An example of how to run Facebook Prophet in Metaflow.
Minimum daily temperatures dataset is used. This dataset describes the minimum daily temperatures over 10 years (1981-1990) in the city Melbourne, Australia. The units are in degrees Celsius and there are 3650 observations. The source of the data is credited as the Australian Bureau of Meteorology.
ProphetFlow is a Metaflow flow using Facebook Prophet to predict future values of a timeseries. A Prophet model is trained to predict next year minimum daily temperatures. Hyperparameters tuning is performed with cross-validation.
Step start
Init configuration
=> load
Step load
Raw data is loaded and prepared
=> hyper_tuning
Step hyper_tuning
Hyperparameters tuning
=> cross_validation
Step cross_validation
Perform cross-validation on given hyperparameters
=> train
Step train
Check cross-validation results and find best parameters.
A new Prophet model is fitted.
=> end
Step end
Last step, process is finished
If you need, install required Python packages (Metaflow & FBProphet) as usual: pip install -r requirements.txt
Simply run training flow with: python src/ProphetFlow.py run
In predict.ipynb notebook it's explained how to use Prophet trained model to predict future values, accessing Metaflow runs artifacts.
A simple flow without hyperparameters tuning: python src/ProphetSimpleFlow.py run
To configure Metaflow to use AWS Cloud, you first need to create stack:
aws s3 cp aws/metaflow-cfn-template.yml s3://<your_bucket>
aws cloudformation create-stack --stack-name metaflow \
--template-url <url_of_template_in_your_bucket> \
--capabilities CAPABILITY_IAM
When Cloudformation stack is created, you must configure Metaflow.
metaflow configure aws
To run all steps in flow in AWS Batch:
python src/ProphetFlow.py run --with batch:image=vnardone/prophet-metaflow
An example of how to run a single step in AWS Batch (using @batch decorator):
python src/ProphetAWSStepFlow.py run