ocean-runner 0.3.9

A fluent API for OceanProtocol algorithms

Ocean Runner

Ocean Runner is a package that eases algorithm creation in the scope of OceanProtocol.

Installation

pip install ocean-runner
# or
uv add ocean-runner

Usage

Annotated Minimal Example

import random
from ocean_runner import Algorithm, EmptyAlgorithm

algorithm: EmptyAlgorithm[int] = Algorithm.create(None)


@algorithm.run
def run(_) -> int:
    return random.randint(0, 100)

This code snippet will:

• Read the OceanProtocol JobDetails from the environment variables and use default configuration file paths.
• Execute the default input validation function, assessing if there are input dids and ddos.
• Execute the run function.
• Execute the default saving function, storing the result in a "result.txt" file within the default outputs path.

Not Annotated Minimal Example

If you do not care about static analysis tools, this snippet will run just fine.

import random
from ocean_runner import Algorithm

algorithm = Algorithm.create(None)


@algorithm.run
def run(_):
    return random.randint(0, 100)

Tuning

Application Config

The application configuration can be tweaked by passing a Config instance to its constructor.

from ocean_runner import Algorithm, Config

algorithm = Algorithm.create(
    Config(
        custom_input: ... # dataclass
        # Custom algorithm parameters dataclass.

        logger: ... # type: logging.Logger
        # Custom logger to use.

        source_paths: ... # type: Iterable[Path]
        # Source paths to include in the PATH

        environment: ...
        # type: ocean_runner.Environment. Mock of environment variables.
    )
)

import logging

from pydantic import BaseModel
from ocean_runner import Algorithm, Config


class CustomInput(BaseModel):
    foobar: string


logger = logging.getLogger(__name__)


algorithm = Algorithm.create(
    Config(
        custom_input=CustomInput,
        """
        Load the Algorithm's Custom Input into a CustomInput instance.
        """

        source_paths=[Path("/algorithm/src")],
        """
        Source paths to include in the PATH. '/algorithm/src' is the default since our templates place the algorithm source files there.
        """

        logger=logger,
        """
        Custom logger to use in the Algorithm.
        """

        environment=Environment(
            base_dir: "./_data",
            """
            Custom data path to use test data.
            """

            dids: '["17feb697190d9f5912e064307006c06019c766d35e4e3f239ebb69fb71096e42"]',
            """
            Dataset DID.
            """

            transformation_did: "1234",
            """
            Random transformation DID to use while testing.
            """

            secret: "1234",
            """
            Random secret to use while testing.
            """
        )
        """
        Should not be needed in production algorithms, used to mock environment variables, defaults to using env.
        """
    )
)

Behaviour Config

To fully configure the behaviour of the algorithm as in the Minimal Example, you can do it decorating your defined function as in the following fully annotated example (Pyton >=3.12), which features all the possible algorithm customization.

from pathlib import Path
from typing import Sequence, Tuple

import pandas as pd
from oceanprotocol_job_details.domain import DID

from ocean_runner import Algorithm
from ocean_runner.runner import EmptyAlgorithm

type ResultT = Tuple[DID, pd.DataFrame]
type ResultsT = Sequence[ResultT]
algorithm: EmptyAlgorithm[ResultsT] = Algorithm.create(None)


@algorithm.on_error
def error_callback(_, ex: Exception):
    algorithm.logger.exception(ex)
    raise algorithm.Error() from ex


@algorithm.validate
def val(_) -> None:
    assert algorithm.job_details.files, "Empty input dir"


@algorithm.run
def run(_) -> ResultsT:
    def describe(df: pd.DataFrame) -> pd.DataFrame:
        return df.describe(include="all")

    return [
        (did, describe(pd.read_csv(file_path)))
        for did, file_path in algorithm.job_details.inputs()
    ]


@algorithm.save_results
def save(_, result: ResultsT, base: Path):
    for did, analysis in result:
        algorithm.logger.info(f"Descriptive statistics {did}: {result}")
        analysis.to_csv(base / f"{did}.csv")

Default implementations

As seen in the minimal example, all methods implemented in Algorithm have a default implementation which will be commented here.

.validate()

    """
    Will validate the algorithm's job detail instance, checking for the existence of:
    - `job_details.ddos`
    - `job_details.files`
    """

.run()

    """
    Has NO default implementation, must pass a callback that returns a result of any type.
    """

.save_results()

    """
    Stores the result of running the algorithm in "outputs/results.txt"
    """

Job Details

To load the OceanProtocol JobDetails instance, the program will read some environment variables, they can be mocked passing an instance of Environment through the configuration of the algorithm.

Environment variables:

• DIDS (optional) Input dataset(s) DID's, must have format: ["abc..90"]. Defaults to reading them automatically from the DDO data directory.
• TRANSFORMATION_DID (optional, default="DEFAULT"): Algorithm DID, must have format: abc..90.
• SECRET (optional, default="DEFAULT"): Algorithm secret.
• BASE_DIR (optional, default="/data"): Base path to the OceanProtocol data directories.