Connect EKM and RunPod to Build Intelligent Automations
Choose a Trigger
When this happens...
New Customer
New Order
New Product
Choose an Action
Automatically do this!
Create a Container Registry
Create a Template
Find a Container Registry Auth
Delete a Container Registry Auth
Delete a Template
Create Registry Authentication
Ready to use EKM and RunPod automations
Actions and Triggers
When this happensTriggers
A trigger is an event that starts a workflow.
New Customer
Runs when a new customer is created.
New Order
Trigger when new order is created in your account.
New Product
Trigger when new product is created in your account.
Do thisActions
Action is the task that follows automatically within your EKM integrations.
Create Customer
Add a new customer to the EKM account.
Create Product
Add a new Product to the EKM account.
Update Product
Update the details of an existing product.
Create a Container Registry
Create a new container registry auth
Create a Template
Create a new template.
Find a Container Registry Auth
Get a single container registry auth
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Frequently Asked Questions
To start, connect both your EKM and RunPod accounts to viaSocket. Once connected, you can set up a workflow where an event in EKM triggers actions in RunPod (or vice versa).
Absolutely. You can customize how EKM data is recorded in RunPod. This includes choosing which data fields go into which fields of RunPod, setting up custom formats, and filtering out unwanted information.
The data sync between EKM and RunPod typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between EKM and RunPod. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About RunPod
RunPod is a platform that provides cloud-based computing resources for running and managing machine learning models, data processing tasks, and other computational workloads. It offers scalable and efficient solutions for developers and data scientists to deploy and execute their projects seamlessly.
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