119 lines
3.2 KiB
Markdown
119 lines
3.2 KiB
Markdown
# docker-jupyterhub
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## Getting started
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[main reference](https://github.com/jupyterhub/jupyterhub-deploy-docker)
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构建自己的docker镜像
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修改自己想要的python环境在文件`Dockerfile.jupyterhub`
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基础镜像是：`jupyter/scipy-notebook` 里面含有conda
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```shell
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docker buildx build -t hotwa/jupyterhub:latest . -f Dockerfile.jupyterhub --load
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```
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修改docker-compose.yml文件环境变量：DOCKER_NOTEBOOK_IMAGE
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```shell
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DOCKER_NOTEBOOK_IMAGE: hotwa/jupyterhub:latest
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```
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也可以通过 docker-compose 构建镜像
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```shell
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docker compose build
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```
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启动docker-compose.yml
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```shell
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docker compose up -d
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```
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停止
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```shell
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docker compose down
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```
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[基本镜像](https://github.com/jupyter/docker-stacks/tree/main/images/base-notebook)
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https://github.com/jupyter/docker-stacks/tree/main/images
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[jupyter-gpu](https://github.com/iot-salzburg/gpu-jupyter)
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构建自己的基础镜像：
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```shell
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git clone https://github.com/jupyter/docker-stacks
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cd docker-stacks/images/base-notebook
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# 修改这个目录下面的 Dockerfile文件, 在后面加就行，里面已经有了mamba、conda
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docker buildx build -t hotwa/notebook:latest . -f Dockerfile
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```
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## Alternatives
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- [rocker/binder](https://rocker-project.org/images/versioned/binder.html) -
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From the R focused [rocker-project](https://rocker-project.org),
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lets you run both RStudio and Jupyter either standalone or in a JupyterHub
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- [jupyter/repo2docker](https://github.com/jupyterhub/repo2docker) -
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Turn git repositories into Jupyter-enabled Docker Images
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- [openshift/source-to-image](https://github.com/openshift/source-to-image) -
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A tool for building artifacts from source code and injecting them into docker images
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- [jupyter-on-openshift/jupyter-notebooks](https://github.com/jupyter-on-openshift/jupyter-notebooks) -
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OpenShift compatible S2I builder for basic notebook images
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## nvidia-container-toolkit add
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参考了 llama [容器部署](https://hub.docker.com/r/ollama/ollama)
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```shell
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curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
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| sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
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curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
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| sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' \
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| sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
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sudo apt-get update
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sudo apt-get install -y nvidia-container-toolkit
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```
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Configure Docker to use Nvidia driver
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```shell
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sudo nvidia-ctk runtime configure --runtime=docker
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sudo systemctl restart docker
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```
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配置完成之后：
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```shell
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docker-compose up -d
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```
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在jupyterhub_config.py中添加：这样jupyterhub的容器启动镜像就可以使用宿主机的显卡了
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```python
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# GPU 和网络配置
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c.DockerSpawner.extra_host_config = {
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#'network_mode': 'host',
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'runtime': 'nvidia'
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}
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c.DockerSpawner.environment = {
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'NVIDIA_DRIVER_CAPABILITIES': 'compute,utility',
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'NVIDIA_VISIBLE_DEVICES': 'all'
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}
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```
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## 清除卷
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```shell
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docker volume ls
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docker-compose down
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docker rm <container_name_or_id>
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docker volume rm basic-example_jupyterhub-data
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docker volume rm docker-jupyterhub_jupyterhub-data
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docker volume rm jupyterhub-user-admin
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``` |