KeepsakeReplicate
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Related Products
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About
ExecuTorch is PyTorch’s open source framework for deploying AI/ML models directly to edge devices, enabling text, vision, speech, recommendation, and multimodal inference without requiring the cloud. It exports models from PyTorch without intermediate conversion formats, preserves ATen operators, and uses ahead-of-time compilation to optimize performance for target hardware before deployment. Its modular design lets developers choose compile-time and runtime optimizations while staying inside the familiar PyTorch ecosystem, including torchao for quantization. A portable C++ runtime with a base footprint of about 50 KB can run on smartphones, desktops, embedded systems, microcontrollers, DSPs, and Cortex-M processors. ExecuTorch supports Android, iOS, Linux, Windows, macOS, and WebAssembly, with native APIs for C++, Swift, Kotlin, and Objective-C.
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About
Keepsake is an open-source Python library designed to provide version control for machine learning experiments and models. It enables users to automatically track code, hyperparameters, training data, model weights, metrics, and Python dependencies, ensuring that all aspects of the machine learning workflow are recorded and reproducible. Keepsake integrates seamlessly with existing workflows by requiring minimal code additions, allowing users to continue training as usual while Keepsake saves code and weights to Amazon S3 or Google Cloud Storage. This facilitates the retrieval of code and weights from any checkpoint, aiding in re-training or model deployment. Keepsake supports various machine learning frameworks, including TensorFlow, PyTorch, scikit-learn, and XGBoost, by saving files and dictionaries in a straightforward manner. It also offers features such as experiment comparison, enabling users to analyze differences in parameters, metrics, and dependencies across experiments.
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Not Supported
On-Premises
Supported
iPhone
Supported
iPad
Supported
Android
Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
AI developers and engineering teams that need to deploy optimized PyTorch models for on-device inference across smartphones, embedded systems, and microcontrollers
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Audience
Developers in need of a tool to manage their code and enhance the efficiency of their workflows
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Supported
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API
Offers API
Supported
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Company InformationExecuTorch
United States
executorch.ai/
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Company InformationReplicate
United States
keepsake.ai/
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Alternatives |
Alternatives |
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Categories |
Categories |
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Integrations
PyTorch
Supported
Amazon S3
Not Supported
C++
Supported
Facebook
Supported
Google Cloud Storage
Not Supported
Instagram
Supported
JSON
Not Supported
Kotlin
Supported
LLaVA
Supported
Llama 3.2
Supported
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Integrations
PyTorch
Supported
Amazon S3
Supported
C++
Not Supported
Facebook
Not Supported
Google Cloud Storage
Supported
Instagram
Not Supported
JSON
Supported
Kotlin
Not Supported
LLaVA
Not Supported
Llama 3.2
Not Supported
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