Asynchronous Execution (Preview)

Many applications require asynchronous execution of functions in a “fire-and-forget” fashion in order to work optimally. In this scenario, you hand off execution of a function to be Cerebrium’s responsibility, while you as the developer are responsible for ensuring that data is able leave the function.

You can enable your function to execute asynchronously by adding the async query parameter to your request , and setting it to true. This would look something like this:

curl -X POST https://api.cortex.cerebrium.ai/v4/<YOUR-PROJECT-ID>/<YOUR-APP>/run?async=true'\
       -H 'Content-Type: application/json'\
       -H 'Authorization: Bearer <YOUR-JWT-TOKEN>\
       --data '{"param": "hello world"}'

This would immediately return a response akin to the following, with a body that only specifies a run_id:

HTTP/1.1 202 Accepted
Connection: close
Content-Length: 50
Content-Type: application/json
Date: Tue, 12 Nov 2024 03:13:25 GMT
Server: envoy
Vary: Origin
X-Envoy-Upstream-Service-Time: 2
X-Request-Id: 21eb3b98-4b10-9ad6-8681-a47172828024

{"run_id":"21eb3b98-4b10-9ad6-8681-a47172828024"}

In the background, Cerebrium will now run this HTTP request for you. Your function is still expected to behave in a synchronous manner. That is, it must open a connection, keep it open while it is doing work, then return a response once it is done. This effectively allows you to treat a synchronous request-responses as asynchronous, and you can do anything within the scope of your function call with a 12 hour timeout. This does not mean your actual Python function cannot be async. Rather, this means that the lifetime of the function is still tied to lifetime of the request made to your app. If you terminate your function early and return a response while the application is still doing work, the Cerebrium system will begin to terminate the application. Therefore, you should only return a response once the container has finished processing the task, and not earlier.

In order to use async execution effectively, you must ensure that your function exports any relevant data you need, since you will no longer be able to receive a request. An easy way to achieve this would be to combine async execution with a specified webhookEndpoint, to have Cerebrium automatically forward the body of the function response once it has returned:

curl -X POST <https://api.cortex.cerebrium.ai/v4/><YOUR-PROJECT-ID>/<YOUR-APP>/run?async=true&webhookEndpoint=https%3A%2F%2Fwebhook.site%2F'\
 -H 'Content-Type: application/json'\
 -H 'Authorization: Bearer <YOUR-JWT-TOKEN>\
 --data '{"param": "hello world"}'

As with webhooks, this a feature of our proxy, and you will not need to modify any part of your code in order to use the webhook functionality. In your dashboard, your function will be marked as async, but will still show the status of the internal synchronous call that was made (in other words, if the call to your function failed, the state of your async request would be failure etc.)