Docker User Guide

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1. Pull Docker Image

You may pull a Docker image from the Docker Hub.

To pull the nightly build version, use

sudo docker pull intelanalytics/bigdl:2.1.0-SNAPSHOT

To pull other versions, please refer to BigDL Docker Hub Tags, select a tag and use

sudo docker pull intelanalytics/bigdl:tag_name

Configuring resources

For Docker Desktop users, the default resources (2 CPUs and 2GB memory) are relatively small, and you may want to change them to larger values (8GB memory and 4 CPUs should be a good estimate for most examples, and the exact memory requirements vary for different applications). For more information, view the Docker documentation for MacOS and Windows.

Speed up pulling image by adding mirrors

To speed up pulling the image from DockerHub, you may add the registry-mirrors key and value by editing daemon.json (located in /etc/docker/ folder on Linux):

{
  "registry-mirrors": ["https://<my-docker-mirror-host>"]
}

For instance, users in China may add the USTC mirror as follows:

{
  "registry-mirrors": ["https://docker.mirrors.ustc.edu.cn"]
}

After that, flush changes and restart docker：

sudo systemctl daemon-reload
sudo systemctl restart docker

2. Launch Docker Container

After pulling the BigDL Docker image, you can launch an BigDL Docker container:

sudo docker run -it --rm --net=host \
    -e http_proxy=http://your-proxy-host:your-proxy-port \
    -e https_proxy=https://your-proxy-host:your-proxy-port \
    intelanalytics/bigdl:2.1.0-SNAPSHOT bash

• The value 12345 is a user specified port number.

• The value “your-token” is a user specified string.

• If you need to use http/https proxy, please use -e http_proxy/https_proxy

Once the container is successfully launched, you will automatically login into the container and see this as the output:

root@[hostname]:/opt/work#

The /opt/work directory contains:

• start-notebook.sh is used for starting the jupyter notebook. You can specify the environment settings and spark settings to start a specified jupyter notebook.

• bigdl-${BigDL_VERSION} is the BigDL home of BigDL distribution.

• spark-${SPARK_VERSION} is the Spark home.

• BigDL is cloned from https://github.com/intel-analytics/BigDL.git, contains apps, examples using BigDL.

• opt/download-bigdl.sh is used for downloading BigDL distributions.

3. Run Jupyter Notebook Examples in the Container

After a Docker container is launched and user login into the container, you can start the Jupyter Notebook service inside the container.

3.1 Start the Jupyter Notebook services

In the /opt/work directory, run this command line to start the Jupyter Notebook service:

./start-notebook.sh

You will see the output message like below. This means the Jupyter Notebook service has started successfully within the container.

[I 07:40:39.354 NotebookApp] Serving notebooks from local directory: /opt/work/bigdl-2.1.0-SNAPSHOT/apps
[I 07:40:39.355 NotebookApp] Jupyter Notebook 6.4.6 is running at:
[I 07:40:39.355 NotebookApp] http://(the-host-name):12345/?token=...
[I 07:40:39.355 NotebookApp]  or http://127.0.0.1:12345/?token=...
[I 07:40:39.355 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).

3.2 Connect to Jupyter Notebook service from a browser

After the Jupyter Notebook service is successfully started, you can connect to the Jupyter Notebook service from a browser.

1. Get the IP address of the container

2. Launch a browser, and connect to the Jupyter Notebook service with the URL: https://container-ip-address:port-number/?token=your-token As a result, you will see the Jupyter Notebook like this:

3.3 Run BigDL Jupyter Notebooks

After connecting to the Jupyter Notebook in the browser, you can run multiple BigDL Jupyter Notebook examples. The example shown below is the “dogs-vs-cats”.

• Click into the “dogs-vs-cats” folder:

• Open the notebook file:

• Start to run the “dogs-vs-cats” notebook:

• Run through the example and check the prediction:

4. Shut Down Docker Container

You should shut down the BigDL Docker container after using it.

1. You can list all the active Docker containers by command line:

sudo docker ps

1. You will see your docker containers:

CONTAINER ID        IMAGE                                        COMMAND                  CREATED             STATUS              PORTS               NAMES
40de2cdad025        intelanalytics/bigdl:2.1.0-SNAPSHOT         "/opt/work/start-n..."   3 hours ago         Up 3 hours                              upbeat_al

1. Shut down the corresponding docker container by its ID:

$sudo docker rm -f 40de2cdad025