Caffe

Caffe

BAIR

About

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Check out our web image classification demo! Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU.

About

Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies.

About

ThinkSQL is a powerful, cross-platform relational database management system designed for modern operating systems and hardware. It aims to comply fully with the ISO SQL standard to provide an open and flexible SQL server for your applications. Compliance with the SQL standard means it supports transactions, sub-selects, views, set operators (union, except, intersect), access privileges and comprehensive referential constraints to ensure the integrity of your data. ThinkSQL uses a multi-versioning engine to provide consistent views of data and concurrent access to rows without locking. The server also has stored procedures and functions (using the standard syntax), and has support for large multimedia objects. It's easy to install, runs on Windows ® and Linux ®, and has native ODBC, JDBC™, dbExpress™ and Python drivers so your applications can connect directly from any platform.

About

Django-MySQL extends Django’s built-in MySQL and MariaDB support their specific features not available on other databases. A new cache backend that makes use of MySQL’s upsert statement and does compression. Named locks for easy locking of e.g. external resources. Extra checks added to Django’s check framework to ensure your Django and MySQL configurations are optimal. Django-MySQL comes with a number of extensions to QuerySet that can be installed in a number of ways - e.g. adding the QuerySetMixin to your existing QuerySet subclass.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

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 an open-source deep learning framework with expression, speed and modularity

Audience

Researchers in need of an open source machine learning solution to accelerate research prototyping and production deployment

Audience

Companies searching for a solution to manage and improve their database operations

Audience

Component Library solution for developers

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

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

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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

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Review this Software

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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Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

BAIR
United States
caffe.berkeleyvision.org

Company Information

PyTorch
Founded: 2016
pytorch.org

Company Information

ThinkSQL
United Kingdom
www.thinksql.co.uk

Company Information

django-mysql
United Kingdom
pypi.org/project/django-mysql/

Alternatives

MXNet

MXNet

The Apache Software Foundation

Alternatives

Alternatives

Alternatives

DeepSpeed

DeepSpeed

Microsoft
Core ML

Core ML

Apple
CA Datacom

CA Datacom

Broadcom
Vertex AI

Vertex AI

Google
Create ML

Create ML

Apple
Tibero

Tibero

TmaxSoft
DeepSpeed

DeepSpeed

Microsoft
AWS Neuron

AWS Neuron

Amazon Web Services
Huawei RDS

Huawei RDS

Huawei Cloud

Categories

Categories

Categories

Categories

Deep Learning Features

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

RDBMS Features

Backup
Data Migration
Monitoring
Performance Analysis
Queries
Storage Optimization

Integrations

3LC
Amazon EC2 Capacity Blocks for ML
Amazon EC2 P4 Instances
Amazon EC2 P5 Instances
Amazon SageMaker Model Building
Dataoorts GPU Cloud
Exafunction
Flyte
GPUonCLOUD
Google Cloud Deep Learning VM Image
Google Cloud Platform
Guild AI
Horovod
Hugging Face Transformers
Modelbit
Pop!_OS
SiMa
Superwise
Vertex AI Notebooks
voyage-3-large

Integrations

3LC
Amazon EC2 Capacity Blocks for ML
Amazon EC2 P4 Instances
Amazon EC2 P5 Instances
Amazon SageMaker Model Building
Dataoorts GPU Cloud
Exafunction
Flyte
GPUonCLOUD
Google Cloud Deep Learning VM Image
Google Cloud Platform
Guild AI
Horovod
Hugging Face Transformers
Modelbit
Pop!_OS
SiMa
Superwise
Vertex AI Notebooks
voyage-3-large

Integrations

3LC
Amazon EC2 Capacity Blocks for ML
Amazon EC2 P4 Instances
Amazon EC2 P5 Instances
Amazon SageMaker Model Building
Dataoorts GPU Cloud
Exafunction
Flyte
GPUonCLOUD
Google Cloud Deep Learning VM Image
Google Cloud Platform
Guild AI
Horovod
Hugging Face Transformers
Modelbit
Pop!_OS
SiMa
Superwise
Vertex AI Notebooks
voyage-3-large

Integrations

3LC
Amazon EC2 Capacity Blocks for ML
Amazon EC2 P4 Instances
Amazon EC2 P5 Instances
Amazon SageMaker Model Building
Dataoorts GPU Cloud
Exafunction
Flyte
GPUonCLOUD
Google Cloud Deep Learning VM Image
Google Cloud Platform
Guild AI
Horovod
Hugging Face Transformers
Modelbit
Pop!_OS
SiMa
Superwise
Vertex AI Notebooks
voyage-3-large
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