зеркало из https://github.com/Azure/AzureDSVM.git
Merge branch 'master' of https://github.com/Azure/AzureDSVM
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@ -25,14 +25,16 @@ Microsoft R extensions.
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* Elasiticity
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* Elasiticity
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* Deployment of a DSVM with customized information such as machine name, machine size, operating system, authentication method, etc.
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* Deployment of a DSVM with customized information such as machine name, machine size (with compute/memory optimized general-purpose CPU, Nvidia K80/M60 GPU, etc.), operating system (Windows Server 2016, Ubunbut 16.04, and CentOS), authentication method (public key based or password based), etc.
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* Enjoy all benefits of a Windows/Linux DSVM. E.g., all tools for data science work such as R/Python/Julia programming languages, SQL Server, Visual Studio with RTVS, etc., remote working environment via RStudio Server or Jupyter Notebook interface, and machine learning & artificial intelligence packages such as Microsoft CNTK, MXNet, and XGBoost.
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* Enjoy all benefits of a Windows/Linux DSVM. E.g., all tools for data science work such as R/Python/Julia programming languages, SQL Server, Visual Studio with RTVS, etc., remote working environment via RStudio Server or Jupyter Notebook interface, and machine learning & artificial intelligence packages such as Microsoft CNTK, MXNet, and XGBoost.
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* Execution of R analytics on DSVM(s) with various Microsoft R Server computing contexts such as "local parallel" and "cluster parallel".
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* Execution of R analytics on DSVM(s) with various Microsoft R Server computing contexts such as "local parallel" and "cluster parallel".
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* Seamless interaction with remote R Server session with `mrsdeploy` functions.
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* Post-deployment installation of extension for customizing system environment, reinstalling/uninstalling software, etc.
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* Scalability
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* Scalability
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* Deployment of a collection of heterogeneous DSVMs for a group of data scientists.
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* Deployment of a collection of heterogeneous DSVMs for a group of data scientists.
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* Scale up DSVM and form them into a cluster for parallel computation with Microsoft R Server backend.
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* Scale up DSVM and form them into a cluster for parallel/distributed computation with Microsoft R Server backend.
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* Usability
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* Usability
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