What is call stack in python
I have looked for a way to clear the Stack in Python but have not found anything. but I am running into stack depth issues.
Kernel visibility into the call stack in Python
Does the OS have visibility into the call stack (e.g. calls made between functions) in CPython? E.g. In what way is the OS involved in the creation, retrieval and/or management of the Python stack and operations of its stack frames?
- My understanding is that the Python interpreter does not support tail call recursion, so this seems to be something left to Python to handle.
- Most OS impose a maximum limit on the size of a stack (e.g. I believe in Linux OS the maximum stack size is 8192 KB by default but it can be changed via e.g. ulimit ), meaning the kernel clearly can get involved in at least limiting the size of the call stack.
In what way is the OS involved in the creation, retrieval and/or management of the python stack and operations of its stack frames?
It isn’t. The stack frames are for the process to take care of, the kernel does not interfere.
My understanding is that the Python interpreter does not support tail call recursion, so this seems to be something left to Python to handle.
Well yes, it’s Python’s job to handle its own stack, regardless of tail recursion. The fact that Python does not support tail recursion may have some disadvantages for deep recursive calls, but the code could always be rewritten as iterative.
See also: What is the maximum recursion depth in Python, and how to increase it?
the kernel clearly can get involved in at least limiting the size of the call stack
Yes, indeed the kernel does limit the stack size. The way it is done is by allocating an invisible guard page just after the top of the stack: when the stack is full, making another call (thus adding another stack frame) will trigger reads and/or writes into the guard page, and the kernel will detect it and increase the stack size. This only happens up to a certain predefined amount though, after which the process is killed for exceeding the maximum allowed stack size.
Stack in Python, A stack is a linear data structure that stores items in a Last-In/First-Out (LIFO) or First-In/Last-Out (FILO) manner. In stack, a new element is added at one end and an element is removed from that end only. The insert and delete operations are often called push and pop. The functions associated with stack are:
Call Stack in Python
Clear the call stack in Python
I am fairly new to Python and stuck on what seems like a simple problem. After months of waiting, I figured I would give in and write my own bot to get my kids a PS5. but I am running into stack depth issues.
The program just looks to see if an item is available and if not if refreshed the page and tries again. But I am throwing an exception after 1000 calls. I have looked for a way to clear the Stack in Python but have not found anything.
I have also tried to restart the program when the stack > 1000 with os. execv() . But this is throwing an Exec format error.
Below is a truncated version with all the login and setup stuff removed. Thank you in advance for any help!
def click_and_buy(): try: print('trying to buy') buy_now = driver.find_element_by_xpath('//*[@id="buy-now-button"]') buy_now.click() except Exception as e: print(len(inspect.stack(0))) if len(inspect.stack(0)) < 5: click_and_buy() time.sleep(1) else: restart() def restart(): # os.system('ps5_bots.py') os.execv(__file__, sys.argv) if __name__ == '__main__': click_and_buy() Recursion is not a good fit for infinite repetitions. This is an x y problem.
Instead make the method iterative by using a while loop. Alternatively wrap the method with another that would invoke the loop:
def click_and_buy(): print('trying to buy') buy_now = driver.find_element_by_xpath('//*[@id="buy-now-button"]') buy_now.click() def click_and_buy_repeated(): while True: click_and_buy() if __name__ == '__main__': click_and_buy_repeated() Log call stack in Python, 1 Answer. Sorted by: 3. If you use the python trace module you can debug each funcion or line where that interpreter executes. The trace moudule can be called from the cli without modifying the program: #print each executed line: python -m trace --count -C . -t MypyFile.py #print only called functions: python …
Error "Function call stack: train_function" occurred in implementation of convLSTM2D()
An error occurs in the implementation of ConvLSTM2D .
I implemented time series data analysis for the first time. So the procedure for arranging the data format may be wrong. I don't know the cause and solution, so please help me.
If you run this program, please prepare 5 images "0.png","1.png" . "4.png" in the same hierarchy. (Link here)
from scipy.sparse import dok import tensorflow as tf import numpy as np from matplotlib import pyplot as plt from keras.models import Model from keras.preprocessing.image import img_to_array, load_img from keras.layers import Input, Dense, Reshape ,ConvLSTM2D,BatchNormalization,Activation from keras.optimizers import Adam from sklearn.model_selection import train_test_split imgSize = 200 dataNum = 5 labels = list(range(dataNum, 0, -1)) imgs = [] for i in range(5): img = img_to_array(load_img(str(i)+'.png', target_size=(imgSize,imgSize))) imgs.append(img) #----------------------------------------------------------------------------------- # Arrange in a format that can be learned in Time-series # original data format: List of the same number of labels(:labels) and images(:imgs) # imgs:[image1,image2,image3,image4,image5. ] # labels:[100,110,150,140,160. ] n_seq = 2 n_sample = dataNum - n_seq x = np.zeros((n_sample, n_seq, imgSize, imgSize, 3)) for i in range(n_sample): x[i] = imgs[i:i + n_seq] del labels[0:n_seq] #----------------------------------------------------------------------------------- # Data-Split(val,test) img_train, img_val, Label_train, Label_val = train_test_split(x,labels, test_size=0.6, shuffle=False) #----------------------------------------------------------------------------------- # Model and Training inputL = Input(shape=(n_seq, imgSize, imgSize, 3)) x0 = ConvLSTM2D(filters=16, kernel_size=(3, 3), padding="same", return_sequences=True, data_format="channels_last")(inputL) x0 = BatchNormalization(momentum=0.6)(x0) x0 = ConvLSTM2D(filters=16, kernel_size=(3, 3), padding="same", return_sequences=True, data_format="channels_last")(x0) x0 = BatchNormalization(momentum=0.8)(x0) x0 = ConvLSTM2D(filters=3, kernel_size=(3, 3), padding="same", return_sequences=False, data_format="channels_last")(x0) out = Activation('tanh')(x0) outputL = Dense(1, name="a")(out) model = Model(inputs=inputL, outputs=outputL) model.compile(Adam(learning_rate=0.01),loss= ,metrics= ) history = model.fit([np.array(img_train)], [np.array(Label_train)], epochs=50, batch_size=16, validation_data=([np.array(img_val)],[np.array(Label_val)])) Traceback (most recent call last): File "c:/Users/UserA/StudyAI/LSTM.py", line 189, in history = model.fit([np.array(img_train)], [np.array(Label_train)], File "C:\Users\UserA\anaconda3\lib\site-packages\keras\engine\training.py", line 1158, in fit tmp_logs = self.train_function(iterator) File "C:\Users\UserA\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 889, in __call__ result = self._call(*args, **kwds) File "C:\Users\UserA\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 950, in _call return self._stateless_fn(*args, **kwds) File "C:\Users\UserA\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 3023, in __call__ return graph_function._call_flat( File "C:\Users\UserA\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 1960, in _call_flat return self._build_call_outputs(self._inference_function.call( File "C:\Users\UserA\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 591, in call outputs = execute.execute( File "C:\Users\UserA\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, tensorflow.python.framework.errors_impl.InvalidArgumentError: required broadcastable shapes at loc(unknown) [[node mean_absolute_error_1/sub (defined at C:\Users\UserA\anaconda3\lib\site-packages\keras\losses.py:1301) ]] [Op:__inference_train_function_10812] Errors may have originated from an input operation. Input Source operations connected to node mean_absolute_error_1/sub: ExpandDims_1 (defined at C:\Users\UserA\anaconda3\lib\site-packages\keras\engine\data_adapter.py:1414) model/b/BiasAdd (defined at C:\Users\UserA\anaconda3\lib\site-packages\keras\layers\core.py:1233) Function call stack: train_function Your model output is (200,200,1) , because you are passing tensor with more than 2 dimension to dense layer, so you will get all other dimensions at output, not just 1 ( Dense(1) ).
You may resolve this issue by adding Flatten layer before the last layer:
inputL = Input(shape=(n_seq, imgSize, imgSize, 3)) x0 = ConvLSTM2D(filters=16, kernel_size=(3, 3), padding="same", return_sequences=True, data_format="channels_last")(inputL) x0 = BatchNormalization(momentum=0.6)(x0) x0 = ConvLSTM2D(filters=16, kernel_size=(3, 3), padding="same", return_sequences=True, data_format="channels_last")(x0) x0 = BatchNormalization(momentum=0.8)(x0) x0 = ConvLSTM2D(filters=3, kernel_size=(3, 3), padding="same", return_sequences=False, data_format="channels_last")(x0) x0 = Activation('tanh')(x0) #Alter this out = keras.layers.Flatten()(x0) #Add this outputL = Dense(1, name="a")(out) P.S: Investigate your model's output with model.summary() .
Print current call stack from a method in code, If you really only want to print the stack to stderr, you can use: traceback.print_stack () Or to print to stdout (useful if want to keep redirected output together), use: traceback.print_stack (file=sys.stdout) But getting it via traceback.format_stack () lets you do whatever you like with it. Share.
See call stack while debugging in Pydev
Is there a way to see the call stack while debugging python in Pydev?
This is the " Debug " view of the " Debug " perspective :
You can see that I was inside a failUnlessEqual method, called by test_01a , called by a new_method .
To have the complete stacktrace you could add the following watch expression:
[stackLine for stackLine in __import__("traceback").format_stack() if not 'pydev' in stackLine] I am not sure if there is a better way to have the complete stacktrace.
Kernel visibility into the call stack in Python, The way it is done is by allocating an invisible guard page just after the top of the stack: when the stack is full, making another call (thus adding another stack frame) will trigger reads and/or writes into the guard page, and the kernel will detect it and increase the stack size. This only happens up to a …