Тензор Dst не инициализируется, распределение памяти ⇐ Python

[Anonymous]

У меня проблема с тем, что тензор Dst не инициализируется, есть похожие сообщения, но решения слишком устарели, и моя проблема довольно специфична.
Я выполняю:
python - 3.11,
Tensorflow - 2.15,
Keras - 2.15,
tensorflow_metal - 2.15
Я использую версия 2.15, чтобы обеспечить совместимость с графическим процессором моего M2 Mac, но мне интересно узнать, есть ли другой способ использовать самую современную версию, упомяните об этом, поскольку это может быть проблемой.
Это код ошибки, который я получаю

Код: Выделить всё

    Traceback (most recent call last):
File "training.py", line 149, in 
main()
File "training.py", line 136, in main
model, history = train_existing_model(MODEL_PATH, X, y)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "training.py", line 69, in train_existing_model
X = tf.convert_to_tensor(X, dtype=tf.float32)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/framework/constant_op.py", line 103, in convert_to_eager_tensor
return ops.EagerTensor(value, ctx.device_name, dtype)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tensorflow.python.framework.errors_impl.InternalError: Failed copying input tensor from /job:localhost/replica:0/task:0/device:CPU:0 to /job:localhost/replica:0/task:0/device:GPU:0 in order to run _EagerConst: Dst tensor is not initialized.

полный код приводит к ошибке ниже

Код: Выделить всё

    import os
import tensorflow as tf
import numpy as np
import random as rdm
import time
from chess import pgn, Board
from tqdm import tqdm
from tensorflow.keras.models import load_model
from tensorflow.keras.utils import to_categorical

os.environ['TF_METAL'] = '1'

def load_pgn(file_path):
with open(file_path, 'r') as pgn_file:
while True:
game = pgn.read_game(pgn_file)
if game is None:
break
yield game

def board_to_matrix(board: Board):
matrix = np.zeros((8, 8, 12))
piece_map = board.piece_map()
for square, piece in piece_map.items():
row, col = divmod(square, 8)
piece_type = piece.piece_type - 1
piece_color = 0 if piece.color else 6
matrix[row, col, piece_type + piece_color] = 1
return matrix

def create_input_for_nn(games):
X, y = [], []
chunk_size = 1000

for i in range(0, len(games), chunk_size):
chunk = games[i:i + chunk_size]
for game in chunk:
board = game.board()
for move in game.mainline_moves():
X.append(board_to_matrix(board))
y.append(move.uci())
board.push(move)

return np.array(X, dtype=np.float32), np.array(y)

def encode_moves(moves):
move_to_int = {move:  idx for idx, move in enumerate(set(moves))}
return [move_to_int[move] for move in moves], move_to_int

def data_generator(X, y, batch_size):
dataset_size = len(X)
while True:
for i in range(0, dataset_size, batch_size):
end = min(i + batch_size, dataset_size)
yield X[i:end], y[i:end]

def train_existing_model(model_path, X, y, batch_size=32, epochs=25):
print(f"\nLoading existing model from {model_path}")
model = load_model(model_path)

# Convert to TensorFlow tensors
X = np.array(X, dtype=np.float32)
X = tf.convert_to_tensor(X, dtype=tf.float32)

y = np.array(y, dtype=np.int32)
y = tf.convert_to_tensor(y, dtype=tf.int32)

# Create data generator
train_gen = data_generator(X, y, batch_size)

# Use legacy optimizer for M1/M2
model.compile(
optimizer=tf.keras.optimizers.legacy.Adam(),
loss=model.loss,
metrics=model.metrics
)

print("\nContinuing training...")
history = model.fit(
train_gen,
validation_split=0.1,
epochs=epochs,
verbose=1
)

print("\nSaving updated model...")
model.save(model_path)
return model, history

def main():
start_time = time.time()

# Configuration
MODEL_PATH = '../assets/chessModel.keras'
FILE_PATH = '../assets/ChessData'
LIMIT_OF_FILES = 3
GAMES_LIMIT = 50000

physical_devices = tf.config.list_physical_devices('GPU')
if physical_devices:
for device in physical_devices:
tf.config.experimental.set_memory_growth(device, False)
tf.config.set_visible_devices(physical_devices[0], 'GPU')
# Load games
print("\nLoading games...")
files = [file for file in os.listdir(FILE_PATH) if file.endswith('.pgn')]
games = []

for i, file in enumerate(tqdm(files)):
games.extend(load_pgn(f"{FILE_PATH}/{file}"))
if i >= LIMIT_OF_FILES - 1:
break

print(f"\nTotal games loaded: {len(games)}")
LIMITED_GAMES = games[:GAMES_LIMIT]

# Prepare training data
print("\nPreparing training data...")
X, y = create_input_for_nn(LIMITED_GAMES)
y, move_to_int = encode_moves(y)
y = to_categorical(y, num_classes=len(move_to_int))
X = np.array(X)

print(f"\nTraining data shapes:")
print(f"X shape: {X.shape}")
print(f"y shape: {y.shape}")

# Train model
model, history = train_existing_model(MODEL_PATH, X, y)

# Create move mapping for predictions
int_to_move = dict(zip(move_to_int.values(), move_to_int.keys()))

# Print training time
end_time = time.time()
print(f"\nTotal training time: {(end_time - start_time) / 60:.2f} minutes")

return model, history, int_to_move

if __name__ == "__main__":
main()

Я не знаю, что еще попробовать. Я настроил конвейеры данных, массивы int32 и float32, как показано выше, но ничего не работает.

Подробнее здесь: https://stackoverflow.com/questions/793 ... llocatiion