LiteRT

LiteRT

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About

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. Kubeflow provides a custom TensorFlow training job operator that you can use to train your ML model. In particular, Kubeflow's job operator can handle distributed TensorFlow training jobs. Configure the training controller to use CPUs or GPUs and to suit various cluster sizes. Kubeflow includes services to create and manage interactive Jupyter notebooks. You can customize your notebook deployment and your compute resources to suit your data science needs. Experiment with your workflows locally, then deploy them to a cloud when you're ready.

About

LiteRT (Lite Runtime), formerly known as TensorFlow Lite, is Google's high-performance runtime for on-device AI. It enables developers to deploy machine learning models across various platforms and microcontrollers. LiteRT supports models from TensorFlow, PyTorch, and JAX, converting them into the efficient FlatBuffers format (.tflite) for optimized on-device inference. Key features include low latency, enhanced privacy by processing data locally, reduced model and binary sizes, and efficient power consumption. The runtime offers SDKs in multiple languages such as Java/Kotlin, Swift, Objective-C, C++, and Python, facilitating integration into diverse applications. Hardware acceleration is achieved through delegates like GPU and iOS Core ML, improving performance on supported devices. LiteRT Next, currently in alpha, introduces a new set of APIs that streamline on-device hardware acceleration.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Anyone looking for a Machine Learning toolkit for Kubernetes

Audience

Mobile application developers in search of a tool to integrate efficient, on-device AI capabilities into their apps

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Kubeflow
www.kubeflow.org

Company Information

Google
Founded: 1998
United States
ai.google.dev/edge/litert

Alternatives

Alternatives

AWS Neuron

AWS Neuron

Amazon Web Services
Union Cloud

Union Cloud

Union.ai

Categories

Categories

Integrations

APERIO DataWise
Azure Marketplace
Camunda
Civo
Comet LLM
DagsHub
Flyte
Gemini Enterprise Agent Platform Notebooks
Giskard
Google AI Edge Gallery
JAX
KServe
Kedro
Kubernetes
Objective-C
PredictKube
PyTorch
Superwise
ZenML

Integrations

APERIO DataWise
Azure Marketplace
Camunda
Civo
Comet LLM
DagsHub
Flyte
Gemini Enterprise Agent Platform Notebooks
Giskard
Google AI Edge Gallery
JAX
KServe
Kedro
Kubernetes
Objective-C
PredictKube
PyTorch
Superwise
ZenML
Claim Kubeflow and update features and information
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