First, I'm working on a Windows machine. I would like to specify a specific version of python to use in RStudio. I would like RStudio to use python 3 in the ArcGIS Pro folder in order to have arcpy available, along with the licensed extensions. I have reticulate installed and have tried the following methods to force RStudio to use the ArcGIS Pro version of python.
First I tried this:
library(reticulate)
use_python("C:/Program Files/ArcGIS/Pro/bin/Python/envs/arcgispro-py3/python.exe", required = TRUE)
The resulting error:
Error in path.expand(path) : invalid 'path' argument
Following some other tips, I tried setting the environment before loading the reticulate library.
Sys.setenv(RETICULATE_PYTHON = "c:/Program Files/ArcGIS/Pro/bin/Python/envs/arcgispro-py3/python.exe")
library(reticulate)
Then I retrieve information about the the version of Python currently being used by reticulate.
py_config
Error in path.expand(path) : invalid 'path' argument
I also tried creating and editing the .Renviron by using the usethis package
usethis::edit_r_environ()
Then entering the following
RETICULATE_PYTHON="C:/Program Files/ArcGIS/Pro/bin/Python/envs/arcgispro-py3/python.exe"
And saving it, restarting R..
library (reticulate)
py_config()
Error in path.expand(path) : invalid 'path' argument
And, to confirm, here is the location...
Any ideas on why I continue to receive invalid 'path' argument
I was having a similar issue. After trying a whole assortment of things, I finally installed an archived version of reticulate (reticulate_1.22) instead of using the most up-to-date version (reticulate_1.23) and now the issue is gone. It appears that this bug has been brought to the developers' attention (https://github.com/rstudio/reticulate/issues/1189).
Try using
use_python("C:/Program Files/ArcGIS/Pro/bin/Python/envs/arcgispro-py3")
Have you tried replacing Program Files with PROGRA~1 and have you maybe also checked for example a command like dir("path/to/your/env") although tbh your screenshot looks ok;
btw just in case - after editing your .Renviron file you need to restart your RStudio/R session for changes to take effect;
RStudio version 2022.02 has Python interpreter selection now available in Global Options
I ran into the same error with R version R-4.1.1, but when I switched back to the previous version R-4.0.5 everything worked as expected. It's a quick workaround but doesn't solve the underlying issue in the current version.
When I am following a Youtube tutorial, my code breaks when they try to import pytorch_lightning. I had a similar problem in my PyCharm environment but it has since gone away, and I am unsure why.
Error provided was
import pytorch_lightning as pl fails with ValueError: transformers.models.auto._spec_ is None.
This is my collab notebook : https://colab.research.google.com/drive/1DeeYFvK7wFsf9VBxjr184VAlulg6M_ly?usp=sharing
I decided to try and see if I can replicate the error using a minimal sub-example in a new notebook : https://colab.research.google.com/drive/1NiHIoIt8v215-lO8KoHKDSkCuw9W5S6a?usp=sharing
In the new notebook, it imports just fine. Why is it then in my actual notebook I get an error?
Update : Ending my runtime session, and restarting it, has solved the issue. However I'd like to know what I did that caused it in the first place, if possible, to avoid having this occur again.
it's a known issue with torchmetrics==0.7.0. Until torchmetrics==0.7.1 will be released, use an older version:
pip install "torchmetrics<0.7"
Here's the tracking issue: https://github.com/PyTorchLightning/pytorch-lightning/issues/11524
I have a 2021 Macbook pro M1.
It is a well recognized problem that it conflicts with Tensoflow library, see here or here; in particular this last one was exactly the issue I experienced: when I tried to import tensorflow in jupyter notebook via the command
import tensorflow as tf
then the message
The kernel appears to have died. It will restart automatically.
appeared. Then searching in the above linked discussions, I have the feeling that the suggestion given at some point, which points at this link, SEEMS to be useful.
FIRST QUESTION: is this a/the solution for the M1-Tensorflow conflict?
I say "it seems" since before trying that I have been into the kind of tornado of desperate attempts leading a beginner like me to search for hints all around the web and then copy-paste commands taken here and there into the Terminal without understanding them all properly.
On one hand it sounds dumb, I admit, on the other the cost of understanding everything goes well beyond my humble intentions of learning some ML.
So, the final result is that I have a complete mess in my computer; the old libraries like numpy don't work anymore (when I import them inside a Python3 page opened with jupyter notebook with the command import numpy as np, the message
ModuleNotFoundError: No module named 'numpy'
appears), then the pip command doesn't work, if I use the pip3 to install, nothing changes. I read somewhere to use a virtual enviroment, and I followed the instructions even if I wasn't really aware of what I was doing; I downloaded XCode, miniforge3...
Well, I guess that there is somebody out there who can relate with this.
SECOND PROBLEM: I would like to clean-up everything dealing with Python/pip/anaconda and so on and install everything from scratch, possibly following the above link to solve the M1-tensorflow conflict...if it is correct. How can I do that?
Can somebody help me, please? Thanks
I use Anaconda on a Windows 10 laptop with Python 2.7 and Spark 2.1. Built a deep learning model using Sknn.mlp package. I have completed the model. When I try to predict using the predict function, it throws an error. I run the same code on my Mac and it works just fine. Wondering what is wrong with my windows packages.
'NoneType' object is not callable
I verified input data. It is numpy.array and it does not have null value. Its dimension is same as training one and all attributed are the same. Not sure what it can be.
I don't work with Python on Windows, so this answer will be very vague, but maybe it will guide you in the right direction. Sometimes there are cross-platform errors due to one module still not being updated for the OS, frequently when another related module gets an update. I recall something happened to me with a django application which required somebody more familiar with Windows to fix it for me.
Maybe you could try with an environment using older versions of your modules until you find the culprit.
I finally solved the problem on windows. Here is the solution in case you face it.
The Theano package was faulty. I installed the latest version from github and then it threw another error as below:
RuntimeError: To use MKL 2018 with Theano you MUST set "MKL_THREADING_LAYER=GNU" in your environment.
In order to solve this, I created a variable named MKL_Threading_Layer under user environment variable and passed GNU. Reset the kernel and it was working.
Hope it helps!
I'm trying to execute a Python script, but I am getting the following error:
Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)
I'm using python 3.5.2 on a Linux Mint 18.1 Serena OS
Can someone tell me why this happens, and how can I solve?
The SIGSEGV signal indicates a "segmentation violation" or a "segfault". More or less, this equates to a read or write of a memory address that's not mapped in the process.
This indicates a bug in your program. In a Python program, this is either a bug in the interpreter or in an extension module being used (and the latter is the most common cause).
To fix the problem, you have several options. One option is to produce a minimal, self-contained, complete example which replicates the problem and then submit it as a bug report to the maintainers of the extension module it uses.
Another option is to try to track down the cause yourself. gdb is a valuable tool in such an endeavor, as is a debug build of Python and all of the extension modules in use.
After you have gdb installed, you can use it to run your Python program:
gdb --args python <more args if you want>
And then use gdb commands to track down the problem. If you use run then your program will run until it would have crashed and you will have a chance to inspect the state using other gdb commands.
Another possible cause (which I encountered today) is that you're trying to read/write a file which is open. In this case, simply closing the file and rerunning the script solved the issue.
After some times I discovered that I was running a new TensorFlow version that gives error on older computers. I solved the problem downgrading the TensorFlow version to 1.4
When I encounter this problem, I realize there are some memory issues. I rebooted PC and solved it.
This can also be the case if your C-program (e.g. using cpython is trying to access a variable out-of-bound
ctypedef struct ReturnRows:
double[10] your_value
cdef ReturnRows s_ReturnRows # Allocate memory for the struct
s_ReturnRows.your_value = [0] * 12
will fail with
Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)
For me, I was using the OpenCV library to apply SIFT.
In my code, I replaced cv2.SIFT() to cv2.SIFT_create() and the problem is gone.
Deleted the python interpreter and the 'venv' folder solve my error.
I got this error in PHP, while running PHPUnit. The reason was a circular dependency.
I received the same error when trying to connect to an Oracle DB using the pyodbc module:
connection = pyodbc.connect()
The error occurred on the following occasions:
The DB connection has been opened multiple times in the same python
file
While in debug mode a breakpoint has been reached
while the connection to the DB being open
The error message could be avoided with the following approaches:
Open the DB only once and reuse the connection at all needed places
Properly close the DB connection after using it
Hope, that will help anyone!
11 : SIGSEGV - This signal is arises when an memory segement is illegally accessed.
There is a module name signal in python through which you can handle this kind of OS signals.
If you want to ignore this SIGSEGV signal, you can do this:
signal.signal(signal.SIGSEGV, signal.SIG_IGN)
However, ignoring the signal can cause some inappropriate behaviours to your code, so it is better to handle the SIGSEGV signal with your defined handler like this:
def SIGSEGV_signal_arises(signalNum, stack):
print(f"{signalNum} : SIGSEGV arises")
# Your code
signal.signal(signal.SIGSEGV, SIGSEGV_signal_arises)
I encountered this problem when I was trying to run my code on an external GPU which was disconnected. I set os.environ['PYOPENCL_CTX']=2 where GPU 2 was not connected. So I just needed to change the code to os.environ['PYOPENCL_CTX'] = 1.
For me these three lines of code already reproduced the error, no matter how much free memory was available:
import numpy as np
from sklearn.cluster import KMeans
X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]])
kmeans = KMeans(n_clusters=1, random_state=0).fit(X)
I could solve the issue by removing an reinstalling the scikit-learn package. A very similar solution to this.
This can also occur if trying to compound threads using concurrent.futures. For example, calling .map inside another .map call.
This can be solved by removing one of the .map calls.
I had the same issue working with kmeans from scikit-learn.
Upgrading from scikit-learn 1.0 to 1.0.2 solved it for me.
This issue is often caused by incompatible libraries in your environment. In my case, it was the pyspark library.
In my case, reverting my most recent conda installs fixed the situation.
I got this error when importing monai. It was solved after I created a new conda environment. Possible reasons I could imagine were either that there were some conflict between different packages, or maybe that my environment name was the same as the package name I wanted to import (monai).
found on other page.
interpreter: python 3.8
cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
this solved issue for me.
i was getting SIGSEGV with 2.7, upgraded my python to 3.8 then got different error with OpenCV. and found answer on OpenCV 4.0.0 SystemError: <class 'cv2.CascadeClassifier'> returned a result with an error set.
but eventually one line of code fixed it.