AI News, How to install Python client libraries for remote access to a Machine Learning Server
- On Wednesday, June 6, 2018
- By Read More
How to install Python client libraries for remote access to a Machine Learning Server
Machine Learning Server includes open-source and Microsoft-specific Python packages for modeling, training, and scoring data for statistical and predictive analytics.
For classic client-server configurations, where multiple clients connect to and use a remote Machine Learning Server, installing the same Python client libraries on a local workstation enables you to write and run script locally and then push execution to the remote server where data resides.
For help on linking the Python interpreter and libraries to tools, see Link Python tools and IDEs, replacing the MLS server paths with the path you defined on your workstation For example, for a Python project in Visual Studio, your custom environment would specify C:\mypythonlibs, C:\mypythonlibs\python.exe and C:\mypythonlibs\pythonw.exe for Prefix path, Interpreter path, and Windowed interpreter, respectively.
On Ubuntu 14.04 - 16.04 With root or sudo permissions, run the following commands: Red Hat and CentOS 6/7 With root or sudo permissions, run the following commands: SUSE Linux Enterprise Server 11 With root or sudo permissions, run the following commands: As a verification step, call functions from the revoscalepy package and from scikit, included in Anaconda.
- On Monday, September 23, 2019
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