测试用Python代码：

# 测试成功：【（禁用xla编译选项）源码编译TensorFlow1.13.1成功！ 】 成功使用计算能力为3.0的GPU进行计算
import tensorflow as tf
import os# 指定使用 GPU
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"print("TensorFlow【版本】（自己源码编译的，最低GPU计算能力3.0）：")
print(tf.__version__)
print("TensorFlow【安装路径】：")
print(tf.__path__)# 显示GPU显卡识别log信息
print("【第1次】GPU显卡的识别log信息：")
sess1 = tf.Session()# 用[tf.matmul]计算【矩阵点积】
# 2-D tensor `a`
# [[1, 2, 3],
#  [4, 5, 6]]
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])# 2-D tensor `b`
# [[ 7,  8],
#  [ 9, 10],
#  [11, 12]]
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])# `a` * `b`
# [[ 58, 64],
#  [139, 154]]
c = tf.matmul(a, b)print("【矩阵点积】计算结果：")
print(c.eval(feed_dict=None, session=sess1))
sess1.close()# 再次显示GPU显卡识别log信息
print("【第2次】GPU显卡的识别log信息：")
sess2 = tf.Session(config=tf.ConfigProto(log_device_placement=True))
sess2.close()###################################################################################
# 再来测试一次
print("再来测试一次！")
sess3 = tf.InteractiveSession()matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.],[2.]])
product = tf.matmul(matrix1, matrix2)# print the product
print("【1行2列的[行向量]】 * 【2行1列的[列向量]】结果：")
print(product.eval())#close the session to release resources
sess3.close()

Wing调试器输出结果：

TensorFlow【版本】（自己源码编译的，最低GPU计算能力3.0）：
1.13.1
TensorFlow【安装路径】：
['/usr/local/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/api', '/usr/local/lib/python3.6/site-packages/tensorflow', '/usr/local/lib/python3.6/site-packages/tensorflow/_api/v1']
【第1次】GPU显卡的识别log信息：
2021-01-12 00:07:57.029353: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-01-12 00:07:57.029643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: Quadro K2100M major: 3 minor: 0 memoryClockRate(GHz): 0.6665
pciBusID: 0000:01:00.0
totalMemory: 1.95GiB freeMemory: 1.93GiB
2021-01-12 00:07:57.029658: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2021-01-12 00:07:57.242745: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-12 00:07:57.242778: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2021-01-12 00:07:57.242784: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2021-01-12 00:07:57.242888: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1699 MB memory) -> physical GPU (device: 0, name: Quadro K2100M, pci bus id: 0000:01:00.0, compute capability: 3.0)
【矩阵点积】计算结果：
[[ 58  64][139 154]]
【第2次】GPU显卡的识别log信息：
2021-01-12 00:07:57.305685: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2021-01-12 00:07:57.305716: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-12 00:07:57.305722: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2021-01-12 00:07:57.305726: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2021-01-12 00:07:57.305788: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1699 MB memory) -> physical GPU (device: 0, name: Quadro K2100M, pci bus id: 0000:01:00.0, compute capability: 3.0)
Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Quadro K2100M, pci bus id: 0000:01:00.0, compute capability: 3.0
2021-01-12 00:07:57.305917: I tensorflow/core/common_runtime/direct_session.cc:317] Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Quadro K2100M, pci bus id: 0000:01:00.0, compute capability: 3.0再来测试一次！
2021-01-12 00:07:57.306311: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2021-01-12 00:07:57.306328: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-01-12 00:07:57.306334: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2021-01-12 00:07:57.306338: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2021-01-12 00:07:57.306388: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1699 MB memory) -> physical GPU (device: 0, name: Quadro K2100M, pci bus id: 0000:01:00.0, compute capability: 3.0)
【1行2列的[行向量]】 * 【2行1列的[列向量]】结果：
[[12.]]

禁用xla之后，源码编译TensorFlow1.13.1成功，测试运行3.0计算能力的GPU显卡K2100M成功！相关推荐

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禁用xla之后，源码编译TensorFlow1.13.1成功，测试运行3.0计算能力的GPU显卡K2100M成功！

禁用xla之后，源码编译TensorFlow1.13.1成功，测试运行3.0计算能力的GPU显卡K2100M成功！

源码编译的【tensorflow-1.13.1-cp36-cp36m-linux_x86_64【禁用xla编译选项，GPU计算能力3.0即可使用】.whl】轮子文件：

测试用Python代码：

Wing调试器输出结果：

禁用xla之后，源码编译TensorFlow1.13.1成功，测试运行3.0计算能力的GPU显卡K2100M成功！相关推荐

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