Running tasks


This is a multi-step process where we create an execution spec file, update the spec file, and then create the execution. More details can be found in the Flytectl API reference.

Generate execution spec file

flytectl get tasks -d development -p flytesnacks workflows.example.generate_normal_df  --latest --execFile exec_spec.yaml

Update the input spec file for arguments to the workflow

iamRoleARN: 'arn:aws:iam::12345678:role/defaultrole'
  n: 200
  mean: 0.0
  sigma: 1.0
kubeServiceAcct: ""
targetDomain: ""
targetProject: ""
task: workflows.example.generate_normal_df
version: "v1"

Create execution using the exec spec file

flytectl create execution -p flytesnacks -d development --execFile exec_spec.yaml

Monitor the execution by providing the execution id from create command

flytectl get execution -p flytesnacks -d development <execid>


A task can be launched via FlyteRemote programmatically.

from flytekit.remote import FlyteRemote
from flytekit.configuration import Config, SerializationSettings

# FlyteRemote object is the main entrypoint to API
remote = FlyteRemote(

# Get Task
flyte_task = remote.fetch_task(name="workflows.example.generate_normal_df", version="v1")

flyte_task = remote.register_task(

# Run Task
execution = remote.execute(
     flyte_task, inputs={"n": 200, "mean": 0.0, "sigma": 1.0}, execution_name="task-execution", wait=True

# Or use execution_name_prefix to avoid repeated execution names
execution = remote.execute(
     flyte_task, inputs={"n": 200, "mean": 0.0, "sigma": 1.0}, execution_name_prefix="flyte", wait=True

# Inspecting execution
# The 'inputs' and 'outputs' correspond to the task execution.
input_keys = execution.inputs.keys()
output_keys = execution.outputs.keys()