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https://github.com/guumaster/playground-docker-gpu-jupyterlab
Docker image with gpu support and jupyterlab installed
https://github.com/guumaster/playground-docker-gpu-jupyterlab
Last synced: 15 days ago
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Docker image with gpu support and jupyterlab installed
- Host: GitHub
- URL: https://github.com/guumaster/playground-docker-gpu-jupyterlab
- Owner: guumaster
- Created: 2018-11-18T20:57:06.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2018-11-19T12:02:14.000Z (about 6 years ago)
- Last Synced: 2024-11-12T05:42:28.946Z (2 months ago)
- Language: Jupyter Notebook
- Size: 83 KB
- Stars: 1
- Watchers: 3
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# JupyterLab with Tensorflow
## Pre-requisites
- Install `nvidia-dirvers` for your graphic card and make sure that `nvidia-smi` outputs information about the installed driver.
## Installation
### nvidia-docker
Add `nvidia-docker` repo and install `nvidia-docker2` plugin.
```
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
echo $distribution
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
apt-get update
apt-get install -y nvidia-docker2
service docker restart
```
Then add `nvidia` as `default-runtime` in the file `/etc/docker/daemon.json`. Edit like this:
```
{
"default-runtime": "nvidia",
"runtimes": ...
}
```
### Test GPU support
You can check if your local configuration is correct and your containers would have GPU support with this commmands:
```
docker run --rm nvidia/cuda:9.0-base nvidia-smi
# Output something like this:
# +-----------------------------------------------------------------------------+
# | NVIDIA-SMI 415.13 Driver Version: 415.13 CUDA Version: 10.0 |
# |-------------------------------+----------------------+----------------------+
# | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
# | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
# |===============================+======================+======================|
# | 0 GeForce GTX 105... Off | 00000000:01:00.0 Off | N/A |
# | N/A 40C P8 N/A / N/A | 3743MiB / 4042MiB | 0% Default |
# +-------------------------------+----------------------+----------------------+
#
# +-----------------------------------------------------------------------------+
# | Processes: GPU Memory |
# | GPU PID Type Process name Usage |
# |=============================================================================|
# +-----------------------------------------------------------------------------+
```
## Build image
### Setup .env
First copy `.env-example` to `.env` and fill the info with the data obtained from `id`
```
$ id
# uid=1000(mr_robot) gid=1002(mr_robot) groups=1002(mr_robot)
# edit .env like this:
NB_USER=mr_robot
NB_UID=1000
NB_GID=1000
```
This is necessary so the notebooks would be mounted with write permissions for your host user
### build with docker
```
docker build -t jupyter-lab .
```
*NOTE:* This is the moment to grab that coffee. It'd take long to build.
## Test JupyterLab GPU support
To test if everything is ok and there is GPU support run this command:
```
docker run --rm jupyter-lab nvidia-smi
# Output similar to the above example
```
## Usage
Once you have your image built. Add a persistence volume to save your notebooks and start the service.
### Run docker-compose
```
docker-compose up
```
## Adding notebooks
You can add notebooks as volumes shared to the container. Edit `docker-compose.yml`. You can use $NB_USER in the path:
```
volumes:
- ./my_notebooks:/home/$NB_USER/my_notebooks
- ./datasets:/home/$NB_USER/datasets
```