Quickstart

Once you have a Union account, install union:

pip install union

Export the following environment variable to build and push images to your own container registry:

        
# replace with your registry name
export IMAGE_SPEC_REGISTRY="<your-container-registry>"

Then run the following commands to run the workflow:

        
    
git clone https://github.com/unionai/unionai-examples
cd unionai-examples
union run --remote tutorials/sentiment_classifier/sentiment_classifier.py main --model distilbert-base-uncased

The source code for this tutorial can be found here {octicon}mark-github.

In this demo and following example, learn how to use DoltTable to annotate DataFrame inputs and outputs in the Flyte tasks.

First, let’s import the libraries.

        
    
import sys
from pathlib import Path

import pandas as pd
from flytekit import task, workflow
from flytekitplugins.dolt.schema import DoltConfig, DoltTable

Next, we initialize Dolt’s config.

        
    
doltdb_path = str(Path(__file__).parent / "foo")

rabbits_conf = DoltConfig(
    db_path=doltdb_path,
    tablename="rabbits",
)

We define a task to create a DataFrame and store the table in Dolt.

        
    
@task
def populate_rabbits(a: int) -> DoltTable:
    rabbits = [("George", a), ("Alice", a * 2), ("Sugar Maple", a * 3)]
    df = pd.DataFrame(rabbits, columns=["name", "count"])
    return DoltTable(data=df, config=rabbits_conf)

unwrap_rabbits task does the exact opposite – reading the table from Dolt and returning a DataFrame.

        
    
@task
def unwrap_rabbits(table: DoltTable) -> pd.DataFrame:
    return table.data

Our workflow combines the above two tasks:

        
    
@workflow
def wf(a: int) -> pd.DataFrame:
    rabbits = populate_rabbits(a=a)
    df = unwrap_rabbits(table=rabbits)
    return df


if __name__ == "__main__":
    print(f"Running {__file__} main...")
    if len(sys.argv) != 2:
        raise ValueError("Expected 1 argument: a (int)")
    a = int(sys.argv[1])
    result = wf(a=a)
    print(f"Running wf(), returns dataframe\n{result}\n{result.dtypes}")

Run this task by issuing the following command:

$ python quickstart_example.py 1