https://flyte.org logo
#ask-ai
Title
# ask-ai
a

Andy Czerwonka

03/18/2024, 8:39 PM
@Glime how does Flyte encode and pass complex data to remote tasks?
g

Glime

03/18/2024, 8:39 PM
Flyte handles the passing of complex data between tasks, especially for remote execution, by utilizing a combination of stack-based parameters (pass by value) and heap parameters (pass by reference/pointers). For simple data types like integers, which are limited to 10MB, these are passed directly from task to task. However, for more complex data types such as files, file-like objects, dataframes, and directories, Flyte employs a different strategy to ensure efficiency, security, and speed. When dealing with these complex data types, the data is first automatically persisted to a remote storage location like S3 during its initial use. Subsequently, instead of passing the actual data between tasks, Flyte passes a reference to its location in the remote storage. This method avoids the need to duplicate the data, thereby saving on storage costs and reducing the time required for data transfer between tasks. It also addresses security concerns by limiting direct access to the data. For accessing the data, Flyte provides abstractions like FlyteFile, FlyteDirectory, and StructuredDataset. These objects carry a reference to the remote file location. Depending on the requirements, the data can either be downloaded on demand to the local filesystem of the container or streamed. Streaming can be faster and is made to appear to the user as if they are opening a file, although it requires the program to be capable of processing a stream of bytes. This approach allows Flyte to efficiently and securely manage the transfer of complex data types in distributed and remote task executions, ensuring that the data storage is both efficient and fast. --- This is an AI-generated response, take its recommendations with caution. Please help us improve the model accuracy by giving this response a thumbs up/thumbs down reaction. Sources https://discuss.flyte.org/t/10262872/hi-all-just-getting-my-hands-dirty-and-trying-to-wrap-my-hea/ 👍 button 👎 button
a

Andy Czerwonka

03/18/2024, 9:16 PM
@Glime what does a StructuredDataset look like?
g

Glime

03/18/2024, 9:16 PM
A
StructuredDataset
in Flyte is designed to represent dataframes abstractly, bridging the gap between various dataframe classes (like Pandas, Spark, Pandera, etc.) and the lack of a native Python type for dataframes. It provides a unified way to work with tabular data across different tasks and workflows in Flyte, offering several benefits: • Elimination of Boilerplate Code: It reduces the need to write custom serialization/deserialization code for moving dataframe instances to and from file objects. • Metadata Management: It removes the necessity for additional inputs/outputs to convey metadata about the tabular data's format. • Loading Flexibility: It adds flexibility in how dataframe files are loaded. • Dataframe Specific Functionality: It supports enforcing compatibility of different schemas (at both compile-time and runtime), storing third-party schema definitions, and potentially providing features like rendering sample data or summary statistics in the future. To use a
StructuredDataset
, you typically define a task that returns a dataframe, such as a Pandas DataFrame. Flytekit automatically detects the return signature of the task as a Pandas DataFrame and converts the task interface to use the
StructuredDataset
type. This process abstracts away the complexities of handling different dataframe formats and focuses on the data and its transformations. Here's a basic example of how a
StructuredDataset
might be used in a task:
Copy code
from flytekit import task from pandas import DataFrame from flytekit.types.structured import StructuredDataset  @task def my_task() -> StructuredDataset:     # Create a Pandas DataFrame     df = DataFrame({"column1": [1, 2, 3], "column2": ["a", "b", "c"]})     # Return it as a StructuredDataset     return StructuredDataset(dataframe=df)
This example demonstrates the creation of a simple Pandas DataFrame and its return as a
StructuredDataset
, showcasing the ease of working with tabular data in Flyte.
a

Andy Czerwonka

03/18/2024, 9:17 PM
@Glime What if I want to use a custom data graph?
@Glime Can you show me an example of using a LiteralMap to pass parameters into a task?
g

Glime

03/18/2024, 9:21 PM
To use a
LiteralMap
to pass parameters into a task in Flyte, you would typically convert a Python dictionary into a
LiteralMap
and then use this map to pass parameters to a task. The
LiteralMap
is a way to encapsulate a set of parameters (key-value pairs) where the keys are strings and the values are
Literals
. This is particularly useful when you want to dynamically pass a set of parameters to a task. Here's a simplified example to illustrate how you might convert a Python dictionary to a
LiteralMap
and use it in a Flyte task:
Copy code
from flytekit import task, workflow from flytekit.core.context_manager import FlyteContext from flytekit.models.literals import LiteralMap, Scalar, Primitive from flytekit.models.types import LiteralType, SimpleType  # Define a task that accepts a LiteralMap as input @task def process_params(params: LiteralMap) -> str:     # Assuming params is a LiteralMap with string values     # Extracting the value for a specific key for demonstration     value = params.literals["example_key"].scalar.primitive.string_value     return f"Processed value: {value}"  # Example function to convert a Python dict to a LiteralMap def dict_to_literal_map(input_dict: dict) -> LiteralMap:     context = FlyteContext.current_context()     literals = {}     for key, value in input_dict.items():         # Assuming all values in the dict are strings for simplicity         # For other types, you would use the appropriate Primitive type         primitive = Primitive(string_value=value)         scalar = Scalar(primitive=primitive)         literal_type = LiteralType(simple=SimpleType.STRING)         literals[key] = context.type_engine.to_literal(context, value, str, literal_type)     return LiteralMap(literals=literals)  # Example workflow to demonstrate usage @workflow def my_workflow():     # Example Python dictionary to be converted     my_dict = {"example_key": "example_value"}     # Convert the Python dict to a LiteralMap     literal_map = dict_to_literal_map(my_dict)     # Pass the LiteralMap to the task     result = process_params(params=literal_map)     return result
In this example, the
dict_to_literal_map
function takes a Python dictionary as input and converts it into a
LiteralMap
by iterating over the dictionary, converting each key-value pair into a
Literal
, and then aggregating these into a
LiteralMap
. The
process_params
task is defined to accept a
LiteralMap
as input, and it demonstrates how you might extract and use the values from the
LiteralMap
. Finally, the
my_workflow
workflow shows how to use these components together. Please note, this example is simplified to illustrate the concept and focuses on string values for simplicity. Depending on your specific use case, you might need to handle different types of values and adjust the conversion logic accordingly.
To use a custom data graph in Flyte, you can leverage the platform's support for dynamic workflows and tasks. Flyte allows you to construct dynamic graphs that adapt to external inputs or conditions, enabling the creation of custom data graphs based on runtime decisions. This feature is particularly useful for scenarios where the workflow needs to be adjusted based on data-driven insights or external triggers. Here's a general approach to creating a custom data graph in Flyte: 1. Define Dynamic Workflows: Use Flyte's dynamic workflow feature to create workflows that can dynamically construct task execution graphs at runtime. This is done by annotating a task with
@dynamic
decorator, which allows the task to return a workflow instead of a direct output. 2. Utilize Conditional Constructs: Flyte supports conditional constructs within workflows, allowing you to branch your logic based on certain conditions. This can be used to dynamically include or exclude parts of your data graph based on runtime conditions. 3. Leverage Subworkflows: For complex data graphs, you can define subworkflows that encapsulate a portion of your data processing logic. These subworkflows can then be dynamically invoked within your main workflow, allowing for modular and reusable data graph components. 4. Customize Task Inputs and Outputs: Flyte's type system allows you to define custom data types for your tasks' inputs and outputs. This can be used to pass complex data structures or references to data stored in external systems, enabling the construction of sophisticated data graphs. 5. Integrate with External Data Sources: If your custom data graph requires integration with external data sources or systems, you can use Flyte's extensible plugin system to create custom task types. These tasks can interact with external systems, allowing your data graph to incorporate data from or trigger actions in external services. Remember, while Flyte provides the flexibility to create dynamic and custom data graphs, maintaining the shareability and reproducibility of these graphs is crucial. Flyte's design ensures that even dynamic graphs have the same properties as static graphs in terms of reproducibility and shareability. For specific examples or further guidance on implementing custom data graphs in Flyte, consider joining the Flyte community or exploring the documentation and resources available on the Flyte website and GitHub repository.