Pycharm use GUP server

1、配置远程服务器信息

　　Tools——Deployment——Configuration

　　

　　然后，点击加号Add一个远程服务信息。

　　我这里命名为server1；Type选择SFTP；Host即ip地址，也就是服务器的地址；Port是端口号；User name即用户名；再输一下password，顺便save一下。

　　

　　Test一下Connection，然后自动生成一下Root path，OK。

　　

2、添加远程调试环境

　　File——Settings——Project——Project Interpreter(解释器)

　　

　　Show all——加号——SSH Interpreter——Existing server configuration——server1——Next——Finish

　　

　　

　　

3、Test

　　新建一个项目文件，跑一下code。

import tensorflow as tf
a = tf.test.is_built_with_cuda()
b = tf.test.is_gpu_available(
    cuda_only=False,
    min_cuda_compute_capability=None
)
print(a)
print(b)

　　结果：

ssh://root@111.44.254.166:43942/usr/bin/python -u /root/tmp/pycharm_project_928/test.py
-- ::51.954587: I tensorflow/core/platform/cpu_feature_guard.cc:] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
-- ::52.058581: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:] successful NUMA node read from SysFS had negative value (-), but there must be at least one NUMA node, so returning NUMA node zero
-- ::52.059029: I tensorflow/core/common_runtime/gpu/gpu_device.cc:] Found device  with properties:
name: GeForce RTX  major:  minor:  memoryClockRate(GHz): 1.71
pciBusID: ::00.0
totalMemory: .77GiB freeMemory: .62GiB
-- ::52.059044: I tensorflow/core/common_runtime/gpu/gpu_device.cc:] Adding visible gpu devices:
-- ::52.275783: I tensorflow/core/common_runtime/gpu/gpu_device.cc:] Device interconnect StreamExecutor with strength  edge matrix:
-- ::52.275834: I tensorflow/core/common_runtime/gpu/gpu_device.cc:]
-- ::52.275842: I tensorflow/core/common_runtime/gpu/gpu_device.cc:] :   N
-- ::52.275968: I tensorflow/core/common_runtime/gpu/gpu_device.cc:] Created TensorFlow device (/device:GPU: with  MB memory) -> physical GPU (device: , name: GeForce RTX , pci bus id: ::00.0, compute capability: 7.5)
True
True

Process finished with exit code