Search Results for "management framework"
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Showing 3 open source projects for "management framework"
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Professional Email Hosting for Small Business | GreatmailDependable cloud based email hosting with spam filtering, antivirus protection, generous storage and webmail. Compatible with Outlook and all other POP3/IMAP clients. High volume SMTP service for responsible senders. Outbound relay service for transactional messages, email marketing campaigns, newsletters and other applications. Dedicated email servers, clustering and multiple IP load balancing for high volume senders. Fixed monthly cost with unlimited sending and reputation monitoring. Greatmail is an email service provider (ESP) specializing in business class email hosting, SMTP hosting and email servers. For ISPs, application programmers and cloud developers, we also provide custom solutions including dedicated IP servers and process specific, load balanced configurations with multiple servers.
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SLM Lab
Modular Deep Reinforcement Learning framework in PyTorch
SLM Lab is a modular and extensible deep reinforcement learning framework designed for research and practical applications. It provides implementations of various state-of-the-art RL algorithms and emphasizes reproducibility, scalability, and detailed experiment tracking. SLM Lab is structured around a flexible experiment management system, allowing users to define, run, and analyze RL experiments efficiently. -
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TensorHouse
A collection of reference Jupyter notebooks and demo AI/ML application
TensorHouse is a scalable reinforcement learning (RL) platform that focuses on high-throughput experience generation and distributed training. It is designed to efficiently train agents across multiple environments and compute resources. TensorHouse enables flexible experiment management, making it suitable for large-scale RL experiments in both research and applied settings. -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make...
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