Products

The Microsoft Access Python Connector by Devart is a robust solution that enables Python applications to interact seamlessly with Microsoft Access databases. Fully implementing the Python DB API 2.0 specification, this connector facilitates efficient create, read, update, and delete operations on Access databases without the need for additional software installations. It supports both .mdb and .accdb file formats, including those from the latest Microsoft Access versions, and is compatible acros

The Python Connector for Microsoft Excel is a robust solution that enables Python applications to seamlessly interact with Microsoft Excel, Apache OpenOffice Calc, and LibreOffice Calc spreadsheets. It allows developers to perform create, read, update, and delete operations on spreadsheet data without the need for additional software installations. Fully compliant with the Python DB API 2.0 specification, this connector is distributed as a wheel package compatible with Windows, macOS, and Linux

The NetSuite Python Connector by Devart is a robust and user-friendly solution designed to facilitate seamless integration between Python applications and NetSuite. It enables developers to perform create, read, update, and delete (CRUD operations on NetSuite data, adhering fully to the Python DB API 2.0 specification. Distributed as a wheel package, it supports both 32-bit and 64-bit Windows platforms, ensuring broad compatibility and ease of installation. Key Features and Functionality: - St

The PostgreSQL Python Connector by Devart is a reliable and efficient solution designed to facilitate seamless interaction between Python applications and PostgreSQL database servers. Fully implementing the Python DB API 2.0 specification, this connector enables developers to perform create, read, update, and delete operations on PostgreSQL databases without the need for additional client libraries. Distributed as a wheel package, it supports multiple platforms, including Windows, macOS, and Lin

The HubSpot Python Connector by Devart is a robust and user-friendly solution designed to facilitate seamless integration between Python applications and HubSpot. It enables developers to perform create, read, update, and delete (CRUD operations on HubSpot data, adhering fully to the Python DB API 2.0 specification. Distributed as a wheel package, it supports both 32-bit and 64-bit Windows platforms, ensuring broad compatibility and ease of installation. Key Features and Functionality: - Stand

The BigCommerce Python Connector is a robust and user-friendly solution designed to facilitate seamless integration between Python applications and BigCommerce stores. By fully implementing the Python DB API 2.0 specification, it enables developers to perform create, read, update, and delete operations on BigCommerce data using standard SQL syntax. This connector is distributed as a wheel package compatible with Windows, macOS, and Linux platforms. Key Features and Functionality: - Standard S

The "Python 3.7 with Debian 10" Amazon Machine Image (AMI) offers a pre-configured environment combining Debian 10 "Buster" with Python 3.7, facilitating the development and deployment of Python applications on AWS. Key Features and Functionality: - Debian 10 "Buster": A stable and secure operating system known for its reliability and extensive package support. - Python 3.7: A versatile programming language suitable for various applications, from web development to data analysis. - AWS CLI I

The "Python 3 Minimal with Debian 10" Amazon Machine Image (AMI) offers a streamlined and secure environment for developers and system administrators seeking a lightweight Debian 10 setup with Python 3 pre-installed. This AMI is designed to facilitate the rapid deployment of Python-based applications and services on AWS EC2 instances. Key Features and Functionality: - Minimal Debian 10 Installation: Provides a clean and efficient Debian 10 Buster environment, reducing overhead and potential vu

PyTorch 0.3 Python 3.6 CPU Production is a pre-configured and fully integrated software stack that combines PyTorch 0.3, an open-source machine learning library, with Python 3.6. This stack provides a stable and tested execution environment optimized for CPU-based tasks, facilitating both training and inference processes. It is designed to support both short and long-running high-performance tasks and can be seamlessly integrated into continuous integration and deployment workflows. Key Featur

The "Caffe Python 3.6 NVidia GPU Production on Ubuntu" is a pre-configured Amazon Machine Image (AMI) designed to facilitate the deployment of deep learning applications using the Caffe framework on Ubuntu. This AMI integrates Python 3.6 and is optimized for NVIDIA GPU acceleration, providing a robust environment for developing and running machine learning models efficiently. Key Features and Functionality: - Pre-Installed Caffe Framework: The AMI comes with the Caffe deep learning framework p

The Python Connector for Google BigQuery is a robust and efficient solution designed to facilitate seamless interaction between Python applications and the Google BigQuery data warehouse. Fully implementing the Python DB API 2.0 specification, this connector enables developers to perform create, read, update, and delete operations on BigQuery data with ease. Distributed as a wheel package, it supports both 32-bit and 64-bit versions of Windows and Windows Server, ensuring broad compatibility acr

The Caffe Python 2.7 CPU Production AMI is a pre-configured and fully integrated software stack designed for deep learning applications. It features Caffe, an open-source deep learning framework developed by UC Berkeley, optimized for image classification and segmentation tasks. This AMI is tailored for CPU-based environments, providing a stable and high-performance execution platform for both training and inference tasks. Key Features and Functionality: - Pre-Configured Environment: The AMI c

Scale native Python/Pandas data science workloads to clusters automatically with extreme supercomputing performance. Bodo is the first automatic parallelization and optimization technology that accelerates and scales data science workloads without any code rewrites.

The "Python 3.8 with Ubuntu 18.04 LTS" Amazon Machine Image (AMI) offers a pre-configured environment combining Python 3.8 with Ubuntu 18.04 LTS, facilitating the rapid deployment of Python applications on AWS. This AMI is designed to streamline the setup process, allowing developers to focus on building and deploying applications without the overhead of manual configuration. Key Features and Functionality: - Pre-Installed Python 3.8: Provides immediate access to Python 3.8, enabling the devel

PythonAnywhere makes it easy to create and run Python programs in the cloud.

The MySQL and MariaDB Python Connector by Devart is a robust and user-friendly solution designed to facilitate seamless interaction between Python applications and MySQL or MariaDB database servers. Fully implementing the Python DB API 2.0 specification, this connector enables developers to perform create, read, update, and delete operations efficiently. Distributed as a wheel package, it supports multiple platforms, including Windows, macOS, and Linux. Key Features and Functionality: - Direct

The MXNet 1 Python 3.6 CPU Production environment is a pre-configured software stack that integrates Apache MXNet, an open-source deep learning framework, with Python 3.6. This setup offers a stable and tested execution environment optimized for CPU-based tasks, facilitating efficient training, inference, and deployment of deep learning models. It is designed to seamlessly integrate into continuous integration and deployment workflows, making it suitable for both short and long-running high-perf

The "MXNet 1 Python 2.7 CPU Production" is an Amazon Machine Image designed to facilitate the development and deployment of deep learning models using Apache MXNet on Amazon EC2 instances. This AMI is pre-configured with MXNet version 1.x and Python 2.7, optimized for CPU-based computations, providing a ready-to-use environment for machine learning practitioners. Key Features and Functionality: - Pre-Installed MXNet Framework: The AMI comes with Apache MXNet 1.x, an open-source deep learning f