How is Flyte integrated with Hamilton? Is there a need for a micro orchestrator like Hamilton? Is Hamilton the best choice?

Flyte does not currently have a native integration with Hamilton, but there is an open issue discussing the potential for such an integration. The idea is to run a Hamilton DAG within a Flyte task more easily than it would be now, potentially through a plugin (GitHub Issue). Hamilton is designed for micro-workflows, while Flyte handles macro workflows. Whether Hamilton is the best choice depends on your specific needs. If you require fine-grained control over small, modular tasks, Hamilton could be beneficial. However, for larger, more complex workflows, Flyte's scalability and reproducibility might be more suitable. Would you like more detail on how to implement Hamilton within Flyte or explore other micro-orchestrators? Sources: - GitHub Issue 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@little-lock-48902 Flyte handles infrastructure and workflows - micro vs macro is maybe latency requirements. At union we have a built a low latency system that keeps the pods up and runs the same workflows much much faster (think milliseconds)

@ancient-wolf-19325 how well is Flyte suited for structuring a data project with separation of concerns, especially execution in differing data and storage contexts?

Flyte is well-suited for structuring data projects with separation of concerns, particularly through its support for multi-tenancy, projects, and domains. Projects allow grouping workflows and tasks within the same problem domain, while domains provide further separation by isolating resources and configurations (Flyte Blog, Flyte Blog). Flyte also supports environment and dependency isolation, allowing different teams or tasks to use different libraries and versions without interference. This is achieved through Docker images and Kubernetes, which provide resource quotas and compute isolation (Flyte Blog). Would you like more detail on how to implement these features in Flyte? Sources: - Flyte Blog - Flyte Blog - Flyte Blog 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 what are recommended or popular ways for caching assets generated with Flyte and providing them for data scientists' use in Jupyter notebooks via API for immediate use, so they are either delivered from cache or by processing those parts of the DAG necessary for a particular asset, on the fly?

Flyte provides robust caching mechanisms to store and retrieve task outputs, which can be leveraged to deliver assets to data scientists in Jupyter notebooks. Here are the recommended ways: 1. Task Caching: Enable caching for tasks using the

cache=True

and

cache_version

parameters in the

@task

decorator. This ensures that repeated executions with the same inputs retrieve results from the cache (Flyte Caching Documentation). 2. FlyteRemote API: Use the

FlyteRemote

API to programmatically execute workflows and tasks, specifying the

overwrite_cache

parameter to control cache usage. This allows you to either fetch cached results or force re-execution as needed (FlyteRemote API). 3. Jupyter Integration: Integrate Flyte with Jupyter notebooks by using the FlyteRemote API within the notebooks. This enables data scientists to trigger workflows and retrieve results directly from the cache or by executing necessary parts of the DAG. Example code snippet for using FlyteRemote in a Jupyter notebook:

import flytekit
from flytekit.remote import FlyteRemote
from flytekit.configuration import Config

# Configure FlyteRemote
remote = FlyteRemote(
    config=Config.auto(),
    default_project="my_project",
    default_domain="development"
)

# Fetch a workflow
workflow = remote.fetch_workflow(name="my_workflow")

# Execute the workflow with caching
execution = remote.execute(
    workflow,
    inputs={"input1": value1, "input2": value2},
    overwrite_cache=False  # Set to True to force re-execution
)

# Wait for execution to complete and fetch results
execution = remote.wait(execution)
results = execution.outputs

Would you like more detail on any specific part of this process? Sources: - Flyte Caching Documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@freezing-airport-6809 thank you for this experience value from practice! Next, I'm looking into execution contexts and whether containerization is a must.

Flyte is designed to operate primarily as a concurrent computing container network, leveraging Kubernetes for scalability and isolation. However, Flyte tasks can be executed locally without containerization for development and testing purposes. 1. Containerized Execution: Flyte tasks and workflows are typically run in isolated containers on Kubernetes, ensuring resource isolation and scalability (Flyte README). 2. Local Execution: Flyte allows for local execution of tasks and workflows using the

pyflyte run

command, which can be useful for development and debugging. This does not require containerization (Flyte Local Execution). 3. Standalone Python Module: While Flyte is optimized for containerized environments, it can be used as a standalone Python module for local development. However, this approach may not leverage Flyte's full capabilities in terms of scalability and resource management. Example of local execution:

pyflyte run hello_world.py hello_world_wf

Would you like more detail on setting up Flyte for local execution or containerized deployment? Sources: - Flyte README - Flyte Local Execution 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.