-file and using `env_file` in docker-compose or a hard-coded token.
The static token can be requested using `docker exec`:
```bash
docker exec -it gpu-jupyter_1 jupyter server list
```
#### Set a custom Password
There are two ways to set a password for GPU-Jupyter:
1. Go to the login page ([http://192.168.48.48:8848/login](http://localhost:8848/login)) when logged out and setup a Password in the corresponding field.
2. Use the `--password` or `--pw` option in the generate-Dockerfile.sh script to specify your desired password, like so:
```bash
bash generate-Dockerfile.sh --password [your_password]
```
This will update automatically the salted hashed token in the `.build/jupyter_notebook_config.json` file. Note that the specified password may be visible in your account's bash history.
### Adaptions for using Tensorboard
Both TensorFlow and PyTorch support [tensorboard](https://www.tensorflow.org/tensorboard/get_started).
This packages is already installed in the GPU-packages and can be used with these settings:
1. Forward the port in the docker command using `-p 6006:6006` (only for usage outside of Juypterlab).
2. Starting tensorboad with port binding within a container or Jupyterlab UI. Make sure the parameter `--bind_all` is set.
```bash
docker exec -it [container-name/ID] bash
root@749eb1a06d60:~# tensorboard --logdir mylogdir --bind_all
```
```jupyter
%tensorboard --logdir logs/[logdir] --bind_all
```
4. Writing the states and results in the tensorboard log-dir, as described in the tutorials for [TensorFlow](https://www.tensorflow.org/tensorboard/get_started) and [PyTorch](https://pytorch.org/tutorials/recipes/recipes/tensorboard_with_pytorch.html) or in the Getting Started section `data/Getting_Started`.
If the port is exposed, tensorboard can be accessed in the browser on [localhost:6006](http://localhost:6006).
### Updates
#### Update CUDA to another version
The GPU-libraries such as PyTorch and Tensorflow in `custom/Docker.gpulibs` must support the CUDA version and NVIDIA drivers on the host machine. Check out the compatibility lists for [PyTorch](https://pytorch.org/get-started/locally/) and [Tensorflow](https://www.tensorflow.org/install/source#gpu) or search online for the explicit versions. In this setup, the NVIDIA Driver has version 530.30.02 and CUDA version 11.6.2 is used, which is compatible with Tensorflow 2.10 and PyTorch 1.12.
The host's CUDA version must be equal to or higher than that used by the container (set within `custom/header.Dockerfile`).
Check the host's version with `nvcc --version` and the version compatibilities
for CUDA-dependent packages as [Pytorch](https://pytorch.org/get-started/locally/)
respectively [Tensorflow](https://www.tensorflow.org/install/gpu) previously.
Then modify, if supported, the CUDA-version (find all tags [here](https://hub.docker.com/r/nvidia/cuda/tags))
in `custom/header.Dockerfile` to, e.g.:
the line:
FROM nvidia/cuda:X.Y-base-ubuntu20.04
Then re-generate, re-build and run the updated image.
Note that a change in the first line of the Dockerfile will re-build the whole image.
```bash
./generate-Dockerfile.sh --slim # generate the Dockerfile with only a python interpreter, --python-only is default
docker build -t gpu-jupyter .build/ # will take a while
docker run --gpus all -d -it -p 8848:8888 -v $(pwd)/data:/home/jovyan/work -e GRANT_SUDO=yes -e JUPYTER_ENABLE_LAB=yes -e NB_UID="$(id -u)" -e NB_GID="$(id -g)" --user root --restart always --name gpu-jupyter_1 gpu-jupyter
```
#### Upgrade / Downgrade the Docker-Stacks
The [docker-stacks](https://github.com/jupyter/docker-stacks) is used as a submodule within `.build/docker-stacks`. Per default, the head of the commit is reset to a commit on which `gpu-jupyter` runs stable.
To set the docker-stacks to a specific version generate the Dockerfile with a specific [docker-stacks commit](https://github.com/jupyter/docker-stacks/commits/main), run:
```bash
./generate-Dockerfile.sh --commit c1c32938438151c7e2a22b5aa338caba2ec01da2
```
To update the generated Dockerfile to the latest commit, run:
```bash
./generate-Dockerfile.sh --commit latest
```
A new build can last some time and may consume a lot of data traffic. Note, that untested versions often result in
a version conflict, as some files have to be adapted. Here are some examples of solutions:
- **Some file is not found:**
```
Step 22/64 : COPY --chown="${NB_UID}:${NB_GID}" initial-condarc "${CONDA_DIR}/.condarc"
COPY failed: file not found in build context or excluded by .dockerignore: stat initial-condarc: file does not exist
```
→ Adapt `generate-Dockerfile.sh` so that it copies `initial-condarc` into the working directory as it does with other files. Renamed files result in a similar issue and solution.
- **The specified package version is not compatible with the drivers.**
```
Step 56/64 : RUN pip install --upgrade pip && pip install --no-cache-dir "tensorflow==2.6.2" && pip install --no-cache-dir keras
---> Running in 7c5701a3d780
Requirement already satisfied: pip in /opt/conda/lib/python3.10/site-packages (22.1.2)
ERROR: Could not find a version that satisfies the requirement tensorflow==2.6.2 (from versions: 2.8.0rc0, 2.8.0rc1, 2.8.0, 2.8.1, 2.8.2, 2.9.0rc0, 2.9.0rc1, 2.9.0rc2, 2.9.0, 2.9.1)
ERROR: No matching distribution found for tensorflow==2.6.2
```
→ Just update the package to a version that is compatible, here tensorflow 2.8.2 was.
- **Branch `master` is not known:**
```
error: pathspec 'master' did not match any file(s) known to git.
```
→ The default branch of [docker-stacks](https://github.com/jupyter/docker-stacks) were
renamed from `master` to `main`. Delete the subdirectory `.build/docker-stacks` and regenerate the the Dockerfile.
General information on submodules can be found in
[this tutorial](https://www.vogella.com/tutorials/GitSubmodules/article.html).
## Deployment in the Docker Swarm
A Jupyter instance often requires data from other services.
If that data source is containerized in Docker and sharing a port for communication shouldn't be allowed, e.g., for security reasons,
then connecting the data source with *GPU-Jupyter* within a Docker Swarm is a great option!
### Set up Docker Swarm and Registry
This step requires a running [Docker Swarm](https://www.youtube.com/watch?v=x843GyFRIIY) on a cluster or at least on this node.
In order to register custom images in a local Docker Swarm cluster,
a registry instance must be deployed in advance.
Note that we are using port 5001, as many services use the default port 5000.
```bash
sudo docker service create --name registry --publish published=5001,target=5000 registry:2
curl 127.0.0.1:5001/v2/
```
This should output `{}`. \
Afterward, check if the registry service is available using `docker service ls`.
### Configure the shared Docker network
Additionally, *GPU-Jupyter* is connected to the data source via the same *docker-network*. Therefore, This network must be set to **attachable** in the source's `docker-compose.yml`:
```yml
services:
data-source-service:
...
networks:
- default
- datastack
...
networks:
datastack:
driver: overlay
attachable: true
```
In this example,
* The docker stack was deployed in Docker swarm with the name **elk** (`docker stack deploy ... elk`),
* The docker network has the name **datastack** within the `docker-compose.yml` file,
* This network is configured to be attachable in the `docker-compose.yml` file
* and the docker network has the name **elk_datastack**, see the following output:
```bash
sudo docker network ls
# ...
# [UID] elk_datastack overlay swarm
# ...
```
The docker network name **elk_datastack** is used in the next step as a parameter.
### Start GPU-Jupyter in Docker Swarm
Finally, *GPU-Jupyter* can be deployed in the Docker Swarm with the shared network, using:
```bash
./generate-Dockerfile.sh
./add-to-swarm.sh -p [port] -n [docker-network] -r [registry-port]
# e.g. ./add-to-swarm.sh -p 8848 -n elk_datastack -r 5001
```
where:
* **-p:** port specifies the port on which the service will be available.
* **-n:** docker-network is the name of the attachable network from the previous step,
e.g., here it is **elk_datastack**.
* **-r:** registry port is the port that is published by the registry service, the default is `5000`.
Now, *GPU-jupyter* will be accessible here on [localhost:8848](http://localhost:8848)
with the default password `gpu-jupyter` and shares the network with the other data source, i.e.,
all ports of the data source will be accessible within *GPU-Jupyter*,
even if they aren't routed it the source's `docker-compose` file.
Check if everything works well using:
```bash
sudo docker service ps gpu_gpu-jupyter
docker service ps gpu_gpu-jupyter
```
To remove the service from the swarm, use:
```bash
./remove-from-swarm.sh
```
## Issues and Contributing
### Frequent Issues:
- **Can't execute the bash scripts.**
```bash
$ bash generate-Dockerfile.sh
generate-Dockerfile.sh: line 2: cd: $'/path/to/gpu-jupyter\r': No such file or directory
generate-Dockerfile.sh: line 3: $'\r': command not found
generate-Dockerfile.sh: line 9: $'\r': command not found
generate-Dockerfile.sh: line 11: syntax error near unexpected token `$'in\r''
generate-Dockerfile.sh: line 11: `while [[ "$#" -gt 0 ]]; do case $1 in
```
The reason for this issue is that the line breaks between Unix and Windows-based systems are different and in the current version different.
**Solution**: Change the line endings of the bash scripts. This can be done e.g. in the *Notepad++* under
*Edit* - *EOL Conversion*, or using `dos2unix`
```bash
sudo apt install dos2unix
dos2unix generate-Dockerfile.sh
```
- **No GPU available - error**
The docker-compose start breaks with:
```bash
ERROR: for fc8d8dfbebe9_gpu-jupyter_gpu-jupyter_1 Cannot start service gpu-jupyter: OCI runtime create failed: container_linux.go:370: starting container process caused: process_linux.go:459: container init ca
used: Running hook #0:: error running hook: exit status 1, stdout: , stderr: nvidia-container-cli: initialization error: driver error: failed to process request: unknown
```
**Solution**:
Check if the GPU is available on the host node via `nvidia-smi` and run with the described `docker`-commands.
If the error still occurs, so try there could be an issue that docker can't use the GPU. Please try [this](https://cschranz.medium.com/set-up-your-own-gpu-based-jupyterlab-e0d45fcacf43) or similar tutorials on how to install the required drivers.
### Contribution
This project has the intention to create a robust image for CUDA-based GPU applications,
which is built on top of the [docker-stacks](https://github.com/jupyter/docker-stacks).
Please help us to improve this project, by:
* [filing a new issue](https://github.com/iot-salzburg/gpu-jupyter/issues/new)
* [open a pull request](https://help.github.com/articles/using-pull-requests/)
## Support
For commercial support, please contact [b-data](https://github.com/b-data) by
email: .
b-data maintains its own CUDA-enabled
[JupyterLab](https://github.com/b-data/jupyterlab-r-docker-stack)
[docker](https://github.com/b-data/jupyterlab-python-docker-stack)
[stacks](https://github.com/b-data/jupyterlab-julia-docker-stack) and tailors
them to your needs, e.g.
- Integration of self-signed CA certificates to enable communication with web
services on the intranet.
- Setting up the necessary environment variables so that everything works
behind a corporate proxy server.
Additionally, the
[JupyterHub](https://github.com/b-data/docker-deployment-jupyter) setup can be
customised to allow
- authentication with AD/LDAP
- mounting CIFS/SMB file shares
and much more.