最近我决定从 Tensorflow（GPU 变体）版本 1.14 迁移到当前版本 2.0。
我当前的设置是：
- Tensorflow（GPU 变体）2.0
- 库德恩7.6.4
- CUDA 10
- Python 3.6
- 集成开发环境：Visual Studio 2019
我原本以为会有一些疼痛，但这让我措手不及。
当我尝试运行我的一个（现已调整的）1.14 项目时，构建的模型出现了问题，并且训练过程顺利开始。直到第三步之后才彻底停下来。
同一个项目在 Tensorflow 2.0 的 CPU 变体上运行得很好，但训练所有模型需要花费几个数量级的时间。
这是我到目前为止所尝试的：
- 更改超参数
- 安装CUDA
- 安装张量流
- 重新安装cudnn
- 禁用验证
- 检查路径变量
这些都没有对解决问题有任何帮助。我唯一的线索是警告消息：
Invoking ptxas not supported on Windows
Relying on driver to perform ptx compilation. This message will be only logged once.
我在 Tf 1.14 中从未遇到过这一点，并且对此感到有些困惑。
我知道 CUDA 可以工作，因为我编译并运行了几个 Nvidia 示例。因此，剩下的唯一真正的选择与 Tensorflow 或它如何处理 GPU 相关。
但我不知道如何继续前进。
会话日志如下：
019-11-27 01:03:57.910895: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll
C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python36_64\lib\site-packages\pandas\core\frame.py:4117: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
errors=errors,
2019-11-27 01:04:02.247959: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2019-11-27 01:04:02.277414: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate(GHz): 1.835
pciBusID: 0000:0a:00.0
2019-11-27 01:04:02.282378: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-11-27 01:04:02.286653: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-11-27 01:04:02.289629: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-11-27 01:04:02.295084: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate(GHz): 1.835
pciBusID: 0000:0a:00.0
2019-11-27 01:04:02.299843: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-11-27 01:04:02.303965: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-11-27 01:04:03.043700: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-27 01:04:03.047132: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0
2019-11-27 01:04:03.049453: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N
2019-11-27 01:04:03.052642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6382 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:0a:00.0, compute capability: 6.1)
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, 154, 64) 896000
_________________________________________________________________
conv1d (Conv1D) (None, 150, 64) 20544
_________________________________________________________________
flatten (Flatten) (None, 9600) 0
_________________________________________________________________
dense (Dense) (None, 300) 2880300
_________________________________________________________________
dense_1 (Dense) (None, 150) 45150
_________________________________________________________________
dense_2 (Dense) (None, 70) 10570
_________________________________________________________________
dense_3 (Dense) (None, 10) 710
_________________________________________________________________
dense_4 (Dense) (None, 2) 22
=================================================================
Total params: 3,853,296
Trainable params: 3,853,296
Non-trainable params: 0
_________________________________________________________________
Train for 10 steps, validate for 50 steps
Epoch 1/40
2019-11-27 01:04:06.199581: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_100.dll
2019-11-27 01:04:06.430358: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2019-11-27 01:04:07.180709: W tensorflow/stream_executor/cuda/redzone_allocator.cc:312] Internal: Invoking ptxas not supported on Windows
Relying on driver to perform ptx compilation. This message will be only logged once.
2019-11-27 01:04:07.425377: I tensorflow/core/profiler/lib/profiler_session.cc:184] Profiler session started.
2019-11-27 01:04:07.431736: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cupti64_100.dll
1/10 [==>...........................] - ETA: 32s - loss: 0.6933 - accuracy: 0.4375 - categorical_accuracy: 0.4375 - precision: 0.4375 - recall: 0.43752019-11-27 01:04:07.655586: I tensorflow/core/platform/default/device_tracer.cc:588] Collecting 148 kernel records, 21 memcpy records.
WARNING: Logging before flag parsing goes to stderr.
W1127 01:04:07.730274 5696 callbacks.py:244] Method (on_train_batch_end) is slow compared to the batch update (0.138531). Check your callbacks.
3/10 [========>.....................] - ETA: 9s - loss: 0.6167 - accuracy: 0.7000 - categorical_accuracy: 0.7000 - precision: 0.7000 - recall: 0.7000
