Installation Issue #597
Comments
|
Very interesting. Thanks for reporting. Do you get the same error if you use keras3? library(keras3)
keras_model_sequential() What do the broken system("ls -l /usr/local/bin/python*")Can you trackdown where the error is coming from? options(error = quote(print(rlang::trace_back())))
<reproduce error> |
|
Thanks, with Furthermore, I receive system("ls -l /usr/local/bin/python*")
lrwxr-xr-x 1 root admin 70 Nov 24 2021 /usr/local/bin/python3 -> ../../../Library/Frameworks/Python.framework/Versions/3.10/bin/python3
lrwxr-xr-x 1 root admin 77 Nov 24 2021 /usr/local/bin/python3-config -> ../../../Library/Frameworks/Python.framework/Versions/3.10/bin/python3-config
lrwxr-xr-x 1 root admin 78 Nov 24 2021 /usr/local/bin/python3-intel64 -> ../../../Library/Frameworks/Python.framework/Versions/3.10/bin/python3-intel64
lrwxr-xr-x 1 root admin 73 Nov 24 2021 /usr/local/bin/python3.10 -> ../../../Library/Frameworks/Python.framework/Versions/3.10/bin/python3.10
lrwxr-xr-x 1 root admin 80 Nov 24 2021 /usr/local/bin/python3.10-config -> ../../../Library/Frameworks/Python.framework/Versions/3.10/bin/python3.10-config
lrwxr-xr-x 1 root admin 81 Nov 24 2021 /usr/local/bin/python3.10-intel64 -> ../../../Library/Frameworks/Python.framework/Versions/3.10/bin/python3.10-intel64and options(error = quote(print(rlang::trace_back())))
model <- keras_model_sequential()
Error:
▆ |
|
Thanks for reporting, the warning about We'll need a traceback to track it down---I can't reproduce the error locally. Here is another trick for getting a traceback if R is being "shy" like this, wrap the offending call in a function that you're tracing in a debugger, and then repeatedly "step into" ( Seeing as how you have some broken symlinks in reticulate::install_python("3.10:latest", force = TRUE) -> python
# TensorFlow version 2.15 is the last version that defaulted to using Keras version 2,
# which is what the {keras} package is for. TensorFlow 2.16 and later default
# to Keras 3, which you can use from R using the {keras3} package.
tensorflow::install_tensorflow(version = "2.15", python_version = python)Also, please double check that is_set <- function(x) !is.na(Sys.getenv(x, NA))
if (is_set(c("PYTHONPATH")))
stop("PYTHONPATH is being set somewhere")
if (any(is_set(c("CONDA_PREFIX", "CONDA_EXE"))))
stop("Conda is being activated somewhere") |
|
Thanks for the information! I tried the following in R to trace the error: profvis::profvis({
keras::keras_model_sequential()
})
profvis: code exited with error:
Error in parse_rprof_lines(lines, expr_source) :
No parsing data available. Maybe your function was too fast? |
|
I don't think profvis will be helpful here. I meant to do something like this fn <- function() {
browser()
keras::keras_model_sequential()
}
fn()
# press the 's' key and then the Enter key
# press the 's' key and then the Enter key
# press the 's' key and then the Enter key
# press the 's' key and then the Enter key
# ... see where we're going
# evaluate `print(rlang::trace_back())` to see the traceback |
|
Thanks! That's the output: > fn <- function() {
+ browser()
+ keras::keras_model_sequential()
+ }
>
> fn()
Called from: fn()
Browse[1]> s
debug at #3: keras::keras_model_sequential()
Browse[2]> s
debugging in: keras::keras_model_sequential()
debug: {
if (length(list(...)))
layers <- c(sequential_model_input_layer(...), layers)
keras$models$Sequential(layers = layers, name = name)
}
Browse[3]> s
debug: if (length(list(...))) layers <- c(sequential_model_input_layer(...),
layers)
Browse[3]> s
debug: keras$models$Sequential(layers = layers, name = name)
Browse[3]> s
debugging in: keras$models$Sequential(layers = layers, name = name)
debug: {
cl <- sys.call()
cl[[1L]] <- list2
call_args <- split_named_unnamed(eval(cl, parent.frame()))
result <- py_call_impl(callable, call_args$unnamed, call_args$named)
if (py_get_convert(callable))
result <- py_to_r(result)
if (is.null(result))
invisible(result)
else result
}
Browse[4]> s
debug: cl <- sys.call()
Browse[4]> s
debugging in: sys.call()
debug: .Internal(sys.call(which))
Browse[5]> s
exiting from: sys.call()
debug: cl[[1L]] <- list2
Browse[4]> s
debug: call_args <- split_named_unnamed(eval(cl, parent.frame()))
Browse[4]> s
debugging in: split_named_unnamed(eval(cl, parent.frame()))
debug: {
nms <- names(x)
if (is.null(nms))
return(list(unnamed = x, named = list()))
named <- nzchar(nms)
list(unnamed = x[!named], named = x[named])
}
Browse[5]> s
debug: nms <- names(x)
Browse[5]> s
debugging in: eval(cl, parent.frame())
debug: .Internal(eval(expr, envir, enclos))
Browse[6]> s
debugging in: parent.frame()
debug: .Internal(parent.frame(n))
Browse[7]> s
exiting from: parent.frame()
debug: baseenv()
Browse[6]> s
debugging in: (function (...)
{
.Call(ffi_dots_list, frame_env = environment(), named = NULL,
ignore_empty = "trailing", preserve_empty = FALSE, unquote_names = TRUE,
homonyms = "keep", check_assign = FALSE)
})(layers = layers, name = name)
debug: {
.Call(ffi_dots_list, frame_env = environment(), named = NULL,
ignore_empty = "trailing", preserve_empty = FALSE, unquote_names = TRUE,
homonyms = "keep", check_assign = FALSE)
}
Browse[8]> s
debug: .Call(ffi_dots_list, frame_env = environment(), named = NULL,
ignore_empty = "trailing", preserve_empty = FALSE, unquote_names = TRUE,
homonyms = "keep", check_assign = FALSE)
Browse[8]> s
debugging in: environment()
debug: .Internal(environment(fun))
Browse[9]> s
exiting from: environment()
exiting from: (function (...)
{
.Call(ffi_dots_list, frame_env = environment(), named = NULL,
ignore_empty = "trailing", preserve_empty = FALSE, unquote_names = TRUE,
homonyms = "keep", check_assign = FALSE)
})(layers = layers, name = name)
exiting from: eval(cl, parent.frame())
debug: if (is.null(nms)) return(list(unnamed = x, named = list()))
Browse[5]> s
debug: named <- nzchar(nms)
Browse[5]> s
debug: list(unnamed = x[!named], named = x[named])
Browse[5]> s
exiting from: split_named_unnamed(eval(cl, parent.frame()))
debug: result <- py_call_impl(callable, call_args$unnamed, call_args$named)
Browse[4]> s
debugging in: py_call_impl(callable, call_args$unnamed, call_args$named)
debug: {
.Call(`_reticulate_py_call_impl`, x, args, keywords)
}
Browse[5]> s
debug: .Call(`_reticulate_py_call_impl`, x, args, keywords)
Browse[5]> s
exiting from: py_call_impl(callable, call_args$unnamed, call_args$named)
debug: if (py_get_convert(callable)) result <- py_to_r(result)
Browse[4]> s
debugging in: py_get_convert(callable)
debug: {
.Call(`_reticulate_py_get_convert`, x)
}
Browse[5]> s
debug: .Call(`_reticulate_py_get_convert`, x)
Browse[5]> s
exiting from: py_get_convert(callable)
debug: result <- py_to_r(result)
Browse[4]> s
debugging in: py_to_r(result)
debug: {
if (!is_py_object(x <- py_to_r_cpp(x)))
return(x)
UseMethod("py_to_r")
}
Browse[5]> s
debug: if (!is_py_object(x <- py_to_r_cpp(x))) return(x)
Browse[5]> s
debugging in: is_py_object(x <- py_to_r_cpp(x))
debug: {
.Call(`_reticulate_is_py_object`, x)
}
Browse[6]> s
debug: .Call(`_reticulate_is_py_object`, x)
Browse[6]> s
debugging in: py_to_r_cpp(x)
debug: {
.Call(`_reticulate_py_to_r_cpp`, x)
}
Browse[7]> s
debug: .Call(`_reticulate_py_to_r_cpp`, x)
Browse[7]> s
exiting from: py_to_r_cpp(x)
exiting from: is_py_object(x <- py_to_r_cpp(x))
debug: UseMethod("py_to_r")
Browse[5]> s
debugging in: py_to_r.default(result)
debug: {
py_to_r_cpp(x)
}
Browse[6]> s
debug: py_to_r_cpp(x)
Browse[6]> s
debugging in: py_to_r_cpp(x)
debug: {
.Call(`_reticulate_py_to_r_cpp`, x)
}
Browse[7]> s
debug: .Call(`_reticulate_py_to_r_cpp`, x)
Browse[7]> s
exiting from: py_to_r_cpp(x)
exiting from: py_to_r.default(result)
exiting from: py_to_r(result)
debug: if (is.null(result)) invisible(result) else result
Browse[4]> s
debug: result
Browse[4]> s
exiting from: keras$models$Sequential(layers = layers, name = name)
exiting from: keras::keras_model_sequential()
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Layer (type) ┃ Output Shape ┃ Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
└─────────────────────────────────────────────────────────────────────────────────────┴─────────────────────────────────────────────────────────────────┴──────────────────────────────────────┘
Total params: 0 (0.00 B)
Trainable params: 0 (0.00 B)
Non-trainable params: 0 (0.00 B) |
|
it seems that there is no error when you step through it with a debugger. Am I reading the output correctly? But the error still occurs outside a debugger? |
|
Yes, you are reading the output correctly. During debugging, the error does not occur, but outside the debugging the error does occur. |
|
I'm genuinely stumped about what might be causing the issue. Do you have anything in |
|
Wow, thank you very much!!! Indeed, there was an exported path in the .Rprofil linking to a conda env, which I deleted and now it works!!! Thank you so much for your great support! |
I followed you installation instructions and receive the following error messages:
Some diagnostic info:
Unfortunately, calling the code on
https://tensorflow.rstudio.com/examples/conv_lstm
yields to an error as well:
(There is no explicit error message)
Thank you very much in advance!
The text was updated successfully, but these errors were encountered: