hey @ancient-wolf-19325 how to resolve "TypeError: bad argument type for built-in operation" ?

The "TypeError: bad argument type for built-in operation" error often occurs due to type mismatches or incorrect type annotations in Flyte workflows or tasks. According to this discussion, one common cause is a discrepancy between the types expected by Flyte and those provided in the workflow or task definitions. Would you like more details on how to debug this issue step-by-step? Sources: - this discussion 👍 1 button 👎 0 button 💡 Hint: Mention @ancient-wolf-19325 in the thread for followups.

hey @ancient-wolf-19325 how to find out for which workflow or task type mismatch has occured?

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hey @ancient-wolf-19325 how to find out for which workflow or task type mismatch has occured?

To identify which workflow or task has a type mismatch, you can follow these steps: 1. Check Error Messages: The error message usually contains details about the specific task or workflow where the type mismatch occurred. Look for keywords like "Node Id" or "Variable" in the error message. For example, an error message might state:

Error 0: Code: MismatchingTypes, Node Id: construct-rate-cards, Description: Variable [o0] (type [structured_dataset_type:<format:"parquet" > ]) doesn't match expected type [structured_dataset_type:<> ].

2. Review Task and Workflow Definitions: Ensure that the input and output types of your tasks and workflows are correctly annotated. Type mismatches often occur when the expected type does not match the provided type. For instance, if a task is expected to return a

pandas.DataFrame

but returns a different type, it will cause a mismatch. 3. Use Type Annotations: Make sure all tasks and workflows have proper type annotations. Missing or incorrect type annotations can lead to type mismatches. For example:

@task
   def t1() -> pandas.DataFrame:
       ...

   @task
   def t2(a: pandas.DataFrame) -> None:
       ...

   @workflow
   def wf() -> None:
       return t2(a=t1())

4. Debugging Tools: Utilize Flyte's debugging tools and logs to trace the source of the error. The logs can provide insights into which part of the workflow or task is causing the issue. Would you like more detailed guidance on any of these steps? Sources: - Flyte GitHub Issue #5045 - Flyte GitHub Issue #5270 - Flyte Discussion 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 stack trace shows below method .

flytekitplugins/spark/models.py:159 in to_flyte_idl                                         │
│                                                                                                                                                                                                      │
│ ❱ 159 │   │   return _spark_task.SparkJob(                                                                                                                                                           │
│

The stack trace indicates that the error is occurring in the

to_flyte_idl

method within the

flytekitplugins/spark/models.py

file. This method is responsible for converting a Spark task to its Flyte IDL (Interface Definition Language) representation. To identify the specific task or workflow causing the type mismatch, follow these steps: 1. Inspect the Stack Trace: The stack trace should provide a sequence of function calls leading to the error. Look for the task or workflow name in the stack trace. The error message might include details about the specific node or variable causing the issue. 2. Check Task Definitions: Review the task definitions in your code, especially those involving Spark tasks. Ensure that the input and output types are correctly annotated and match the expected types. 3. Validate Type Annotations: Ensure that the types used in your tasks and workflows are compatible with Flyte's type system. For example, if you are using a

pandas.DataFrame

, make sure it is correctly annotated and handled. 4. Debugging Example: Here is an example of how to define a Spark task with proper type annotations:

from flytekit import task, workflow
   from flytekitplugins.spark import Spark

   @task(task_config=Spark(spark_conf={"spark.driver.memory": "1000M"}))
   def spark_task() -> str:
       # Your Spark task logic here
       return "result"

   @workflow
   def my_workflow() -> str:
       return spark_task()

5. Check for Known Issues: Refer to known issues and discussions related to Spark tasks in Flyte. For example, this GitHub issue discusses a similar problem with Spark tasks. Would you like more detailed guidance on any of these steps? Sources: - Flyte GitHub Issue #4877 - Flyte GitHub Issue #3904 - Flyte Discussion 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 How to resolve "Can only use mocks on tasks/workflows declared in Python." error?

Sorry, there was a temporary error when processing your request. Please try again. If this problem persists, please contact support. 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

@ancient-wolf-19325 How to resolve "Can only use mocks on tasks/workflows declared in Python." error?

The error "Can only use mocks on tasks/workflows declared in Python." typically occurs when you attempt to mock a task or workflow that is not defined using Flyte's Python SDK. This can happen if the task or workflow is defined in another language or if there is a type mismatch. To resolve this error, follow these steps: 1. Ensure Python Declaration: Verify that the task or workflow you are trying to mock is declared using Flyte's Python SDK. For example:

from flytekit import task, workflow

   @task
   def my_task() -> str:
       return "Hello, Flyte!"

   @workflow
   def my_workflow() -> str:
       return my_task()

2. Use Correct Mocking Utilities: Use the

task_mock

or

patch

utilities provided by Flytekit for mocking tasks and workflows. Ensure that you are importing these utilities correctly:

from flytekit.testing import task_mock, patch

   def test_my_task():
       with task_mock(my_task) as mock:
           mock.return_value = "Mocked Result"
           assert my_task() == "Mocked Result"

   @patch(my_task)
   def test_my_workflow(mock_task):
       mock_task.return_value = "Mocked Result"
       assert my_workflow() == "Mocked Result"

3. Check for Type Mismatches: Ensure that the types used in your tasks and workflows are compatible with Flyte's type system. Type mismatches can cause this error. For example, if you are using a

pandas.DataFrame

, make sure it is correctly annotated and handled. 4. Review Task and Workflow Definitions: Ensure that the task or workflow you are trying to mock is correctly defined and registered in Flyte. If the task or workflow is defined in another language or not properly registered, you will encounter this error. Would you like more detailed guidance on any of these steps? Sources: - Flyte GitHub Issue #5312 - Flyte GitHub Issue #4505 - Flytekit Testing Documentation - Flytekit Mocking Tasks Guide 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.