服务器 ¶

ssh name@ip

ssh -p <port> name@ip

exit

cd .ssh

ssh-keygen -t rsa

ssh-copy-id -p 10086 name@ip

ssh <alias>

C:\Users\username\.ssh\

/home/username/.vscode-server/

conda create -n <>_py<version> python=<version>

conda activate <name>

pip install torch torchaudio torchvision

pip install scipy seaborn tqdm jupyter pandas matplotlib opencv-python

pip install d2l

import torch
print("PyTorch Version:", torch.__version__)

if torch.cuda.is_available(): # 检查 CUDA 是否可用
    print("CUDA is available.")
    num_gpus = torch.cuda.device_count() # 获取显卡总数
    print(f"Number of GPUs: {num_gpus}")
    if num_gpus >= 1:
        device = torch.device(f"cuda:{num_gpus - 1}") # 指定使用最后一块显卡
        print(f"Using GPU: {torch.cuda.get_device_name(device)}")
        tensor = torch.rand(3, 3).to(device) # 在指定显卡上创建一个简单的张量计算
        print("Tensor on GPU:\n", tensor)
    else:
        print("No GPUs available.")
else:
    print("CUDA is not available.")

PyTorch Version: 2.4.1+cu121
CUDA is available.
Number of GPUs: 6
Using GPU: NVIDIA GeForce RTX 3090
Tensor on GPU:
 tensor([[0.9610, 0.1389, 0.1536],
        [0.7481, 0.7573, 0.2097],
        [0.2123, 0.6865, 0.0611]], device='cuda:5')

gpustat

nvidia-msi

nvidia-smi -l

free -m 

tasklist

top

htop

kill -9 <pid>

taskkill /PID 进程号 -F -T  

ps -f -p PID号

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9 # 占用GPU90%的显存
session = tf.Session(config=config)

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)