gpu-jupyter/README.md

# GPU-Jupyter
#### Leverage Jupyter Notebooks with the power of your NVIDIA GPU and perform GPU calculations using Tensorflow and Pytorch in collaborative notebooks. 

![Jupyterlab Overview](https://raw.githubusercontent.com/iot-salzburg/gpu-jupyter/master/extra/jupyterlab-overview.png)

First of all, thanks to [docker-stacks](https://github.com/jupyter/docker-stacks) 
for creating and maintaining a robost  Python, R and Julia toolstack for Data Analytics/Science 
applications. This project uses the NVIDIA CUDA image as the base image and installs their 
toolstack on top of it to enable GPU calculations in the Jupyter notebooks. 
The image of this repository is available on [Dockerhub](https://hub.docker.com/r/cschranz/gpu-jupyter).

## Contents

1. [Quickstart](#quickstart)
2. [Build your own image](#build-your-own-image)
3. [Tracing](#tracing)
4. [Configuration](#configuration)
5. [Deployment](#deployment-in-the-docker-swarm)
6. [Issues and Contributing](#issues-and-contributing)


## Quickstart

1.  A computer with an NVIDIA GPU is required.
2.  Install [Docker](https://www.docker.com/community-edition#/download) version **1.10.0+**
 and [Docker Compose](https://docs.docker.com/compose/install/) version **1.6.0+**.
3.  Get access to your GPU via CUDA drivers within Docker containers.
    You can be sure that you can access your GPU within Docker, 
    if the command `docker run --gpus all nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 nvidia-smi`
    returns a result similar to this one:
    ```bash
    Tue Jan  5 09:38:21 2021       
    +-----------------------------------------------------------------------------+
    | NVIDIA-SMI 450.80.02    Driver Version: 450.80.02    CUDA Version: 10.1     |
    |-------------------------------+----------------------+----------------------+
    | 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  GeForce RTX 207...  Off  | 00000000:01:00.0  On |                  N/A |
    |  0%   40C    P8     7W / 215W |    360MiB /  7974MiB |      1%      Default |
    |                               |                      |                  N/A |
    +-------------------------------+----------------------+----------------------+
                                                                                   
    +-----------------------------------------------------------------------------+
    | Processes:                                                                  |
    |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
    |        ID   ID                                                   Usage      |
    |=============================================================================|
    +-----------------------------------------------------------------------------+
    ``` 
    If you don't get an output similar than this one, follow the installation steps in this 
[medium article](https://medium.com/@christoph.schranz/set-up-your-own-gpu-based-jupyterlab-e0d45fcacf43).
    The CUDA toolkit is not required on the host system, as it will be 
    installed within the Docker containers using [NVIDIA-docker](https://github.com/NVIDIA/nvidia-docker).
    It is also important to keep your installed CUDA version in mind, when you pull images. 
    **You can't run images based on `nvidia/cuda:11.1` if you have only CUDA version 10.1 installed.**
    Check your host's CUDA-version with `nvcc --version` and update to at least 
    the same version you want to pull.
    
4. Pull and run the image. This can last some hours, as a whole data-science 
    environment will be downloaded:
   ```bash
   cd your-working-directory 
   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:v1.2_cuda-10.1_ubuntu-18.04_python-only
   ```
   This starts an instance with of *GPU-Jupyter* the tag `v1.2_cuda-10.1_ubuntu-18.04_python-only` at [http://localhost:8848](http://localhost:8848) (port `8484`).
   The default password is `asdf` which should be changed as described [below](#set-password). 
   Furthermore, data within the host's `data` directory is shared with the container.
   Other versions of GPU-Jupyter are available and listed on Dockerhub under  [Tags](https://hub.docker.com/r/cschranz/gpu-jupyter/tags?page=1&ordering=last_updated).

   
Within the Jupyterlab instance, you can check if you can access your GPU by opening a new terminal window and running
`nvidia-smi`. In terminal windows, you can also install new packages for your own projects. 
Some example code can be found in the repository under `extra/Getting_Started`.
If you want to learn more about Jupyterlab, check out this [tutorial](https://www.youtube.com/watch?v=7wfPqAyYADY). 


## Build your own Image

First, it is necessary to generate the `Dockerfile` in `.build`, that is based on 
the NIVIDA base image and the [docker-stacks](https://github.com/jupyter/docker-stacks).
As soon as you have access to your GPU within Docker containers 
(make sure the command `docker run --gpus all nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04 nvidia-smi` 
shows your GPU statistics), you can generate the Dockerfile, build and run it.
The following commands will start *GPU-Jupyter* on [localhost:8848](http://localhost:8848) 
with the default password `asdf`.

  ```bash
  git clone https://github.com/iot-salzburg/gpu-jupyter.git
  cd gpu-jupyter
  # generate a Dockerfile with python and without Julia and R
  ./generate-Dockerfile.sh --no-datascience-notebook  
  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 starts a container WITH GPU support, a shared local data volume `data`  
and some other configurations like root permissions which are necessary to install packages within the container.
For more configurations, scroll down to [Configuration of the Dockerfile-Generation](#configuration-of-the-dockerfile-generation).

### Start via Docker Compose

The script `start-local.sh` is a wrapper for a quick configuration of the 
underlying `docker-compose.yml`:

  ```bash
  ./start-local.sh -p 8848  # the default port is 8888
  ```


## Tracing
  
With these commands we can see if everything worked well:
```bash
docker ps
docker logs [service-name]  # or
bash show-local.sh  # a env-var safe wrapper for 'docker-compose logs -f'
```

In order to stop the local deployment, run:

  ```bash
docker ps
docker rm -f [service-name]  # or
  ./stop-local.sh
  ```
 

## Configuration

### Configuration of the Dockerfile-Generation

The script `generate-Dockerfile.sh` generates a Dockerfile within the `.build/`
directory. 
This implies that this Dockerfile is overwritten by each generation.
The Dockerfile-generation script `generate-Dockerfile.sh`
has the following parameters (note that 2, 3 and 4 are exclusive): 

* `-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 `src/Dockerfile.usefulpackages` is installed.

* `--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 `src/Dockerfile.usefulpackages` 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 `src/Dockerfile.usefulpackages`.


### Custom Installations

Custom packages can be installed within a container, or by modifying the file
`src/Dockerfile.usefulpackages`.
**As `.build/Dockerfile` is overwritten each time a Dockerfile is generated, 
it is suggested to append custom installations either
within `src/Dockerfile.usefulpackages` or in `generate-Dockerfile.sh`.**
If an essential package is missing in the default stack, please let us know!



### Set Password

Please set a new password using `src/jupyter_notebook_config.json`.
Therefore, hash your password in the form (password)(salt) using a sha1 hash generator, e.g., the sha1 generator of [sha1-online.com](http://www.sha1-online.com/). 
The input with the default password `asdf` is appended by a arbitrary salt `e49e73b0eb0e` to `asdfe49e73b0eb0e` and should yield the hash string as shown in the config below.
**Never give away your own unhashed password!**

Then update the config file as shown below and restart the service.

```json
{
  "NotebookApp": {
    "password": "sha1:e49e73b0eb0e:32edae7a5fd119045e699a0bd04f90819ca90cd6"
  }
}
```


### Updates
 
#### Update CUDA to another version

The host's CUDA-version must be equal or higher than that of the
container itself (in `Dockerfile.header`). 
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 in `Dockerfile.header` to, e.g.:
the line:

    FROM nvidia/cuda:11.1-base-ubuntu20.04
    
and in the `Dockerfile.pytorch` the line:

    cudatoolkit=11.1

Then re-generate, re-build and run the updated image, as closer described above:
Note that a change in the first line of the Dockerfile will re-build the whole image.

```bash
./generate-Dockerfile.sh
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 --user root --restart always --name gpu-jupyter_1 gpu-jupyter 
```


#### Update Docker-Stack

The [docker-stacks](https://github.com/jupyter/docker-stacks) are 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 update the generated Dockerfile to a specific commit, 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 the latest version may result in
a version conflict!
More info to 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 the we are using the 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 `{}`. \

Afterwards, 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, default is `5000`.

Now, *gpu-jupyter* will be accessible here on [localhost:8848](http://localhost:8848) 
with the default password `asdf` 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
```

In order to remove the service from the swarm, use:
```bash
./remove-from-swarm.sh
```


## Issues and Contributing

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). 
You are free to help 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/)