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Praefatio

Cum TensorFlow 2 utens ad exercitationem aut praedictionem, administratio propria GPU memoriae pendet. Defectum ad memoriam efficaciter administrare et GPU dimittere memoriam potest in memoriam perducere, quae computatione subsequenti opera afficere potest. In hoc articulo varias vias explorabimus ut GPU memoriam efficaciter liberaret, et convenienter et cum negotium terminare coactus est.

1. Conventional video memoriam administratione modi

1. Reset default image

Quoties novum TensorFlow graphum curritis, vocando tf.keras.backend.clear_session() ut hodiernam TensorFlow graphi liberam et memoriam purgare.

import tensorflow as tf
tf.keras.backend.clear_session()

2. Limit usus memoria GPU

Ponens consilium in usu memoriae video, potes ne memoriam video GPU nimis occupari.

• Crescere video memoriam usus in demanda：

  import tensorflow as tf
  
  gpus = tf.config.experimental.list_physical_devices('GPU')
  if gpus:
      try:
          for gpu in gpus:
              tf.config.experimental.set_memory_growth(gpu, True)
      except RuntimeError as e:
          print(e)
  

• Terminus video memoriam usus：

  import tensorflow as tf
  
  gpus = tf.config.experimental.list_physical_devices('GPU')
  if gpus:
      try:
          tf.config.experimental.set_virtual_device_configuration(
              gpus[0],
              [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4096)])  # 限制为 4096 MB
      except RuntimeError as e:
          print(e)
  

3. Manually dimittis memoriam GPU

Post institutionem sive praedictionem, utere gc Moduli et TensorFlow memoria administrationis functiones GPU memoriae manually emittunt.

import tensorflow as tf
import gc

tf.keras.backend.clear_session()
gc.collect()

4. usus with Procuratio enuntiationis contextus

Usus est in disciplina seu praedictio codice with dicitur ad automatice resource release administrare.

import tensorflow as tf

def train_model():
    with tf.device('/GPU:0'):
        model = tf.keras.models.Sequential([
            tf.keras.layers.Dense(64, activation='relu', input_shape=(32,)),
            tf.keras.layers.Dense(10, activation='softmax')
        ])
        model.compile(optimizer='adam', loss='categorical_crossentropy')
        # 假设 X_train 和 y_train 是训练数据
        model.fit(X_train, y_train, epochs=10)

train_model()

2. Video memoriam administratione cum fortiter termi- nere

Interdum opus est ut opus TensorFlow fortiter terminetur ad memoriam GPU dimittendam.In hoc casu utere Pythonismultiprocessing modulus vel "os Modi facultates efficaciter administrare possunt.

1. usus multiprocessing modulus

Currens TensorFlow officia in processibus separatis, totum processum occidi potest, ut memoriam video, cum opus fuerit, liberare.

import multiprocessing as mp
import tensorflow as tf
import time

def train_model():
    model = tf.keras.models.Sequential([
        tf.keras.layers.Dense(64, activation='relu', input_shape=(32,)),
        tf.keras.layers.Dense(10, activation='softmax')
    ])
    model.compile(optimizer='adam', loss='categorical_crossentropy')
    # 假设 X_train 和 y_train 是训练数据
    model.fit(X_train, y_train, epochs=10)

if __name__ == '__main__':
    p = mp.Process(target=train_model)
    p.start()
    time.sleep(60)  # 例如，等待60秒
    p.terminate()
    p.join()  # 等待进程完全终止

2. usus os modulus processus terminatur

Per questus processus id quod utens os Module, qui processus TensorFlow fortiter terminare potest.

import os
import signal
import tensorflow as tf
import multiprocessing as mp

def train_model():
    pid = os.getpid()
    with open('pid.txt', 'w') as f:
        f.write(str(pid))

    model = tf.keras.models.Sequential([
        tf.keras.layers.Dense(64, activation='relu', input_shape=(32,)),
        tf.keras.layers.Dense(10, activation='softmax')
    ])
    model.compile(optimizer='adam', loss='categorical_crossentropy')
    # 假设 X_train 和 y_train 是训练数据
    model.fit(X_train, y_train, epochs=10)

if __name__ == '__main__':
    p = mp.Process(target=train_model)
    p.start()
    time.sleep(60)  # 例如，等待60秒
    with open('pid.txt', 'r') as f:
        pid = int(f.read())
    os.kill(pid, signal.SIGKILL)
    p.join()

Summatim

Cum TensorFlow 2 utens ad disciplinam vel praedictionem, crucialus est ut recte administrare et dimittere GPU memoriam.Per tabulam defaltam resetting, limitans usum memoriae video, manually memoriam video solvens et utenswith Contextus administrationis enuntiationis efficaciter vitare memoriam difficultates effluo potest.Cum opus fortiter terminare negotium, uteremultiprocessing modules etos Modulus efficere potest ut memoria videndi tempore dimittatur. Per has rationes, utilitas efficientis facultatum GPU conservari potest et stabilitas et effectus computandi munia emendari possunt.