Motivation Currently it is hard to implement backend plugins, especially for data-scientists & MLE’s who do not have working knowledge of Golang. Also, performance requirements, maintenance and development is cumbersome. The document here proposes a path to make it possible to write plugins rapidly, while decoupling them from the core flytepropeller engine. Goals Plugins should be easy to author - no need of code generation, using tools that MLEs and Data Scientists are not accustomed to using. Most important plugins for Flyte today are plugins that communicate with external services. It should be possible to test these plugins independently and also deploy them privately. It should be possible for users to use backend plugins for local development, especially in flytekit and unionML
12/28/2023PR: https://github.com/flyteorg/flytekit/pull/1782
9/8/2023Issues Discussion Motivation: Why do you think this is important? Currently flyteadmin notifications are delivered using the PagerDuty, Github and Slack email APIs. On AWS deployments FlyteAdmin uses SES to trigger emails, for all others the only alternative email implementation is SendGrid integration. Setting up SES or SendGrid can be somewhat complicated. Furthermore, asking your Flyte users to configure the aforementioned services with email integrations adds even more overhead. It would be simpler as an alternative to provide webhook integration for notification so that users only have to configure existing API keys for PagerDuty/Github/Slack. Flyte currently only allows sending notifications by email and requires users to explicitly define notification rules in their launchplans. FlyteAdmin Webhook
6/5/2023Save Model to submarine model registry by adding submarine.save_model(model, "tensorflow") in your script import tensorflow_datasets as tfds import tensorflow as tf from tensorflow.keras import layers, models import submarine def make_datasets_unbatched(): BUFFER_SIZE = 10000
5/19/2022or
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