Better analysis in all of its forms
This project serves as a repository of Temporal workers and workflows written in Python (useful in machine learning) for use with Datashare. Install with
make install
To create new workers, you can follow asr_worker with the file/dir structure
activities.py --> Workflow activities
constants.py --> Worker/workflow constants
models.py --> Workflow and activity inputs/outputs and other data classes
worker.py --> Worker definition
workflow.py --> Workflow definition
Use docker-compose to run the dev server on localhost, which will start elasticsearch
(port 9200), postgres (5432), and redis (6379) services, as well as the Temporal
server and ui (7233 and 8233), and datashare (8080). Note that container build and
startup times can be long if workers and workflows rely on large models, so allocate memory
to Docker accordingly.