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DASH ENTERPRISE
Job Queue
Queueing is key to building scalable ML and AI apps.
About Job Queue
Background jobs can dramatically improve the scalability of a Dash app by enabling it to offload slow or CPU-intensive tasks from its callback loops. This helps ensure that the Dash front end can handle incoming web requests promptly, reducing the likelihood of performance issues that occur when requests become backlogged.
The Dash Enterprise Job Queue makes all of this seamless and scalable in Python. Combine Job Queue with Snapshot Engine to email a PDF or Dash app link when the job is done.
You’ll need a Job Queue.
Many Dash apps need to run scheduled or long-running jobs. Here are a few examples of Python tasks that easily tie into the Dash Enterprise Job Queue:
- Polling a remote API every 5 minutes
- Sending an email report every night at midnight
- Retraining a long-running ML model based on user input
The cron tool is commonly used for this use case, but it is ill-suited for data scientists and horizontally scalable systems like Dash Enterprise. A more powerful, flexible solution is a job scheduler like the Dash Enterprise Job Queue.
80% of Dash Enterprise customers use Job Queues when they bring their Dash applications to production.
You’ll need a Job Queue.
Many Dash apps need to run scheduled or long-running jobs. Here are a few examples of Python tasks that easily tie into the Dash Enterprise Job Queue:
- Polling a remote API every 5 minutes
- Sending an email report every night at midnight
- Retraining a long-running ML model based on user input
The cron tool is commonly used for this use case, but it is ill-suited for data scientists and horizontally scalable systems like Dash Enterprise. A more powerful, flexible solution is a job scheduler like the Dash Enterprise Job Queue.
80% of Dash Enterprise customers use Job Queues when they bring their Dash applications to production.
Running ML & AI models.
Running ML & AI models in Python from a Dash app UI can take a long time if not run on HPC hardware. This is a perfect use case for the Dash Enterprise Job Queue.
This example Dash app runs an ML model as a background task on the Dash Enterprise Job Queue. When the job has finished running, it automatically generates and archives an interactive report with Snapshot Engine. The report displays and archives metrics and graphs from the model run such as ROC curves, PR curves, and confusion matrices.
Data Cache.
Behind the scenes, the Dash Enterprise Job Queue interfaces with as an onboard Postgres database and Redis cache to ensure production-grade performance.
Intuitive API.
The Dash Enterprise Job Queue and Snapshot Engine feature an API that is intuitive and accessible to Python beginners. Dash Enterprise also ships with dozens of canned Job Queue and Snapshot Engine examples that are designed for easy copy/paste into your Dash apps.
See Dash in action.
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