Skip to content

prefect.flows

Module containing the base workflow class and decorator - for most use cases, using the @flow decorator is preferred.

Flow

Bases: Generic[P, R]

A Prefect workflow definition.

Note

We recommend using the @flow decorator for most use-cases.

Wraps a function with an entrypoint to the Prefect engine. To preserve the input and output types, we use the generic type variables P and R for "Parameters" and "Returns" respectively.

Parameters:

Name Type Description Default
fn Callable[P, R]

The function defining the workflow.

required
name Optional[str]

An optional name for the flow; if not provided, the name will be inferred from the given function.

None
version Optional[str]

An optional version string for the flow; if not provided, we will attempt to create a version string as a hash of the file containing the wrapped function; if the file cannot be located, the version will be null.

None
flow_run_name Optional[Union[Callable[[], str], str]]

An optional name to distinguish runs of this flow; this name can be provided as a string template with the flow's parameters as variables, or a function that returns a string.

None
task_runner Union[Type[BaseTaskRunner], BaseTaskRunner]

An optional task runner to use for task execution within the flow; if not provided, a ConcurrentTaskRunner will be used.

ConcurrentTaskRunner
description str

An optional string description for the flow; if not provided, the description will be pulled from the docstring for the decorated function.

None
timeout_seconds Union[int, float]

An optional number of seconds indicating a maximum runtime for the flow. If the flow exceeds this runtime, it will be marked as failed. Flow execution may continue until the next task is called.

None
validate_parameters bool

By default, parameters passed to flows are validated by Pydantic. This will check that input values conform to the annotated types on the function. Where possible, values will be coerced into the correct type; for example, if a parameter is defined as x: int and "5" is passed, it will be resolved to 5. If set to False, no validation will be performed on flow parameters.

True
retries Optional[int]

An optional number of times to retry on flow run failure.

None
retry_delay_seconds Optional[Union[int, float]]

An optional number of seconds to wait before retrying the flow after failure. This is only applicable if retries is nonzero.

None
persist_result Optional[bool]

An optional toggle indicating whether the result of this flow should be persisted to result storage. Defaults to None, which indicates that Prefect should choose whether the result should be persisted depending on the features being used.

None
result_storage Optional[ResultStorage]

An optional block to use to persist the result of this flow. This value will be used as the default for any tasks in this flow. If not provided, the local file system will be used unless called as a subflow, at which point the default will be loaded from the parent flow.

None
result_serializer Optional[ResultSerializer]

An optional serializer to use to serialize the result of this flow for persistence. This value will be used as the default for any tasks in this flow. If not provided, the value of PREFECT_RESULTS_DEFAULT_SERIALIZER will be used unless called as a subflow, at which point the default will be loaded from the parent flow.

None
on_failure Optional[List[Callable[[Flow, FlowRun, State], None]]]

An optional list of callables to run when the flow enters a failed state.

None
on_completion Optional[List[Callable[[Flow, FlowRun, State], None]]]

An optional list of callables to run when the flow enters a completed state.

None
on_cancellation Optional[List[Callable[[Flow, FlowRun, State], None]]]

An optional list of callables to run when the flow enters a cancelling state.

None
on_crashed Optional[List[Callable[[Flow, FlowRun, State], None]]]

An optional list of callables to run when the flow enters a crashed state.

None
on_running Optional[List[Callable[[Flow, FlowRun, State], None]]]

An optional list of callables to run when the flow enters a running state.

None
Source code in prefect/flows.py
 137
 138
 139
 140
 141
 142
 143
 144
 145
 146
 147
 148
 149
 150
 151
 152
 153
 154
 155
 156
 157
 158
 159
 160
 161
 162
 163
 164
 165
 166
 167
 168
 169
 170
 171
 172
 173
 174
 175
 176
 177
 178
 179
 180
 181
 182
 183
 184
 185
 186
 187
 188
 189
 190
 191
 192
 193
 194
 195
 196
 197
 198
 199
 200
 201
 202
 203
 204
 205
 206
 207
 208
 209
 210
 211
 212
 213
 214
 215
 216
 217
 218
 219
 220
 221
 222
 223
 224
 225
 226
 227
 228
 229
 230
 231
 232
 233
 234
 235
 236
 237
 238
 239
 240
 241
 242
 243
 244
 245
 246
 247
 248
 249
 250
 251
 252
 253
 254
 255
 256
 257
 258
 259
 260
 261
 262
 263
 264
 265
 266
 267
 268
 269
 270
 271
 272
 273
 274
 275
 276
 277
 278
 279
 280
 281
 282
 283
 284
 285
 286
 287
 288
 289
 290
 291
 292
 293
 294
 295
 296
 297
 298
 299
 300
 301
 302
 303
 304
 305
 306
 307
 308
 309
 310
 311
 312
 313
 314
 315
 316
 317
 318
 319
 320
 321
 322
 323
 324
 325
 326
 327
 328
 329
 330
 331
 332
 333
 334
 335
 336
 337
 338
 339
 340
 341
 342
 343
 344
 345
 346
 347
 348
 349
 350
 351
 352
 353
 354
 355
 356
 357
 358
 359
 360
 361
 362
 363
 364
 365
 366
 367
 368
 369
 370
 371
 372
 373
 374
 375
 376
 377
 378
 379
 380
 381
 382
 383
 384
 385
 386
 387
 388
 389
 390
 391
 392
 393
 394
 395
 396
 397
 398
 399
 400
 401
 402
 403
 404
 405
 406
 407
 408
 409
 410
 411
 412
 413
 414
 415
 416
 417
 418
 419
 420
 421
 422
 423
 424
 425
 426
 427
 428
 429
 430
 431
 432
 433
 434
 435
 436
 437
 438
 439
 440
 441
 442
 443
 444
 445
 446
 447
 448
 449
 450
 451
 452
 453
 454
 455
 456
 457
 458
 459
 460
 461
 462
 463
 464
 465
 466
 467
 468
 469
 470
 471
 472
 473
 474
 475
 476
 477
 478
 479
 480
 481
 482
 483
 484
 485
 486
 487
 488
 489
 490
 491
 492
 493
 494
 495
 496
 497
 498
 499
 500
 501
 502
 503
 504
 505
 506
 507
 508
 509
 510
 511
 512
 513
 514
 515
 516
 517
 518
 519
 520
 521
 522
 523
 524
 525
 526
 527
 528
 529
 530
 531
 532
 533
 534
 535
 536
 537
 538
 539
 540
 541
 542
 543
 544
 545
 546
 547
 548
 549
 550
 551
 552
 553
 554
 555
 556
 557
 558
 559
 560
 561
 562
 563
 564
 565
 566
 567
 568
 569
 570
 571
 572
 573
 574
 575
 576
 577
 578
 579
 580
 581
 582
 583
 584
 585
 586
 587
 588
 589
 590
 591
 592
 593
 594
 595
 596
 597
 598
 599
 600
 601
 602
 603
 604
 605
 606
 607
 608
 609
 610
 611
 612
 613
 614
 615
 616
 617
 618
 619
 620
 621
 622
 623
 624
 625
 626
 627
 628
 629
 630
 631
 632
 633
 634
 635
 636
 637
 638
 639
 640
 641
 642
 643
 644
 645
 646
 647
 648
 649
 650
 651
 652
 653
 654
 655
 656
 657
 658
 659
 660
 661
 662
 663
 664
 665
 666
 667
 668
 669
 670
 671
 672
 673
 674
 675
 676
 677
 678
 679
 680
 681
 682
 683
 684
 685
 686
 687
 688
 689
 690
 691
 692
 693
 694
 695
 696
 697
 698
 699
 700
 701
 702
 703
 704
 705
 706
 707
 708
 709
 710
 711
 712
 713
 714
 715
 716
 717
 718
 719
 720
 721
 722
 723
 724
 725
 726
 727
 728
 729
 730
 731
 732
 733
 734
 735
 736
 737
 738
 739
 740
 741
 742
 743
 744
 745
 746
 747
 748
 749
 750
 751
 752
 753
 754
 755
 756
 757
 758
 759
 760
 761
 762
 763
 764
 765
 766
 767
 768
 769
 770
 771
 772
 773
 774
 775
 776
 777
 778
 779
 780
 781
 782
 783
 784
 785
 786
 787
 788
 789
 790
 791
 792
 793
 794
 795
 796
 797
 798
 799
 800
 801
 802
 803
 804
 805
 806
 807
 808
 809
 810
 811
 812
 813
 814
 815
 816
 817
 818
 819
 820
 821
 822
 823
 824
 825
 826
 827
 828
 829
 830
 831
 832
 833
 834
 835
 836
 837
 838
 839
 840
 841
 842
 843
 844
 845
 846
 847
 848
 849
 850
 851
 852
 853
 854
 855
 856
 857
 858
 859
 860
 861
 862
 863
 864
 865
 866
 867
 868
 869
 870
 871
 872
 873
 874
 875
 876
 877
 878
 879
 880
 881
 882
 883
 884
 885
 886
 887
 888
 889
 890
 891
 892
 893
 894
 895
 896
 897
 898
 899
 900
 901
 902
 903
 904
 905
 906
 907
 908
 909
 910
 911
 912
 913
 914
 915
 916
 917
 918
 919
 920
 921
 922
 923
 924
 925
 926
 927
 928
 929
 930
 931
 932
 933
 934
 935
 936
 937
 938
 939
 940
 941
 942
 943
 944
 945
 946
 947
 948
 949
 950
 951
 952
 953
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
@PrefectObjectRegistry.register_instances
class Flow(Generic[P, R]):
    """
    A Prefect workflow definition.

    !!! note
        We recommend using the [`@flow` decorator][prefect.flows.flow] for most use-cases.

    Wraps a function with an entrypoint to the Prefect engine. To preserve the input
    and output types, we use the generic type variables `P` and `R` for "Parameters" and
    "Returns" respectively.

    Args:
        fn: The function defining the workflow.
        name: An optional name for the flow; if not provided, the name will be inferred
            from the given function.
        version: An optional version string for the flow; if not provided, we will
            attempt to create a version string as a hash of the file containing the
            wrapped function; if the file cannot be located, the version will be null.
        flow_run_name: An optional name to distinguish runs of this flow; this name can
            be provided as a string template with the flow's parameters as variables,
            or a function that returns a string.
        task_runner: An optional task runner to use for task execution within the flow;
            if not provided, a `ConcurrentTaskRunner` will be used.
        description: An optional string description for the flow; if not provided, the
            description will be pulled from the docstring for the decorated function.
        timeout_seconds: An optional number of seconds indicating a maximum runtime for
            the flow. If the flow exceeds this runtime, it will be marked as failed.
            Flow execution may continue until the next task is called.
        validate_parameters: By default, parameters passed to flows are validated by
            Pydantic. This will check that input values conform to the annotated types
            on the function. Where possible, values will be coerced into the correct
            type; for example, if a parameter is defined as `x: int` and "5" is passed,
            it will be resolved to `5`. If set to `False`, no validation will be
            performed on flow parameters.
        retries: An optional number of times to retry on flow run failure.
        retry_delay_seconds: An optional number of seconds to wait before retrying the
            flow after failure. This is only applicable if `retries` is nonzero.
        persist_result: An optional toggle indicating whether the result of this flow
            should be persisted to result storage. Defaults to `None`, which indicates
            that Prefect should choose whether the result should be persisted depending on
            the features being used.
        result_storage: An optional block to use to persist the result of this flow.
            This value will be used as the default for any tasks in this flow.
            If not provided, the local file system will be used unless called as
            a subflow, at which point the default will be loaded from the parent flow.
        result_serializer: An optional serializer to use to serialize the result of this
            flow for persistence. This value will be used as the default for any tasks
            in this flow. If not provided, the value of `PREFECT_RESULTS_DEFAULT_SERIALIZER`
            will be used unless called as a subflow, at which point the default will be
            loaded from the parent flow.
        on_failure: An optional list of callables to run when the flow enters a failed state.
        on_completion: An optional list of callables to run when the flow enters a completed state.
        on_cancellation: An optional list of callables to run when the flow enters a cancelling state.
        on_crashed: An optional list of callables to run when the flow enters a crashed state.
        on_running: An optional list of callables to run when the flow enters a running state.
    """

    # NOTE: These parameters (types, defaults, and docstrings) should be duplicated
    #       exactly in the @flow decorator
    def __init__(
        self,
        fn: Callable[P, R],
        name: Optional[str] = None,
        version: Optional[str] = None,
        flow_run_name: Optional[Union[Callable[[], str], str]] = None,
        retries: Optional[int] = None,
        retry_delay_seconds: Optional[Union[int, float]] = None,
        task_runner: Union[Type[BaseTaskRunner], BaseTaskRunner] = ConcurrentTaskRunner,
        description: str = None,
        timeout_seconds: Union[int, float] = None,
        validate_parameters: bool = True,
        persist_result: Optional[bool] = None,
        result_storage: Optional[ResultStorage] = None,
        result_serializer: Optional[ResultSerializer] = None,
        cache_result_in_memory: bool = True,
        log_prints: Optional[bool] = None,
        on_completion: Optional[
            List[Callable[[FlowSchema, FlowRun, State], None]]
        ] = None,
        on_failure: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
        on_cancellation: Optional[
            List[Callable[[FlowSchema, FlowRun, State], None]]
        ] = None,
        on_crashed: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
        on_running: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
    ):
        if name is not None and not isinstance(name, str):
            raise TypeError(
                "Expected string for flow parameter 'name'; got {} instead. {}".format(
                    type(name).__name__,
                    (
                        "Perhaps you meant to call it? e.g."
                        " '@flow(name=get_flow_run_name())'"
                        if callable(name)
                        else ""
                    ),
                )
            )

        # Validate if hook passed is list and contains callables
        hook_categories = [
            on_completion,
            on_failure,
            on_cancellation,
            on_crashed,
            on_running,
        ]
        hook_names = [
            "on_completion",
            "on_failure",
            "on_cancellation",
            "on_crashed",
            "on_running",
        ]
        for hooks, hook_name in zip(hook_categories, hook_names):
            if hooks is not None:
                if not hooks:
                    raise ValueError(f"Empty list passed for '{hook_name}'")
                try:
                    hooks = list(hooks)
                except TypeError:
                    raise TypeError(
                        f"Expected iterable for '{hook_name}'; got"
                        f" {type(hooks).__name__} instead. Please provide a list of"
                        f" hooks to '{hook_name}':\n\n"
                        f"@flow({hook_name}=[hook1, hook2])\ndef"
                        " my_flow():\n\tpass"
                    )

                for hook in hooks:
                    if not callable(hook):
                        raise TypeError(
                            f"Expected callables in '{hook_name}'; got"
                            f" {type(hook).__name__} instead. Please provide a list of"
                            f" hooks to '{hook_name}':\n\n"
                            f"@flow({hook_name}=[hook1, hook2])\ndef"
                            " my_flow():\n\tpass"
                        )

        if not callable(fn):
            raise TypeError("'fn' must be callable")

        # Validate name if given
        if name:
            raise_on_name_with_banned_characters(name)

        self.name = name or fn.__name__.replace("_", "-")

        if flow_run_name is not None:
            if not isinstance(flow_run_name, str) and not callable(flow_run_name):
                raise TypeError(
                    "Expected string or callable for 'flow_run_name'; got"
                    f" {type(flow_run_name).__name__} instead."
                )
        self.flow_run_name = flow_run_name

        task_runner = task_runner or ConcurrentTaskRunner()
        self.task_runner = (
            task_runner() if isinstance(task_runner, type) else task_runner
        )

        self.log_prints = log_prints

        self.description = description or inspect.getdoc(fn)
        update_wrapper(self, fn)
        self.fn = fn
        self.isasync = is_async_fn(self.fn)

        raise_for_reserved_arguments(self.fn, ["return_state", "wait_for"])

        # Version defaults to a hash of the function's file
        flow_file = inspect.getsourcefile(self.fn)
        if not version:
            try:
                version = file_hash(flow_file)
            except (FileNotFoundError, TypeError, OSError):
                pass  # `getsourcefile` can return null values and "<stdin>" for objects in repls
        self.version = version

        self.timeout_seconds = float(timeout_seconds) if timeout_seconds else None

        # FlowRunPolicy settings
        # TODO: We can instantiate a `FlowRunPolicy` and add Pydantic bound checks to
        #       validate that the user passes positive numbers here
        self.retries = (
            retries if retries is not None else PREFECT_FLOW_DEFAULT_RETRIES.value()
        )

        self.retry_delay_seconds = (
            retry_delay_seconds
            if retry_delay_seconds is not None
            else PREFECT_FLOW_DEFAULT_RETRY_DELAY_SECONDS.value()
        )

        self.parameters = parameter_schema(self.fn)
        self.should_validate_parameters = validate_parameters

        if self.should_validate_parameters:
            # Try to create the validated function now so that incompatibility can be
            # raised at declaration time rather than at runtime
            # We cannot, however, store the validated function on the flow because it
            # is not picklable in some environments
            try:
                ValidatedFunction(self.fn, config={"arbitrary_types_allowed": True})
            except pydantic.ConfigError as exc:
                raise ValueError(
                    "Flow function is not compatible with `validate_parameters`. "
                    "Disable validation or change the argument names."
                ) from exc

        self.persist_result = persist_result
        self.result_storage = result_storage
        self.result_serializer = result_serializer
        self.cache_result_in_memory = cache_result_in_memory
        self.on_completion = on_completion
        self.on_failure = on_failure
        self.on_cancellation = on_cancellation
        self.on_crashed = on_crashed
        self.on_running = on_running

        # Used for flows loaded from remote storage
        self._storage: Optional[RunnerStorage] = None
        self._entrypoint: Optional[str] = None

        module = fn.__module__
        if module in ("__main__", "__prefect_loader__"):
            module_name = inspect.getfile(fn)
            module = module_name if module_name != "__main__" else module

        self._entrypoint = f"{module}:{fn.__name__}"

    def with_options(
        self,
        *,
        name: str = None,
        version: str = None,
        retries: Optional[int] = None,
        retry_delay_seconds: Optional[Union[int, float]] = None,
        description: str = None,
        flow_run_name: Optional[Union[Callable[[], str], str]] = None,
        task_runner: Union[Type[BaseTaskRunner], BaseTaskRunner] = None,
        timeout_seconds: Union[int, float] = None,
        validate_parameters: bool = None,
        persist_result: Optional[bool] = NotSet,
        result_storage: Optional[ResultStorage] = NotSet,
        result_serializer: Optional[ResultSerializer] = NotSet,
        cache_result_in_memory: bool = None,
        log_prints: Optional[bool] = NotSet,
        on_completion: Optional[
            List[Callable[[FlowSchema, FlowRun, State], None]]
        ] = None,
        on_failure: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
        on_cancellation: Optional[
            List[Callable[[FlowSchema, FlowRun, State], None]]
        ] = None,
        on_crashed: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
        on_running: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
    ) -> Self:
        """
        Create a new flow from the current object, updating provided options.

        Args:
            name: A new name for the flow.
            version: A new version for the flow.
            description: A new description for the flow.
            flow_run_name: An optional name to distinguish runs of this flow; this name
                can be provided as a string template with the flow's parameters as variables,
                or a function that returns a string.
            task_runner: A new task runner for the flow.
            timeout_seconds: A new number of seconds to fail the flow after if still
                running.
            validate_parameters: A new value indicating if flow calls should validate
                given parameters.
            retries: A new number of times to retry on flow run failure.
            retry_delay_seconds: A new number of seconds to wait before retrying the
                flow after failure. This is only applicable if `retries` is nonzero.
            persist_result: A new option for enabling or disabling result persistence.
            result_storage: A new storage type to use for results.
            result_serializer: A new serializer to use for results.
            cache_result_in_memory: A new value indicating if the flow's result should
                be cached in memory.
            on_failure: A new list of callables to run when the flow enters a failed state.
            on_completion: A new list of callables to run when the flow enters a completed state.
            on_cancellation: A new list of callables to run when the flow enters a cancelling state.
            on_crashed: A new list of callables to run when the flow enters a crashed state.
            on_running: A new list of callables to run when the flow enters a running state.

        Returns:
            A new `Flow` instance.

        Examples:

            Create a new flow from an existing flow and update the name:

            >>> @flow(name="My flow")
            >>> def my_flow():
            >>>     return 1
            >>>
            >>> new_flow = my_flow.with_options(name="My new flow")

            Create a new flow from an existing flow, update the task runner, and call
            it without an intermediate variable:

            >>> from prefect.task_runners import SequentialTaskRunner
            >>>
            >>> @flow
            >>> def my_flow(x, y):
            >>>     return x + y
            >>>
            >>> state = my_flow.with_options(task_runner=SequentialTaskRunner)(1, 3)
            >>> assert state.result() == 4

        """
        new_flow = Flow(
            fn=self.fn,
            name=name or self.name,
            description=description or self.description,
            flow_run_name=flow_run_name or self.flow_run_name,
            version=version or self.version,
            task_runner=task_runner or self.task_runner,
            retries=retries if retries is not None else self.retries,
            retry_delay_seconds=(
                retry_delay_seconds
                if retry_delay_seconds is not None
                else self.retry_delay_seconds
            ),
            timeout_seconds=(
                timeout_seconds if timeout_seconds is not None else self.timeout_seconds
            ),
            validate_parameters=(
                validate_parameters
                if validate_parameters is not None
                else self.should_validate_parameters
            ),
            persist_result=(
                persist_result if persist_result is not NotSet else self.persist_result
            ),
            result_storage=(
                result_storage if result_storage is not NotSet else self.result_storage
            ),
            result_serializer=(
                result_serializer
                if result_serializer is not NotSet
                else self.result_serializer
            ),
            cache_result_in_memory=(
                cache_result_in_memory
                if cache_result_in_memory is not None
                else self.cache_result_in_memory
            ),
            log_prints=log_prints if log_prints is not NotSet else self.log_prints,
            on_completion=on_completion or self.on_completion,
            on_failure=on_failure or self.on_failure,
            on_cancellation=on_cancellation or self.on_cancellation,
            on_crashed=on_crashed or self.on_crashed,
            on_running=on_running or self.on_running,
        )
        new_flow._storage = self._storage
        new_flow._entrypoint = self._entrypoint
        return new_flow

    def validate_parameters(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
        """
        Validate parameters for compatibility with the flow by attempting to cast the inputs to the
        associated types specified by the function's type annotations.

        Returns:
            A new dict of parameters that have been cast to the appropriate types

        Raises:
            ParameterTypeError: if the provided parameters are not valid
        """
        args, kwargs = parameters_to_args_kwargs(self.fn, parameters)

        if HAS_PYDANTIC_V2:
            has_v1_models = any(isinstance(o, V1BaseModel) for o in args) or any(
                isinstance(o, V1BaseModel) for o in kwargs.values()
            )
            has_v2_types = any(is_v2_type(o) for o in args) or any(
                is_v2_type(o) for o in kwargs.values()
            )

            if has_v1_models and has_v2_types:
                raise ParameterTypeError(
                    "Cannot mix Pydantic v1 and v2 types as arguments to a flow."
                )

            if has_v1_models:
                validated_fn = V1ValidatedFunction(
                    self.fn, config={"arbitrary_types_allowed": True}
                )
            else:
                validated_fn = V2ValidatedFunction(
                    self.fn, config={"arbitrary_types_allowed": True}
                )

        else:
            validated_fn = ValidatedFunction(
                self.fn, config={"arbitrary_types_allowed": True}
            )

        try:
            model = validated_fn.init_model_instance(*args, **kwargs)
        except pydantic.ValidationError as exc:
            # We capture the pydantic exception and raise our own because the pydantic
            # exception is not picklable when using a cythonized pydantic installation
            raise ParameterTypeError.from_validation_error(exc) from None
        except V2ValidationError as exc:
            # We capture the pydantic exception and raise our own because the pydantic
            # exception is not picklable when using a cythonized pydantic installation
            raise ParameterTypeError.from_validation_error(exc) from None

        # Get the updated parameter dict with cast values from the model
        cast_parameters = {
            k: v
            for k, v in model._iter()
            if k in model.__fields_set__ or model.__fields__[k].default_factory
        }
        return cast_parameters

    def serialize_parameters(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
        """
        Convert parameters to a serializable form.

        Uses FastAPI's `jsonable_encoder` to convert to JSON compatible objects without
        converting everything directly to a string. This maintains basic types like
        integers during API roundtrips.
        """
        serialized_parameters = {}
        for key, value in parameters.items():
            try:
                serialized_parameters[key] = jsonable_encoder(value)
            except (TypeError, ValueError):
                logger.debug(
                    f"Parameter {key!r} for flow {self.name!r} is of unserializable "
                    f"type {type(value).__name__!r} and will not be stored "
                    "in the backend."
                )
                serialized_parameters[key] = f"<{type(value).__name__}>"
        return serialized_parameters

    @sync_compatible
    @deprecated_parameter(
        "schedule",
        start_date="Mar 2024",
        when=lambda p: p is not None,
        help="Use `schedules` instead.",
    )
    @deprecated_parameter(
        "is_schedule_active",
        start_date="Mar 2024",
        when=lambda p: p is not None,
        help="Use `paused` instead.",
    )
    async def to_deployment(
        self,
        name: str,
        interval: Optional[
            Union[
                Iterable[Union[int, float, datetime.timedelta]],
                int,
                float,
                datetime.timedelta,
            ]
        ] = None,
        cron: Optional[Union[Iterable[str], str]] = None,
        rrule: Optional[Union[Iterable[str], str]] = None,
        paused: Optional[bool] = None,
        schedules: Optional[List["FlexibleScheduleList"]] = None,
        schedule: Optional[SCHEDULE_TYPES] = None,
        is_schedule_active: Optional[bool] = None,
        parameters: Optional[dict] = None,
        triggers: Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]] = None,
        description: Optional[str] = None,
        tags: Optional[List[str]] = None,
        version: Optional[str] = None,
        enforce_parameter_schema: bool = False,
        work_pool_name: Optional[str] = None,
        work_queue_name: Optional[str] = None,
        job_variables: Optional[Dict[str, Any]] = None,
        entrypoint_type: EntrypointType = EntrypointType.FILE_PATH,
    ) -> "RunnerDeployment":
        """
        Creates a runner deployment object for this flow.

        Args:
            name: The name to give the created deployment.
            interval: An interval on which to execute the new deployment. Accepts either a number
                or a timedelta object. If a number is given, it will be interpreted as seconds.
            cron: A cron schedule of when to execute runs of this deployment.
            rrule: An rrule schedule of when to execute runs of this deployment.
            paused: Whether or not to set this deployment as paused.
            schedules: A list of schedule objects defining when to execute runs of this deployment.
                Used to define multiple schedules or additional scheduling options such as `timezone`.
            schedule: A schedule object defining when to execute runs of this deployment.
            is_schedule_active: Whether or not to set the schedule for this deployment as active. If
                not provided when creating a deployment, the schedule will be set as active. If not
                provided when updating a deployment, the schedule's activation will not be changed.
            parameters: A dictionary of default parameter values to pass to runs of this deployment.
            triggers: A list of triggers that will kick off runs of this deployment.
            description: A description for the created deployment. Defaults to the flow's
                description if not provided.
            tags: A list of tags to associate with the created deployment for organizational
                purposes.
            version: A version for the created deployment. Defaults to the flow's version.
            enforce_parameter_schema: Whether or not the Prefect API should enforce the
                parameter schema for the created deployment.
            work_pool_name: The name of the work pool to use for this deployment.
            work_queue_name: The name of the work queue to use for this deployment's scheduled runs.
                If not provided the default work queue for the work pool will be used.
            job_variables: Settings used to override the values specified default base job template
                of the chosen work pool. Refer to the base job template of the chosen work pool for
            entrypoint_type: Type of entrypoint to use for the deployment. When using a module path
                entrypoint, ensure that the module will be importable in the execution environment.

        Examples:
            Prepare two deployments and serve them:

            ```python
            from prefect import flow, serve

            @flow
            def my_flow(name):
                print(f"hello {name}")

            @flow
            def my_other_flow(name):
                print(f"goodbye {name}")

            if __name__ == "__main__":
                hello_deploy = my_flow.to_deployment("hello", tags=["dev"])
                bye_deploy = my_other_flow.to_deployment("goodbye", tags=["dev"])
                serve(hello_deploy, bye_deploy)
            ```
        """
        from prefect.deployments.runner import RunnerDeployment

        if not name.endswith(".py"):
            raise_on_name_with_banned_characters(name)
        if self._storage and self._entrypoint:
            return await RunnerDeployment.from_storage(
                storage=self._storage,
                entrypoint=self._entrypoint,
                name=name,
                interval=interval,
                cron=cron,
                rrule=rrule,
                paused=paused,
                schedules=schedules,
                schedule=schedule,
                is_schedule_active=is_schedule_active,
                tags=tags,
                triggers=triggers,
                parameters=parameters or {},
                description=description,
                version=version,
                enforce_parameter_schema=enforce_parameter_schema,
                work_pool_name=work_pool_name,
                work_queue_name=work_queue_name,
                job_variables=job_variables,
            )
        else:
            return RunnerDeployment.from_flow(
                self,
                name=name,
                interval=interval,
                cron=cron,
                rrule=rrule,
                paused=paused,
                schedules=schedules,
                schedule=schedule,
                is_schedule_active=is_schedule_active,
                tags=tags,
                triggers=triggers,
                parameters=parameters or {},
                description=description,
                version=version,
                enforce_parameter_schema=enforce_parameter_schema,
                work_pool_name=work_pool_name,
                work_queue_name=work_queue_name,
                job_variables=job_variables,
                entrypoint_type=entrypoint_type,
            )

    @sync_compatible
    async def serve(
        self,
        name: Optional[str] = None,
        interval: Optional[
            Union[
                Iterable[Union[int, float, datetime.timedelta]],
                int,
                float,
                datetime.timedelta,
            ]
        ] = None,
        cron: Optional[Union[Iterable[str], str]] = None,
        rrule: Optional[Union[Iterable[str], str]] = None,
        paused: Optional[bool] = None,
        schedules: Optional[List["FlexibleScheduleList"]] = None,
        schedule: Optional[SCHEDULE_TYPES] = None,
        is_schedule_active: Optional[bool] = None,
        triggers: Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]] = None,
        parameters: Optional[dict] = None,
        description: Optional[str] = None,
        tags: Optional[List[str]] = None,
        version: Optional[str] = None,
        enforce_parameter_schema: bool = False,
        pause_on_shutdown: bool = True,
        print_starting_message: bool = True,
        limit: Optional[int] = None,
        webserver: bool = False,
        entrypoint_type: EntrypointType = EntrypointType.FILE_PATH,
    ):
        """
        Creates a deployment for this flow and starts a runner to monitor for scheduled work.

        Args:
            name: The name to give the created deployment. Defaults to the name of the flow.
            interval: An interval on which to execute the deployment. Accepts a number or a
                timedelta object to create a single schedule. If a number is given, it will be
                interpreted as seconds. Also accepts an iterable of numbers or timedelta to create
                multiple schedules.
            cron: A cron schedule string of when to execute runs of this deployment.
                Also accepts an iterable of cron schedule strings to create multiple schedules.
            rrule: An rrule schedule string of when to execute runs of this deployment.
                Also accepts an iterable of rrule schedule strings to create multiple schedules.
            triggers: A list of triggers that will kick off runs of this deployment.
            paused: Whether or not to set this deployment as paused.
            schedules: A list of schedule objects defining when to execute runs of this deployment.
                Used to define multiple schedules or additional scheduling options like `timezone`.
            schedule: A schedule object defining when to execute runs of this deployment. Used to
                define additional scheduling options such as `timezone`.
            is_schedule_active: Whether or not to set the schedule for this deployment as active. If
                not provided when creating a deployment, the schedule will be set as active. If not
                provided when updating a deployment, the schedule's activation will not be changed.
            parameters: A dictionary of default parameter values to pass to runs of this deployment.
            description: A description for the created deployment. Defaults to the flow's
                description if not provided.
            tags: A list of tags to associate with the created deployment for organizational
                purposes.
            version: A version for the created deployment. Defaults to the flow's version.
            enforce_parameter_schema: Whether or not the Prefect API should enforce the
                parameter schema for the created deployment.
            pause_on_shutdown: If True, provided schedule will be paused when the serve function is stopped.
                If False, the schedules will continue running.
            print_starting_message: Whether or not to print the starting message when flow is served.
            limit: The maximum number of runs that can be executed concurrently.
            webserver: Whether or not to start a monitoring webserver for this flow.
            entrypoint_type: Type of entrypoint to use for the deployment. When using a module path
                entrypoint, ensure that the module will be importable in the execution environment.

        Examples:
            Serve a flow:

            ```python
            from prefect import flow

            @flow
            def my_flow(name):
                print(f"hello {name}")

            if __name__ == "__main__":
                my_flow.serve("example-deployment")
            ```

            Serve a flow and run it every hour:

            ```python
            from prefect import flow

            @flow
            def my_flow(name):
                print(f"hello {name}")

            if __name__ == "__main__":
                my_flow.serve("example-deployment", interval=3600)
            ```
        """
        from prefect.runner import Runner

        if not name:
            name = self.name
        else:
            # Handling for my_flow.serve(__file__)
            # Will set name to name of file where my_flow.serve() without the extension
            # Non filepath strings will pass through unchanged
            name = Path(name).stem

        runner = Runner(name=name, pause_on_shutdown=pause_on_shutdown, limit=limit)
        deployment_id = await runner.add_flow(
            self,
            name=name,
            triggers=triggers,
            interval=interval,
            cron=cron,
            rrule=rrule,
            paused=paused,
            schedules=schedules,
            schedule=schedule,
            is_schedule_active=is_schedule_active,
            parameters=parameters,
            description=description,
            tags=tags,
            version=version,
            enforce_parameter_schema=enforce_parameter_schema,
            entrypoint_type=entrypoint_type,
        )
        if print_starting_message:
            help_message = (
                f"[green]Your flow {self.name!r} is being served and polling for"
                " scheduled runs!\n[/]\nTo trigger a run for this flow, use the"
                " following command:\n[blue]\n\t$ prefect deployment run"
                f" '{self.name}/{name}'\n[/]"
            )
            if PREFECT_UI_URL:
                help_message += (
                    "\nYou can also run your flow via the Prefect UI:"
                    f" [blue]{PREFECT_UI_URL.value()}/deployments/deployment/{deployment_id}[/]\n"
                )

            console = Console()
            console.print(help_message, soft_wrap=True)
        await runner.start(webserver=webserver)

    @classmethod
    @sync_compatible
    async def from_source(
        cls: Type[F],
        source: Union[str, RunnerStorage, ReadableDeploymentStorage],
        entrypoint: str,
    ) -> F:
        """
        Loads a flow from a remote source.

        Args:
            source: Either a URL to a git repository or a storage object.
            entrypoint:  The path to a file containing a flow and the name of the flow function in
                the format `./path/to/file.py:flow_func_name`.

        Returns:
            A new `Flow` instance.

        Examples:
            Load a flow from a public git repository:


            ```python
            from prefect import flow
            from prefect.runner.storage import GitRepository
            from prefect.blocks.system import Secret

            my_flow = flow.from_source(
                source="https://github.com/org/repo.git",
                entrypoint="flows.py:my_flow",
            )

            my_flow()
            ```

            Load a flow from a private git repository using an access token stored in a `Secret` block:

            ```python
            from prefect import flow
            from prefect.runner.storage import GitRepository
            from prefect.blocks.system import Secret

            my_flow = flow.from_source(
                source=GitRepository(
                    url="https://github.com/org/repo.git",
                    credentials={"access_token": Secret.load("github-access-token")}
                ),
                entrypoint="flows.py:my_flow",
            )

            my_flow()
            ```
        """
        if isinstance(source, str):
            storage = create_storage_from_source(source)
        elif isinstance(source, RunnerStorage):
            storage = source
        elif hasattr(source, "get_directory"):
            storage = BlockStorageAdapter(source)
        else:
            raise TypeError(
                f"Unsupported source type {type(source).__name__!r}. Please provide a"
                " URL to remote storage or a storage object."
            )

        with tempfile.TemporaryDirectory() as tmpdir:
            if not isinstance(storage, LocalStorage):
                storage.set_base_path(Path(tmpdir))
                await storage.pull_code()

            full_entrypoint = str(storage.destination / entrypoint)
            flow: "Flow" = await from_async.wait_for_call_in_new_thread(
                create_call(load_flow_from_entrypoint, full_entrypoint)
            )
            flow._storage = storage
            flow._entrypoint = entrypoint

        return flow

    @sync_compatible
    async def deploy(
        self,
        name: str,
        work_pool_name: Optional[str] = None,
        image: Optional[Union[str, DeploymentImage]] = None,
        build: bool = True,
        push: bool = True,
        work_queue_name: Optional[str] = None,
        job_variables: Optional[dict] = None,
        interval: Optional[Union[int, float, datetime.timedelta]] = None,
        cron: Optional[str] = None,
        rrule: Optional[str] = None,
        paused: Optional[bool] = None,
        schedules: Optional[List[MinimalDeploymentSchedule]] = None,
        schedule: Optional[SCHEDULE_TYPES] = None,
        is_schedule_active: Optional[bool] = None,
        triggers: Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]] = None,
        parameters: Optional[dict] = None,
        description: Optional[str] = None,
        tags: Optional[List[str]] = None,
        version: Optional[str] = None,
        enforce_parameter_schema: bool = False,
        entrypoint_type: EntrypointType = EntrypointType.FILE_PATH,
        print_next_steps: bool = True,
        ignore_warnings: bool = False,
    ) -> UUID:
        """
        Deploys a flow to run on dynamic infrastructure via a work pool.

        By default, calling this method will build a Docker image for the flow, push it to a registry,
        and create a deployment via the Prefect API that will run the flow on the given schedule.

        If you want to use an existing image, you can pass `build=False` to skip building and pushing
        an image.

        Args:
            name: The name to give the created deployment.
            work_pool_name: The name of the work pool to use for this deployment. Defaults to
                the value of `PREFECT_DEFAULT_WORK_POOL_NAME`.
            image: The name of the Docker image to build, including the registry and
                repository. Pass a DeploymentImage instance to customize the Dockerfile used
                and build arguments.
            build: Whether or not to build a new image for the flow. If False, the provided
                image will be used as-is and pulled at runtime.
            push: Whether or not to skip pushing the built image to a registry.
            work_queue_name: The name of the work queue to use for this deployment's scheduled runs.
                If not provided the default work queue for the work pool will be used.
            job_variables: Settings used to override the values specified default base job template
                of the chosen work pool. Refer to the base job template of the chosen work pool for
                available settings.
            interval: An interval on which to execute the deployment. Accepts a number or a
                timedelta object to create a single schedule. If a number is given, it will be
                interpreted as seconds. Also accepts an iterable of numbers or timedelta to create
                multiple schedules.
            cron: A cron schedule string of when to execute runs of this deployment.
                Also accepts an iterable of cron schedule strings to create multiple schedules.
            rrule: An rrule schedule string of when to execute runs of this deployment.
                Also accepts an iterable of rrule schedule strings to create multiple schedules.
            triggers: A list of triggers that will kick off runs of this deployment.
            paused: Whether or not to set this deployment as paused.
            schedules: A list of schedule objects defining when to execute runs of this deployment.
                Used to define multiple schedules or additional scheduling options like `timezone`.
            schedule: A schedule object defining when to execute runs of this deployment. Used to
                define additional scheduling options like `timezone`.
            is_schedule_active: Whether or not to set the schedule for this deployment as active. If
                not provided when creating a deployment, the schedule will be set as active. If not
                provided when updating a deployment, the schedule's activation will not be changed.
            parameters: A dictionary of default parameter values to pass to runs of this deployment.
            description: A description for the created deployment. Defaults to the flow's
                description if not provided.
            tags: A list of tags to associate with the created deployment for organizational
                purposes.
            version: A version for the created deployment. Defaults to the flow's version.
            enforce_parameter_schema: Whether or not the Prefect API should enforce the
                parameter schema for the created deployment.
            entrypoint_type: Type of entrypoint to use for the deployment. When using a module path
                entrypoint, ensure that the module will be importable in the execution environment.
            print_next_steps_message: Whether or not to print a message with next steps
                after deploying the deployments.
            ignore_warnings: Whether or not to ignore warnings about the work pool type.

        Returns:
            The ID of the created/updated deployment.

        Examples:
            Deploy a local flow to a work pool:

            ```python
            from prefect import flow

            @flow
            def my_flow(name):
                print(f"hello {name}")

            if __name__ == "__main__":
                my_flow.deploy(
                    "example-deployment",
                    work_pool_name="my-work-pool",
                    image="my-repository/my-image:dev",
                )
            ```

            Deploy a remotely stored flow to a work pool:

            ```python
            from prefect import flow

            if __name__ == "__main__":
                flow.from_source(
                    source="https://github.com/org/repo.git",
                    entrypoint="flows.py:my_flow",
                ).deploy(
                    "example-deployment",
                    work_pool_name="my-work-pool",
                    image="my-repository/my-image:dev",
                )
            ```
        """
        work_pool_name = work_pool_name or PREFECT_DEFAULT_WORK_POOL_NAME.value()

        try:
            async with get_client() as client:
                work_pool = await client.read_work_pool(work_pool_name)
        except ObjectNotFound as exc:
            raise ValueError(
                f"Could not find work pool {work_pool_name!r}. Please create it before"
                " deploying this flow."
            ) from exc

        deployment = await self.to_deployment(
            name=name,
            interval=interval,
            cron=cron,
            rrule=rrule,
            schedules=schedules,
            paused=paused,
            schedule=schedule,
            is_schedule_active=is_schedule_active,
            triggers=triggers,
            parameters=parameters,
            description=description,
            tags=tags,
            version=version,
            enforce_parameter_schema=enforce_parameter_schema,
            work_queue_name=work_queue_name,
            job_variables=job_variables,
            entrypoint_type=entrypoint_type,
        )

        deployment_ids = await deploy(
            deployment,
            work_pool_name=work_pool_name,
            image=image,
            build=build,
            push=push,
            print_next_steps_message=False,
            ignore_warnings=ignore_warnings,
        )

        if print_next_steps:
            console = Console()
            if not work_pool.is_push_pool and not work_pool.is_managed_pool:
                console.print(
                    "\nTo execute flow runs from this deployment, start a worker in a"
                    " separate terminal that pulls work from the"
                    f" {work_pool_name!r} work pool:"
                )
                console.print(
                    f"\n\t$ prefect worker start --pool {work_pool_name!r}",
                    style="blue",
                )
            console.print(
                "\nTo schedule a run for this deployment, use the following command:"
            )
            console.print(
                f"\n\t$ prefect deployment run '{self.name}/{name}'\n",
                style="blue",
            )
            if PREFECT_UI_URL:
                message = (
                    "\nYou can also run your flow via the Prefect UI:"
                    f" [blue]{PREFECT_UI_URL.value()}/deployments/deployment/{deployment_ids[0]}[/]\n"
                )
                console.print(message, soft_wrap=True)

        return deployment_ids[0]

    @overload
    def __call__(self: "Flow[P, NoReturn]", *args: P.args, **kwargs: P.kwargs) -> None:
        # `NoReturn` matches if a type can't be inferred for the function which stops a
        # sync function from matching the `Coroutine` overload
        ...

    @overload
    def __call__(
        self: "Flow[P, Coroutine[Any, Any, T]]", *args: P.args, **kwargs: P.kwargs
    ) -> Awaitable[T]:
        ...

    @overload
    def __call__(
        self: "Flow[P, T]",
        *args: P.args,
        **kwargs: P.kwargs,
    ) -> T:
        ...

    @overload
    def __call__(
        self: "Flow[P, T]",
        *args: P.args,
        return_state: Literal[True],
        **kwargs: P.kwargs,
    ) -> State[T]:
        ...

    def __call__(
        self,
        *args: "P.args",
        return_state: bool = False,
        wait_for: Optional[Iterable[PrefectFuture]] = None,
        **kwargs: "P.kwargs",
    ):
        """
        Run the flow and return its result.


        Flow parameter values must be serializable by Pydantic.

        If writing an async flow, this call must be awaited.

        This will create a new flow run in the API.

        Args:
            *args: Arguments to run the flow with.
            return_state: Return a Prefect State containing the result of the
                flow run.
            wait_for: Upstream task futures to wait for before starting the flow if called as a subflow
            **kwargs: Keyword arguments to run the flow with.

        Returns:
            If `return_state` is False, returns the result of the flow run.
            If `return_state` is True, returns the result of the flow run
                wrapped in a Prefect State which provides error handling.

        Examples:

            Define a flow

            >>> @flow
            >>> def my_flow(name):
            >>>     print(f"hello {name}")
            >>>     return f"goodbye {name}"

            Run a flow

            >>> my_flow("marvin")
            hello marvin
            "goodbye marvin"

            Run a flow with additional tags

            >>> from prefect import tags
            >>> with tags("db", "blue"):
            >>>     my_flow("foo")
        """
        from prefect.engine import enter_flow_run_engine_from_flow_call

        # Convert the call args/kwargs to a parameter dict
        parameters = get_call_parameters(self.fn, args, kwargs)

        return_type = "state" if return_state else "result"

        task_viz_tracker = get_task_viz_tracker()
        if task_viz_tracker:
            # this is a subflow, for now return a single task and do not go further
            # we can add support for exploring subflows for tasks in the future.
            return track_viz_task(self.isasync, self.name, parameters)

        if PREFECT_EXPERIMENTAL_ENABLE_NEW_ENGINE.value():
            from prefect.new_flow_engine import run_flow, run_flow_sync

            run_kwargs = dict(
                flow=self,
                parameters=parameters,
                wait_for=wait_for,
                return_type=return_type,
            )
            if self.isasync:
                # this returns an awaitable coroutine
                return run_flow(**run_kwargs)
            else:
                return run_flow_sync(**run_kwargs)

        return enter_flow_run_engine_from_flow_call(
            self,
            parameters,
            wait_for=wait_for,
            return_type=return_type,
        )

    @overload
    def _run(self: "Flow[P, NoReturn]", *args: P.args, **kwargs: P.kwargs) -> State[T]:
        # `NoReturn` matches if a type can't be inferred for the function which stops a
        # sync function from matching the `Coroutine` overload
        ...

    @overload
    def _run(
        self: "Flow[P, Coroutine[Any, Any, T]]", *args: P.args, **kwargs: P.kwargs
    ) -> Awaitable[T]:
        ...

    @overload
    def _run(self: "Flow[P, T]", *args: P.args, **kwargs: P.kwargs) -> State[T]:
        ...

    def _run(
        self,
        *args: "P.args",
        wait_for: Optional[Iterable[PrefectFuture]] = None,
        **kwargs: "P.kwargs",
    ):
        """
        Run the flow and return its final state.

        Examples:

            Run a flow and get the returned result

            >>> state = my_flow._run("marvin")
            >>> state.result()
           "goodbye marvin"
        """
        from prefect.engine import enter_flow_run_engine_from_flow_call

        # Convert the call args/kwargs to a parameter dict
        parameters = get_call_parameters(self.fn, args, kwargs)

        return enter_flow_run_engine_from_flow_call(
            self,
            parameters,
            wait_for=wait_for,
            return_type="state",
        )

    @sync_compatible
    async def visualize(self, *args, **kwargs):
        """
        Generates a graphviz object representing the current flow. In IPython notebooks,
        it's rendered inline, otherwise in a new window as a PNG.

        Raises:
            - ImportError: If `graphviz` isn't installed.
            - GraphvizExecutableNotFoundError: If the `dot` executable isn't found.
            - FlowVisualizationError: If the flow can't be visualized for any other reason.
        """
        if not PREFECT_UNIT_TEST_MODE:
            warnings.warn(
                "`flow.visualize()` will execute code inside of your flow that is not"
                " decorated with `@task` or `@flow`."
            )

        try:
            with TaskVizTracker() as tracker:
                if self.isasync:
                    await self.fn(*args, **kwargs)
                else:
                    self.fn(*args, **kwargs)

                graph = build_task_dependencies(tracker)

                visualize_task_dependencies(graph, self.name)

        except GraphvizImportError:
            raise
        except GraphvizExecutableNotFoundError:
            raise
        except VisualizationUnsupportedError:
            raise
        except FlowVisualizationError:
            raise
        except Exception as e:
            msg = (
                "It's possible you are trying to visualize a flow that contains "
                "code that directly interacts with the result of a task"
                " inside of the flow. \nTry passing a `viz_return_value` "
                "to the task decorator, e.g. `@task(viz_return_value=[1, 2, 3]).`"
            )

            new_exception = type(e)(str(e) + "\n" + msg)
            # Copy traceback information from the original exception
            new_exception.__traceback__ = e.__traceback__
            raise new_exception

deploy async

Deploys a flow to run on dynamic infrastructure via a work pool.

By default, calling this method will build a Docker image for the flow, push it to a registry, and create a deployment via the Prefect API that will run the flow on the given schedule.

If you want to use an existing image, you can pass build=False to skip building and pushing an image.

Parameters:

Name Type Description Default
name str

The name to give the created deployment.

required
work_pool_name Optional[str]

The name of the work pool to use for this deployment. Defaults to the value of PREFECT_DEFAULT_WORK_POOL_NAME.

None
image Optional[Union[str, DeploymentImage]]

The name of the Docker image to build, including the registry and repository. Pass a DeploymentImage instance to customize the Dockerfile used and build arguments.

None
build bool

Whether or not to build a new image for the flow. If False, the provided image will be used as-is and pulled at runtime.

True
push bool

Whether or not to skip pushing the built image to a registry.

True
work_queue_name Optional[str]

The name of the work queue to use for this deployment's scheduled runs. If not provided the default work queue for the work pool will be used.

None
job_variables Optional[dict]

Settings used to override the values specified default base job template of the chosen work pool. Refer to the base job template of the chosen work pool for available settings.

None
interval Optional[Union[int, float, timedelta]]

An interval on which to execute the deployment. Accepts a number or a timedelta object to create a single schedule. If a number is given, it will be interpreted as seconds. Also accepts an iterable of numbers or timedelta to create multiple schedules.

None
cron Optional[str]

A cron schedule string of when to execute runs of this deployment. Also accepts an iterable of cron schedule strings to create multiple schedules.

None
rrule Optional[str]

An rrule schedule string of when to execute runs of this deployment. Also accepts an iterable of rrule schedule strings to create multiple schedules.

None
triggers Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]]

A list of triggers that will kick off runs of this deployment.

None
paused Optional[bool]

Whether or not to set this deployment as paused.

None
schedules Optional[List[MinimalDeploymentSchedule]]

A list of schedule objects defining when to execute runs of this deployment. Used to define multiple schedules or additional scheduling options like timezone.

None
schedule Optional[SCHEDULE_TYPES]

A schedule object defining when to execute runs of this deployment. Used to define additional scheduling options like timezone.

None
is_schedule_active Optional[bool]

Whether or not to set the schedule for this deployment as active. If not provided when creating a deployment, the schedule will be set as active. If not provided when updating a deployment, the schedule's activation will not be changed.

None
parameters Optional[dict]

A dictionary of default parameter values to pass to runs of this deployment.

None
description Optional[str]

A description for the created deployment. Defaults to the flow's description if not provided.

None
tags Optional[List[str]]

A list of tags to associate with the created deployment for organizational purposes.

None
version Optional[str]

A version for the created deployment. Defaults to the flow's version.

None
enforce_parameter_schema bool

Whether or not the Prefect API should enforce the parameter schema for the created deployment.

False
entrypoint_type EntrypointType

Type of entrypoint to use for the deployment. When using a module path entrypoint, ensure that the module will be importable in the execution environment.

FILE_PATH
print_next_steps_message

Whether or not to print a message with next steps after deploying the deployments.

required
ignore_warnings bool

Whether or not to ignore warnings about the work pool type.

False

Returns:

Type Description
UUID

The ID of the created/updated deployment.

Examples:

Deploy a local flow to a work pool:

from prefect import flow

@flow
def my_flow(name):
    print(f"hello {name}")

if __name__ == "__main__":
    my_flow.deploy(
        "example-deployment",
        work_pool_name="my-work-pool",
        image="my-repository/my-image:dev",
    )

Deploy a remotely stored flow to a work pool:

from prefect import flow

if __name__ == "__main__":
    flow.from_source(
        source="https://github.com/org/repo.git",
        entrypoint="flows.py:my_flow",
    ).deploy(
        "example-deployment",
        work_pool_name="my-work-pool",
        image="my-repository/my-image:dev",
    )
Source code in prefect/flows.py
 942
 943
 944
 945
 946
 947
 948
 949
 950
 951
 952
 953
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
@sync_compatible
async def deploy(
    self,
    name: str,
    work_pool_name: Optional[str] = None,
    image: Optional[Union[str, DeploymentImage]] = None,
    build: bool = True,
    push: bool = True,
    work_queue_name: Optional[str] = None,
    job_variables: Optional[dict] = None,
    interval: Optional[Union[int, float, datetime.timedelta]] = None,
    cron: Optional[str] = None,
    rrule: Optional[str] = None,
    paused: Optional[bool] = None,
    schedules: Optional[List[MinimalDeploymentSchedule]] = None,
    schedule: Optional[SCHEDULE_TYPES] = None,
    is_schedule_active: Optional[bool] = None,
    triggers: Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]] = None,
    parameters: Optional[dict] = None,
    description: Optional[str] = None,
    tags: Optional[List[str]] = None,
    version: Optional[str] = None,
    enforce_parameter_schema: bool = False,
    entrypoint_type: EntrypointType = EntrypointType.FILE_PATH,
    print_next_steps: bool = True,
    ignore_warnings: bool = False,
) -> UUID:
    """
    Deploys a flow to run on dynamic infrastructure via a work pool.

    By default, calling this method will build a Docker image for the flow, push it to a registry,
    and create a deployment via the Prefect API that will run the flow on the given schedule.

    If you want to use an existing image, you can pass `build=False` to skip building and pushing
    an image.

    Args:
        name: The name to give the created deployment.
        work_pool_name: The name of the work pool to use for this deployment. Defaults to
            the value of `PREFECT_DEFAULT_WORK_POOL_NAME`.
        image: The name of the Docker image to build, including the registry and
            repository. Pass a DeploymentImage instance to customize the Dockerfile used
            and build arguments.
        build: Whether or not to build a new image for the flow. If False, the provided
            image will be used as-is and pulled at runtime.
        push: Whether or not to skip pushing the built image to a registry.
        work_queue_name: The name of the work queue to use for this deployment's scheduled runs.
            If not provided the default work queue for the work pool will be used.
        job_variables: Settings used to override the values specified default base job template
            of the chosen work pool. Refer to the base job template of the chosen work pool for
            available settings.
        interval: An interval on which to execute the deployment. Accepts a number or a
            timedelta object to create a single schedule. If a number is given, it will be
            interpreted as seconds. Also accepts an iterable of numbers or timedelta to create
            multiple schedules.
        cron: A cron schedule string of when to execute runs of this deployment.
            Also accepts an iterable of cron schedule strings to create multiple schedules.
        rrule: An rrule schedule string of when to execute runs of this deployment.
            Also accepts an iterable of rrule schedule strings to create multiple schedules.
        triggers: A list of triggers that will kick off runs of this deployment.
        paused: Whether or not to set this deployment as paused.
        schedules: A list of schedule objects defining when to execute runs of this deployment.
            Used to define multiple schedules or additional scheduling options like `timezone`.
        schedule: A schedule object defining when to execute runs of this deployment. Used to
            define additional scheduling options like `timezone`.
        is_schedule_active: Whether or not to set the schedule for this deployment as active. If
            not provided when creating a deployment, the schedule will be set as active. If not
            provided when updating a deployment, the schedule's activation will not be changed.
        parameters: A dictionary of default parameter values to pass to runs of this deployment.
        description: A description for the created deployment. Defaults to the flow's
            description if not provided.
        tags: A list of tags to associate with the created deployment for organizational
            purposes.
        version: A version for the created deployment. Defaults to the flow's version.
        enforce_parameter_schema: Whether or not the Prefect API should enforce the
            parameter schema for the created deployment.
        entrypoint_type: Type of entrypoint to use for the deployment. When using a module path
            entrypoint, ensure that the module will be importable in the execution environment.
        print_next_steps_message: Whether or not to print a message with next steps
            after deploying the deployments.
        ignore_warnings: Whether or not to ignore warnings about the work pool type.

    Returns:
        The ID of the created/updated deployment.

    Examples:
        Deploy a local flow to a work pool:

        ```python
        from prefect import flow

        @flow
        def my_flow(name):
            print(f"hello {name}")

        if __name__ == "__main__":
            my_flow.deploy(
                "example-deployment",
                work_pool_name="my-work-pool",
                image="my-repository/my-image:dev",
            )
        ```

        Deploy a remotely stored flow to a work pool:

        ```python
        from prefect import flow

        if __name__ == "__main__":
            flow.from_source(
                source="https://github.com/org/repo.git",
                entrypoint="flows.py:my_flow",
            ).deploy(
                "example-deployment",
                work_pool_name="my-work-pool",
                image="my-repository/my-image:dev",
            )
        ```
    """
    work_pool_name = work_pool_name or PREFECT_DEFAULT_WORK_POOL_NAME.value()

    try:
        async with get_client() as client:
            work_pool = await client.read_work_pool(work_pool_name)
    except ObjectNotFound as exc:
        raise ValueError(
            f"Could not find work pool {work_pool_name!r}. Please create it before"
            " deploying this flow."
        ) from exc

    deployment = await self.to_deployment(
        name=name,
        interval=interval,
        cron=cron,
        rrule=rrule,
        schedules=schedules,
        paused=paused,
        schedule=schedule,
        is_schedule_active=is_schedule_active,
        triggers=triggers,
        parameters=parameters,
        description=description,
        tags=tags,
        version=version,
        enforce_parameter_schema=enforce_parameter_schema,
        work_queue_name=work_queue_name,
        job_variables=job_variables,
        entrypoint_type=entrypoint_type,
    )

    deployment_ids = await deploy(
        deployment,
        work_pool_name=work_pool_name,
        image=image,
        build=build,
        push=push,
        print_next_steps_message=False,
        ignore_warnings=ignore_warnings,
    )

    if print_next_steps:
        console = Console()
        if not work_pool.is_push_pool and not work_pool.is_managed_pool:
            console.print(
                "\nTo execute flow runs from this deployment, start a worker in a"
                " separate terminal that pulls work from the"
                f" {work_pool_name!r} work pool:"
            )
            console.print(
                f"\n\t$ prefect worker start --pool {work_pool_name!r}",
                style="blue",
            )
        console.print(
            "\nTo schedule a run for this deployment, use the following command:"
        )
        console.print(
            f"\n\t$ prefect deployment run '{self.name}/{name}'\n",
            style="blue",
        )
        if PREFECT_UI_URL:
            message = (
                "\nYou can also run your flow via the Prefect UI:"
                f" [blue]{PREFECT_UI_URL.value()}/deployments/deployment/{deployment_ids[0]}[/]\n"
            )
            console.print(message, soft_wrap=True)

    return deployment_ids[0]

from_source async classmethod

Loads a flow from a remote source.

Parameters:

Name Type Description Default
source Union[str, RunnerStorage, ReadableDeploymentStorage]

Either a URL to a git repository or a storage object.

required
entrypoint str

The path to a file containing a flow and the name of the flow function in the format ./path/to/file.py:flow_func_name.

required

Returns:

Type Description
F

A new Flow instance.

Examples:

Load a flow from a public git repository:

from prefect import flow
from prefect.runner.storage import GitRepository
from prefect.blocks.system import Secret

my_flow = flow.from_source(
    source="https://github.com/org/repo.git",
    entrypoint="flows.py:my_flow",
)

my_flow()

Load a flow from a private git repository using an access token stored in a Secret block:

from prefect import flow
from prefect.runner.storage import GitRepository
from prefect.blocks.system import Secret

my_flow = flow.from_source(
    source=GitRepository(
        url="https://github.com/org/repo.git",
        credentials={"access_token": Secret.load("github-access-token")}
    ),
    entrypoint="flows.py:my_flow",
)

my_flow()
Source code in prefect/flows.py
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
@classmethod
@sync_compatible
async def from_source(
    cls: Type[F],
    source: Union[str, RunnerStorage, ReadableDeploymentStorage],
    entrypoint: str,
) -> F:
    """
    Loads a flow from a remote source.

    Args:
        source: Either a URL to a git repository or a storage object.
        entrypoint:  The path to a file containing a flow and the name of the flow function in
            the format `./path/to/file.py:flow_func_name`.

    Returns:
        A new `Flow` instance.

    Examples:
        Load a flow from a public git repository:


        ```python
        from prefect import flow
        from prefect.runner.storage import GitRepository
        from prefect.blocks.system import Secret

        my_flow = flow.from_source(
            source="https://github.com/org/repo.git",
            entrypoint="flows.py:my_flow",
        )

        my_flow()
        ```

        Load a flow from a private git repository using an access token stored in a `Secret` block:

        ```python
        from prefect import flow
        from prefect.runner.storage import GitRepository
        from prefect.blocks.system import Secret

        my_flow = flow.from_source(
            source=GitRepository(
                url="https://github.com/org/repo.git",
                credentials={"access_token": Secret.load("github-access-token")}
            ),
            entrypoint="flows.py:my_flow",
        )

        my_flow()
        ```
    """
    if isinstance(source, str):
        storage = create_storage_from_source(source)
    elif isinstance(source, RunnerStorage):
        storage = source
    elif hasattr(source, "get_directory"):
        storage = BlockStorageAdapter(source)
    else:
        raise TypeError(
            f"Unsupported source type {type(source).__name__!r}. Please provide a"
            " URL to remote storage or a storage object."
        )

    with tempfile.TemporaryDirectory() as tmpdir:
        if not isinstance(storage, LocalStorage):
            storage.set_base_path(Path(tmpdir))
            await storage.pull_code()

        full_entrypoint = str(storage.destination / entrypoint)
        flow: "Flow" = await from_async.wait_for_call_in_new_thread(
            create_call(load_flow_from_entrypoint, full_entrypoint)
        )
        flow._storage = storage
        flow._entrypoint = entrypoint

    return flow

serialize_parameters

Convert parameters to a serializable form.

Uses FastAPI's jsonable_encoder to convert to JSON compatible objects without converting everything directly to a string. This maintains basic types like integers during API roundtrips.

Source code in prefect/flows.py
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
def serialize_parameters(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
    """
    Convert parameters to a serializable form.

    Uses FastAPI's `jsonable_encoder` to convert to JSON compatible objects without
    converting everything directly to a string. This maintains basic types like
    integers during API roundtrips.
    """
    serialized_parameters = {}
    for key, value in parameters.items():
        try:
            serialized_parameters[key] = jsonable_encoder(value)
        except (TypeError, ValueError):
            logger.debug(
                f"Parameter {key!r} for flow {self.name!r} is of unserializable "
                f"type {type(value).__name__!r} and will not be stored "
                "in the backend."
            )
            serialized_parameters[key] = f"<{type(value).__name__}>"
    return serialized_parameters

serve async

Creates a deployment for this flow and starts a runner to monitor for scheduled work.

Parameters:

Name Type Description Default
name Optional[str]

The name to give the created deployment. Defaults to the name of the flow.

None
interval Optional[Union[Iterable[Union[int, float, timedelta]], int, float, timedelta]]

An interval on which to execute the deployment. Accepts a number or a timedelta object to create a single schedule. If a number is given, it will be interpreted as seconds. Also accepts an iterable of numbers or timedelta to create multiple schedules.

None
cron Optional[Union[Iterable[str], str]]

A cron schedule string of when to execute runs of this deployment. Also accepts an iterable of cron schedule strings to create multiple schedules.

None
rrule Optional[Union[Iterable[str], str]]

An rrule schedule string of when to execute runs of this deployment. Also accepts an iterable of rrule schedule strings to create multiple schedules.

None
triggers Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]]

A list of triggers that will kick off runs of this deployment.

None
paused Optional[bool]

Whether or not to set this deployment as paused.

None
schedules Optional[List[FlexibleScheduleList]]

A list of schedule objects defining when to execute runs of this deployment. Used to define multiple schedules or additional scheduling options like timezone.

None
schedule Optional[SCHEDULE_TYPES]

A schedule object defining when to execute runs of this deployment. Used to define additional scheduling options such as timezone.

None
is_schedule_active Optional[bool]

Whether or not to set the schedule for this deployment as active. If not provided when creating a deployment, the schedule will be set as active. If not provided when updating a deployment, the schedule's activation will not be changed.

None
parameters Optional[dict]

A dictionary of default parameter values to pass to runs of this deployment.

None
description Optional[str]

A description for the created deployment. Defaults to the flow's description if not provided.

None
tags Optional[List[str]]

A list of tags to associate with the created deployment for organizational purposes.

None
version Optional[str]

A version for the created deployment. Defaults to the flow's version.

None
enforce_parameter_schema bool

Whether or not the Prefect API should enforce the parameter schema for the created deployment.

False
pause_on_shutdown bool

If True, provided schedule will be paused when the serve function is stopped. If False, the schedules will continue running.

True
print_starting_message bool

Whether or not to print the starting message when flow is served.

True
limit Optional[int]

The maximum number of runs that can be executed concurrently.

None
webserver bool

Whether or not to start a monitoring webserver for this flow.

False
entrypoint_type EntrypointType

Type of entrypoint to use for the deployment. When using a module path entrypoint, ensure that the module will be importable in the execution environment.

FILE_PATH

Examples:

Serve a flow:

from prefect import flow

@flow
def my_flow(name):
    print(f"hello {name}")

if __name__ == "__main__":
    my_flow.serve("example-deployment")

Serve a flow and run it every hour:

from prefect import flow

@flow
def my_flow(name):
    print(f"hello {name}")

if __name__ == "__main__":
    my_flow.serve("example-deployment", interval=3600)
Source code in prefect/flows.py
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
@sync_compatible
async def serve(
    self,
    name: Optional[str] = None,
    interval: Optional[
        Union[
            Iterable[Union[int, float, datetime.timedelta]],
            int,
            float,
            datetime.timedelta,
        ]
    ] = None,
    cron: Optional[Union[Iterable[str], str]] = None,
    rrule: Optional[Union[Iterable[str], str]] = None,
    paused: Optional[bool] = None,
    schedules: Optional[List["FlexibleScheduleList"]] = None,
    schedule: Optional[SCHEDULE_TYPES] = None,
    is_schedule_active: Optional[bool] = None,
    triggers: Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]] = None,
    parameters: Optional[dict] = None,
    description: Optional[str] = None,
    tags: Optional[List[str]] = None,
    version: Optional[str] = None,
    enforce_parameter_schema: bool = False,
    pause_on_shutdown: bool = True,
    print_starting_message: bool = True,
    limit: Optional[int] = None,
    webserver: bool = False,
    entrypoint_type: EntrypointType = EntrypointType.FILE_PATH,
):
    """
    Creates a deployment for this flow and starts a runner to monitor for scheduled work.

    Args:
        name: The name to give the created deployment. Defaults to the name of the flow.
        interval: An interval on which to execute the deployment. Accepts a number or a
            timedelta object to create a single schedule. If a number is given, it will be
            interpreted as seconds. Also accepts an iterable of numbers or timedelta to create
            multiple schedules.
        cron: A cron schedule string of when to execute runs of this deployment.
            Also accepts an iterable of cron schedule strings to create multiple schedules.
        rrule: An rrule schedule string of when to execute runs of this deployment.
            Also accepts an iterable of rrule schedule strings to create multiple schedules.
        triggers: A list of triggers that will kick off runs of this deployment.
        paused: Whether or not to set this deployment as paused.
        schedules: A list of schedule objects defining when to execute runs of this deployment.
            Used to define multiple schedules or additional scheduling options like `timezone`.
        schedule: A schedule object defining when to execute runs of this deployment. Used to
            define additional scheduling options such as `timezone`.
        is_schedule_active: Whether or not to set the schedule for this deployment as active. If
            not provided when creating a deployment, the schedule will be set as active. If not
            provided when updating a deployment, the schedule's activation will not be changed.
        parameters: A dictionary of default parameter values to pass to runs of this deployment.
        description: A description for the created deployment. Defaults to the flow's
            description if not provided.
        tags: A list of tags to associate with the created deployment for organizational
            purposes.
        version: A version for the created deployment. Defaults to the flow's version.
        enforce_parameter_schema: Whether or not the Prefect API should enforce the
            parameter schema for the created deployment.
        pause_on_shutdown: If True, provided schedule will be paused when the serve function is stopped.
            If False, the schedules will continue running.
        print_starting_message: Whether or not to print the starting message when flow is served.
        limit: The maximum number of runs that can be executed concurrently.
        webserver: Whether or not to start a monitoring webserver for this flow.
        entrypoint_type: Type of entrypoint to use for the deployment. When using a module path
            entrypoint, ensure that the module will be importable in the execution environment.

    Examples:
        Serve a flow:

        ```python
        from prefect import flow

        @flow
        def my_flow(name):
            print(f"hello {name}")

        if __name__ == "__main__":
            my_flow.serve("example-deployment")
        ```

        Serve a flow and run it every hour:

        ```python
        from prefect import flow

        @flow
        def my_flow(name):
            print(f"hello {name}")

        if __name__ == "__main__":
            my_flow.serve("example-deployment", interval=3600)
        ```
    """
    from prefect.runner import Runner

    if not name:
        name = self.name
    else:
        # Handling for my_flow.serve(__file__)
        # Will set name to name of file where my_flow.serve() without the extension
        # Non filepath strings will pass through unchanged
        name = Path(name).stem

    runner = Runner(name=name, pause_on_shutdown=pause_on_shutdown, limit=limit)
    deployment_id = await runner.add_flow(
        self,
        name=name,
        triggers=triggers,
        interval=interval,
        cron=cron,
        rrule=rrule,
        paused=paused,
        schedules=schedules,
        schedule=schedule,
        is_schedule_active=is_schedule_active,
        parameters=parameters,
        description=description,
        tags=tags,
        version=version,
        enforce_parameter_schema=enforce_parameter_schema,
        entrypoint_type=entrypoint_type,
    )
    if print_starting_message:
        help_message = (
            f"[green]Your flow {self.name!r} is being served and polling for"
            " scheduled runs!\n[/]\nTo trigger a run for this flow, use the"
            " following command:\n[blue]\n\t$ prefect deployment run"
            f" '{self.name}/{name}'\n[/]"
        )
        if PREFECT_UI_URL:
            help_message += (
                "\nYou can also run your flow via the Prefect UI:"
                f" [blue]{PREFECT_UI_URL.value()}/deployments/deployment/{deployment_id}[/]\n"
            )

        console = Console()
        console.print(help_message, soft_wrap=True)
    await runner.start(webserver=webserver)

to_deployment async

Creates a runner deployment object for this flow.

Parameters:

Name Type Description Default
name str

The name to give the created deployment.

required
interval Optional[Union[Iterable[Union[int, float, timedelta]], int, float, timedelta]]

An interval on which to execute the new deployment. Accepts either a number or a timedelta object. If a number is given, it will be interpreted as seconds.

None
cron Optional[Union[Iterable[str], str]]

A cron schedule of when to execute runs of this deployment.

None
rrule Optional[Union[Iterable[str], str]]

An rrule schedule of when to execute runs of this deployment.

None
paused Optional[bool]

Whether or not to set this deployment as paused.

None
schedules Optional[List[FlexibleScheduleList]]

A list of schedule objects defining when to execute runs of this deployment. Used to define multiple schedules or additional scheduling options such as timezone.

None
schedule Optional[SCHEDULE_TYPES]

A schedule object defining when to execute runs of this deployment.

None
is_schedule_active Optional[bool]

Whether or not to set the schedule for this deployment as active. If not provided when creating a deployment, the schedule will be set as active. If not provided when updating a deployment, the schedule's activation will not be changed.

None
parameters Optional[dict]

A dictionary of default parameter values to pass to runs of this deployment.

None
triggers Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]]

A list of triggers that will kick off runs of this deployment.

None
description Optional[str]

A description for the created deployment. Defaults to the flow's description if not provided.

None
tags Optional[List[str]]

A list of tags to associate with the created deployment for organizational purposes.

None
version Optional[str]

A version for the created deployment. Defaults to the flow's version.

None
enforce_parameter_schema bool

Whether or not the Prefect API should enforce the parameter schema for the created deployment.

False
work_pool_name Optional[str]

The name of the work pool to use for this deployment.

None
work_queue_name Optional[str]

The name of the work queue to use for this deployment's scheduled runs. If not provided the default work queue for the work pool will be used.

None
job_variables Optional[Dict[str, Any]]

Settings used to override the values specified default base job template of the chosen work pool. Refer to the base job template of the chosen work pool for

None
entrypoint_type EntrypointType

Type of entrypoint to use for the deployment. When using a module path entrypoint, ensure that the module will be importable in the execution environment.

FILE_PATH

Examples:

Prepare two deployments and serve them:

from prefect import flow, serve

@flow
def my_flow(name):
    print(f"hello {name}")

@flow
def my_other_flow(name):
    print(f"goodbye {name}")

if __name__ == "__main__":
    hello_deploy = my_flow.to_deployment("hello", tags=["dev"])
    bye_deploy = my_other_flow.to_deployment("goodbye", tags=["dev"])
    serve(hello_deploy, bye_deploy)
Source code in prefect/flows.py
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
@sync_compatible
@deprecated_parameter(
    "schedule",
    start_date="Mar 2024",
    when=lambda p: p is not None,
    help="Use `schedules` instead.",
)
@deprecated_parameter(
    "is_schedule_active",
    start_date="Mar 2024",
    when=lambda p: p is not None,
    help="Use `paused` instead.",
)
async def to_deployment(
    self,
    name: str,
    interval: Optional[
        Union[
            Iterable[Union[int, float, datetime.timedelta]],
            int,
            float,
            datetime.timedelta,
        ]
    ] = None,
    cron: Optional[Union[Iterable[str], str]] = None,
    rrule: Optional[Union[Iterable[str], str]] = None,
    paused: Optional[bool] = None,
    schedules: Optional[List["FlexibleScheduleList"]] = None,
    schedule: Optional[SCHEDULE_TYPES] = None,
    is_schedule_active: Optional[bool] = None,
    parameters: Optional[dict] = None,
    triggers: Optional[List[Union[DeploymentTriggerTypes, TriggerTypes]]] = None,
    description: Optional[str] = None,
    tags: Optional[List[str]] = None,
    version: Optional[str] = None,
    enforce_parameter_schema: bool = False,
    work_pool_name: Optional[str] = None,
    work_queue_name: Optional[str] = None,
    job_variables: Optional[Dict[str, Any]] = None,
    entrypoint_type: EntrypointType = EntrypointType.FILE_PATH,
) -> "RunnerDeployment":
    """
    Creates a runner deployment object for this flow.

    Args:
        name: The name to give the created deployment.
        interval: An interval on which to execute the new deployment. Accepts either a number
            or a timedelta object. If a number is given, it will be interpreted as seconds.
        cron: A cron schedule of when to execute runs of this deployment.
        rrule: An rrule schedule of when to execute runs of this deployment.
        paused: Whether or not to set this deployment as paused.
        schedules: A list of schedule objects defining when to execute runs of this deployment.
            Used to define multiple schedules or additional scheduling options such as `timezone`.
        schedule: A schedule object defining when to execute runs of this deployment.
        is_schedule_active: Whether or not to set the schedule for this deployment as active. If
            not provided when creating a deployment, the schedule will be set as active. If not
            provided when updating a deployment, the schedule's activation will not be changed.
        parameters: A dictionary of default parameter values to pass to runs of this deployment.
        triggers: A list of triggers that will kick off runs of this deployment.
        description: A description for the created deployment. Defaults to the flow's
            description if not provided.
        tags: A list of tags to associate with the created deployment for organizational
            purposes.
        version: A version for the created deployment. Defaults to the flow's version.
        enforce_parameter_schema: Whether or not the Prefect API should enforce the
            parameter schema for the created deployment.
        work_pool_name: The name of the work pool to use for this deployment.
        work_queue_name: The name of the work queue to use for this deployment's scheduled runs.
            If not provided the default work queue for the work pool will be used.
        job_variables: Settings used to override the values specified default base job template
            of the chosen work pool. Refer to the base job template of the chosen work pool for
        entrypoint_type: Type of entrypoint to use for the deployment. When using a module path
            entrypoint, ensure that the module will be importable in the execution environment.

    Examples:
        Prepare two deployments and serve them:

        ```python
        from prefect import flow, serve

        @flow
        def my_flow(name):
            print(f"hello {name}")

        @flow
        def my_other_flow(name):
            print(f"goodbye {name}")

        if __name__ == "__main__":
            hello_deploy = my_flow.to_deployment("hello", tags=["dev"])
            bye_deploy = my_other_flow.to_deployment("goodbye", tags=["dev"])
            serve(hello_deploy, bye_deploy)
        ```
    """
    from prefect.deployments.runner import RunnerDeployment

    if not name.endswith(".py"):
        raise_on_name_with_banned_characters(name)
    if self._storage and self._entrypoint:
        return await RunnerDeployment.from_storage(
            storage=self._storage,
            entrypoint=self._entrypoint,
            name=name,
            interval=interval,
            cron=cron,
            rrule=rrule,
            paused=paused,
            schedules=schedules,
            schedule=schedule,
            is_schedule_active=is_schedule_active,
            tags=tags,
            triggers=triggers,
            parameters=parameters or {},
            description=description,
            version=version,
            enforce_parameter_schema=enforce_parameter_schema,
            work_pool_name=work_pool_name,
            work_queue_name=work_queue_name,
            job_variables=job_variables,
        )
    else:
        return RunnerDeployment.from_flow(
            self,
            name=name,
            interval=interval,
            cron=cron,
            rrule=rrule,
            paused=paused,
            schedules=schedules,
            schedule=schedule,
            is_schedule_active=is_schedule_active,
            tags=tags,
            triggers=triggers,
            parameters=parameters or {},
            description=description,
            version=version,
            enforce_parameter_schema=enforce_parameter_schema,
            work_pool_name=work_pool_name,
            work_queue_name=work_queue_name,
            job_variables=job_variables,
            entrypoint_type=entrypoint_type,
        )

validate_parameters

Validate parameters for compatibility with the flow by attempting to cast the inputs to the associated types specified by the function's type annotations.

Returns:

Type Description
Dict[str, Any]

A new dict of parameters that have been cast to the appropriate types

Raises:

Type Description
ParameterTypeError

if the provided parameters are not valid

Source code in prefect/flows.py
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
def validate_parameters(self, parameters: Dict[str, Any]) -> Dict[str, Any]:
    """
    Validate parameters for compatibility with the flow by attempting to cast the inputs to the
    associated types specified by the function's type annotations.

    Returns:
        A new dict of parameters that have been cast to the appropriate types

    Raises:
        ParameterTypeError: if the provided parameters are not valid
    """
    args, kwargs = parameters_to_args_kwargs(self.fn, parameters)

    if HAS_PYDANTIC_V2:
        has_v1_models = any(isinstance(o, V1BaseModel) for o in args) or any(
            isinstance(o, V1BaseModel) for o in kwargs.values()
        )
        has_v2_types = any(is_v2_type(o) for o in args) or any(
            is_v2_type(o) for o in kwargs.values()
        )

        if has_v1_models and has_v2_types:
            raise ParameterTypeError(
                "Cannot mix Pydantic v1 and v2 types as arguments to a flow."
            )

        if has_v1_models:
            validated_fn = V1ValidatedFunction(
                self.fn, config={"arbitrary_types_allowed": True}
            )
        else:
            validated_fn = V2ValidatedFunction(
                self.fn, config={"arbitrary_types_allowed": True}
            )

    else:
        validated_fn = ValidatedFunction(
            self.fn, config={"arbitrary_types_allowed": True}
        )

    try:
        model = validated_fn.init_model_instance(*args, **kwargs)
    except pydantic.ValidationError as exc:
        # We capture the pydantic exception and raise our own because the pydantic
        # exception is not picklable when using a cythonized pydantic installation
        raise ParameterTypeError.from_validation_error(exc) from None
    except V2ValidationError as exc:
        # We capture the pydantic exception and raise our own because the pydantic
        # exception is not picklable when using a cythonized pydantic installation
        raise ParameterTypeError.from_validation_error(exc) from None

    # Get the updated parameter dict with cast values from the model
    cast_parameters = {
        k: v
        for k, v in model._iter()
        if k in model.__fields_set__ or model.__fields__[k].default_factory
    }
    return cast_parameters

visualize async

Generates a graphviz object representing the current flow. In IPython notebooks, it's rendered inline, otherwise in a new window as a PNG.

Raises:

Type Description
-ImportError

If graphviz isn't installed.

-GraphvizExecutableNotFoundError

If the dot executable isn't found.

-FlowVisualizationError

If the flow can't be visualized for any other reason.

Source code in prefect/flows.py
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
@sync_compatible
async def visualize(self, *args, **kwargs):
    """
    Generates a graphviz object representing the current flow. In IPython notebooks,
    it's rendered inline, otherwise in a new window as a PNG.

    Raises:
        - ImportError: If `graphviz` isn't installed.
        - GraphvizExecutableNotFoundError: If the `dot` executable isn't found.
        - FlowVisualizationError: If the flow can't be visualized for any other reason.
    """
    if not PREFECT_UNIT_TEST_MODE:
        warnings.warn(
            "`flow.visualize()` will execute code inside of your flow that is not"
            " decorated with `@task` or `@flow`."
        )

    try:
        with TaskVizTracker() as tracker:
            if self.isasync:
                await self.fn(*args, **kwargs)
            else:
                self.fn(*args, **kwargs)

            graph = build_task_dependencies(tracker)

            visualize_task_dependencies(graph, self.name)

    except GraphvizImportError:
        raise
    except GraphvizExecutableNotFoundError:
        raise
    except VisualizationUnsupportedError:
        raise
    except FlowVisualizationError:
        raise
    except Exception as e:
        msg = (
            "It's possible you are trying to visualize a flow that contains "
            "code that directly interacts with the result of a task"
            " inside of the flow. \nTry passing a `viz_return_value` "
            "to the task decorator, e.g. `@task(viz_return_value=[1, 2, 3]).`"
        )

        new_exception = type(e)(str(e) + "\n" + msg)
        # Copy traceback information from the original exception
        new_exception.__traceback__ = e.__traceback__
        raise new_exception

with_options

Create a new flow from the current object, updating provided options.

Parameters:

Name Type Description Default
name str

A new name for the flow.

None
version str

A new version for the flow.

None
description str

A new description for the flow.

None
flow_run_name Optional[Union[Callable[[], str], str]]

An optional name to distinguish runs of this flow; this name can be provided as a string template with the flow's parameters as variables, or a function that returns a string.

None
task_runner Union[Type[BaseTaskRunner], BaseTaskRunner]

A new task runner for the flow.

None
timeout_seconds Union[int, float]

A new number of seconds to fail the flow after if still running.

None
validate_parameters bool

A new value indicating if flow calls should validate given parameters.

None
retries Optional[int]

A new number of times to retry on flow run failure.

None
retry_delay_seconds Optional[Union[int, float]]

A new number of seconds to wait before retrying the flow after failure. This is only applicable if retries is nonzero.

None
persist_result Optional[bool]

A new option for enabling or disabling result persistence.

NotSet
result_storage Optional[ResultStorage]

A new storage type to use for results.

NotSet
result_serializer Optional[ResultSerializer]

A new serializer to use for results.

NotSet
cache_result_in_memory bool

A new value indicating if the flow's result should be cached in memory.

None
on_failure Optional[List[Callable[[Flow, FlowRun, State], None]]]

A new list of callables to run when the flow enters a failed state.

None
on_completion Optional[List[Callable[[Flow, FlowRun, State], None]]]

A new list of callables to run when the flow enters a completed state.

None
on_cancellation Optional[List[Callable[[Flow, FlowRun, State], None]]]

A new list of callables to run when the flow enters a cancelling state.

None
on_crashed Optional[List[Callable[[Flow, FlowRun, State], None]]]

A new list of callables to run when the flow enters a crashed state.

None
on_running Optional[List[Callable[[Flow, FlowRun, State], None]]]

A new list of callables to run when the flow enters a running state.

None

Returns:

Type Description
Self

A new Flow instance.

Create a new flow from an existing flow and update the name:

>>> @flow(name="My flow")
>>> def my_flow():
>>>     return 1
>>>
>>> new_flow = my_flow.with_options(name="My new flow")

Create a new flow from an existing flow, update the task runner, and call
it without an intermediate variable:

>>> from prefect.task_runners import SequentialTaskRunner
>>>
>>> @flow
>>> def my_flow(x, y):
>>>     return x + y
>>>
>>> state = my_flow.with_options(task_runner=SequentialTaskRunner)(1, 3)
>>> assert state.result() == 4
Source code in prefect/flows.py
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
def with_options(
    self,
    *,
    name: str = None,
    version: str = None,
    retries: Optional[int] = None,
    retry_delay_seconds: Optional[Union[int, float]] = None,
    description: str = None,
    flow_run_name: Optional[Union[Callable[[], str], str]] = None,
    task_runner: Union[Type[BaseTaskRunner], BaseTaskRunner] = None,
    timeout_seconds: Union[int, float] = None,
    validate_parameters: bool = None,
    persist_result: Optional[bool] = NotSet,
    result_storage: Optional[ResultStorage] = NotSet,
    result_serializer: Optional[ResultSerializer] = NotSet,
    cache_result_in_memory: bool = None,
    log_prints: Optional[bool] = NotSet,
    on_completion: Optional[
        List[Callable[[FlowSchema, FlowRun, State], None]]
    ] = None,
    on_failure: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
    on_cancellation: Optional[
        List[Callable[[FlowSchema, FlowRun, State], None]]
    ] = None,
    on_crashed: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
    on_running: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
) -> Self:
    """
    Create a new flow from the current object, updating provided options.

    Args:
        name: A new name for the flow.
        version: A new version for the flow.
        description: A new description for the flow.
        flow_run_name: An optional name to distinguish runs of this flow; this name
            can be provided as a string template with the flow's parameters as variables,
            or a function that returns a string.
        task_runner: A new task runner for the flow.
        timeout_seconds: A new number of seconds to fail the flow after if still
            running.
        validate_parameters: A new value indicating if flow calls should validate
            given parameters.
        retries: A new number of times to retry on flow run failure.
        retry_delay_seconds: A new number of seconds to wait before retrying the
            flow after failure. This is only applicable if `retries` is nonzero.
        persist_result: A new option for enabling or disabling result persistence.
        result_storage: A new storage type to use for results.
        result_serializer: A new serializer to use for results.
        cache_result_in_memory: A new value indicating if the flow's result should
            be cached in memory.
        on_failure: A new list of callables to run when the flow enters a failed state.
        on_completion: A new list of callables to run when the flow enters a completed state.
        on_cancellation: A new list of callables to run when the flow enters a cancelling state.
        on_crashed: A new list of callables to run when the flow enters a crashed state.
        on_running: A new list of callables to run when the flow enters a running state.

    Returns:
        A new `Flow` instance.

    Examples:

        Create a new flow from an existing flow and update the name:

        >>> @flow(name="My flow")
        >>> def my_flow():
        >>>     return 1
        >>>
        >>> new_flow = my_flow.with_options(name="My new flow")

        Create a new flow from an existing flow, update the task runner, and call
        it without an intermediate variable:

        >>> from prefect.task_runners import SequentialTaskRunner
        >>>
        >>> @flow
        >>> def my_flow(x, y):
        >>>     return x + y
        >>>
        >>> state = my_flow.with_options(task_runner=SequentialTaskRunner)(1, 3)
        >>> assert state.result() == 4

    """
    new_flow = Flow(
        fn=self.fn,
        name=name or self.name,
        description=description or self.description,
        flow_run_name=flow_run_name or self.flow_run_name,
        version=version or self.version,
        task_runner=task_runner or self.task_runner,
        retries=retries if retries is not None else self.retries,
        retry_delay_seconds=(
            retry_delay_seconds
            if retry_delay_seconds is not None
            else self.retry_delay_seconds
        ),
        timeout_seconds=(
            timeout_seconds if timeout_seconds is not None else self.timeout_seconds
        ),
        validate_parameters=(
            validate_parameters
            if validate_parameters is not None
            else self.should_validate_parameters
        ),
        persist_result=(
            persist_result if persist_result is not NotSet else self.persist_result
        ),
        result_storage=(
            result_storage if result_storage is not NotSet else self.result_storage
        ),
        result_serializer=(
            result_serializer
            if result_serializer is not NotSet
            else self.result_serializer
        ),
        cache_result_in_memory=(
            cache_result_in_memory
            if cache_result_in_memory is not None
            else self.cache_result_in_memory
        ),
        log_prints=log_prints if log_prints is not NotSet else self.log_prints,
        on_completion=on_completion or self.on_completion,
        on_failure=on_failure or self.on_failure,
        on_cancellation=on_cancellation or self.on_cancellation,
        on_crashed=on_crashed or self.on_crashed,
        on_running=on_running or self.on_running,
    )
    new_flow._storage = self._storage
    new_flow._entrypoint = self._entrypoint
    return new_flow

flow

Decorator to designate a function as a Prefect workflow.

This decorator may be used for asynchronous or synchronous functions.

Flow parameters must be serializable by Pydantic.

Parameters:

Name Type Description Default
name Optional[str]

An optional name for the flow; if not provided, the name will be inferred from the given function.

None
version Optional[str]

An optional version string for the flow; if not provided, we will attempt to create a version string as a hash of the file containing the wrapped function; if the file cannot be located, the version will be null.

None
flow_run_name Optional[Union[Callable[[], str], str]]

An optional name to distinguish runs of this flow; this name can be provided as a string template with the flow's parameters as variables, or a function that returns a string.

None
retries int

An optional number of times to retry on flow run failure.

None
retry_delay_seconds Union[int, float]

An optional number of seconds to wait before retrying the flow after failure. This is only applicable if retries is nonzero.

None
task_runner BaseTaskRunner

An optional task runner to use for task execution within the flow; if not provided, a ConcurrentTaskRunner will be instantiated.

ConcurrentTaskRunner
description str

An optional string description for the flow; if not provided, the description will be pulled from the docstring for the decorated function.

None
timeout_seconds Union[int, float]

An optional number of seconds indicating a maximum runtime for the flow. If the flow exceeds this runtime, it will be marked as failed. Flow execution may continue until the next task is called.

None
validate_parameters bool

By default, parameters passed to flows are validated by Pydantic. This will check that input values conform to the annotated types on the function. Where possible, values will be coerced into the correct type; for example, if a parameter is defined as x: int and "5" is passed, it will be resolved to 5. If set to False, no validation will be performed on flow parameters.

True
persist_result Optional[bool]

An optional toggle indicating whether the result of this flow should be persisted to result storage. Defaults to None, which indicates that Prefect should choose whether the result should be persisted depending on the features being used.

None
result_storage Optional[ResultStorage]

An optional block to use to persist the result of this flow. This value will be used as the default for any tasks in this flow. If not provided, the local file system will be used unless called as a subflow, at which point the default will be loaded from the parent flow.

None
result_serializer Optional[ResultSerializer]

An optional serializer to use to serialize the result of this flow for persistence. This value will be used as the default for any tasks in this flow. If not provided, the value of PREFECT_RESULTS_DEFAULT_SERIALIZER will be used unless called as a subflow, at which point the default will be loaded from the parent flow.

None
cache_result_in_memory bool

An optional toggle indicating whether the cached result of a running the flow should be stored in memory. Defaults to True.

True
log_prints Optional[bool]

If set, print statements in the flow will be redirected to the Prefect logger for the flow run. Defaults to None, which indicates that the value from the parent flow should be used. If this is a parent flow, the default is pulled from the PREFECT_LOGGING_LOG_PRINTS setting.

None
on_completion Optional[List[Callable[[Flow, FlowRun, State], Union[Awaitable[None], None]]]]

An optional list of functions to call when the flow run is completed. Each function should accept three arguments: the flow, the flow run, and the final state of the flow run.

None
on_failure Optional[List[Callable[[Flow, FlowRun, State], Union[Awaitable[None], None]]]]

An optional list of functions to call when the flow run fails. Each function should accept three arguments: the flow, the flow run, and the final state of the flow run.

None
on_cancellation Optional[List[Callable[[Flow, FlowRun, State], None]]]

An optional list of functions to call when the flow run is cancelled. These functions will be passed the flow, flow run, and final state.

None
on_crashed Optional[List[Callable[[Flow, FlowRun, State], None]]]

An optional list of functions to call when the flow run crashes. Each function should accept three arguments: the flow, the flow run, and the final state of the flow run.

None
on_running Optional[List[Callable[[Flow, FlowRun, State], None]]]

An optional list of functions to call when the flow run is started. Each function should accept three arguments: the flow, the flow run, and the current state

None

Returns:

Type Description

A callable Flow object which, when called, will run the flow and return its

final state.

Examples:

Define a simple flow

>>> from prefect import flow
>>> @flow
>>> def add(x, y):
>>>     return x + y

Define an async flow

>>> @flow
>>> async def add(x, y):
>>>     return x + y

Define a flow with a version and description

>>> @flow(version="first-flow", description="This flow is empty!")
>>> def my_flow():
>>>     pass

Define a flow with a custom name

>>> @flow(name="The Ultimate Flow")
>>> def my_flow():
>>>     pass

Define a flow that submits its tasks to dask

>>> from prefect_dask.task_runners import DaskTaskRunner
>>>
>>> @flow(task_runner=DaskTaskRunner)
>>> def my_flow():
>>>     pass
Source code in prefect/flows.py
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
def flow(
    __fn=None,
    *,
    name: Optional[str] = None,
    version: Optional[str] = None,
    flow_run_name: Optional[Union[Callable[[], str], str]] = None,
    retries: int = None,
    retry_delay_seconds: Union[int, float] = None,
    task_runner: BaseTaskRunner = ConcurrentTaskRunner,
    description: str = None,
    timeout_seconds: Union[int, float] = None,
    validate_parameters: bool = True,
    persist_result: Optional[bool] = None,
    result_storage: Optional[ResultStorage] = None,
    result_serializer: Optional[ResultSerializer] = None,
    cache_result_in_memory: bool = True,
    log_prints: Optional[bool] = None,
    on_completion: Optional[
        List[Callable[[FlowSchema, FlowRun, State], Union[Awaitable[None], None]]]
    ] = None,
    on_failure: Optional[
        List[Callable[[FlowSchema, FlowRun, State], Union[Awaitable[None], None]]]
    ] = None,
    on_cancellation: Optional[
        List[Callable[[FlowSchema, FlowRun, State], None]]
    ] = None,
    on_crashed: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
    on_running: Optional[List[Callable[[FlowSchema, FlowRun, State], None]]] = None,
):
    """
    Decorator to designate a function as a Prefect workflow.

    This decorator may be used for asynchronous or synchronous functions.

    Flow parameters must be serializable by Pydantic.

    Args:
        name: An optional name for the flow; if not provided, the name will be inferred
            from the given function.
        version: An optional version string for the flow; if not provided, we will
            attempt to create a version string as a hash of the file containing the
            wrapped function; if the file cannot be located, the version will be null.
        flow_run_name: An optional name to distinguish runs of this flow; this name can
            be provided as a string template with the flow's parameters as variables,
            or a function that returns a string.
        retries: An optional number of times to retry on flow run failure.
        retry_delay_seconds: An optional number of seconds to wait before retrying the
            flow after failure. This is only applicable if `retries` is nonzero.
        task_runner: An optional task runner to use for task execution within the flow; if
            not provided, a `ConcurrentTaskRunner` will be instantiated.
        description: An optional string description for the flow; if not provided, the
            description will be pulled from the docstring for the decorated function.
        timeout_seconds: An optional number of seconds indicating a maximum runtime for
            the flow. If the flow exceeds this runtime, it will be marked as failed.
            Flow execution may continue until the next task is called.
        validate_parameters: By default, parameters passed to flows are validated by
            Pydantic. This will check that input values conform to the annotated types
            on the function. Where possible, values will be coerced into the correct
            type; for example, if a parameter is defined as `x: int` and "5" is passed,
            it will be resolved to `5`. If set to `False`, no validation will be
            performed on flow parameters.
        persist_result: An optional toggle indicating whether the result of this flow
            should be persisted to result storage. Defaults to `None`, which indicates
            that Prefect should choose whether the result should be persisted depending on
            the features being used.
        result_storage: An optional block to use to persist the result of this flow.
            This value will be used as the default for any tasks in this flow.
            If not provided, the local file system will be used unless called as
            a subflow, at which point the default will be loaded from the parent flow.
        result_serializer: An optional serializer to use to serialize the result of this
            flow for persistence. This value will be used as the default for any tasks
            in this flow. If not provided, the value of `PREFECT_RESULTS_DEFAULT_SERIALIZER`
            will be used unless called as a subflow, at which point the default will be
            loaded from the parent flow.
        cache_result_in_memory: An optional toggle indicating whether the cached result of
            a running the flow should be stored in memory. Defaults to `True`.
        log_prints: If set, `print` statements in the flow will be redirected to the
            Prefect logger for the flow run. Defaults to `None`, which indicates that
            the value from the parent flow should be used. If this is a parent flow,
            the default is pulled from the `PREFECT_LOGGING_LOG_PRINTS` setting.
        on_completion: An optional list of functions to call when the flow run is
            completed. Each function should accept three arguments: the flow, the flow
            run, and the final state of the flow run.
        on_failure: An optional list of functions to call when the flow run fails. Each
            function should accept three arguments: the flow, the flow run, and the
            final state of the flow run.
        on_cancellation: An optional list of functions to call when the flow run is
            cancelled. These functions will be passed the flow, flow run, and final state.
        on_crashed: An optional list of functions to call when the flow run crashes. Each
            function should accept three arguments: the flow, the flow run, and the
            final state of the flow run.
        on_running: An optional list of functions to call when the flow run is started. Each
            function should accept three arguments: the flow, the flow run, and the current state

    Returns:
        A callable `Flow` object which, when called, will run the flow and return its
        final state.

    Examples:
        Define a simple flow

        >>> from prefect import flow
        >>> @flow
        >>> def add(x, y):
        >>>     return x + y

        Define an async flow

        >>> @flow
        >>> async def add(x, y):
        >>>     return x + y

        Define a flow with a version and description

        >>> @flow(version="first-flow", description="This flow is empty!")
        >>> def my_flow():
        >>>     pass

        Define a flow with a custom name

        >>> @flow(name="The Ultimate Flow")
        >>> def my_flow():
        >>>     pass

        Define a flow that submits its tasks to dask

        >>> from prefect_dask.task_runners import DaskTaskRunner
        >>>
        >>> @flow(task_runner=DaskTaskRunner)
        >>> def my_flow():
        >>>     pass
    """
    if __fn:
        return cast(
            Flow[P, R],
            Flow(
                fn=__fn,
                name=name,
                version=version,
                flow_run_name=flow_run_name,
                task_runner=task_runner,
                description=description,
                timeout_seconds=timeout_seconds,
                validate_parameters=validate_parameters,
                retries=retries,
                retry_delay_seconds=retry_delay_seconds,
                persist_result=persist_result,
                result_storage=result_storage,
                result_serializer=result_serializer,
                cache_result_in_memory=cache_result_in_memory,
                log_prints=log_prints,
                on_completion=on_completion,
                on_failure=on_failure,
                on_cancellation=on_cancellation,
                on_crashed=on_crashed,
                on_running=on_running,
            ),
        )
    else:
        return cast(
            Callable[[Callable[P, R]], Flow[P, R]],
            partial(
                flow,
                name=name,
                version=version,
                flow_run_name=flow_run_name,
                task_runner=task_runner,
                description=description,
                timeout_seconds=timeout_seconds,
                validate_parameters=validate_parameters,
                retries=retries,
                retry_delay_seconds=retry_delay_seconds,
                persist_result=persist_result,
                result_storage=result_storage,
                result_serializer=result_serializer,
                cache_result_in_memory=cache_result_in_memory,
                log_prints=log_prints,
                on_completion=on_completion,
                on_failure=on_failure,
                on_cancellation=on_cancellation,
                on_crashed=on_crashed,
                on_running=on_running,
            ),
        )

is_entrypoint_async

Determine if the function specified in the entrypoint is asynchronous.

Parameters:

Name Type Description Default
entrypoint str

A string in the format <path_to_script>:<func_name> or a module path to a function.

required

Returns:

Type Description
bool

True if the function is asynchronous, False otherwise.

Source code in prefect/flows.py
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
def is_entrypoint_async(entrypoint: str) -> bool:
    """
    Determine if the function specified in the entrypoint is asynchronous.

    Args:
        entrypoint: A string in the format `<path_to_script>:<func_name>` or
          a module path to a function.

    Returns:
        True if the function is asynchronous, False otherwise.
    """
    func_def, _ = _entrypoint_definition_and_source(entrypoint)
    return isinstance(func_def, ast.AsyncFunctionDef)

load_flow_arguments_from_entrypoint

Extract flow arguments from an entrypoint string.

Loads the source code of the entrypoint and extracts the flow arguments from the flow decorator.

Parameters:

Name Type Description Default
entrypoint str

a string in the format <path_to_script>:<flow_func_name> or a module path to a flow function

required
Source code in prefect/flows.py
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
def load_flow_arguments_from_entrypoint(
    entrypoint: str, arguments: Optional[Union[List[str], Set[str]]] = None
) -> Dict[str, Any]:
    """
    Extract flow arguments from an entrypoint string.

    Loads the source code of the entrypoint and extracts the flow arguments
    from the `flow` decorator.

    Args:
        entrypoint: a string in the format `<path_to_script>:<flow_func_name>`
          or a module path to a flow function
    """

    func_def, source_code = _entrypoint_definition_and_source(entrypoint)

    if arguments is None:
        # If no arguments are provided default to known arguments that are of
        # built-in types.
        arguments = {
            "name",
            "version",
            "retries",
            "retry_delay_seconds",
            "description",
            "timeout_seconds",
            "validate_parameters",
            "persist_result",
            "cache_result_in_memory",
            "log_prints",
        }

    result = {}

    for decorator in func_def.decorator_list:
        if (
            isinstance(decorator, ast.Call)
            and getattr(decorator.func, "id", "") == "flow"
        ):
            for keyword in decorator.keywords:
                if keyword.arg not in arguments:
                    continue

                if isinstance(keyword.value, ast.Constant):
                    # Use the string value of the argument
                    result[keyword.arg] = str(keyword.value.value)
                    continue

                # if the arg value is not a raw str (i.e. a variable or expression),
                # then attempt to evaluate it
                namespace = safe_load_namespace(source_code)
                literal_arg_value = ast.get_source_segment(source_code, keyword.value)
                cleaned_value = (
                    literal_arg_value.replace("\n", "") if literal_arg_value else ""
                )

                try:
                    evaluated_value = eval(cleaned_value, namespace)  # type: ignore
                    result[keyword.arg] = str(evaluated_value)
                except Exception as e:
                    logger.info(
                        "Failed to parse @flow argument: `%s=%s` due to the following error. Ignoring and falling back to default behavior.",
                        keyword.arg,
                        literal_arg_value,
                        exc_info=e,
                    )
                    # ignore the decorator arg and fallback to default behavior
                    continue

    if "name" in arguments and "name" not in result:
        # If no matching decorator or keyword argument for `name' is found
        # fallback to the function name.
        result["name"] = func_def.name.replace("_", "-")

    return result

load_flow_from_entrypoint

Extract a flow object from a script at an entrypoint by running all of the code in the file.

Parameters:

Name Type Description Default
entrypoint str

a string in the format <path_to_script>:<flow_func_name> or a module path to a flow function

required

Returns:

Type Description
Flow

The flow object from the script

Raises:

Type Description
FlowScriptError

If an exception is encountered while running the script

MissingFlowError

If the flow function specified in the entrypoint does not exist

Source code in prefect/flows.py
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
def load_flow_from_entrypoint(
    entrypoint: str,
) -> Flow:
    """
    Extract a flow object from a script at an entrypoint by running all of the code in the file.

    Args:
        entrypoint: a string in the format `<path_to_script>:<flow_func_name>` or a module path
            to a flow function

    Returns:
        The flow object from the script

    Raises:
        FlowScriptError: If an exception is encountered while running the script
        MissingFlowError: If the flow function specified in the entrypoint does not exist
    """
    with PrefectObjectRegistry(
        block_code_execution=True,
        capture_failures=True,
    ):
        if ":" in entrypoint:
            # split by the last colon once to handle Windows paths with drive letters i.e C:\path\to\file.py:do_stuff
            path, func_name = entrypoint.rsplit(":", maxsplit=1)
        else:
            path, func_name = entrypoint.rsplit(".", maxsplit=1)
        try:
            flow = import_object(entrypoint)
        except AttributeError as exc:
            raise MissingFlowError(
                f"Flow function with name {func_name!r} not found in {path!r}. "
            ) from exc
        except ScriptError as exc:
            # If the flow has dependencies that are not installed in the current
            # environment, fallback to loading the flow via AST parsing. The
            # drawback of this approach is that we're unable to actually load the
            # function, so we create a placeholder flow that will re-raise this
            # exception when called.

            flow = load_placeholder_flow(entrypoint=entrypoint, raises=exc)

        if not isinstance(flow, Flow):
            raise MissingFlowError(
                f"Function with name {func_name!r} is not a flow. Make sure that it is "
                "decorated with '@flow'."
            )

        return flow

load_flow_from_script

Extract a flow object from a script by running all of the code in the file.

If the script has multiple flows in it, a flow name must be provided to specify the flow to return.

Parameters:

Name Type Description Default
path str

A path to a Python script containing flows

required
flow_name str

An optional flow name to look for in the script

None

Returns:

Type Description
Flow

The flow object from the script

Raises:

Type Description
FlowScriptError

If an exception is encountered while running the script

MissingFlowError

If no flows exist in the iterable

MissingFlowError

If a flow name is provided and that flow does not exist

UnspecifiedFlowError

If multiple flows exist but no flow name was provided

Source code in prefect/flows.py
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
def load_flow_from_script(path: str, flow_name: str = None) -> Flow:
    """
    Extract a flow object from a script by running all of the code in the file.

    If the script has multiple flows in it, a flow name must be provided to specify
    the flow to return.

    Args:
        path: A path to a Python script containing flows
        flow_name: An optional flow name to look for in the script

    Returns:
        The flow object from the script

    Raises:
        FlowScriptError: If an exception is encountered while running the script
        MissingFlowError: If no flows exist in the iterable
        MissingFlowError: If a flow name is provided and that flow does not exist
        UnspecifiedFlowError: If multiple flows exist but no flow name was provided
    """
    return select_flow(
        load_flows_from_script(path),
        flow_name=flow_name,
        from_message=f"in script '{path}'",
    )

load_flow_from_text

Load a flow from a text script.

The script will be written to a temporary local file path so errors can refer to line numbers and contextual tracebacks can be provided.

Source code in prefect/flows.py
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
def load_flow_from_text(script_contents: AnyStr, flow_name: str):
    """
    Load a flow from a text script.

    The script will be written to a temporary local file path so errors can refer
    to line numbers and contextual tracebacks can be provided.
    """
    with NamedTemporaryFile(
        mode="wt" if isinstance(script_contents, str) else "wb",
        prefix=f"flow-script-{flow_name}",
        suffix=".py",
        delete=False,
    ) as tmpfile:
        tmpfile.write(script_contents)
        tmpfile.flush()
    try:
        flow = load_flow_from_script(tmpfile.name, flow_name=flow_name)
    finally:
        # windows compat
        tmpfile.close()
        os.remove(tmpfile.name)
    return flow

load_flows_from_script

Load all flow objects from the given python script. All of the code in the file will be executed.

Returns:

Type Description
List[Flow]

A list of flows

Raises:

Type Description
FlowScriptError

If an exception is encountered while running the script

Source code in prefect/flows.py
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
def load_flows_from_script(path: str) -> List[Flow]:
    """
    Load all flow objects from the given python script. All of the code in the file
    will be executed.

    Returns:
        A list of flows

    Raises:
        FlowScriptError: If an exception is encountered while running the script
    """
    return registry_from_script(path).get_instances(Flow)

load_placeholder_flow

Load a placeholder flow that is initialized with the same arguments as the flow specified in the entrypoint. If called the flow will raise raises.

This is useful when a flow can't be loaded due to missing dependencies or other issues but the base metadata defining the flow is still needed.

Parameters:

Name Type Description Default
entrypoint str

a string in the format <path_to_script>:<flow_func_name> or a module path to a flow function

required
raises Exception

an exception to raise when the flow is called

required
Source code in prefect/flows.py
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
def load_placeholder_flow(entrypoint: str, raises: Exception):
    """
    Load a placeholder flow that is initialized with the same arguments as the
    flow specified in the entrypoint. If called the flow will raise `raises`.

    This is useful when a flow can't be loaded due to missing dependencies or
    other issues but the base metadata defining the flow is still needed.

    Args:
        entrypoint: a string in the format `<path_to_script>:<flow_func_name>`
          or a module path to a flow function
        raises: an exception to raise when the flow is called
    """

    def _base_placeholder():
        raise raises

    def sync_placeholder_flow(*args, **kwargs):
        _base_placeholder()

    async def async_placeholder_flow(*args, **kwargs):
        _base_placeholder()

    placeholder_flow = (
        async_placeholder_flow
        if is_entrypoint_async(entrypoint)
        else sync_placeholder_flow
    )

    arguments = load_flow_arguments_from_entrypoint(entrypoint)
    arguments["fn"] = placeholder_flow

    return Flow(**arguments)

select_flow

Select the only flow in an iterable or a flow specified by name.

Returns A single flow object

Raises:

Type Description
MissingFlowError

If no flows exist in the iterable

MissingFlowError

If a flow name is provided and that flow does not exist

UnspecifiedFlowError

If multiple flows exist but no flow name was provided

Source code in prefect/flows.py
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
def select_flow(
    flows: Iterable[Flow], flow_name: str = None, from_message: str = None
) -> Flow:
    """
    Select the only flow in an iterable or a flow specified by name.

    Returns
        A single flow object

    Raises:
        MissingFlowError: If no flows exist in the iterable
        MissingFlowError: If a flow name is provided and that flow does not exist
        UnspecifiedFlowError: If multiple flows exist but no flow name was provided
    """
    # Convert to flows by name
    flows = {f.name: f for f in flows}

    # Add a leading space if given, otherwise use an empty string
    from_message = (" " + from_message) if from_message else ""
    if not flows:
        raise MissingFlowError(f"No flows found{from_message}.")

    elif flow_name and flow_name not in flows:
        raise MissingFlowError(
            f"Flow {flow_name!r} not found{from_message}. "
            f"Found the following flows: {listrepr(flows.keys())}. "
            "Check to make sure that your flow function is decorated with `@flow`."
        )

    elif not flow_name and len(flows) > 1:
        raise UnspecifiedFlowError(
            (
                f"Found {len(flows)} flows{from_message}:"
                f" {listrepr(sorted(flows.keys()))}. Specify a flow name to select a"
                " flow."
            ),
        )

    if flow_name:
        return flows[flow_name]
    else:
        return list(flows.values())[0]

serve async

Serve the provided list of deployments.

Parameters:

Name Type Description Default
*args RunnerDeployment

A list of deployments to serve.

()
pause_on_shutdown bool

A boolean for whether or not to automatically pause deployment schedules on shutdown.

True
print_starting_message bool

Whether or not to print message to the console on startup.

True
limit Optional[int]

The maximum number of runs that can be executed concurrently.

None
**kwargs

Additional keyword arguments to pass to the runner.

{}

Examples:

Prepare two deployments and serve them:

import datetime

from prefect import flow, serve

@flow
def my_flow(name):
    print(f"hello {name}")

@flow
def my_other_flow(name):
    print(f"goodbye {name}")

if __name__ == "__main__":
    # Run once a day
    hello_deploy = my_flow.to_deployment(
        "hello", tags=["dev"], interval=datetime.timedelta(days=1)
    )

    # Run every Sunday at 4:00 AM
    bye_deploy = my_other_flow.to_deployment(
        "goodbye", tags=["dev"], cron="0 4 * * sun"
    )

    serve(hello_deploy, bye_deploy)
Source code in prefect/flows.py
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
@sync_compatible
async def serve(
    *args: "RunnerDeployment",
    pause_on_shutdown: bool = True,
    print_starting_message: bool = True,
    limit: Optional[int] = None,
    **kwargs,
):
    """
    Serve the provided list of deployments.

    Args:
        *args: A list of deployments to serve.
        pause_on_shutdown: A boolean for whether or not to automatically pause
            deployment schedules on shutdown.
        print_starting_message: Whether or not to print message to the console
            on startup.
        limit: The maximum number of runs that can be executed concurrently.
        **kwargs: Additional keyword arguments to pass to the runner.

    Examples:
        Prepare two deployments and serve them:

        ```python
        import datetime

        from prefect import flow, serve

        @flow
        def my_flow(name):
            print(f"hello {name}")

        @flow
        def my_other_flow(name):
            print(f"goodbye {name}")

        if __name__ == "__main__":
            # Run once a day
            hello_deploy = my_flow.to_deployment(
                "hello", tags=["dev"], interval=datetime.timedelta(days=1)
            )

            # Run every Sunday at 4:00 AM
            bye_deploy = my_other_flow.to_deployment(
                "goodbye", tags=["dev"], cron="0 4 * * sun"
            )

            serve(hello_deploy, bye_deploy)
        ```
    """
    from rich.console import Console, Group
    from rich.table import Table

    from prefect.runner import Runner

    runner = Runner(pause_on_shutdown=pause_on_shutdown, limit=limit, **kwargs)
    for deployment in args:
        await runner.add_deployment(deployment)

    if print_starting_message:
        help_message_top = (
            "[green]Your deployments are being served and polling for"
            " scheduled runs!\n[/]"
        )

        table = Table(title="Deployments", show_header=False)

        table.add_column(style="blue", no_wrap=True)

        for deployment in args:
            table.add_row(f"{deployment.flow_name}/{deployment.name}")

        help_message_bottom = (
            "\nTo trigger any of these deployments, use the"
            " following command:\n[blue]\n\t$ prefect deployment run"
            " [DEPLOYMENT_NAME]\n[/]"
        )
        if PREFECT_UI_URL:
            help_message_bottom += (
                "\nYou can also trigger your deployments via the Prefect UI:"
                f" [blue]{PREFECT_UI_URL.value()}/deployments[/]\n"
            )

        console = Console()
        console.print(
            Group(help_message_top, table, help_message_bottom), soft_wrap=True
        )

    await runner.start()