In fdavidcl/ruta: Implementation of Unsupervised Neural Architectures

This article gathers some common problems users may encounter while installing or using Ruta.

Using Ruta with condaenvs

If you are using Ruta over an Anaconda installation, you will need to choose the correct conda environment where Tensorflow and Keras are installed. To do this, you can try asking reticulate to use it (replace "deeplearning" with the name of your condaenv):

reticulate::use_condaenv("deeplearning", required = TRUE)

If reticulate still uses a different Python installation, you can opt to set the RETICULATE_PYTHON environment variable before reticulate is initialized. That is, in a new R session, execute the following command:

Sys.setenv(RETICULATE_PYTHON = PATH)

where PATH is the path to the convenient Python executable (you can discover this path by looking at available versions in reticulate::py_config()).

CUDA error: `CUBLAS_STATUS_NOT_INITIALIZED`

Reset your R session and try allowing Tensorflow to grow the size of allocated memory:

config = tensorflow::tf$ConfigProto(gpu_options = list(allow_growth = TRUE))
sess = tensorflow::tf$Session(config = config)
keras::k_set_session(session = sess)