tracemalloc

tracemalloc 模块用于追踪 Python 的内存分配，能够精确定位内存在哪行代码被分配。

tracemalloc

基本内存追踪

import tracemalloc

tracemalloc.start()

data = [i ** 2 for i in range(100000)]
big_dict = {str(i): i for i in range(50000)}

snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics("lineno")

print("Top 5 内存分配:")
for stat in top_stats[:5]:
    print(stat)

比较两个时间点的内存差异

import tracemalloc

tracemalloc.start()

snapshot1 = tracemalloc.take_snapshot()

# 模拟一些操作
x = [bytearray(1024) for _ in range(1000)]
y = list(range(100000))

snapshot2 = tracemalloc.take_snapshot()

top_stats = snapshot2.compare_to(snapshot1, "lineno")

print("内存增长 Top 5:")
for stat in top_stats[:5]:
    print(stat)

按文件统计内存

import tracemalloc

tracemalloc.start()

data = [i for i in range(1000000)]
text = "hello " * 100000

snapshot = tracemalloc.take_snapshot()
stats = snapshot.statistics("filename")

print("按文件统计:")
for stat in stats[:10]:
    print(f"  {stat.traceback}: {stat.size / 1024:.1f} KiB")

获取当前内存使用

import tracemalloc

tracemalloc.start()

arrays = []
for i in range(10):
    arrays.append(bytearray(1024 * 1024))  # 每次 1MB
    current, peak = tracemalloc.get_traced_memory()
    print(f"第 {i+1} 轮: 当前 {current / 1024 / 1024:.1f} MB, 峰值 {peak / 1024 / 1024:.1f} MB")

tracemalloc.stop()

显示分配的完整调用链

import tracemalloc

tracemalloc.start(25)  # 保存最多25层调用栈

def allocate():
    return [bytearray(1024) for _ in range(100)]

def process():
    return allocate()

def main():
    return process()

data = main()

snapshot = tracemalloc.take_snapshot()
top_stats = snapshot.statistics("traceback")

# 查看最大内存分配的完整调用链
stat = top_stats[0]
print(f"最大分配: {stat.size / 1024:.1f} KiB, {stat.count} 次")
for line in stat.traceback.format():
    print(f"  {line}")

过滤特定模块的内存

import tracemalloc

tracemalloc.start()

import json
data = json.dumps({str(i): list(range(100)) for i in range(1000)})

snapshot = tracemalloc.take_snapshot()

# 只看当前文件的分配
filtered = snapshot.filter_traces([
    tracemalloc.Filter(True, "<string>"),
])
for stat in filtered.statistics("lineno")[:5]:
    print(stat)

# 排除 importlib 的分配
filtered2 = snapshot.filter_traces([
    tracemalloc.Filter(False, "<frozen importlib._bootstrap>"),
    tracemalloc.Filter(False, "<frozen importlib._bootstrap_external>"),
])
for stat in filtered2.statistics("lineno")[:5]:
    print(stat)

tip

在 AI 场景中，内存问题非常常见：

• 数据加载：大数据集未及时释放
• 模型推理：tensor 缓存累积
• 特征工程：中间 DataFrame 占用过多

tracemalloc 配合 gc 模块是排查 Python 层内存问题的标准组合。对于 C 扩展层（如 PyTorch CUDA 内存），需要使用框架自带的工具。