69
Plugins
220
Modules
453,215
Monthly Downloads
9
Categories
Popular Plugins
Ray
flytekitplugins-ray
Flyte backend can be connected with Ray. Once enabled, it allows you to run flyte task on Ray cluster
Spark
flytekitplugins-spark
Flyte can execute Spark jobs natively on a Kubernetes Cluster, which manages a virtual cluster’s lifecycle, spin-up, and tear down. It leverages the open-sourced Spark On K8s Operator and can be enabled without signing up for any service. This is like running a transient spark cluster — a type of cluster spun up for a specific Spark job and torn down after completion.
Kubernetes Pod
flytekitplugins-pod
By default, Flyte tasks decorated with @task are essentially single functions that are loaded in one container. But often, there is a need to run a job with more than one container.
Deck
flytekitplugins-deck-standard
This plugin provides additional renderers to improve task visibility within Flytekit.
Kubeflow MPI
flytekitplugins-kfmpi
This plugin uses the Kubeflow MPI Operator and provides an extremely simplified interface for executing distributed training.
Kubeflow PyTorch
flytekitplugins-kfpytorch
This plugin uses the Kubeflow PyTorch Operator and provides an extremely simplified interface for executing distributed training using various PyTorch backends.
Envd
flytekitplugins-envd
envd is a command-line tool that helps you create the container-based development environment for AI/ML.
Flyte Interactive
flytekitplugins-flyteinteractive
FlyteInteractive plugin provides users' favorite interface to develop and debug a flyte task interactively. We support vscode, jupyter (WIP), and neovim (WIP).
Ray
flytekitplugins-ray
Flyte backend can be connected with Ray. Once enabled, it allows you to run flyte task on Ray cluster
Spark
flytekitplugins-spark
Flyte can execute Spark jobs natively on a Kubernetes Cluster, which manages a virtual cluster’s lifecycle, spin-up, and tear down. It leverages the open-sourced Spark On K8s Operator and can be enabled without signing up for any service. This is like running a transient spark cluster — a type of cluster spun up for a specific Spark job and torn down after completion.
Kubernetes Pod
flytekitplugins-pod
By default, Flyte tasks decorated with @task are essentially single functions that are loaded in one container. But often, there is a need to run a job with more than one container.
Deck
flytekitplugins-deck-standard
This plugin provides additional renderers to improve task visibility within Flytekit.
Kubeflow MPI
flytekitplugins-kfmpi
This plugin uses the Kubeflow MPI Operator and provides an extremely simplified interface for executing distributed training.
Kubeflow PyTorch
flytekitplugins-kfpytorch
This plugin uses the Kubeflow PyTorch Operator and provides an extremely simplified interface for executing distributed training using various PyTorch backends.
Envd
flytekitplugins-envd
envd is a command-line tool that helps you create the container-based development environment for AI/ML.
Flyte Interactive
flytekitplugins-flyteinteractive
FlyteInteractive plugin provides users' favorite interface to develop and debug a flyte task interactively. We support vscode, jupyter (WIP), and neovim (WIP).
Ray
flytekitplugins-ray
Flyte backend can be connected with Ray. Once enabled, it allows you to run flyte task on Ray cluster
Spark
flytekitplugins-spark
Flyte can execute Spark jobs natively on a Kubernetes Cluster, which manages a virtual cluster’s lifecycle, spin-up, and tear down. It leverages the open-sourced Spark On K8s Operator and can be enabled without signing up for any service. This is like running a transient spark cluster — a type of cluster spun up for a specific Spark job and torn down after completion.
Kubernetes Pod
flytekitplugins-pod
By default, Flyte tasks decorated with @task are essentially single functions that are loaded in one container. But often, there is a need to run a job with more than one container.
Deck
flytekitplugins-deck-standard
This plugin provides additional renderers to improve task visibility within Flytekit.
Kubeflow MPI
flytekitplugins-kfmpi
This plugin uses the Kubeflow MPI Operator and provides an extremely simplified interface for executing distributed training.
Kubeflow PyTorch
flytekitplugins-kfpytorch
This plugin uses the Kubeflow PyTorch Operator and provides an extremely simplified interface for executing distributed training using various PyTorch backends.
Envd
flytekitplugins-envd
envd is a command-line tool that helps you create the container-based development environment for AI/ML.
Flyte Interactive
flytekitplugins-flyteinteractive
FlyteInteractive plugin provides users' favorite interface to develop and debug a flyte task interactively. We support vscode, jupyter (WIP), and neovim (WIP).
Ray
flytekitplugins-ray
Flyte backend can be connected with Ray. Once enabled, it allows you to run flyte task on Ray cluster
Spark
flytekitplugins-spark
Flyte can execute Spark jobs natively on a Kubernetes Cluster, which manages a virtual cluster’s lifecycle, spin-up, and tear down. It leverages the open-sourced Spark On K8s Operator and can be enabled without signing up for any service. This is like running a transient spark cluster — a type of cluster spun up for a specific Spark job and torn down after completion.
Kubernetes Pod
flytekitplugins-pod
By default, Flyte tasks decorated with @task are essentially single functions that are loaded in one container. But often, there is a need to run a job with more than one container.
Deck
flytekitplugins-deck-standard
This plugin provides additional renderers to improve task visibility within Flytekit.
Kubeflow MPI
flytekitplugins-kfmpi
This plugin uses the Kubeflow MPI Operator and provides an extremely simplified interface for executing distributed training.
Kubeflow PyTorch
flytekitplugins-kfpytorch
This plugin uses the Kubeflow PyTorch Operator and provides an extremely simplified interface for executing distributed training using various PyTorch backends.
Envd
flytekitplugins-envd
envd is a command-line tool that helps you create the container-based development environment for AI/ML.
Flyte Interactive
flytekitplugins-flyteinteractive
FlyteInteractive plugin provides users' favorite interface to develop and debug a flyte task interactively. We support vscode, jupyter (WIP), and neovim (WIP).
OmegaConf
flytekitplugins-omegaconf
Flytekit python natively supports serialization of many data types for exchanging information between tasks.
Papermill
flytekitplugins-papermill
It is possible to run a Jupyter notebook as a Flyte task using Papermill. Papermill executes the notebook as a whole, so before using this plugin, it is essential to construct your notebook as recommended by Papermill.
Pandera
flytekitplugins-pandera
Flytekit python natively supports many data types, including a FlyteSchema type for type-annotating pandas DataFrames. The Flytekit Pandera plugin provides an alternative for defining DataFrame schemas by integrating with Pandera, a runtime data validation tool for pandas DataFrames.
Spark
flyteplugins-spark
Union can execute Spark jobs natively on a Kubernetes Cluster, which manages a virtual cluster’s lifecycle, spin-up, and tear down. It leverages the open-sourced Spark On K8s Operator and can be enabled without signing up for any service. This is like running a transient spark cluster — a type of cluster spun up for a specific Spark job and torn down after completion.
Bigquery
flytekitplugins-bigquery
BigQuery enables us to build data-intensive applications without operational burden. Flyte backend can be connected with the BigQuery service. Once enabled, it can allow you to query a BigQuery table.
Polars
flytekitplugins-polars
Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as memory model.
whylogs
flytekitplugins-whylogs
whylogs is an open source library for logging any kind of data. With whylogs,
MLflow
flytekitplugins-mlflow
MLflow enables us to log parameters, code, and results in machine learning experiments and compare them using an interactive UI.
OmegaConf
flytekitplugins-omegaconf
Flytekit python natively supports serialization of many data types for exchanging information between tasks.
Papermill
flytekitplugins-papermill
It is possible to run a Jupyter notebook as a Flyte task using Papermill. Papermill executes the notebook as a whole, so before using this plugin, it is essential to construct your notebook as recommended by Papermill.
Pandera
flytekitplugins-pandera
Flytekit python natively supports many data types, including a FlyteSchema type for type-annotating pandas DataFrames. The Flytekit Pandera plugin provides an alternative for defining DataFrame schemas by integrating with Pandera, a runtime data validation tool for pandas DataFrames.
Spark
flyteplugins-spark
Union can execute Spark jobs natively on a Kubernetes Cluster, which manages a virtual cluster’s lifecycle, spin-up, and tear down. It leverages the open-sourced Spark On K8s Operator and can be enabled without signing up for any service. This is like running a transient spark cluster — a type of cluster spun up for a specific Spark job and torn down after completion.
Bigquery
flytekitplugins-bigquery
BigQuery enables us to build data-intensive applications without operational burden. Flyte backend can be connected with the BigQuery service. Once enabled, it can allow you to query a BigQuery table.
Polars
flytekitplugins-polars
Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as memory model.
whylogs
flytekitplugins-whylogs
whylogs is an open source library for logging any kind of data. With whylogs,
MLflow
flytekitplugins-mlflow
MLflow enables us to log parameters, code, and results in machine learning experiments and compare them using an interactive UI.
OmegaConf
flytekitplugins-omegaconf
Flytekit python natively supports serialization of many data types for exchanging information between tasks.
Papermill
flytekitplugins-papermill
It is possible to run a Jupyter notebook as a Flyte task using Papermill. Papermill executes the notebook as a whole, so before using this plugin, it is essential to construct your notebook as recommended by Papermill.
Pandera
flytekitplugins-pandera
Flytekit python natively supports many data types, including a FlyteSchema type for type-annotating pandas DataFrames. The Flytekit Pandera plugin provides an alternative for defining DataFrame schemas by integrating with Pandera, a runtime data validation tool for pandas DataFrames.
Spark
flyteplugins-spark
Union can execute Spark jobs natively on a Kubernetes Cluster, which manages a virtual cluster’s lifecycle, spin-up, and tear down. It leverages the open-sourced Spark On K8s Operator and can be enabled without signing up for any service. This is like running a transient spark cluster — a type of cluster spun up for a specific Spark job and torn down after completion.
Bigquery
flytekitplugins-bigquery
BigQuery enables us to build data-intensive applications without operational burden. Flyte backend can be connected with the BigQuery service. Once enabled, it can allow you to query a BigQuery table.
Polars
flytekitplugins-polars
Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as memory model.
whylogs
flytekitplugins-whylogs
whylogs is an open source library for logging any kind of data. With whylogs,
MLflow
flytekitplugins-mlflow
MLflow enables us to log parameters, code, and results in machine learning experiments and compare them using an interactive UI.
OmegaConf
flytekitplugins-omegaconf
Flytekit python natively supports serialization of many data types for exchanging information between tasks.
Papermill
flytekitplugins-papermill
It is possible to run a Jupyter notebook as a Flyte task using Papermill. Papermill executes the notebook as a whole, so before using this plugin, it is essential to construct your notebook as recommended by Papermill.
Pandera
flytekitplugins-pandera
Flytekit python natively supports many data types, including a FlyteSchema type for type-annotating pandas DataFrames. The Flytekit Pandera plugin provides an alternative for defining DataFrame schemas by integrating with Pandera, a runtime data validation tool for pandas DataFrames.
Spark
flyteplugins-spark
Union can execute Spark jobs natively on a Kubernetes Cluster, which manages a virtual cluster’s lifecycle, spin-up, and tear down. It leverages the open-sourced Spark On K8s Operator and can be enabled without signing up for any service. This is like running a transient spark cluster — a type of cluster spun up for a specific Spark job and torn down after completion.
Bigquery
flytekitplugins-bigquery
BigQuery enables us to build data-intensive applications without operational burden. Flyte backend can be connected with the BigQuery service. Once enabled, it can allow you to query a BigQuery table.
Polars
flytekitplugins-polars
Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as memory model.
whylogs
flytekitplugins-whylogs
whylogs is an open source library for logging any kind of data. With whylogs,
MLflow
flytekitplugins-mlflow
MLflow enables us to log parameters, code, and results in machine learning experiments and compare them using an interactive UI.
Recently Added
BigQuery
v2Flyte SDK (v2)flyteplugins-bigquery
This plugin provides BigQuery integration for Flyte, enabling you to run BigQuery queries as Flyte tasks.
Databricks
v2Flyte SDK (v2)flyteplugins-databricks
This plugin provides Databricks integration for Flyte, enabling you to run Spark jobs on Databricks as Flyte tasks.
Snowflake
v2Flyte SDK (v2)flyteplugins-snowflake
Run Snowflake SQL queries as Flyte tasks with parameterized inputs, key-pair authentication, batch inserts, and DataFrame support.
polars
v2Flyte SDK (v2)flyteplugins-polars
This plugin provides native support for Polars DataFrames and LazyFrames in Flyte, enabling efficient data processing with Polars' high-performance DataFrame library.
Weights & Biases
v2Flyte SDK (v2)flyteplugins-wandb
This plugin provides integration between Flyte and Weights & Biases (W&B) for experiment tracking, including support for distributed training with PyTorch Elastic.
SGLang
v2Flyte SDK (v2)flyteplugins-sglang
Serve large language models using SGLang with Flyte Apps.
vLLM
v2Flyte SDK (v2)flyteplugins-vllm
Serve large language models using vLLM with Flyte Apps.
Dgxc-lepton
Flytekitflytekitplugins-dgxc-lepton
A professional Flytekit plugin that enables seamless deployment and management of AI inference endpoints using Lepton AI infrastructure within Flyte workflows.
Browse by Category
Data & DataFrame
Databases & Warehouses
Cloud & Infrastructure
ML Training
Model Serving
Experiment Tracking
Data Validation
Workflow
Developer Tools
Get Involved
Learn, connect, and contribute to the Flyte ecosystem.
@task
from flytekit import task, workflow
@workflow
def my_pipeline(data: str) -> int:
ImageSpec
image = ImageSpec(packages=["pandas"])
map_task
map_task(process)(data=inputs)
Resources
requests=Resources(cpu="2", mem="4Gi")
Secret
secret=Secret(group="aws", key="s3")
@task
from flytekit import task, workflow
@workflow
def my_pipeline(data: str) -> int:
ImageSpec
image = ImageSpec(packages=["pandas"])
map_task
map_task(process)(data=inputs)
Resources
requests=Resources(cpu="2", mem="4Gi")
Secret
secret=Secret(group="aws", key="s3")
@task
from flytekit import task, workflow
@workflow
def my_pipeline(data: str) -> int:
ImageSpec
image = ImageSpec(packages=["pandas"])
map_task
map_task(process)(data=inputs)
Resources
requests=Resources(cpu="2", mem="4Gi")
Secret
secret=Secret(group="aws", key="s3")
@task
from flytekit import task, workflow
@workflow
def my_pipeline(data: str) -> int:
ImageSpec
image = ImageSpec(packages=["pandas"])
map_task
map_task(process)(data=inputs)
Resources
requests=Resources(cpu="2", mem="4Gi")
Secret
secret=Secret(group="aws", key="s3")
Documentation
Learn how to build, test, and publish your own Flyte plugins.
Community
Join thousands of developers on Slack to discuss Flyte and share plugins.
Contribute
Submit your plugin to the registry. Open and extensible.