install_tensorflow()
installs just the tensorflow python package and it's
direct dependencies. For a more complete installation that includes
additional optional dependencies, use keras3::install_keras()
.
install_tensorflow(
method = c("auto", "virtualenv", "conda"),
conda = "auto",
version = "default",
envname = "r-tensorflow",
extra_packages = NULL,
restart_session = TRUE,
conda_python_version = NULL,
...,
cuda = NULL,
metal = FALSE,
pip_ignore_installed = FALSE,
new_env = identical(envname, "r-tensorflow"),
python_version = NULL
)
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.
The path to a conda
executable. Use "auto"
to allow
reticulate
to automatically find an appropriate conda
binary.
See Finding Conda and conda_binary()
for more details.
TensorFlow version to install. Valid values include:
"default"
installs 2.16
"release"
installs the latest release version of tensorflow (which may
be incompatible with the current version of the R package)
A version specification like "2.4"
or "2.4.0"
. Note that if the patch
version is not supplied, the latest patch release is installed (e.g.,
"2.4"
today installs version "2.4.2")
nightly
for the latest available nightly build.
To any specification, you can append "-cpu" to install the cpu version
only of the package (e.g., "2.4-cpu"
)
The full URL or path to a installer binary or python *.whl file.
The name, or full path, of the environment in which Python
packages are to be installed. When NULL
(the default), the active
environment as set by the RETICULATE_PYTHON_ENV
variable will be used;
if that is unset, then the r-reticulate
environment will be used.
Additional Python packages to install along with TensorFlow.
Restart R session after installing (note this will only occur within RStudio).
other arguments passed to reticulate::conda_install()
or
reticulate::virtualenv_install()
, depending on the method
used.
logical TRUE
or FALSE
. If install_tensorflow()
detects the platform is
Linux, an Nvidia GPU is available, and the TensorFlow version is 2.14 (the
default), it will install also install the required CUDA libraries through pip.
Whether to install tensorflow-metal
pip package on Arm Macs.
This enables tensorflow to use the GPU. Pass a string to install a specific
version like "tensorflow-metal==0.7.*
.
Whether pip should ignore installed python packages and reinstall all already installed python packages.
If TRUE
, any existing Python virtual environment and/or
conda environment specified by envname
is deleted first.
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
install_tensorflow()
or
keras3::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")
Beginning with Tensorflow version 2.13, the default tensorflow package now works on Apple Silicon. See https://developer.apple.com/metal/tensorflow-plugin/ for instructions on how to install older versions of Tensorflow on macOS. Please note that not all operations are supported on Arm Mac GPUs. You can work around the missing operations by pinning operations to CPU. For example:
x <- array(runif(64*64), c(1, 64, 64))
keras3::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"), keras3::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 ?keras3::install_keras
for details.
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()
.
keras3::install_keras()
https://tensorflow.rstudio.com/reference/tensorflow/install_tensorflow