install_tensorflow | R Documentation |
install_tensorflow()
installs just the tensorflow python package and it's
direct dependencies. For a more complete installation that includes
additional optional dependencies, use keras::install_keras()
.
install_tensorflow( method = c("auto", "virtualenv", "conda"), conda = "auto", version = "default", envname = NULL, extra_packages = NULL, restart_session = TRUE, conda_python_version = NULL, ..., pip_ignore_installed = TRUE, python_version = conda_python_version )
method |
Installation method. By default, "auto" automatically finds a method that will work in the local environment. Change the default to force a specific installation method. Note that the "virtualenv" method is not available on Windows. |
conda |
The path to a |
version |
TensorFlow version to install. Valid values include:
|
envname |
The name, or full path, of the environment in which Python
packages are to be installed. When |
extra_packages |
Additional Python packages to install along with TensorFlow. |
restart_session |
Restart R session after installing (note this will only occur within RStudio). |
... |
other arguments passed to |
pip_ignore_installed |
Whether pip should ignore installed python
packages and reinstall all already installed python packages. This defaults
to |
python_version, conda_python_version |
Pass a string like "3.8" to request that conda install a specific Python version. This is ignored when attempting to install in a Python virtual environment. Note that the Python version must be compatible with the requested Tensorflow version, documented here: https://www.tensorflow.org/install/pip#system-requirements |
You may be prompted to download and install miniconda if reticulate did not find a non-system installation of python. Miniconda is the recommended installation method for most users, as it ensures that the R python installation is isolated from other python installations. All python packages will by default be installed into a self-contained conda or venv environment named "r-reticulate". Note that "conda" is the only supported method on M1 Mac.
If you initially declined the miniconda installation prompt, you can later
manually install miniconda by running reticulate::install_miniconda()
.
install_tensorflow()
or
keras::install_keras()
isn't required to use tensorflow with the package.
If you manually configure a python environment with the required
dependencies, you can tell R to use it by pointing reticulate at it,
commonly by setting an environment variable:
Sys.setenv("RETICULATE_PYTHON" = "~/path/to/python-env/bin/python")
Tensorflow on Apple Silicon is not officially
supported by the Tensorflow maintainers. However Apple has published a
custom version of Tensorflow compatible with Arm Macs.
install_tensorflow()
will install the special packages tensorflow-macos
and tensorflow-metal
on Arm Macs. See
https://developer.apple.com/metal/tensorflow-plugin/ for instructions
on how to do the equivalent manually. Please note that this is an
experimental build of both Python and Tensorflow, with known issues. In
particular, certain operations will cause errors, but can often be remedied
by pinning them to the CPU. For example:
x <- array(runif(64*64), c(1, 64, 64)) keras::layer_random_rotation(x, .5) # Error: # No registered 'RngReadAndSkip' OpKernel for 'GPU' devices # Pin the operation to the CPU to avoid the error with(tf$device("CPU"), keras::layer_random_rotation(x, .5) ) # No Error
If you wish to add additional PyPI packages to your Keras / TensorFlow
environment you can either specify the packages in the extra_packages
argument of install_tensorflow()
or install_keras()
, or alternatively
install them into an existing environment using the
reticulate::py_install()
function. Note that install_keras()
includes a
set of additional python packages by default, see ?keras::install_keras
for details.
keras::install_keras()
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