Process finished with exit code -1073740791 (0xC0000409) tensorflow-gpu

Here is the code I am using:

import math
import pandas_datareader as web
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
import matplotlib.pyplot as plt
    
Stock = 'BTC-USD'

#Get the stock quote
df = web.DataReader(Stock, data_source='yahoo', start='2016-01-01', end='2020-12-17')

#Show the Data
#print(df)
#Get the number of rows and columns in the data set
#print(df.shape)
#Visualize the closing price history
plt.figure(figsize=(16,8))
plt.title("Close Price History")
plt.plot(df['Close'])
plt.xlabel('Data', fontsize=18)
plt.ylabel('Close Price USD ($)', fontsize=18)
plt.show()
print(1)

#Create a new Dataframe with only the 'close column'
data = df.filter(['Close'])

#Convert the dataframe to a numpy array
dataset = data.values

#Get the number of rows to train the model on
training_data_len = math.ceil(len(dataset) * .8)
print(2)

#print(training_data_len)
#Scale the data
scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(dataset)
print(3)

#print(scaled_data)
#Create the training data set
#Create the scaled training data set
train_data = scaled_data[0:training_data_len, :]
#Split the data into x_train and y_train data sets
x_train = []
y_train = []
print(4)

for i in range(60, len(train_data)):
    x_train.append(train_data[i-60:i, 0])
    y_train.append(train_data[i, 0])
    if i<= 61:
        print(x_train)
        print(y_train)
        print()
print(5)

#Convert the x_train and y_train to numpy arrays
x_train, y_train = np.array(x_train), np.array(y_train)
print(6)

#Reshape the x_train data set
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
#print(x_train.shape)
print(7)

#Build the LSTM model
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(LSTM(50, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))
print(8)

#Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')
print(9)

#Train the model
model.fit(x_train, y_train, batch_size=1, epochs=1)
print(10)

#Create the test data set
#Create a new array containing scaled values from index 1543 to 2003
test_data = scaled_data[training_data_len - 60: 2003]

#Create the data sets x_test and y_test
x_test = []
y_test = dataset[training_data_len:, :]
for i in range(60, len(test_data)):
    x_test.append(test_data[i-60:i, 0])
print(11)

#Convert the data to a numpy array
x_test = np.array(x_test)

#print(x_test.shape)
#Reshape the data
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
#Get the models predicted price values
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)

#Evaluate the model. Get the root mean squared error (RMSE)
rmse = np.sqrt(np.mean(predictions - y_test)**2)
print(rmse)

#Plot the data
train = data[:training_data_len]
valid = data[training_data_len: ]
valid['Predictions'] = predictions

#Visualize the data
plt.figure(figsize=(16,8))
plt.title('Model')
plt.xlabel('Date', fontsize=18)
plt.ylabel('Close Price USD ($)', fontsize=18)
plt.plot(train['Close'])
plt.plot(valid[['Close', 'Predictions']])
plt.legend(['Train', 'Val', 'Predictions'], loc='lower right')
plt.show()

#Show the valid and predicted prices
print(valid)

It stops between 9 and 10 if you look at my "print" debugging method and this is the output:

C:\Users\gunne\anaconda3\envs\EnvBioWell\python.exe C:/Users/gunne/PycharmProjects/pythonProject/Stocks_Price.py
2021-02-19 09:57:52.322041: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
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[array([0.00307386, 0.00303452, 0.00288404, 0.00301928, 0.00296962,
       0.00284426, 0.00411515, 0.00390359, 0.00365686, 0.00367355,
       0.00369274, 0.00313341, 0.00298767, 0.00289699, 0.        ,
       0.00101894, 0.00078898, 0.00100278, 0.00069457, 0.00245455,
       0.00201685, 0.00079746, 0.00101697, 0.0016967 , 0.00120293,
       0.00122168, 0.00134546, 0.00070072, 0.00066494, 0.00061141,
       0.00019479, 0.00038312, 0.00044424, 0.00024669, 0.00110931,
       0.0009756 , 0.00053531, 0.00053962, 0.00040029, 0.00051366,
       0.00076044, 0.00067284, 0.00087522, 0.00120881, 0.00188371,
       0.00157436, 0.00189504, 0.00228295, 0.00254865, 0.00247892,
       0.00319813, 0.00326988, 0.00322377, 0.00247677, 0.00266203,
       0.00264398, 0.00297805, 0.00299417, 0.00303742, 0.00322153])]
[0.003108507179665692]
[array([0.00307386, 0.00303452, 0.00288404, 0.00301928, 0.00296962,
       0.00284426, 0.00411515, 0.00390359, 0.00365686, 0.00367355,
       0.00369274, 0.00313341, 0.00298767, 0.00289699, 0.        ,
       0.00101894, 0.00078898, 0.00100278, 0.00069457, 0.00245455,
       0.00201685, 0.00079746, 0.00101697, 0.0016967 , 0.00120293,
       0.00122168, 0.00134546, 0.00070072, 0.00066494, 0.00061141,
       0.00019479, 0.00038312, 0.00044424, 0.00024669, 0.00110931,
       0.0009756 , 0.00053531, 0.00053962, 0.00040029, 0.00051366,
       0.00076044, 0.00067284, 0.00087522, 0.00120881, 0.00188371,
       0.00157436, 0.00189504, 0.00228295, 0.00254865, 0.00247892,
       0.00319813, 0.00326988, 0.00322377, 0.00247677, 0.00266203,
       0.00264398, 0.00297805, 0.00299417, 0.00303742, 0.00322153]), array([0.00303452, 0.00288404, 0.00301928, 0.00296962, 0.00284426,
       0.00411515, 0.00390359, 0.00365686, 0.00367355, 0.00369274,
       0.00313341, 0.00298767, 0.00289699, 0.        , 0.00101894,
       0.00078898, 0.00100278, 0.00069457, 0.00245455, 0.00201685,
       0.00079746, 0.00101697, 0.0016967 , 0.00120293, 0.00122168,
       0.00134546, 0.00070072, 0.00066494, 0.00061141, 0.00019479,
       0.00038312, 0.00044424, 0.00024669, 0.00110931, 0.0009756 ,
       0.00053531, 0.00053962, 0.00040029, 0.00051366, 0.00076044,
       0.00067284, 0.00087522, 0.00120881, 0.00188371, 0.00157436,
       0.00189504, 0.00228295, 0.00254865, 0.00247892, 0.00319813,
       0.00326988, 0.00322377, 0.00247677, 0.00266203, 0.00264398,
       0.00297805, 0.00299417, 0.00303742, 0.00322153, 0.00310851])]
[0.003108507179665692, 0.0026196096172032522]
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2021-02-19 09:57:55.479350: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-02-19 09:57:55.479830: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2021-02-19 09:57:55.506019: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
pciBusID: 0000:0e:00.0 name: GeForce RTX 3080 computeCapability: 8.6
coreClock: 1.74GHz coreCount: 68 deviceMemorySize: 10.00GiB deviceMemoryBandwidth: 707.88GiB/s
2021-02-19 09:57:55.506184: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-02-19 09:57:55.509645: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-02-19 09:57:55.509728: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-02-19 09:57:55.511906: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-02-19 09:57:55.512542: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-02-19 09:57:55.516144: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-02-19 09:57:55.517468: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-02-19 09:57:55.518014: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-02-19 09:57:55.518133: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-02-19 09:57:55.518454: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-02-19 09:57:55.519487: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: 
pciBusID: 0000:0e:00.0 name: GeForce RTX 3080 computeCapability: 8.6
coreClock: 1.74GHz coreCount: 68 deviceMemorySize: 10.00GiB deviceMemoryBandwidth: 707.88GiB/s
2021-02-19 09:57:55.519678: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-02-19 09:57:55.519763: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-02-19 09:57:55.519836: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-02-19 09:57:55.519915: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-02-19 09:57:55.519995: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-02-19 09:57:55.520066: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-02-19 09:57:55.520138: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-02-19 09:57:55.520212: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-02-19 09:57:55.520304: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-02-19 09:57:56.033929: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-02-19 09:57:56.034021: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 
2021-02-19 09:57:56.034068: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N 
2021-02-19 09:57:56.034278: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8444 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3080, pci bus id: 0000:0e:00.0, compute capability: 8.6)
2021-02-19 09:57:56.035130: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
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2021-02-19 09:57:56.610845: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2021-02-19 09:57:57.789418: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-02-19 09:57:58.358210: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-02-19 09:57:58.377765: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
Process finished with exit code -1073740791 (0xC0000409)

I'm using my RTX 3080 GPU with Cuddn 8.0 and Cuda Toolkit 11.0 and tensorflow-gpu 2.4.0 with python 3.7. Please let me know if anyone has any suggestions! I've tried everything I can think of and when I run code to check for GPU it pulls it up, so it should be working, but I just can't get it to. I had it working a month ago before I messed something up and now it shows all of those time stamps in the output with packages it's opening. Not sure why, but I just need help.