Simplicity at its core — both in development and deployment

Both Flyte and Kubeflow are platforms designed for orchestrating ML workflows and infrastructure within Kubernetes. Both platforms vary in the degree of scalability under intense workload and they leverage Kubernetes in different ways. In consequence Flyte and Kubeflow offer distinct developer experiences.

Kubeflow primarily focuses on ML pipelines, while Flyte is a versatile platform suitable for various use cases, including data and ML pipelines.

Let’s take a closer look at how Flyte and Kubeflow stack up.

Please note that the mentioned features are accurate as of the time of writing, but they may be subject to change in the future.

Simplify infrastructure jargon and manage Kubernetes complexity

Flyte lets ML practitioners create without having to navigate infrastructure jargon and Kubernetes details. It segregates the user and the platform teams and lets the user team — data scientists, ML practitioners and data engineers — focus on building models instead of setting up infrastructure. Kubeflow requires Kubernetes and DevOps expertise, which may slow down the development of ML pipelines because not all ML practitioners are comfortable with Kubernetes and Ops.

Define diverse data types and transformations

You can pass Pandas DataFrames among Flyte tasks, load a DataFrame to a BigQuery table using structured datasets, offload data to and download data from cloud URIs using FlyteFiles, and more. Meanwhile, Kubeflow enforces a type system, but it doesn’t support data types beyond fundamental Python types and artifacts/files. Kubeflow needs to be told what to do when it encounters another type, such as an s3 URI. Flyte, however, automates the interaction with S3 (and GCS); supports intra- and intercommunication among different cloud services and the local file system; and reduces the need to write boilerplate code.

Say goodbye to peculiar DSL

Flyte’s Python SDK (Flytekit) lets ML practitioners write Python code. In contrast, the Kubeflow Python SDK feels like a new domain-specific language (DSL) that heavily relies on Kubernetes concepts. The v2 Python DSL in Kubeflow is not purely Pythonic, which poses challenges for Python developers.

Constructing pipelines in Kubeflow is not as intuitive as in Flyte due to the following reasons:

1. The output type annotation needs to be declared within the input arguments declaration in case of custom container components and output artifacts.
2. Passing output from a lightweight Python component as an input to a downstream component requires using the `.output` attribute of the source task.
3. Kubeflow’s containerized and custom container components lean towards an infrastructure DSL, unlike lightweight components which are Pythonic. This means that code imported from different modules needs to be refactored to use containerized components, creating friction in the developer experience and impeding development cycles.
4. Kubeflow emphasizes the use of containerized and custom container components for production usage, which brings it closer to an infrastructure DSL.

Simplify the orchestration of dynamic ML pipelines

For the most part, ML is dynamic, so it’s important to be able to construct dynamic DAGs. Here are some example use cases:

• If a dynamic modification is required in the code logic, such as determining the number of training regions, programmatically stopping the training if the error surges, introducing validation steps dynamically, or data-parallel and sharded training
• During feature extraction, if there’s a need to decide on the parameters dynamically
• Building an AutoML pipeline
• Tuning hyperparameters dynamically while a pipeline is in progress

Kubeflow supports dynamism with DSL recursion, which presents its own set of problems: It’s a little awkward to construct dynamic DAGs with recursion, it doesn’t work well with deep workflows, output cannot be dynamically resolved and types are ignored. Moreover, KFP v2 doesn’t yet provide support for recursion.

In Flyte, dynamic workflows enable the construction of dynamic DAGs. When a Flyte task is decorated with `@dynamic`, Flyte evaluates the code at runtime and determines DAG structure. Flyte dynamic workflows offer much more flexibility to compose and run dynamic DAGs:

• Dynamism isn’t restricted to recursion
• Data passing isn’t any different from general Flyte tasks
• Types are respected

Connect with the tools you love

Flyte offers extensibility at all levels, and with the inclusion of Flyte Agents, the integration process has never been smoother.

Kubeflow’s native integrations:

• Kale
• KServe
• Fairing
• Seldon core
• BentoML
• MLRun Serving
• TensorFlow Serving

Flyte’s native integrations:

• HuggingFace Datasets
• Vaex
• Polars
• Modin
• Great Expectations
• Pandera
• DuckDB
• BigQuery
• Snowflake
• Dolthub
• SQLAlchemy
• Hive
• Databricks
• DBT
• Apache Spark
• AWS Athena
• AWS Sagemaker
• Dask
• Kubeflow MPI
• Ray
• Kubeflow TensorFlow
• Kubeflow PyTorch
• ONNX TensorFlow, PyTorch, Scikit Learn
• MLFlow
• Whylogs
• Kubernetes Pods
• AWS Batch
• Papermill

Bottom line: If you’re looking to enhance user experience, simplify data and ML workflow development, while also enjoying the benefits of modern orchestration and simplified infrastructure management, we built Flyte just for you.