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这篇文章主要介绍了大数据中常用框架的测试方法有哪些，具有一定借鉴价值，感兴趣的朋友可以参考下，希望大家阅读完这篇文章之后大有收获，下面让小编带着大家一起了解一下。
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TensorFlow1.x与TensorFlow2.x测试方法是一样的，代码如下：
import tensorflow as tf print(tf.test.is_gpu_available())
上述代码保存为.py文件，使用需要测试环境即可运行，输出：上面是一下log信息，关键的是的最后True，表示测试成功
2020-09-28 15:43:03.197710: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 2020-09-28 15:43:03.204525: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll 2020-09-28 15:43:03.232432: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: name: GeForce RTX 2070 with Max-Q Design major: 7 minor: 5 memoryClockRate(GHz): 1.125 pciBusID: 0000:01:00.0 2020-09-28 15:43:03.235352: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll 2020-09-28 15:43:03.242823: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_100.dll 2020-09-28 15:43:03.261932: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_100.dll 2020-09-28 15:43:03.268757: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_100.dll 2020-09-28 15:43:03.297478: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_100.dll 2020-09-28 15:43:03.315410: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_100.dll 2020-09-28 15:43:03.330562: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll 2020-09-28 15:43:03.332846: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0 2020-09-28 15:43:05.198465: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix: 2020-09-28 15:43:05.200423: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0 2020-09-28 15:43:05.201540: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N 2020-09-28 15:43:05.203863: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/device:GPU:0 with 6306 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070 with Max-Q Design, pci bus id: 0000:01:00.0, compute capability: 7.5) True
上面是一下log信息，关键的是的最后True，表示测试成功。其实我们还可以发现很多GPU信息
GPU型号：name: GeForce RTX 2070 with Max-Q Design
cuda版本：Successfully opened dynamic library cudart64_100.dll（10.0）
cudnn版本：Successfully opened dynamic library cudnn64_7.dll（7.x）
GPU数目：Adding visible gpu devices: 0（1）
GPU显存：/device:GPU:0 with 6306 MB memory（8G）
PyTorch与TensorFlow测试方法类似，都有GPU测试接口。PyTorch的GPU测试代码如下：
import torch print(torch.cuda.is_available())
上述代码保存为.py文件，使用需要测试环境即可运行，输出：True，表示测试成功
True
可以看出PyTorch输出信息简洁很多。其实TensorFlow的log信息输出也是可以控制的。
MXNet与PyTorch，TensorFlow测试方法不同，由于MXNet'没有GPU测试接口（或者说笔者没有找到）。所以MXNet的GPU测试代码采用try-catch捕捉异常的方法来测试，代码如下：
import mxnet as mx mxgpu_ok = False try: _ = mx.nd.array(1,ctx=mx.gpu(0)) mxgpu_ok = True except: mxgpu_ok = False print(mxgpu_ok)
上述代码保存为.py文件，使用需要测试环境即可运行，输出：True，表示测试成功
PaddlePaddle与TensorFlow测试方法类似，都有GPU测试接口。PyTorch的GPU测试代码如下：
import paddle paddle.fluid.install_check.run_check()
上述代码保存为.py文件，使用需要测试环境即可运行，输出：Your Paddle Fluid works well on MUTIPLE GPU or CPU.，表示测试成功
Running Verify Fluid Program ... W0928 16:23:17.825171 10572 device_context.cc:252] Please NOTE: device: 0, CUDA Capability: 75, Driver API Version: 11.0, Runtime API Version: 10.0 W0928 16:23:17.836141 10572 device_context.cc:260] device: 0, cuDNN Version: 7.6. Your Paddle Fluid works well on SINGLE GPU or CPU. W0928 16:23:19.349067 10572 build_strategy.cc:170] fusion_group is not enabled for Windows/MacOS now, and only effective when running with CUDA GPU. Your Paddle Fluid works well on MUTIPLE GPU or CPU. Your Paddle Fluid is installed successfully! Let's start deep Learning with Paddle Fluid now
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