<@U06RKAMK0UC> What is the reason for the below er...
# ask-ai
c
@RunLLM What is the reason for the below errors while registering the workflow?
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Error 1: Code: ParameterNotBound, Node Id: n1, Description: Parameter not bound [events_per_day_paths].
	Error 2: Code: ParameterNotBound, Node Id: n1, Description: Parameter not bound [output_path].
r
The errors indicate that the parameters
events_per_day_paths
and
output_path
are not bound to any task invocation in the workflow. This typically happens when the workflow definition does not correctly pass these parameters to the tasks that require them. Would you like more detail on how to resolve this issue? Sources: - Flyte Discussion on Parameter Binding - Flyte Issue on Parameter Binding 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
c
@RunLLM how to resolve below error? Error 0: Code: MismatchingTypes, Node Id: n1, Description: Variable [config] (type [collection_type{simpleSTRING}]) doesn't match expected type [simple:STRING].
r
The error indicates a type mismatch where the variable
config
is expected to be a simple string (
simple:STRING
), but it is being provided as a collection of strings (
collection_type:{simple:STRING}
). This usually happens when a list or array is passed to a task or workflow expecting a single string. To resolve this, ensure that the
config
variable is correctly defined and passed as a single string. Here is an example of how to correct this in a Flyte workflow:
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from flytekit import task, workflow

@task
def process_config(config: str) -> str:
    # Process the config string
    return config

@workflow
def my_workflow(config: str) -> str:
    result = process_config(config=config)
    return result
Make sure that when you call the workflow, you pass a single string, not a list of strings. Would you like more detail on this? Sources: - Flyte Discussion on Type Mismatch - Flyte Issue on Type Mismatch 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
c
@RunLLM I have a "main" workflow which creates an object of class called "Config". "main" workflow passed this "config" object to "sub workflow". sub workflow definition is like below:
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def sub_workflow(date: str, config: Config) -> str:
    input_paths = [config.path_1, config.path_2]
    task_output = my_task(paths: input_paths)
    return task_output
I get this error while running the "sub_workflow" via "main" workflow
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Error 0: Code: MismatchingTypes, Node Id: n1, Description: Variable [config] (type [collection_type:{simple:STRING}]) doesn't match expected type [simple:STRING].
r
The error indicates that the
config
parameter in the
sub_workflow
is expected to be a simple string, but it is being passed as an object of type
Config
. To resolve this, ensure that the
Config
class is properly defined and used in both the
main
and
sub_workflow
. Here is an example of how to correctly define and use the
Config
class in Flyte workflows: 1. Define the
Config
class
:
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from dataclasses import dataclass
    from flytekit import task, workflow

    @dataclass
    class Config:
        path_1: str
        path_2: str
2. Define the
sub_workflow
:
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@workflow
    def sub_workflow(date: str, config: Config) -> str:
        input_paths = [config.path_1, config.path_2]
        task_output = my_task(paths=input_paths)
        return task_output
3. Define the
main
workflow
:
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@workflow
    def main_workflow(date: str) -> str:
        config = Config(path_1="path/to/file1", path_2="path/to/file2")
        result = sub_workflow(date=date, config=config)
        return result
4. Define the task:
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@task
    def my_task(paths: list[str]) -> str:
        # Process the paths
        return "processed"
Ensure that the
Config
object is correctly passed from the
main_workflow
to the
sub_workflow
and that the
sub_workflow
is expecting a
Config
object, not a simple string. Would you like more detail on this? Sources: - Flyte Discussion on Type Mismatch - Flyte Issue on Type Mismatch 👍 1 button 👎 0 button 💡 Hint: Mention @RunLLM in the thread for followups.
c
@RunLLM how to resolve this error?
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SPARK_SESSION not available as a parameter in Flyte context - are you in right task-type?
r
The error indicates that the Spark session is not available in the Flyte context, which usually happens when the task configuration does not correctly initialize the Spark session. To resolve this, ensure that the task is configured with the
Spark
task type and that the Spark session is correctly accessed within the task. Here is how you can configure and access the Spark session in a Flyte task: 1. Import necessary modules:
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from flytekit import task, workflow
    from flytekitplugins.spark import Spark
    from flytekit.types.schema import FlyteSchema
    from pyspark.sql import SparkSession
2. Define the task with Spark configuration:
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@task(
        task_config=Spark(
            spark_conf={
                "spark.driver.memory": "1000M",
                "spark.executor.memory": "1000M",
                "spark.executor.cores": "1",
                "spark.executor.instances": "2",
                "spark.driver.cores": "1",
            }
        ),
        cache_version="1"
    )
    def spark_task() -> FlyteSchema:
        # Access the Spark session from the Flyte context
        spark = flytekit.current_context().spark_session
        
        # Use the Spark session to perform operations
        df = spark.read.format("csv").option("header", "true").load("path/to/csv")
        return df
3. Define the workflow:
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@workflow
    def my_workflow() -> FlyteSchema:
        return spark_task()
Ensure that you are running this in a configured Flyte environment with Spark backend support. If you are running this locally, make sure that the Flyte environment is correctly set up to support Spark tasks. Would you like more detail on this? Sources: - Flyte Discussion on Spark Session - Flyte Issue on Spark Session 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
c
@RunLLM How to mock "file sensors" for unit testing?
r
To mock file sensors for unit testing in Flyte, you can use the
flytekit.testing
module to create mock tasks and workflows. Here is an example of how to mock a
FileSensor
for unit testing: 1. Import necessary modules:
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import pytest
    from flytekit import task, workflow
    from flytekit.testing import task_mock
    from flytekit.sensor.file_sensor import FileSensor
2. Define the
FileSensor
and tasks
:
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@task
    def process_file(file_path: str) -> str:
        # Process the file
        return f"Processed {file_path}"

    @workflow
    def my_workflow(file_path: str) -> str:
        sensor = FileSensor(name="test_file_sensor")
        sensor(file_path=file_path)
        return process_file(file_path=file_path)
3. Create a test function to mock the
FileSensor
:
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@pytest.mark.parametrize("file_path", ["path/to/file.txt"])
    def test_my_workflow(file_path):
        with task_mock(FileSensor) as mock_sensor:
            mock_sensor.return_value = file_path
            result = my_workflow(file_path=file_path)
            assert result == f"Processed {file_path}"
This setup allows you to mock the
FileSensor
and test the workflow without needing an actual file to be present. The
task_mock
context manager is used to mock the sensor's behavior and return a predefined file path. Would you like more detail on this? Sources: - Flyte Unit Testing Discussion 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
c
@RunLLM how to implement above test "test_my_workflow" using @patch or @flyte_patch annotations instead of using with clause?
r
To implement the
test_my_workflow
using the
@patch
or
@flyte_patch
annotations instead of the
with
clause, you can use the
flytekit.testing.patch
decorator. Here is how you can do it: 1. Import necessary modules:
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import pytest
    from flytekit import task, workflow
    from flytekit.testing import patch
    from flytekit.sensor.file_sensor import FileSensor
2. Define the
FileSensor
and tasks
:
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@task
    def process_file(file_path: str) -> str:
        # Process the file
        return f"Processed {file_path}"

    @workflow
    def my_workflow(file_path: str) -> str:
        sensor = FileSensor(name="test_file_sensor")
        sensor(file_path=file_path)
        return process_file(file_path=file_path)
3. Create a test function using the
@patch
decorator
:
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@patch(FileSensor)
    def test_my_workflow(mock_sensor, file_path="path/to/file.txt"):
        mock_sensor.return_value = file_path
        result = my_workflow(file_path=file_path)
        assert result == f"Processed {file_path}"
This setup uses the
@patch
decorator from
flytekit.testing
to mock the
FileSensor
and test the workflow. The
mock_sensor
parameter in the test function is the mock object for the
FileSensor
. Would you like more detail on this? Sources: - Flyte Mocking Tasks - Flytekit Testing 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
c
@RunLLM Using "with task_mock(FileSensor) as mock_sensor:" and " @patch(FileSensor)" I get "Exception: Can only be used for tasks" . How to resolve this error?
r
The error "Exception: Can only be used for tasks" indicates that the
task_mock
and
@patch
utilities in Flyte are designed to mock Flyte tasks, workflows, or reference entities, but not sensors directly. To mock sensors, you need to mock the underlying functionality that the sensor relies on, such as file existence checks or other I/O operations. Here is an example of how to mock a
FileSensor
by patching the method that checks for file existence: 1. Import necessary modules:
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import pytest
    from unittest.mock import patch
    from flytekit import task, workflow
    from flytekit.sensor.file_sensor import FileSensor
2. Define the
FileSensor
and tasks
:
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@task
    def process_file(file_path: str) -> str:
        # Process the file
        return f"Processed {file_path}"

    @workflow
    def my_workflow(file_path: str) -> str:
        sensor = FileSensor(name="test_file_sensor")
        sensor(file_path=file_path)
        return process_file(file_path=file_path)
3. Create a test function using the
unittest.mock.patch
decorator
:
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@patch("flytekit.sensor.file_sensor.FileSensor._check_file_exists")
    def test_my_workflow(mock_check_file_exists):
        file_path = "path/to/file.txt"
        mock_check_file_exists.return_value = True  # Mock the file existence check to always return True

        result = my_workflow(file_path=file_path)
        assert result == f"Processed {file_path}"
In this example, the
@patch
decorator is used to mock the
_check_file_exists
method of the
FileSensor
class. This allows you to simulate the behavior of the file sensor without needing an actual file. Would you like more detail on this? Sources: - Flyte Mocking Tasks - Flytekit Testing 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
c
@RunLLM Why does Flyte throws "AttributeError: 'list' object has no attribute 'items'" for a task which accepts
List[str]
type argument?
r
The error "AttributeError: 'list' object has no attribute 'items'" typically occurs when Flyte expects a dictionary but receives a list instead. This can happen if the task or workflow is incorrectly defined or if the input is incorrectly passed. To resolve this issue, ensure that the task and workflow definitions correctly specify the input types and that the inputs are correctly passed as lists. Here is an example of how to define a task and workflow that accepts a
List[str]
type argument: 1. Define the task:
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from flytekit import task

    @task
    def process_files(file_paths: list[str]) -> int:
        # Process the list of file paths
        return len(file_paths)
2. Define the workflow:
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from flytekit import workflow

    @workflow
    def my_workflow(file_paths: list[str]) -> int:
        return process_files(file_paths=file_paths)
3. Run the workflow:
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if __name__ == "__main__":
        result = my_workflow(file_paths=["path/to/file1.txt", "path/to/file2.txt"])
        print(f"Number of files processed: {result}")
Ensure that the input to the
process_files
task is a list of strings. If the input is incorrectly passed as a dictionary, Flyte will raise the "AttributeError: 'list' object has no attribute 'items'" error. If you continue to encounter the issue, please provide more details about the task and workflow definitions, as well as the input being passed. Would you like more detail on this? Sources: - Flyte Issue on Type Mismatch - Flyte Discussion on Type Mismatch 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
c
@RunLLM can we give a custom name to Flyte task?
r
Yes, you can give a custom name to a Flyte task using the
name
parameter in the task decorator. This allows you to assign a more descriptive and user-friendly name to the task, which can be useful for better identification and management. Here is an example of how to define a Flyte task with a custom name: 1. Import necessary modules:
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from flytekit import task, workflow
2. Define the task with a custom name:
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@task(name="custom_task_name")
    def my_task(input_str: str) -> str:
        return f"Processed {input_str}"
3. Define the workflow:
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@workflow
    def my_workflow(input_str: str) -> str:
        return my_task(input_str=input_str)
4. Run the workflow:
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if __name__ == "__main__":
        result = my_workflow(input_str="example")
        print(result)
In this example, the task
my_task
is given the custom name
custom_task_name
. Would you like more detail on this? Sources: - Flyte Discussion on Custom Task Names - Flyte Issue on Custom Task Names 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
c
@RunLLM Can we print custom name of the task when task is running?
r
Yes, you can print the custom name of the task when it is running by accessing the task's context. Flyte provides a way to access the current context using
flytekit.current_context()
, which contains various metadata, including the task's name. Here is an example of how to print the custom name of the task when it is running: 1. Import necessary modules:
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from flytekit import task, workflow, current_context
2. Define the task with a custom name and print the name:
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@task(name="custom_task_name")
    def my_task(input_str: str) -> str:
        ctx = current_context()
        print(f"Running task: {ctx.task_name}")
        return f"Processed {input_str}"
3. Define the workflow:
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@workflow
    def my_workflow(input_str: str) -> str:
        return my_task(input_str=input_str)
4. Run the workflow:
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if __name__ == "__main__":
        result = my_workflow(input_str="example")
        print(result)
In this example, the task
my_task
is given the custom name
custom_task_name
, and the task's name is printed when the task is running. Would you like more detail on this? Sources: - Flyte Context - Flyte Discussion on Custom Task Names 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
c
@RunLLM Flyte throws below error when we give name to a task
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TypeError: task() got an unexpected keyword argument 'name'
r
The error "TypeError: task() got an unexpected keyword argument 'name'" indicates that the
task
decorator does not accept a
name
parameter directly. Instead, you can use the
with_overrides
method to set a custom name for the task. Here is how you can define a task with a custom name using the
with_overrides
method: 1. Import necessary modules:
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from flytekit import task, workflow, current_context
2. Define the task:
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@task
    def my_task(input_str: str) -> str:
        ctx = current_context()
        print(f"Running task: {ctx.task_name}")
        return f"Processed {input_str}"
3. Define the workflow and use
with_overrides
to set the custom task name
:
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@workflow
    def my_workflow(input_str: str) -> str:
        custom_named_task = my_task.with_overrides(name="custom_task_name")
        return custom_named_task(input_str=input_str)
4. Run the workflow:
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if __name__ == "__main__":
        result = my_workflow(input_str="example")
        print(result)
In this example, the
my_task
task is given the custom name
custom_task_name
using the
with_overrides
method, and the task's name is printed when the task is running. Would you like more detail on this? Sources: - Flyte Discussion on Custom Task Names 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.