Error in .Call(`_reticulate_py_call_impl`, x, args, keywords) : reached elapsed time limit

I keep getting this error using reticulate with python 3.6, keras 2.2, tensorflow 1.8. I am forcing reticulate to use python 3.6 with use_python("/usr/local/bin/python3", required = T). Error occurs when trying to invoke tensorflow with many rows or from few rows but large files. Example I am following is from here --> https://tensorflow.rstudio.com/blog/simple-audio-classification-keras.html

Script that produces error is below;

library(stringr)
library(dplyr)
library(fs)
library(tfdatasets)
library(tensorflow)
library(utils)
library(keras)
library(reticulate)
use_python("/usr/local/bin/python3", required = T)

Download the data
dir.create("data")

download.file(
url = "http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz",
destfile = "data/speech_commands_v0.01.tar.gz"
)

untar("data/speech_commands_v0.01.tar.gz", exdir = "data/speech_commands_v0.01")

Get list of all files

files <- fs::dir_ls(
path = "data/speech_commands_v0.01/",
recursive = TRUE,
glob = "*.wav"
)

files <- files[!str_detect(files, "background_noise")]

df <- data_frame(
fname = files,
class = fname %>% str_extract("1/.*/") %>%
str_replace_all("1/", "") %>%
str_replace_all("/", ""),
class_id = class %>% as.factor() %>% as.integer() - 1L
)

timeout error occurs here in trying to process 64721 rows or when trying to open small numbers of large wav files.

suggests a memory or sockets problem in reticular or tfdatasets. If I reduce sample data problem repeats in other

tf calls later in the scripts shown here --> https://tensorflow.rstudio.com/blog/simple-audio-classification-keras.html

ds <- tensor_slices_dataset(df) # problem occurs here. Error in .Call(_reticulate_py_call_impl, x, args, keywords) : reached elapsed time limit
traceback()
sessionInfo()
py_config()

And my console report of the above;

library(stringr)
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

filter, lag

The following objects are masked from ‘package:base’:

intersect, setdiff, setequal, union

library(fs)
library(tfdatasets)
library(tensorflow)
library(utils)
library(keras)
library(reticulate)
use_python("/usr/local/bin/python3", required = T)
files <- fs::dir_ls(

• path = "data/speech_commands_v0.01/",
• recursive = TRUE,
• glob = "*.wav"
• )

files <- files[!str_detect(files, "background_noise")]

df <- data_frame(

• fname = files,
• class = fname %>% str_extract("1/.*/") %>%

• str_replace_all("1/", "") %>%
  

• str_replace_all("/", ""),
  

• class_id = class %>% as.factor() %>% as.integer() - 1L
• )

timeout error occurs here in trying to process 64721 rows or when trying to open small numbers of large wav files.

suggests a memory or sockets problem in reticular or tfdatasets. If I reduce sample data problem repeats in other

tf calls later in the scripts shown here --> https://tensorflow.rstudio.com/blog/simple-audio-classification-keras.html

ds <- tensor_slices_dataset(df) # problem occurs here. Error in .Call(_reticulate_py_call_impl, x, args, keywords) : reached elapsed time limit
traceback()
No traceback available
sessionInfo()
R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.5

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] reticulate_1.8.0.9000 keras_2.1.6.9001 tensorflow_1.8.0.9000 tfdatasets_1.5 fs_1.2.3
[6] dplyr_0.7.5 stringr_1.3.1

loaded via a namespace (and not attached):
[1] Rcpp_0.12.17 whisker_0.3-2 bindr_0.1.1 magrittr_1.5 tidyselect_0.2.4 lattice_0.20-35 R6_2.2.2
[8] rlang_0.2.1 tools_3.5.0 grid_3.5.0 tfruns_1.3 yaml_2.1.19 assertthat_0.2.0 tibble_1.4.2
[15] Matrix_1.2-14 bindrcpp_0.2.2 purrr_0.2.5 base64enc_0.1-3 zeallot_0.1.0 glue_1.2.0 stringi_1.2.3
[22] compiler_3.5.0 pillar_1.2.3 jsonlite_1.5 pkgconfig_2.0.1

py_config()
python: /usr/local/bin/python3
libpython: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/config-3.6m-darwin/libpython3.6.dylib
pythonhome: /Library/Frameworks/Python.framework/Versions/3.6:/Library/Frameworks/Python.framework/Versions/3.6
version: 3.6.3 (v3.6.3:2c5fed86e0, Oct 3 2017, 00:32:08) [GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]
numpy: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/numpy
numpy_version: 1.15.0
keras: /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras

NOTE: Python version was forced by use_python function
2018-06-21 16:45:52.907052: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Error in .Call(_reticulate_py_call_impl, x, args, keywords) :
reached elapsed time limit

Thanks for the help to untangle this.

Reproduce the supplied R script with the listed reticulate, Keras, TensorFlow, and Python versions. Start at tensor_slices_dataset(df) and the .Call(_reticulate_py_call_impl) timeout, using traceback(), sessionInfo(), and py_config() to isolate the failure; done means the cause is fixed or its supported configuration is documented.