keras: install_keras – R documentation

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install_keras

Install Keras and the TensorFlow backend

Description

Keras and TensorFlow will be installed into an "r-tensorflow" virtual or conda environment. Note that "virtualenv" is not available on Windows (as this isn't supported by TensorFlow).

Usage

install_keras(
  method = c("auto", "virtualenv", "conda"),
  conda = "auto",
  version = "default",
  tensorflow = "default",
  extra_packages = c("tensorflow-hub"),
  ...
)

Arguments

`method`	Installation method ("virtualenv" or "conda")
`conda`	The path to a `conda` executable. Use `"auto"` to allow `reticulate` to automatically find an appropriate `conda` binary. See Finding Conda for more details.
`version`	Version of Keras to install. Specify "default" to install the latest release. Otherwise specify an alternate version (e.g. "2.2.2").
`tensorflow`	TensorFlow version to install. Specify "default" to install the CPU version of the latest release. Specify "gpu" to install the GPU version of the latest release. You can also provide a full major.minor.patch specification (e.g. "1.1.0"), appending "-gpu" if you want the GPU version (e.g. "1.1.0-gpu"). Alternatively, you can provide the full URL to an installer binary (e.g. for a nightly binary).
`extra_packages`	Additional PyPI packages to install along with Keras and TensorFlow.
`...`	Other arguments passed to `tensorflow::install_tensorflow()`.

GPU Installation

Keras and TensorFlow can be configured to run on either CPUs or GPUs. The CPU version is much easier to install and configure so is the best starting place especially when you are first learning how to use Keras. Here's the guidance on CPU vs. GPU versions from the TensorFlow website:

TensorFlow with CPU support only. If your system does not have a NVIDIA® GPU, you must install this version. Note that this version of TensorFlow is typically much easier to install, so even if you have an NVIDIA GPU, we recommend installing this version first.
TensorFlow with GPU support. TensorFlow programs typically run significantly faster on a GPU than on a CPU. Therefore, if your system has a NVIDIA® GPU meeting all prerequisites and you need to run performance-critical applications, you should ultimately install this version.

To install the GPU version:

Ensure that you have met all installation prerequisites including installation of the CUDA and cuDNN libraries as described in TensorFlow GPU Prerequistes.
Pass tensorflow = "gpu" to install_keras(). For example:
```
  install_keras(tensorflow = "gpu")
```

Windows Installation

The only supported installation method on Windows is "conda". This means that you should install Anaconda 3.x for Windows prior to installing Keras.

Custom Installation

Installing Keras and TensorFlow using install_keras() isn't required to use the Keras R package. You can do a custom installation of Keras (and desired backend) as described on the Keras website and the Keras R package will find and use that version.

See the documentation on custom installations for additional information on how version of Keras and TensorFlow are located by the Keras package.

Additional Packages

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_keras(), or alternatively install them into an existing environment using the reticulate::py_install() function.

Examples

## Not run: 

# default installation
library(keras)
install_keras()

# install using a conda environment (default is virtualenv)
install_keras(method = "conda")

# install with GPU version of TensorFlow
# (NOTE: only do this if you have an NVIDIA GPU + CUDA!)
install_keras(tensorflow = "gpu")

# install a specific version of TensorFlow
install_keras(tensorflow = "1.2.1")
install_keras(tensorflow = "1.2.1-gpu")


## End(Not run)

keras

R Interface to 'Keras'

v2.4.0

MIT + file LICENSE

Authors

Daniel Falbel [ctb, cph, cre], JJ Allaire [aut, cph], François Chollet [aut, cph], RStudio [ctb, cph, fnd], Google [ctb, cph, fnd], Yuan Tang [ctb, cph] (<https://orcid.org/0000-0001-5243-233X>), Wouter Van Der Bijl [ctb, cph], Martin Studer [ctb, cph], Sigrid Keydana [ctb]

Initial release