Get started with Flows

Make sure you have a good understanding of the key concepts before you get started with Domino Flows.

Basic flow

This section demonstrates a basic example that:

Takes two integers as an input to a flow.
The first task adds the integers together and passes the result as an input to the second task.
The second task takes the square root of the input and returns the result as the final output of the flow.

This example flow can be visualized as follows:

$Simple Math Flow Graph$

To create a Domino Flow:

Create a workspace using the Domino Standard Environment (DSE), or a custom environment that is built on top of the DSE.

Create a file named add.py in the root directory. Add the following code to the file to add two integer inputs together:

from pathlib import Path

# Read inputs
a = Path("/workflow/inputs/first_value").read_text()
b = Path("/workflow/inputs/second_value").read_text()

# Calculate sum
sum = int(a) + int(b)
print(f"The sum of {a} + {b} is {sum}")

# Write output
Path("/workflow/outputs/sum").write_text(str(sum))

Create a file named sqrt.py in the root directory. Add the following code to the file to calculate the square root of the input:

from pathlib import Path

# Read input
value = Path("/workflow/inputs/value").read_text()

# Calculate square root
sqrt = int(value) ** 0.5
print(f"The square root of {value} is {sqrt}")

# Write output
Path("/workflow/outputs/sqrt").write_text(str(sqrt))

Create a file named workflow.py in the root directory. Add the following code to the file to define the flow:

from flytekit import workflow
from flytekitplugins.domino.task import DominoJobConfig, DominoJobTask

@workflow
def simple_math_workflow(a: int, b: int) -> float:

    # Create first task
    add_task = DominoJobTask(
        name='Add numbers',
        domino_job_config=DominoJobConfig(Command="python add.py"),
        inputs={'first_value': int, 'second_value': int},
        outputs={'sum': int},
        use_latest=True
    )
    sum = add_task(first_value=a, second_value=b)

    # Create second task
    sqrt_task = DominoJobTask(
        name='Square root',
        domino_job_config=DominoJobConfig(Command="python sqrt.py"),
        inputs={'value': int},
        outputs={'sqrt': float},
        use_latest=True
    )
    sqrt = sqrt_task(value=sum)

    return sqrt

Commit the code and run the following command in the Workspace terminal to register and run the flow:
```
pyflyte run --remote workflow.py simple_math_workflow --a 10 --b 6
```
Once you run the command above, navigate to Flows > Runs > Run Name in the Domino UI to monitor the results and view the outputs that were produced by the execution.
To visualize the full execution flow, click the View graph button to navigate to the Flyte UI.

AI Hub Flow

Rather than beginning from scratch, you can start from a pre-built template in the AI project hub. This section uses a template that demonstrates a basic training flow example:

It takes the path to a dataset as an input to a flow.
The first task does some basic data processing and returns a CSV file that gets passed as input to the next task.
The second task takes the CSV file and uses it to train a model that is returned as the output to the flow.

The training flow can be visualized as follows:

Training Flow Graph

To create a training flow:

Go to AI Hub and select the AI Training Flow template. Create a Project from this template.
Create a Workspace using the Domino Standard Environment (DSE), or a custom environment that is built on top of the DSE.

Inspect the workflow.py file for the definition of the flow. Note how a helper method called run_domino_job_task is used here instead of the DominoJobConfig and DominoJobTask in the basic example above.

data_prep_results = run_domino_job_task(
    flyte_task_name="Prepare data",
    command="python /mnt/code/scripts/prep-data.py",
    environment_name="Domino Standard Environment Py3.11 R4.4",
    hardware_tier_name="Small",
    inputs=[
        Input(name="data_path", type=str, value=data_path)
    ],
    output_specs=[
        Output(name="processed_data", type=FlyteFile[TypeVar("csv")])
    ],
    use_project_defaults_for_omitted=True
)

training_results = run_domino_job_task(
    flyte_task_name="Train model",
    command="python /mnt/code/scripts/train-model.py",
    environment_name="Domino Standard Environment Py3.11 R4.4",
    hardware_tier_name="Small",
    inputs=[
        Input(name="processed_data", type=FlyteFile[TypeVar("csv")], value=data_prep_results['processed_data']),
        Input(name="epochs", type=int, value=10),
        Input(name="batch_size", type=int, value=32)
    ],
    output_specs=[
        Output(name="model", type=FlyteFile)
    ],
    use_project_defaults_for_omitted=True
)

Run the following command in the Workspace terminal to register and run the flow:

pyflyte run --remote workflow.py training_workflow --data_path /mnt/code/data/data.csv

Navigate to Flows > Runs > Run name to monitor the results and view the outputs that were produced by the execution.
To visualize the full execution flow, click the View graph button to navigate to the Flyte UI.

Get started with Flows

Basic flow

AI Hub Flow

Next steps