我使用 PyCharm，是 Python 的新手。
在花了两天时间弄清楚 TensorFlow 的工作原理之后我取得了成功，但启动时间很慢。在这句话之前一切都很好：“Adding visible gpu device : 0”，它需要大约 7/8 分钟的时间。
我搜索并尝试了像“export CUDA_CACHE_MAXSIZE”和“CUDA_FORCE_PTX_JIT=1”这样的方法，但都不起作用。我是 Python 特别是 TensorFlow 的初学者，因此如果有比较明确的解决方案当然会很感激。
所以，如果有人有解决方案，请让我知道，我将非常感激。
祝您拥有愉快的一天，对我的英语表示抱歉。
系统信息：
Windows 10 x64，Gtx 1060，i5，16Go RAM
Python 3.8.7
Cuda v10.1
Tensorflow 2.2.0
cuDNN 7.6
编辑：
我正在从“freecodchamp”的 YouTube 视频中学习，所以我稍微遵循了一下开头的代码。
以下是我的源代码：
import tensorflow as tf
import numpy
import tensorflow_datasets
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tensor1 = tf.ones([2, 3]) # shape 2x3
print('shape tensor 1 = ', tf.shape(tensor1)) # Doing some test for learning
tensor2 = tf.reshape(tensor1, [1, 2, 3])
print('shape tensor 2 = ', tf.shape(tensor2))
tensor1 = tf.reshape(tensor1, [1, 1, 1, 1, 1, 6])
print('shape tensor 1 reshaped = ', tf.shape(tensor1))
输出：
2021-01-04 10:04:31.355144: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-04 10:04:43.401510: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2021-01-04 10:04:43.498316: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1060 computeCapability: 6.1
coreClock: 1.6705GHz coreCount: 10 deviceMemorySize: 3.00GiB deviceMemoryBandwidth: 178.99GiB/s
2021-01-04 10:04:43.499195: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-04 10:04:43.548183: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-04 10:04:43.583967: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2021-01-04 10:04:43.595863: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2021-01-04 10:04:43.637187: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2021-01-04 10:04:43.668089: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2021-01-04 10:04:43.773909: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2021-01-04 10:04:43.774488: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2021-01-04 10:04:43.779002: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2021-01-04 10:04:43.820840: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x16676079d90 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-01-04 10:04:43.821794: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2021-01-04 10:04:43.824342: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1060 computeCapability: 6.1
coreClock: 1.6705GHz coreCount: 10 deviceMemorySize: 3.00GiB deviceMemoryBandwidth: 178.99GiB/s
2021-01-04 10:04:43.825340: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2021-01-04 10:04:43.825837: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2021-01-04 10:04:43.826331: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2021-01-04 10:04:43.826816: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2021-01-04 10:04:43.827303: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2021-01-04 10:04:43.827801: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2021-01-04 10:04:43.828297: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2021-01-04 10:04:43.828914: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2021-01-04 10:16:39.045025: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-04 10:16:39.045537: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0
2021-01-04 10:16:39.045836: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N
2021-01-04 10:16:39.047684: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2095 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0, compute capability: 6.1)
2021-01-04 10:16:39.056201: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1661e481430 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2021-01-04 10:16:39.056798: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce GTX 1060, Compute Capability 6.1
shape tensor 1 = tf.Tensor([2 3], shape=(2,), dtype=int32)
shape tensor 2 = tf.Tensor([1 2 3], shape=(3,), dtype=int32)
shape tensor 1 reshaped = tf.Tensor([1 1 1 1 1 6], shape=(6,), dtype=int32)
Process finished with exit code 0
从“Adding visible gpu devices:0”这一行到下一行之间，可以看出它花费了12分钟时间。 一切都正常运行，只是我想让它运行得更快，因为每次运行程序等那么长时间我无法忍受。