flytekit.bin.entrypoint

Directory

Classes

Errors

• FlyteException
• FlyteNonRecoverableSystemException
• FlyteRecoverableException
• FlyteUserRuntimeException
• IgnoreOutputs

flytekit.bin.entrypoint.ExecutionParameters

This is a run-time user-centric context object that is accessible to every @task method. It can be accessed using

.. code-block:: python

flytekit.current_context()

This object provides the following

• a statsd handler
• a logging handler
• the execution ID as an :py:class:flytekit.models.core.identifier.WorkflowExecutionIdentifier object
• a working directory for the user to write arbitrary files to

Please do not confuse this object with the :py:class:flytekit.FlyteContext object.

def ExecutionParameters(
    execution_date,
    tmp_dir,
    stats,
    execution_id: typing.Optional[_identifier.WorkflowExecutionIdentifier],
    logging,
    raw_output_prefix,
    output_metadata_prefix,
    checkpoint,
    decks,
    task_id: typing.Optional[_identifier.Identifier],
    enable_deck: bool,
    kwargs,
):

Methods

builder()

def builder()

get()

def get(
    key: str,
):

Returns task specific context if present else raise an error. The returned context will match the key

has_attr()

def has_attr(
    attr_name: str,
):

new_builder()

def new_builder(
    current: Optional[ExecutionParameters],
):

with_enable_deck()

def with_enable_deck(
    enable_deck: bool,
):

with_task_sandbox()

def with_task_sandbox()

Properties

flytekit.bin.entrypoint.ExecutionState

This is the context that is active when executing a task or a local workflow. This carries the necessary state to execute. Some required things during execution deal with temporary directories, ExecutionParameters that are passed to the user etc.

Attributes: mode (ExecutionState.Mode): Defines the context in which the task is executed (local, hosted, etc). working_dir (os.PathLike): Specifies the remote, external directory where inputs, outputs and other protobufs are uploaded engine_dir (os.PathLike): branch_eval_mode Optional[BranchEvalMode]: Used to determine whether a branch node should execute. user_space_params Optional[ExecutionParameters]: Provides run-time, user-centric context such as a statsd handler, a logging handler, the current execution id and a working directory.

def ExecutionState(
    working_dir: Union[os.PathLike, str],
    mode: Optional[ExecutionState.Mode],
    engine_dir: Optional[Union[os.PathLike, str]],
    branch_eval_mode: Optional[BranchEvalMode],
    user_space_params: Optional[ExecutionParameters],
):

Methods

branch_complete()

def branch_complete()

Indicates that we are within a conditional / ifelse block and the active branch is not done. Default to SKIPPED

is_local_execution()

def is_local_execution()

take_branch()

def take_branch()

Indicates that we are within an if-else block and the current branch has evaluated to true. Useful only in local execution mode

with_params()

def with_params(
    working_dir: Optional[os.PathLike],
    mode: Optional[Mode],
    engine_dir: Optional[os.PathLike],
    branch_eval_mode: Optional[BranchEvalMode],
    user_space_params: Optional[ExecutionParameters],
):

Produces a copy of the current execution state and overrides the copy’s parameters with passed parameter values.

flytekit.bin.entrypoint.FastSerializationSettings

This object hold information about settings necessary to serialize an object so that it can be fast-registered.

def FastSerializationSettings(
    enabled: bool,
    destination_dir: Optional[str],
    distribution_location: Optional[str],
):

Methods

from_dict()

def from_dict(
    kvs: typing.Union[dict, list, str, int, float, bool, NoneType],
    infer_missing,
):

from_json()

def from_json(
    s: typing.Union[str, bytes, bytearray],
    parse_float,
    parse_int,
    parse_constant,
    infer_missing,
    kw,
):

schema()

def schema(
    infer_missing: bool,
    only,
    exclude,
    many: bool,
    context,
    load_only,
    dump_only,
    partial: bool,
    unknown,
):

to_dict()

def to_dict(
    encode_json,
):

to_json()

def to_json(
    skipkeys: bool,
    ensure_ascii: bool,
    check_circular: bool,
    allow_nan: bool,
    indent: typing.Union[int, str, NoneType],
    separators: typing.Tuple[str, str],
    default: typing.Callable,
    sort_keys: bool,
    kw,
):

flytekit.bin.entrypoint.FileAccessProvider

This is the class that is available through the FlyteContext and can be used for persisting data to the remote durable store.

def FileAccessProvider(
    local_sandbox_dir: typing.Union[str, os.PathLike],
    raw_output_prefix: str,
    data_config: typing.Optional[flytekit.configuration.DataConfig],
    execution_metadata: typing.Optional[dict],
):

Methods

async_get_data()

def async_get_data(
    remote_path: str,
    local_path: str,
    is_multipart: bool,
    kwargs,
):

async_put_data()

def async_put_data(
    local_path: typing.Union[str, os.PathLike],
    remote_path: str,
    is_multipart: bool,
    kwargs,
):

The implication here is that we’re always going to put data to the remote location, so we .remote to ensure we don’t use the true local proxy if the remote path is a file://

async_put_raw_data()

def async_put_raw_data(
    lpath: typing.Union[str, os.PathLike, pathlib.Path, bytes, _io.BufferedReader, _io.BytesIO, _io.StringIO],
    upload_prefix: typing.Optional[str],
    file_name: typing.Optional[str],
    read_chunk_size_bytes: int,
    encoding: str,
    skip_raw_data_prefix: bool,
    kwargs,
):

This is a more flexible version of put that accepts a file-like object or a string path. Writes to the raw output prefix only. If you want to write to another fs use put_data or get the fsspec file system directly. FYI: Currently the raw output prefix set by propeller is already unique per retry and looks like s3://my-s3-bucket/data/o4/feda4e266c748463a97d-n0-0

If lpath is a folder, then recursive will be set. If lpath is a streamable, then it can only be a single file.

Writes to: {raw output prefix}/{upload_prefix}/{file_name}

download()

def download(
    remote_path: str,
    local_path: str,
    kwargs,
):

Downloads from remote to local

download_directory()

def download_directory(
    remote_path: str,
    local_path: str,
    kwargs,
):

Downloads directory from given remote to local path

exists()

def exists(
    path: str,
):

generate_new_custom_path()

def generate_new_custom_path(
    fs: typing.Optional[fsspec.spec.AbstractFileSystem],
    alt: typing.Optional[str],
    stem: typing.Optional[str],
):

Generates a new path with the raw output prefix and a random string appended to it. Optionally, you can provide an alternate prefix and a stem. If stem is provided, it will be appended to the path instead of a random string. If alt is provided, it will replace the first part of the output prefix, e.g. the S3 or GCS bucket.

If wanting to write to a non-random prefix in a non-default S3 bucket, this can be called with alt=“my-alt-bucket” and stem=“my-stem” to generate a path like s3://my-alt-bucket/default-prefix-part/my-stem

get()

def get(
    from_path: str,
    to_path: str,
    recursive: bool,
    kwargs,
):

get_async_filesystem_for_path()

def get_async_filesystem_for_path(
    path: str,
    anonymous: bool,
    kwargs,
):

get_data()

def get_data(
    remote_path: str,
    local_path: str,
    is_multipart: bool,
    kwargs,
):

get_file_tail()

def get_file_tail(
    file_path_or_file_name: str,
):

get_filesystem()

def get_filesystem(
    protocol: typing.Optional[str],
    anonymous: bool,
    path: typing.Optional[str],
    kwargs,
):

get_filesystem_for_path()

def get_filesystem_for_path(
    path: str,
    anonymous: bool,
    kwargs,
):

get_random_local_directory()

def get_random_local_directory()

get_random_local_path()

def get_random_local_path(
    file_path_or_file_name: typing.Optional[str],
):

Use file_path_or_file_name, when you want a random directory, but want to preserve the leaf file name

get_random_remote_directory()

def get_random_remote_directory()

get_random_remote_path()

def get_random_remote_path(
    file_path_or_file_name: typing.Optional[str],
):

get_random_string()

def get_random_string()

is_remote()

def is_remote(
    path: typing.Union[str, os.PathLike],
):

Deprecated. Let’s find a replacement

join()

def join(
    args: `*args`,
    unstrip: bool,
    fs: typing.Optional[fsspec.spec.AbstractFileSystem],
):

put_data()

def put_data(
    local_path: typing.Union[str, os.PathLike],
    remote_path: str,
    is_multipart: bool,
    kwargs,
):

The implication here is that we’re always going to put data to the remote location, so we .remote to ensure we don’t use the true local proxy if the remote path is a file://

put_raw_data()

def put_raw_data(
    lpath: typing.Union[str, os.PathLike, pathlib.Path, bytes, _io.BufferedReader, _io.BytesIO, _io.StringIO],
    upload_prefix: typing.Optional[str],
    file_name: typing.Optional[str],
    read_chunk_size_bytes: int,
    encoding: str,
    skip_raw_data_prefix: bool,
    kwargs,
):

This is a more flexible version of put that accepts a file-like object or a string path. Writes to the raw output prefix only. If you want to write to another fs use put_data or get the fsspec file system directly. FYI: Currently the raw output prefix set by propeller is already unique per retry and looks like s3://my-s3-bucket/data/o4/feda4e266c748463a97d-n0-0

If lpath is a folder, then recursive will be set. If lpath is a streamable, then it can only be a single file.

Writes to: {raw output prefix}/{upload_prefix}/{file_name}

recursive_paths()

def recursive_paths(
    f: str,
    t: str,
):

sep()

def sep(
    file_system: typing.Optional[fsspec.spec.AbstractFileSystem],
):

strip_file_header()

def strip_file_header(
    path: str,
    trim_trailing_sep: bool,
):

Drops file:// if it exists from the file

upload()

def upload(
    file_path: str,
    to_path: str,
    kwargs,
):

upload_directory()

def upload_directory(
    local_path: str,
    remote_path: str,
    kwargs,
):

Properties

flytekit.bin.entrypoint.FlyteContext

This is an internal-facing context object, that most users will not have to deal with. It’s essentially a globally available grab bag of settings and objects that allows flytekit to do things like convert complex types, run and compile workflows, serialize Flyte entities, etc.

Even though this object as a current_context function on it, it should not be called directly. Please use the :py:class:flytekit.FlyteContextManager object instead.

Please do not confuse this object with the :py:class:flytekit.ExecutionParameters object.

def FlyteContext(
    file_access: FileAccessProvider,
    level: int,
    flyte_client: Optional['friendly_client.SynchronousFlyteClient'],
    compilation_state: Optional[CompilationState],
    execution_state: Optional[ExecutionState],
    serialization_settings: Optional[SerializationSettings],
    in_a_condition: bool,
    origin_stackframe: Optional[traceback.FrameSummary],
    output_metadata_tracker: Optional[OutputMetadataTracker],
    worker_queue: Optional[Controller],
):

Methods

current_context()

def current_context()

This method exists only to maintain backwards compatibility. Please use FlyteContextManager.current_context() instead.

Users of flytekit should be wary not to confuse the object returned from this function with :py:func:flytekit.current_context

enter_conditional_section()

def enter_conditional_section()

get_deck()

def get_deck()

Returns the deck that was created as part of the last execution.

The return value depends on the execution environment. In a notebook, the return value is compatible with IPython.display and should be rendered in the notebook.

.. code-block:: python

with flytekit.new_context() as ctx: my_task(…) ctx.get_deck()

OR if you wish to explicitly display

.. code-block:: python

from IPython import display display(ctx.get_deck())

get_origin_stackframe_repr()

def get_origin_stackframe_repr()

new_builder()

def new_builder()

new_compilation_state()

def new_compilation_state(
    prefix: str,
):

Creates and returns a default compilation state. For most of the code this should be the entrypoint of compilation, otherwise the code should always uses - with_compilation_state

new_execution_state()

def new_execution_state(
    working_dir: Optional[os.PathLike],
):

Creates and returns a new default execution state. This should be used at the entrypoint of execution, in all other cases it is preferable to use with_execution_state

set_stackframe()

def set_stackframe(
    s: traceback.FrameSummary,
):

with_client()

def with_client(
    c: SynchronousFlyteClient,
):

with_compilation_state()

def with_compilation_state(
    c: CompilationState,
):

with_execution_state()

def with_execution_state(
    es: ExecutionState,
):

with_file_access()

def with_file_access(
    fa: FileAccessProvider,
):

with_new_compilation_state()

def with_new_compilation_state()

with_output_metadata_tracker()

def with_output_metadata_tracker(
    t: OutputMetadataTracker,
):

with_serialization_settings()

def with_serialization_settings(
    ss: SerializationSettings,
):

with_worker_queue()

def with_worker_queue(
    wq: Controller,
):

Properties

flytekit.bin.entrypoint.FlyteContextManager

FlyteContextManager manages the execution context within Flytekit. It holds global state of either compilation or Execution. It is not thread-safe and can only be run as a single threaded application currently. Context’s within Flytekit is useful to manage compilation state and execution state. Refer to CompilationState and ExecutionState for more information. FlyteContextManager provides a singleton stack to manage these contexts.

Typical usage is

.. code-block:: python

FlyteContextManager.initialize() with FlyteContextManager.with_context(o) as ctx: pass

If required - not recommended you can use

FlyteContextManager.push_context()

but correspondingly a pop_context should be called

FlyteContextManager.pop_context()

Methods

add_signal_handler()

def add_signal_handler(
    handler: typing.Callable[[int, FrameType], typing.Any],
):

current_context()

def current_context()

get_origin_stackframe()

def get_origin_stackframe(
    limit,
):

initialize()

def initialize()

Re-initializes the context and erases the entire context

pop_context()

def pop_context()

push_context()

def push_context(
    ctx: FlyteContext,
    f: Optional[traceback.FrameSummary],
):

size()

def size()

with_context()

def with_context(
    b: FlyteContext.Builder,
):

flytekit.bin.entrypoint.FlyteException

Common base class for all non-exit exceptions.

def FlyteException(
    args,
    timestamp: typing.Optional[float],
):

Properties

flytekit.bin.entrypoint.FlyteNonRecoverableSystemException

Common base class for all non-exit exceptions.

def FlyteNonRecoverableSystemException(
    exc_value: Exception,
):

FlyteNonRecoverableSystemException is thrown when a system code raises an exception.

Properties

flytekit.bin.entrypoint.FlyteRecoverableException

Common base class for all non-exit exceptions.

def FlyteRecoverableException(
    args,
    timestamp: typing.Optional[float],
):

Properties

flytekit.bin.entrypoint.FlyteUserRuntimeException

Common base class for all non-exit exceptions.

def FlyteUserRuntimeException(
    exc_value: Exception,
    timestamp: typing.Optional[float],
):

FlyteUserRuntimeException is thrown when a user code raises an exception.

Properties

flytekit.bin.entrypoint.IgnoreOutputs

This exception should be used to indicate that the outputs generated by this can be safely ignored. This is useful in case of distributed training or peer-to-peer parallel algorithms.

flytekit.bin.entrypoint.ImageConfig

We recommend you to use ImageConfig.auto(img_name=None) to create an ImageConfig. For example, ImageConfig.auto(img_name=““ghcr.io/flyteorg/flytecookbook:v1.0.0"”) will create an ImageConfig.

ImageConfig holds available images which can be used at registration time. A default image can be specified along with optional additional images. Each image in the config must have a unique name.

Attributes: default_image (Optional[Image]): The default image to be used as a container for task serialization. images (List[Image]): Optional, additional images which can be used in task container definitions.

def ImageConfig(
    default_image: Optional[Image],
    images: Optional[List[Image]],
):

Methods

auto()

def auto(
    config_file: typing.Union[str, ConfigFile, None],
    img_name: Optional[str],
):

Reads from config file or from img_name Note that this function does not take into account the flytekit default images (see the Dockerfiles at the base of this repo). To pick those up, see the auto_default_image function..

auto_default_image()

def auto_default_image()

create_from()

def create_from(
    default_image: Optional[Image],
    other_images: typing.Optional[typing.List[Image]],
):

find_image()

def find_image(
    name,
):

Return an image, by name, if it exists.

from_dict()

def from_dict(
    kvs: typing.Union[dict, list, str, int, float, bool, NoneType],
    infer_missing,
):

from_images()

def from_images(
    default_image: str,
    m: typing.Optional[typing.Dict[str, str]],
):

Allows you to programmatically create an ImageConfig. Usually only the default_image is required, unless your workflow uses multiple images

.. code:: python

ImageConfig.from_dict( “ghcr.io/flyteorg/flytecookbook:v1.0.0”, { “spark”: “ghcr.io/flyteorg/myspark:…”, “other”: “…”, } )

urn:

from_json()

def from_json(
    s: typing.Union[str, bytes, bytearray],
    parse_float,
    parse_int,
    parse_constant,
    infer_missing,
    kw,
):

schema()

def schema(
    infer_missing: bool,
    only,
    exclude,
    many: bool,
    context,
    load_only,
    dump_only,
    partial: bool,
    unknown,
):

to_dict()

def to_dict(
    encode_json,
):

to_json()

def to_json(
    skipkeys: bool,
    ensure_ascii: bool,
    check_circular: bool,
    allow_nan: bool,
    indent: typing.Union[int, str, NoneType],
    separators: typing.Tuple[str, str],
    default: typing.Callable,
    sort_keys: bool,
    kw,
):

validate_image()

def validate_image(
    _: typing.Any,
    param: str,
    values: tuple,
):

Validates the image to match the standard format. Also validates that only one default image is provided. a default image, is one that is specified as default=<image_uri> or just <image_uri>. All other images should be provided with a name, in the format name=<image_uri> This method can be used with the CLI

flytekit.bin.entrypoint.OutputMetadataTracker

This class is for the users to set arbitrary metadata on output literals.

Attributes: output_metadata Optional[TaskOutputMetadata]: is a sparse dictionary of metadata that the user wants to attach to each output of a task. The key is the output value (object) and the value is an OutputMetadata object.

def OutputMetadataTracker(
    output_metadata: typing.Dict[typing.Any, OutputMetadata],
):

Methods

add()

def add(
    obj: typing.Any,
    metadata: OutputMetadata,
):

get()

def get(
    obj: typing.Any,
):

with_params()

def with_params(
    output_metadata: Optional[TaskOutputMetadata],
):

Produces a copy of the current object and set new things

flytekit.bin.entrypoint.PythonTask

Base Class for all Tasks with a Python native Interface. This should be directly used for task types, that do not have a python function to be executed. Otherwise refer to :py:class:flytekit.PythonFunctionTask.

def PythonTask(
    task_type: str,
    name: str,
    task_config: typing.Optional[~T],
    interface: typing.Optional[flytekit.core.interface.Interface],
    environment: typing.Optional[typing.Dict[str, str]],
    disable_deck: typing.Optional[bool],
    enable_deck: typing.Optional[bool],
    deck_fields: typing.Optional[typing.Tuple[flytekit.deck.deck.DeckField, ...]],
    kwargs,
):

Methods

compile()

def compile(
    ctx: flytekit.core.context_manager.FlyteContext,
    args,
    kwargs,
):

Generates a node that encapsulates this task in a workflow definition.

construct_node_metadata()

def construct_node_metadata()

Used when constructing the node that encapsulates this task as part of a broader workflow definition.

dispatch_execute()

def dispatch_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
):

This method translates Flyte’s Type system based input values and invokes the actual call to the executor This method is also invoked during runtime.

• VoidPromise is returned in the case when the task itself declares no outputs.
• Literal Map is returned when the task returns either one more outputs in the declaration. Individual outputs may be none
• DynamicJobSpec is returned when a dynamic workflow is executed

execute()

def execute(
    kwargs,
):

This method will be invoked to execute the task.

find_lhs()

def find_lhs()

get_config()

def get_config(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the task config as a serializable dictionary. This task config consists of metadata about the custom defined for this task.

get_container()

def get_container(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the container definition (if any) that is used to run the task on hosted Flyte.

get_custom()

def get_custom(
    settings: flytekit.configuration.SerializationSettings,
):

Return additional plugin-specific custom data (if any) as a serializable dictionary.

get_extended_resources()

def get_extended_resources(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the extended resources to allocate to the task on hosted Flyte.

get_input_types()

def get_input_types()

Returns the names and python types as a dictionary for the inputs of this task.

get_k8s_pod()

def get_k8s_pod(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the kubernetes pod definition (if any) that is used to run the task on hosted Flyte.

get_sql()

def get_sql(
    settings: flytekit.configuration.SerializationSettings,
):

Returns the Sql definition (if any) that is used to run the task on hosted Flyte.

get_type_for_input_var()

def get_type_for_input_var(
    k: str,
    v: typing.Any,
):

Returns the python type for an input variable by name.

get_type_for_output_var()

def get_type_for_output_var(
    k: str,
    v: typing.Any,
):

Returns the python type for the specified output variable by name.

local_execute()

def local_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    kwargs,
):

This function is used only in the local execution path and is responsible for calling dispatch execute. Use this function when calling a task with native values (or Promises containing Flyte literals derived from Python native values).

local_execution_mode()

def local_execution_mode()

post_execute()

def post_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
    rval: typing.Any,
):

Post execute is called after the execution has completed, with the user_params and can be used to clean-up, or alter the outputs to match the intended tasks outputs. If not overridden, then this function is a No-op

pre_execute()

def pre_execute(
    user_params: typing.Optional[flytekit.core.context_manager.ExecutionParameters],
):

This is the method that will be invoked directly before executing the task method and before all the inputs are converted. One particular case where this is useful is if the context is to be modified for the user process to get some user space parameters. This also ensures that things like SparkSession are already correctly setup before the type transformers are called

This should return either the same context of the mutated context

sandbox_execute()

def sandbox_execute(
    ctx: flytekit.core.context_manager.FlyteContext,
    input_literal_map: flytekit.models.literals.LiteralMap,
):

Call dispatch_execute, in the context of a local sandbox execution. Not invoked during runtime.

Properties

flytekit.bin.entrypoint.SerializationSettings

These settings are provided while serializing a workflow and task, before registration. This is required to get runtime information at serialization time, as well as some defaults.

Attributes: project (str): The project (if any) with which to register entities under. domain (str): The domain (if any) with which to register entities under. version (str): The version (if any) with which to register entities under. image_config (ImageConfig): The image config used to define task container images. env (Optional[Dict[str, str]]): Environment variables injected into task container definitions. flytekit_virtualenv_root (Optional[str]): During out of container serialize the absolute path of the flytekit virtualenv at serialization time won’t match the in-container value at execution time. This optional value is used to provide the in-container virtualenv path python_interpreter (Optional[str]): The python executable to use. This is used for spark tasks in out of container execution. entrypoint_settings (Optional[EntrypointSettings]): Information about the command, path and version of the entrypoint program. fast_serialization_settings (Optional[FastSerializationSettings]): If the code is being serialized so that it can be fast registered (and thus omit building a Docker image) this object contains additional parameters for serialization. source_root (Optional[str]): The root directory of the source code.

def SerializationSettings(
    image_config: ImageConfig,
    project: typing.Optional[str],
    domain: typing.Optional[str],
    version: typing.Optional[str],
    env: Optional[Dict[str, str]],
    git_repo: Optional[str],
    python_interpreter: str,
    flytekit_virtualenv_root: Optional[str],
    fast_serialization_settings: Optional[FastSerializationSettings],
    source_root: Optional[str],
):

Methods

default_entrypoint_settings()

def default_entrypoint_settings(
    interpreter_path: str,
):

Assumes the entrypoint is installed in a virtual-environment where the interpreter is

for_image()

def for_image(
    image: str,
    version: str,
    project: str,
    domain: str,
    python_interpreter_path: str,
):

from_dict()

def from_dict(
    kvs: typing.Union[dict, list, str, int, float, bool, NoneType],
    infer_missing,
):

from_json()

def from_json(
    s: typing.Union[str, bytes, bytearray],
    parse_float,
    parse_int,
    parse_constant,
    infer_missing,
    kw,
):

from_transport()

def from_transport(
    s: str,
):

new_builder()

def new_builder()

Creates a SerializationSettings.Builder that copies the existing serialization settings parameters and allows for customization.

schema()

def schema(
    infer_missing: bool,
    only,
    exclude,
    many: bool,
    context,
    load_only,
    dump_only,
    partial: bool,
    unknown,
):

should_fast_serialize()

def should_fast_serialize()

Whether or not the serialization settings specify that entities should be serialized for fast registration.

to_dict()

def to_dict(
    encode_json,
):

to_json()

def to_json(
    skipkeys: bool,
    ensure_ascii: bool,
    check_circular: bool,
    allow_nan: bool,
    indent: typing.Union[int, str, NoneType],
    separators: typing.Tuple[str, str],
    default: typing.Callable,
    sort_keys: bool,
    kw,
):

venv_root_from_interpreter()

def venv_root_from_interpreter(
    interpreter_path: str,
):

Computes the path of the virtual environment root, based on the passed in python interpreter path for example /opt/venv/bin/python3 -> /opt/venv

with_serialized_context()

def with_serialized_context()

Use this method to create a new SerializationSettings that has an environment variable set with the SerializedContext This is useful in transporting SerializedContext to serialized and registered tasks. The setting will be available in the env field with the key SERIALIZED_CONTEXT_ENV_VAR :return: A newly constructed SerializationSettings, or self, if it already has the serializationSettings

Properties

flytekit.bin.entrypoint.StatsConfig

Configuration for sending statsd.

def StatsConfig(
    host: str,
    port: int,
    disabled: bool,
    disabled_tags: bool,
):

Methods

auto()

def auto(
    config_file: typing.Union[str, ConfigFile],
):

Reads from environment variable, followed by ConfigFile provided

flytekit.bin.entrypoint.SyncCheckpoint

This class is NOT THREAD-SAFE! Sync Checkpoint, will synchronously checkpoint a user given file or folder. It will also synchronously download / restore previous checkpoints, when restore is invoked.

TODO: Implement an async checkpoint system

def SyncCheckpoint(
    checkpoint_dest: str,
    checkpoint_src: typing.Optional[str],
):

Methods

prev_exists()

def prev_exists()

read()

def read()

This should only be used if there is a singular checkpoint file written. If more than one checkpoint file is found, this will raise a ValueError

restore()

def restore(
    path: typing.Union[pathlib.Path, str, NoneType],
):

Given a path, if a previous checkpoint exists, will be downloaded to this path. If download is successful the downloaded path is returned

.. note:

Download will not be performed, if the checkpoint was previously restored. The method will return the previously downloaded path.

save()

def save(
    cp: typing.Union[pathlib.Path, str, _io.BufferedReader],
):

write()

def write(
    b: bytes,
):

This will overwrite the checkpoint. It can be retrieved using read or restore

flytekit.bin.entrypoint.Timestamp

A ProtocolMessage

Methods

FromDatetime()

def FromDatetime(
    dt,
):

Converts datetime to Timestamp.

FromJsonString()

def FromJsonString(
    value,
):

Parse a RFC 3339 date string format to Timestamp.

FromMicroseconds()

def FromMicroseconds(
    micros,
):

Converts microseconds since epoch to Timestamp.

FromMilliseconds()

def FromMilliseconds(
    millis,
):

Converts milliseconds since epoch to Timestamp.

FromNanoseconds()

def FromNanoseconds(
    nanos,
):

Converts nanoseconds since epoch to Timestamp.

FromSeconds()

def FromSeconds(
    seconds,
):

Converts seconds since epoch to Timestamp.

GetCurrentTime()

def GetCurrentTime()

Get the current UTC into Timestamp.

ToDatetime()

def ToDatetime(
    tzinfo,
):

Converts Timestamp to a datetime.

ToJsonString()

def ToJsonString()

Converts Timestamp to RFC 3339 date string format.

Returns: A string converted from timestamp. The string is always Z-normalized and uses 3, 6 or 9 fractional digits as required to represent the exact time. Example of the return format: ‘1972-01-01T10:00:20.021Z’

ToMicroseconds()

def ToMicroseconds()

Converts Timestamp to microseconds since epoch.

ToMilliseconds()

def ToMilliseconds()

Converts Timestamp to milliseconds since epoch.

ToNanoseconds()

def ToNanoseconds()

Converts Timestamp to nanoseconds since epoch.

ToSeconds()

def ToSeconds()

Converts Timestamp to seconds since epoch.

flytekit.bin.entrypoint.VoidPromise

This object is returned for tasks that do not return any outputs (declared interface is empty) VoidPromise cannot be interacted with and does not allow comparisons or any operations

def VoidPromise(
    task_name: str,
    ref: Optional[NodeOutput],
):

Methods

runs_before()

def runs_before(
    args,
    kwargs,
):

This is a placeholder and should do nothing. It is only here to enable local execution of workflows where a task returns nothing.

with_overrides()

def with_overrides(
    args,
    kwargs,
):

Properties