get_tscv_train_test_splits

sarima_task

Encountered error while executing workflow 'parallel-tscv.time_series_workflow':
  Error encountered while executing 'time_series_workflow':
  Failed to convert inputs of task 'parallel-tscv.map_sarima_task_99b7f0e6767e1ec825145f7d10320b35':
  type object 'DataFrame' has no attribute '__args__'

from flytekit import task
from flytekit.types.file import FlyteFile


@task
def create_map_inputs(
    paths: list[str],
    spec: dict,
    gcs_output_dir: str,
) -> list[dict]:
    map_inputs = []
    for path in paths:
        map_inputs.append(
            {
                "bam_file": FlyteFile(path),
                "spec": spec,
                "gcs_output_dir": gcs_output_dir,
            }
        )
    return map_inputs


create_map_inputs(paths=["test.bam"], spec={}, gcs_output_dir="")

raise TypeError(
TypeError: Failed to convert return value for var o0 for function create_map_inputs with error <class 'TypeError'>: Object of type FlyteFile is not JSON serializable

flytectl update launchplan ... --activate

--archive

--version

flytectl get launchplan

$ flytectl get launchplan -p io-erisyon-people-thomas -d development indexer_worker_production_10minutes
 ----------------------------- ------------------------------------- ------ ------- -------------------------------------- -------- --------- 
| VERSION (8)                 | NAME                                | TYPE | STATE | SCHEDULE                             | INPUTS | OUTPUTS |
 ----------------------------- ------------------------------------- ------ ------- -------------------------------------- -------- --------- 
| tfb23.06.08a_tfb23.06.08a_0 | indexer_worker_production_10minutes |      |       | map[rate:map[value:%!s(float64=10)]] |        |         |
 ----------------------------- ------------------------------------- ------ ------- -------------------------------------- -------- ---------

flyte-secret-auth

flyte

kubectl edit secret -n flyte flyte-secret-auth

$ kubectl get secrets -n flyte
NAME                                       TYPE     DATA   AGE
flyte-sandbox-db-pass                      Opaque   1      19m
flyte-sandbox-docker-registry-secret       Opaque   3      19m
flyte-sandbox-kubernetes-dashboard-certs   Opaque   0      19m
flyte-sandbox-minio                        Opaque   2      19m
flyte-sandbox-postgresql                   Opaque   1      19m
kubernetes-dashboard-csrf                  Opaque   1      19m
kubernetes-dashboard-key-holder            Opaque   2      19m
flyte-sandbox-webhook-secret               Opaque   3      18m

$ kubectl edit secret -n flyte flyte-secret-auth

Console

from flytekit import dynamic, task, workflow

class Something:
    n: int

@task
def tk_inner() -> Something:
    s = Something()
    s.n = 3
    return s

@task
def tk_agg(s: list[Something]) -> int:
    return sum(x.n for x in s)

@dynamic
def tk_outer() -> int:
    s = [tk_inner() for _ in range(5)]
    return tk_agg(s=s)

@workflow
def wf_outer() -> int:
    return tk_outer()

assert wf_outer() == sum(range(5))

@dataclass_json
@dataclass
class WFConfig:
    some_key: Dict[int, List[int]] = field(default_factory=lambda: {1: [2, 3, 4]})

from dataclasses import dataclass, field
from typing import List, Dict

import flytekit
from dataclasses_json import dataclass_json
from flytekit.core.type_engine import TypeEngine


@dataclass_json
@dataclass
class WFConfig:
    some_key: Dict[int, List[int]] = field(default_factory=lambda: {1: [2, 3, 4]})


config = WFConfig()
transformer = TypeEngine.get_transformer(type(config))
flyte_context = flytekit.FlyteContextManager.current_context()

literal_type = transformer.get_literal_type(type(config))
literal = transformer.to_literal(flyte_context, config, type(config), literal_type)

python_type = transformer.guess_python_type(literal_type)
TypeEngine.to_python_value(flytekit.FlyteContextManager.current_context(), literal, python_type)

sudo flytectl sandbox start --source .

Starting k3s cluster...

flytectl demo start

@workflow
def wf(region: str = 'us') -> None:
       mem = '80Gi' if region == 'us' else '40Gi'
       train(region=region).with_overrides(requests=Resources(mem=mem))  # train() is a task