RStudio Workbench runs as a separate server, and unlike RStudio, can shared by many simultaneous users. These instructions will not work with the RStudio Server Open Source version but only work for RStudio Workbench. This topic covers setting up and configuring RStudio Workbench (previously RStudio Server Pro) for use with LabKey Server. Learn more or contact LabKey for details. You can override this behavior by specifying the version parameter.Premium Feature - This feature supports RStudio, which is part of the Professional and Enterprise Editions of LabKey Server. Alternate Versionsīy default, install_tensorflow() install the latest release version of TensorFlow.

Please refer to ‘Installing Python Packages’ for more information. Install_tensorflow() is a wrapper around reticulate::py_install. Note that install_tensorflow() will intentionally not install into a system Python installation (e.g., /usr/bin/python). Install into an Anaconda Python environment named r-reticulate Install into a Python virtual environment at ~/.virtualenvs/r-reticulate These are the available methods and their behavior: MethodĪutomatically choose an appropriate default for the current platform.

By default, the install_tensorflow() function attempts to install TensorFlow within an isolated Python environment (“r-reticulate”). TensorFlow is distributed as a Python package and so needs to be installed within a Python environment on your system. Read on if you want to learn about additional installation options, including installing a version of TensorFlow that takes advantage of Nvidia GPUs if you have the correct CUDA libraries installed.

This will provide you with a default installation of TensorFlow suitable for use with the tensorflow R package. Tf.Tensor(b'Hello Tensorflow!', shape=(), dtype=string)

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