Workbench

The workbench is not a standalone application but a library that need to be imported. It is used to communicate with a node and submit Artifacts, that encapsulate instructions for the job scheduling and execution.

Installation is straightforward:

pip install flotta[workbench]

Once installed, just create a context object and obtain a project handler with a token. A project is a collection of data sources. The token is created by the node network administrator and it is unique for each project.

Following an example of how to use the workbench library to connect to a node.

from flotta.core.distributions import Collect
from flotta.core.model_operations import Aggregation, Train, TrainTest
from flotta.core.models import FederatedRandomForestClassifier, StrategyRandomForestClassifier
from flotta.core.steps import Finalize, Parallel
from flotta.core.transformers import FederatedSplitter
from flotta.workbench import Context, Artifact

server_url = "http://localhost:1456"
project_token = "58981bcbab77ef4b8e01207134c38873e0936a9ab88cd76b243a2e2c85390b94"

# create the context
ctx = Context(server_url)

# load a project
project = ctx.project(project_token)

# an aggregated view on data
ds = project.data

# print all available features
for feature in ds.features:
    print(feature)

# create a query starting from the project's data
q = ds.extract()
q = q.add(q["feature"] < 2)

# create a Federated model
model = FederatedRandomForestClassifier(
    n_estimators=10,
    strategy=StrategyRandomForestClassifier.MERGE,
)

label = "label"

# describe how to distribute the work and how to train teh model
steps = [
    Parallel(
        TrainTest(
            query=project.extract().add(
                FederatedSplitter(
                    random_state=42,
                    test_percentage=0.2,
                    label=label,
                )
            ),
            trainer=Train(model=model),
            model=model,
        ),
        Collect(),
    ),
    Finalize(
        Aggregation(model=model),
    ),
]

# submit Artifact
Artifact: Artifact = ctx.submit(project, steps)