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Commit d6c0f248 authored by chenpangpang's avatar chenpangpang
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import tensorflow as tf
import numpy as np
import time
def check_gpu_availability():
"""检查GPU是否可用"""
print("TensorFlow版本:", tf.__version__)
print("\n检查GPU设备:")
# 列出所有可用设备
devices = tf.config.list_physical_devices()
print("可用设备:", devices)
# 检查GPU
gpus = tf.config.list_physical_devices('GPU')
if gpus:
print(f"\n✅ 发现 {len(gpus)} 个GPU:")
for gpu in gpus:
print(f" - {gpu}")
# 设置内存增长
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
print("已启用内存动态增长")
except RuntimeError as e:
print(e)
else:
print("\n❌ 未找到GPU设备，将使用CPU")
return len(gpus) > 0
def matrix_multiplication_demo():
"""矩阵乘法GPU加速演示"""
print("\n" + "="*50)
print("矩阵乘法性能测试")
print("="*50)
# 创建大型矩阵
size = 5000
print(f"创建 {size}x{size} 的矩阵...")
# 在CPU上创建随机矩阵
with tf.device('/CPU:0'):
a_cpu = tf.random.normal((size, size))
b_cpu = tf.random.normal((size, size))
# CPU计算
print("\n在CPU上计算...")
start_time = time.time()
with tf.device('/CPU:0'):
result_cpu = tf.matmul(a_cpu, b_cpu)
cpu_time = time.time() - start_time
print(f"CPU计算时间: {cpu_time:.2f}秒")
# GPU计算（如果可用）
if tf.config.list_physical_devices('GPU'):
print("\n在GPU上计算...")
# 将数据传输到GPU
with tf.device('/GPU:0'):
a_gpu = tf.identity(a_cpu)
b_gpu = tf.identity(b_cpu)
# 预热GPU（第一次计算可能较慢）
with tf.device('/GPU:0'):
_ = tf.matmul(a_gpu, b_gpu)
# 正式计算
start_time = time.time()
with tf.device('/GPU:0'):
result_gpu = tf.matmul(a_gpu, b_gpu)
gpu_time = time.time() - start_time
print(f"GPU计算时间: {gpu_time:.2f}秒")
print(f"加速比: {cpu_time/gpu_time:.1f}x")
# 验证结果一致性
print("\n验证CPU和GPU计算结果一致性...")
difference = tf.reduce_max(tf.abs(result_cpu - result_gpu))
print(f"最大差异: {difference:.6f}")
if difference < 1e-4:
print("✅ 计算结果一致")
else:
print("⚠️ 计算结果存在差异")
def neural_network_demo():
"""神经网络GPU加速演示"""
print("\n" + "="*50)
print("神经网络训练演示")
print("="*50)
# 创建模拟数据
print("生成模拟数据...")
num_samples = 10000
num_features = 100
X = np.random.randn(num_samples, num_features).astype(np.float32)
y = np.random.randint(0, 2, size=(num_samples, 1)).astype(np.float32)
# 创建简单的神经网络模型
def create_model():
model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation='relu', input_shape=(num_features,)),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(
optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy']
)
return model
# 在CPU上训练
print("\n在CPU上训练神经网络...")
with tf.device('/CPU:0'):
model_cpu = create_model()
start_time = time.time()
history_cpu = model_cpu.fit(
X, y,
epochs=10,
batch_size=32,
verbose=0
)
cpu_time = time.time() - start_time
print(f"CPU训练时间: {cpu_time:.2f}秒")
print(f"最终准确率: {history_cpu.history['accuracy'][-1]:.4f}")
# 在GPU上训练（如果可用）
if tf.config.list_physical_devices('GPU'):
print("\n在GPU上训练神经网络...")
# 清理之前的模型
tf.keras.backend.clear_session()
with tf.device('/GPU:0'):
model_gpu = create_model()
start_time = time.time()
history_gpu = model_gpu.fit(
X, y,
epochs=10,
batch_size=32,
verbose=0
)
gpu_time = time.time() - start_time
print(f"GPU训练时间: {gpu_time:.2f}秒")
print(f"最终准确率: {history_gpu.history['accuracy'][-1]:.4f}")
print(f"加速比: {cpu_time/gpu_time:.1f}x")
def mixed_precision_demo():
"""混合精度训练演示（需要GPU支持）"""
print("\n" + "="*50)
print("混合精度训练演示")
print("="*50)
if not tf.config.list_physical_devices('GPU'):
print("需要GPU支持混合精度训练")
return
# 启用混合精度
policy = tf.keras.mixed_precision.Policy('mixed_float16')
tf.keras.mixed_precision.set_global_policy(policy)
print(f"计算数据类型: {policy.compute_dtype}")
print(f"变量数据类型: {policy.variable_dtype}")
# 创建模型
model = tf.keras.Sequential([
tf.keras.layers.Dense(256, activation='relu', input_shape=(100,)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1)
])
# 使用混合精度时需要确保输出层使用float32
model.add(tf.keras.layers.Activation('linear', dtype='float32'))
model.compile(
optimizer=tf.keras.optimizers.Adam(),
loss='mse'
)
# 生成数据
X = tf.random.normal((1000, 100))
y = tf.random.normal((1000, 1))
print("\n使用混合精度训练...")
history = model.fit(X, y, epochs=5, batch_size=32, verbose=1)
print("✅ 混合精度训练完成")
def gpu_memory_info():
"""显示GPU内存信息"""
if not tf.config.list_physical_devices('GPU'):
return
print("\n" + "="*50)
print("GPU内存信息")
print("="*50)
# 获取GPU设备
gpus = tf.config.list_physical_devices('GPU')
# 使用TensorFlow的GPU内存统计
for i, gpu in enumerate(gpus):
print(f"\nGPU {i}: {gpu}")
# 创建一些张量来观察内存使用
with tf.device(f'/GPU:{i}'):
# 分配1GB的GPU内存
size = 250000000 # 大约1GB (4 bytes per float32 * 250M)
large_tensor = tf.random.normal((size, 1))
memory_mb = large_tensor.shape[0] * 4 / 1024 / 1024
print(f" 已分配张量: {memory_mb:.2f} MB")
# 检查内存统计
memory_info = tf.config.experimental.get_memory_info(f'GPU:{i}')
if memory_info:
print(f" 当前内存使用: {memory_info['current'] / 1024**2:.2f} MB")
print(f" 峰值内存使用: {memory_info['peak'] / 1024**2:.2f} MB")
# 清理
del large_tensor
def main():
"""主函数"""
print("TensorFlow 2.8.0 GPU演示程序")
print("="*50)
# 检查GPU
has_gpu = check_gpu_availability()
if has_gpu:
# 运行各种演示
matrix_multiplication_demo()
neural_network_demo()
mixed_precision_demo()
gpu_memory_info()
print("\n" + "="*50)
print("所有演示完成！")
print("="*50)
else:
print("\n⚠️ 未检测到GPU，只运行CPU演示")
matrix_multiplication_demo()
print("\n提示：")
print("1. 请确保已安装NVIDIA显卡驱动")
print("2. 安装CUDA 11.2和cuDNN 8.1")
print("3. 安装TensorFlow 2.8.0 GPU版本: pip install tensorflow-gpu==2.8.0")
if __name__ == "__main__":
main()
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