@Marvin what pattern should I use for jobs that take a long time to spin up but then generate results fast? E.g. predictions with large machine learning models. I'd like to be able to keep the model loaded in GPU memory waiting for new prediction requests.

To address the issue of long spin-up times for machine learning models and keeping them loaded in memory for fast predictions, you'll want to explore solutions like warm starts, task caching, and long-lived task execution environments within the context of Prefect. I'll search the Prefect documentation for guidance on these topics. Let's start by researching these topics specifically: - "warm start for tasks" - "cache tasks in prefect" - "long lived tasks execution" - "reuse execution environment" Allow me to perform these searches and get back to you with the relevant information. Based on the documentation and resources gathered, here are some strategies you can use in Prefect to manage long spin-up times for machine learning tasks and keep models loaded for fast predictions: 1. Deferred Tasks: - Prefect allows running tasks in a background worker, similar to how task queues like Celery operate. This helps in decoupling long-running initialization from fast prediction tasks. - More details can be found in Prefect 3.x documentation: https://docs-3.prefect.io/3.0/develop/deferred-tasks 2. Task Caching: - Prefect provides caching mechanisms where you can cache the results of certain tasks. This is useful if the task computations can be reused, thus saving time on subsequent runs. - Example: You could cache model loading or initialization tasks so that they don't have to be re-executed, keeping the model in memory. - More details are found here: https://docs-3.prefect.io/3.0/develop/task-caching 3. Background Tasks: - Prefect can run long processes in the background using separate architectures for scaling. It is encouraged to spin up models as background tasks so they don't delay HTTP request responses directly. - Practical insights can be taken from Prefect's blog discussing task queue deployment strategies: https://www.prefect.io/blog/successfully-deploying-task-queue By utilizing these techniques, you can efficiently manage your machine learning models within Prefect, keeping them ready for fast predictions while managing long initialization times effectively. If you need further specific examples or help with implementation, feel free to ask!

@Marvin in the minimal example, both processes (runner and scheduler) have to import the task code directly. Is there a way to schedule tasks without importing the code?

From the documentation, Prefect does not inherently allow scheduling tasks without having the task's code imported. However, there are some strategies you can employ to decouple scheduling from execution which might help in achieving a setup closer to what you're describing: 1. Deferred Tasks and Task Workers: - You can use Prefect's task worker infrastructure to separate the execution process. By deploying task workers, you can have these workers execute tasks that are scheduled by another process. However, you will still need to have task definitions accessible to the task workers. - This method is documented in Prefect's guides on task scheduling and execution management: https://docs-3.prefect.io/3.0/develop/deferred-tasks 2. Serverless or Container-based Execution: - Another approach mentioned involves utilizing serverless solutions like AWS Lambda or containers orchestrated via Kubernetes. These methods allow tasks to be executed without direct dependency on the code being present in the scheduler's environment. - For an in-depth discussion on background tasks and potential methods, refer to Prefect's blog on deploying task queues: https://www.prefect.io/blog/successfully-deploying-task-queue Essentially, you can design a system where the task definitions are packaged and made available in an environment (like Docker containers) which can be spun up to execute the tasks on demand, following a trigger from the scheduler. This setup abstracts the physical presence of task definitions from the scheduling process.

@Marvin can I have multiple task workers executing the same kind of task? Can I start the runners on different k8s nodes?

Yes, Prefect does support having multiple task workers executing the same kind of task. This is useful for scaling out and distributing the workload across multiple machines or containers. Here's how you can do it: 1. Multiple Task Workers: - You can start multiple task workers to run the same type of task concurrently. Prefect allows you to distribute tasks across various workers, and these tasks can be handled concurrently to scale your workflows. - You simply need to start additional instances of your task worker processes. In the Prefect 3.x documentation, you can see examples of such setups using deferred tasks: https://docs-3.prefect.io/3.0/develop/deferred-tasks 2. Running on Different Kubernetes Nodes: - Prefect supports deploying and managing tasks on Kubernetes clusters, which means you can leverage Kubernetes' capabilities to schedule tasks onto different nodes. - You can use Kubernetes job configurations and Prefect's Kubernetes integration to ensure that task workers run on different nodes. Details and examples of Kubernetes deployments are available here: https://docs-3.prefect.io/3.0/deploy/infrastructure-examples/kubernetes By configuring Prefect with Kubernetes, you can dictate how and where your task workers are run, allowing fine-grained control over resource allocation and scaling seamlessly across different nodes in your cluster. If you have further questions on getting started or need configuration help, feel free to ask!

@Marvin how does the queue work? does one task runner correspond to one slot, which is freed after the task is completed? does the runner actively poll the server for new tasks, like the other kinds of workers?

In Prefect, work queues and task execution are designed to be flexible and efficient. Here's a summary of how they operate based on available information: 1. Work Queue and Task Slots: - Prefect's task runners and workers can execute tasks from a queue asynchronously or concurrently. There is no strict one-to-one correspondence between a task runner and a task slot. Instead, workers can take available tasks from a queue as resources permit, execute them, and then return results or update statuses when they are done. 2. Task Polling Mechanism: - Prefect uses a task polling mechanism where workers can actively poll a Prefect API or server for available tasks. This means that task runners can ask the server for new tasks once they have completed their current execution. This method ensures that the system remains responsive to demand, dispatching tasks dynamically as workers become available. 3. Resource Management: - In terms of resource management, tasks may run sequentially or concurrently based on the configuration of deployments and infrastructure settings (like Kubernetes). Prefect can manage these dynamically with scalable infrastructure, meaning the consumption of resources like CPU, memory, etc., is coordinated based on availability. These mechanisms are designed to allow both scale and fluidity in task execution across potentially distributed environments such as cloud infrastructures with Kubernetes, AWS Lambda, or other similar orchestrations. For more detailed configurations or to see practical examples of task execution architecture, you may refer to Prefect's resources, such as their blog on deploying a robust task queue: https://www.prefect.io/blog/successfully-deploying-task-queue. If you have more specific queries regarding setup and architecture, feel free to ask!