| About
            DeepSpeed is an open source deep learning optimization library for PyTorch.  It's designed to reduce computing power and memory use, and to train large distributed models with better parallelism on existing computer hardware.  DeepSpeed is optimized for low latency, high throughput training.
DeepSpeed can train DL models with over a hundred billion parameters on the current generation of GPU clusters.  It can also train up to 13 billion parameters in a single GPU.
DeepSpeed is developed by Microsoft and aims to offer distributed training for large-scale models. It's built on top of PyTorch, which specializes in data parallelism. 
             | About
            A hybrid front-end seamlessly transitions between Gluon eager imperative mode and symbolic mode to provide both flexibility and speed. Scalable distributed training and performance optimization in research and production is enabled by the dual parameter server and Horovod support. Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl. A thriving ecosystem of tools and libraries extends MXNet and enables use-cases in computer vision, NLP, time series and more. Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision-making process have stabilized in a manner consistent with other successful ASF projects. Join the MXNet scientific community to contribute, learn, and get answers to your questions.
             | 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
            python-sql is a library to write SQL queries in a pythonic way. Simple selects, select with where condition. Select with join or select with multiple joins. Select with group_by and select with output name. Select with order_by, or select with sub-select. Select on other schema and insert query with default values. Insert query with values, and insert query with query. Update query with values. Update query with where condition. Update query with from the list. Delete query with where condition, and delete query with sub-query. Provides limit style, qmark style, and numeric style.
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        Researchers in need of an open source machine learning solution to accelerate research prototyping and production deployment
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        Developers searching for a solution offering a library to write SQL queries 
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| Company InformationMicrosoft Founded: 1975 United States www.deepspeed.ai/ | Company InformationThe Apache Software Foundation Founded: 1999 United States mxnet.apache.org | Company InformationPyTorch Founded: 2016 pytorch.org | Company InformationPython Software Foundation United States pypi.org/project/python-sql/ | |||
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