Use Chronos in Container (docker)#

This page helps user to build and use a docker image where Chronos-nightly build version is deployed.

Download image from Docker Hub#

We provide docker image with Chronos-nightly build version deployed in Docker Hub. You can directly download it by running command:

docker pull intelanalytics/bigdl-chronos:latest

Build an image (Optional)#

If you have downloaded docker image, you can just skip this part and go on Use Chronos.

First clone the repo BigDL to the local.

git clone https://github.com/intel-analytics/BigDL.git

Then cd to the root directory of BigDL, and copy the Dockerfile to it.

cd BigDL
cp docker/chronos-nightly/Dockerfile ./Dockerfile

When building image, you can specify some build args to install chronos with necessary dependencies according to your own needs. The build args are similar to the install options in Chronos documentation.

model: which model or framework you want. 
       value: pytorch
              tensorflow
              prophet
              arima
              ml (default, for machine learning models).

auto_tuning: whether to enable auto tuning.
             value: y (for yes)
                    n (default, for no).

hardware: run chronos on a single machine or a cluster.
          value: single (default)
                 cluster

inference: whether to install dependencies for inference optimization (e.g. onnx, openvino, ...).
           value: y (for yes)
                  n (default, for no)

extra_dep: whether to install some extra dependencies.
           value: y (for yes)
                  n (default, for no)
           if specified to y, the following dependencies will be installed:
           tsfresh, pyarrow, prometheus_pandas, xgboost, jupyter, matplotlib

If you want to build image with the default options, you can simply use the following command:

sudo docker build -t intelanalytics/bigdl-chronos:latest . # You may choose any NAME:TAG you want.

You can also build with other options by specifying the build args:

sudo docker build \
    --build-arg model=pytorch \
    --build-arg auto_tuning=y \
    --build-arg hardware=single \
    --build-arg inference=n \
    --build-arg extra_dep=n \
     -t intelanalytics/bigdl-chronos:latest . # You may choose any NAME:TAG you want.

(Optional) If you need a proxy, you can add two additional build args to specify it:

# typically, you need a proxy for building since there will be some downloading.
sudo docker build \
    --build-arg http_proxy=http://<your_proxy_ip>:<your_proxy_port> \ #optional
    --build-arg https_proxy=http://<your_proxy_ip>:<your_proxy_port> \ #optional
    -t intelanalytics/bigdl-chronos:latest . # You may choose any NAME:TAG you want.

According to your network status, this building will cost 15-30 mins.

Tips: When errors happen like failed: Connection timed out., it’s usually related to the bad network status. Please build with a proxy.

Run the image#

sudo docker run -it --rm --net=host intelanalytics/bigdl-chronos:latest bash

Use Chronos#

A conda environment is created for you automatically. bigdl-chronos and the necessary depenencies (based on the build args when you build image) are installed inside this environment.

(chronos) root@icx-5:/opt/work#

Important

Considering the image size, we build docker image with the default args and upload it to Docker Hub. If you use it directly, only bigdl-chronos is installed inside this environment. There are two methods to install other necessary dependencies according to your own needs:

Make sure network is available and run install command following Install using Conda , such as pip install --pre --upgrade bigdl-chronos[pytorch].
Make sure network is available and bash /opt/install-python-env.sh with build args. The values are introduced in Build an image.

# bash /opt/install-python-env.sh ${model} ${auto_tuning} ${hardware} ${inference} ${extra_dep}
# For example, if you want to install bigdl-chronos[pytorch,inference]
bash /opt/install-python-env.sh pytorch n single y n

Run unittest examples on Jupyter Notebook for a quick use#

Note: To use jupyter notebook, you need to specify the build arg extra_dep to y.

You can run these on Jupyter Notebook on single node server if you pursue a quick use on Chronos.

(chronos) root@icx-5:/opt/work# cd /opt/work/colab-notebook #Unittest examples are here.

(chronos) root@icx-5:/opt/work/colab-notebook# jupyter notebook --notebook-dir=./ --ip=* --allow-root #Start the Jupyter Notebook services.

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

Get the IP address of the container
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 opened.
Open one of these .ipynb files, run through the example and learn how to use Chronos to predict time series.

Shut down docker container#

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

First, use ctrl+p+q to quit the container when you are still in it.

Then, you can list all the active Docker containers by command line:

sudo docker ps

You will see your docker containers:

CONTAINER ID   IMAGE                                 COMMAND   CREATED       STATUS       PORTS     NAMES
ef133bd732d1   intelanalytics/bigdl-chronos:latest   "bash"    2 hours ago   Up 2 hours             happy_babbage

Shut down the corresponding docker container by its ID:
```
sudo docker rm -f ef133bd732d1
```