@ancient-wolf-19325 How flyte directory works? can you use to store the data generated in each task. how much data can be used to store

FlyteDirectory is a Flyte type used to handle directories in workflows, allowing tasks to pass directories between each other. It represents directories in remote storage, and Flyte automatically manages the download and upload of these directories as needed. You can use FlyteDirectory to store data generated in each task, and it supports both local and remote storage, making it suitable for handling large datasets. The amount of data you can store using FlyteDirectory is primarily limited by the storage backend you configure, such as AWS S3 or Google Cloud Storage. Flyte itself does not impose strict limits on the size of data stored, but practical limits will depend on your storage provider's constraints and your cluster's configuration. Would you like more detail on how to configure FlyteDirectory for your specific use case? Sources: - Downloading with FlyteFile and FlyteDirectory - FlyteFile and FlyteDirectory 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

share the example to use flyte directory suppose two task are there one task is running after wards and the data genrated by task1 should use by task2

To use FlyteDirectory to pass data between two tasks, you can define a workflow where the first task generates data and stores it in a FlyteDirectory, and the second task consumes this data. Here's an example:

import os
from flytekit import task, workflow
from flytekit.types.directory import FlyteDirectory

@task
def task1() -> FlyteDirectory:
    # Create a directory and write some files
    local_dir = os.path.join(os.getcwd(), "my_data")
    os.makedirs(local_dir, exist_ok=True)
    with open(os.path.join(local_dir, "file1.txt"), "w") as f:
        f.write("This is file 1.")
    with open(os.path.join(local_dir, "file2.txt"), "w") as f:
        f.write("This is file 2.")
    return FlyteDirectory(local_dir)

@task
def task2(fd: FlyteDirectory):
    # List files in the directory and read contents
    for file_name in os.listdir(fd.path):
        with open(os.path.join(fd.path, file_name), "r") as f:
            print(f"Contents of {file_name}: {f.read()}")

@workflow
def my_workflow():
    fd = task1()
    task2(fd=fd)

if __name__ == "__main__":
    my_workflow()

In this example,

task1

creates a directory with some files and returns it as a

FlyteDirectory

.

task2

takes this

FlyteDirectory

as input, lists the files, and reads their contents. The workflow

my_workflow

orchestrates these tasks. Would you like more details on any specific part of this example? Sources: - FlyteFile and FlyteDirectory 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 where it will store, which resource use to store? is it uses flyte database or anything else?

Flyte stores data in an object store rather than a database. When using FlyteDirectory, the data is stored in a configured object store like AWS S3, Google Cloud Storage, or Azure Blob Storage. Flyte manages the references to these data objects, while the actual data resides in the object store. This separation allows Flyte to handle large datasets efficiently without directly managing the data itself. The Flyte control plane does not store the raw data; it only manages metadata and references to the data stored in the object store. This setup ensures that Flyte can scale and handle large volumes of data by leveraging the capabilities of the underlying storage service. Would you like more information on configuring the storage backend for Flyte? Sources: - Task input and output - Data handling 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.