python - 如何解释 TensorFlow 输出？

Question

如何解释 TensorFlow 输出以在 GPGPU 上构建和执行计算图？

给定以下使用 python API 执行任意 tensorflow 脚本的命令。

python3 tensorflow_test.py > out

第一部分 stream_executor 似乎是它的加载依赖项。

I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally

什么是NUMA节点？

I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:900] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

我假设这是它找到可用 GPU 的时候

I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties: 
name: Tesla K40c
major: 3 minor: 5 memoryClockRate (GHz) 0.745
pciBusID 0000:01:00.0
Total memory: 11.25GiB
Free memory: 11.15GiB

一些gpu初始化？什么是 DMA？

I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:755] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K40c, pci bus id: 0000:01:00.0)

为什么会抛出错误E？

E tensorflow/stream_executor/cuda/cuda_driver.cc:932] failed to allocate 11.15G (11976531968 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY

pool_allocator 的作用的最佳答案:https://stackoverflow.com/a/35166985/4233809

I tensorflow/core/common_runtime/gpu/pool_allocator.cc:244] PoolAllocator: After 3160 get requests, put_count=2958 evicted_count=1000 eviction_rate=0.338066 and unsatisfied allocation rate=0.412025
I tensorflow/core/common_runtime/gpu/pool_allocator.cc:256] Raising pool_size_limit_ from 100 to 110
I tensorflow/core/common_runtime/gpu/pool_allocator.cc:244] PoolAllocator: After 1743 get requests, put_count=1970 evicted_count=1000 eviction_rate=0.507614 and unsatisfied allocation rate=0.456684
I tensorflow/core/common_runtime/gpu/pool_allocator.cc:256] Raising pool_size_limit_ from 256 to 281
I tensorflow/core/common_runtime/gpu/pool_allocator.cc:244] PoolAllocator: After 1986 get requests, put_count=2519 evicted_count=1000 eviction_rate=0.396983 and unsatisfied allocation rate=0.264854
I tensorflow/core/common_runtime/gpu/pool_allocator.cc:256] Raising pool_size_limit_ from 655 to 720
I tensorflow/core/common_runtime/gpu/pool_allocator.cc:244] PoolAllocator: After 28728 get requests, put_count=28680 evicted_count=1000 eviction_rate=0.0348675 and unsatisfied allocation rate=0.0418407
I tensorflow/core/common_runtime/gpu/pool_allocator.cc:256] Raising pool_size_limit_ from 1694 to 1863

Answer 1

关于 NUMA -- https://software.intel.com/en-us/articles/optimizing-applications-for-numa

粗略地说，如果您有双路 CPU，它们将各自拥有自己的内存，并且必须通过较慢的 QPI 链接访问其他处理器的内存。所以每个CPU+内存都是一个NUMA节点。

您可能会将两个不同的 NUMA 节点视为两个不同的设备，并构建您的网络以针对不同的节点内/节点间带宽进行优化

但是，我认为目前 TF 中没有足够的布线来执行此操作。检测也不起作用——我只是在一台有 2 个 NUMA 节点的机器上尝试过，它仍然打印相同的消息并初始化为 1 个 NUMA 节点。

DMA = 直接内存访问。您可以在不使用 CPU(即通过 NVlink)的情况下将内容从一个 GPU 复制到另一个 GPU。 NVLink 集成尚未实现。

就错误而言，TensorFlow 尝试分配接近 GPU 最大内存，所以听起来您的一些 GPU 内存已经分配给其他东西，并且分配失败。

您可以执行以下操作以避免分配太多内存

config = tf.ConfigProto(log_device_placement=True)
config.gpu_options.per_process_gpu_memory_fraction=0.3 # don't hog all vRAM
config.operation_timeout_in_ms=15000   # terminate on long hangs
sess = tf.InteractiveSession("", config=config)