system info is : Apple M1 Pro Monterey 12.1 32GB tensorflow-metal:0.2.0 tensorflow-macos:2.6.0 With the increase of epochs, the memory usage in the activity monitor is also rapidly increasing, and can even reach 100G, and then the computer restarts....... use memory_profiler tools，show that the model does not consume so much memory。 Even if the model consumes a lot of memory, it should be a memory overflow, not a computer restart。 Below is a small demo： from tensorflow import keras as k from keras.models import Sequential from keras.layers import Dense,Activation import numpy as np from tqdm import tqdm import tensorflow as tf from memory_profiler import profile input_dim = 500 model_layers =10 class my_model(k.Model): def __init__(self,input_dim): super(my_model, self).__init__() self.input_dim = input_dim self.denses = [Dense(32, activation='relu') for item in range(input_dim)] self.l2 = Dense(2) def call(self,inputs): for i in range((self.input_dim)): inputs = self.denses[i](inputs) inputs = self.l2(inputs) return inputs batch_all = 10000000 data = np.random.random((batch_all, input_dim)) y = np.random.randint(2, size=(batch_all)) model = my_model(model_layers) epochs = 10 def compute_loss(predict,ground_true): loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=predict, labels= tf.one_hot(ground_true,depth=2,axis = -1)) return loss batch_size =4096*2 optimizer = tf.keras.optimizers.Adam(learning_rate=1e-2) m = tf.keras.metrics.Accuracy() from keras import backend as k from tensorflow.python.framework import ops import psutil @profile(precision=4,stream=open('../logs/keras_test/memory_profiler_buiild.log','w+')) def train(batch_size): with tf.GradientTape() as tp: input_x = data[num:num+batch_size] labels = y[num:num+batch_size] predict = model(input_x) loss = compute_loss(predict,labels) vears = tp.watched_variables() grads = tp.gradient(loss,vears) optimizer.apply_gradients(zip(grads,vears)) for item in tqdm(range(epochs)) : for num in tqdm(range(0, batch_all, batch_size)): train(batch_size) memory_profiler_buiild.log is shown below: Line # Mem usage Increment Occurences Line Contents ============================================================ 45 4514.1562 MiB 4514.1562 MiB 1 @profile(precision=4,stream=open('../logs/keras_test/memory_profiler_buiild.log','w+')) 46 def train(batch_size): 47 48 4514.1562 MiB 0.0000 MiB 1 with tf.GradientTape() as tp: 49 50 4514.1562 MiB 0.0000 MiB 1 input_x = data[num:num+batch_size] 51 4514.1562 MiB 0.0000 MiB 1 labels = y[num:num+batch_size] 52 4514.1562 MiB 0.0000 MiB 1 predict = model(input_x) 53 54 4514.1562 MiB 0.0000 MiB 1 loss = compute_loss(predict,labels) 55 4514.1562 MiB 0.0000 MiB 1 vears = tp.watched_variables() 56 4514.1562 MiB 0.0000 MiB 1 grads = tp.gradient(loss,vears) 57 58 4514.1719 MiB 0.0156 MiB 1 optimizer.apply_gradients(zip(grads,vears)) the memory show in activity monitor  is shown as 37G. how to solve this problem？ When the memory leak, I expect the program to be killed, not the computer to restart!!!!