nvidb 1.2.0

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nvidb

A package that provides an aggregated view of the NVIDIA GPU information on several hosts.

1.Installation

1.1 Install using pip

You can install nvidb using pip. First, clone the repository:

git clone https://github.com/FanBB2333/nvidb.git
cd nvidb
pip install .

Or using pip directly:

pip install nvidb
# If the specified version is unavailable in your custom repository, use pypi.org as the source:
pip install nvidb -i https://pypi.org/simple

---

1.2 [Optional] Manually Add a Configuration File

To monitor the status of remote servers, a configuration file is required. nvidb will look for the config.yml file in the ~/.nvidb/ directory.

To create the configuration file, follow these steps:

mkdir -p ~/.nvidb/
cd ~/.nvidb/
touch config.yml

Then, edit the config.yml file with the following structure:

servers:
  - host: "example1.com"
    port: 8080
    username: "user1"
    description: "Description of the first server"
  - host: "example2.com"
    port: 9090
    username: "user2"
    password: "password2" # Optional, if password-based authentication is required
    description: "Description of the second server"

• The password field is optional, omit the field if the server can be accessed with the public key (By default, the program will read the key located in ~/.ssh). If your key is not accessed or the filled password is incorrect, the program will prompt you to enter the password.

2.Usage

After installation, the command nvidb will be available in the terminal. Run the command to get the aggregated view of the NVIDIA GPU information on several hosts.

nvidb # for local machine only
nvidb --remote # for local and remote servers

The output format will be like:

[Local Machine Info]
[Remote Server0 GPU Info]
[Remote Server1 GPU Info]
...

One sample output for a remote server might look like:

⏰ Time: 09:41:00

Local Machine (l1ght@localhost)
Driver: 570.169 | CUDA: 12.8 | GPUs: 1
GPU  |         name         |   fan    |   util   | mem_util |   temp   |     rx     |     tx     |       power        |   memory[used/total]   |      processes      
-----+----------------------+----------+----------+----------+----------+------------+------------+--------------------+------------------------+---------------------
 0   |     RTX 3090 Ti      |   0 %    |   0 %    |   0 %    |   39 C   |  350KB/s   |  500KB/s   |  P8 32.72/450.00   |        41/24564        |       gdm(17M)      

Server 1
Driver: 575.57.08 | CUDA: 12.9 | GPUs: 8
GPU  |         name         |   fan    |   util   | mem_util |   temp   |     rx     |     tx     |       power        |   memory[used/total]   |      processes      
-----+----------------------+----------+----------+----------+----------+------------+------------+--------------------+------------------------+---------------------
 0   |       RTX 3090       |   39 %   |   6 %    |   4 %    |   50 C   |  350KB/s   |  350KB/s   |  P2 150.35/350.00  |      16147/24576       | user1(16124M) gdm(4M) 
 1   |       RTX 3090       |   57 %   |   13 %   |   11 %   |   62 C   |  350KB/s   |  400KB/s   |  P2 174.41/350.00  |      17581/24576       | user1(17558M) gdm(4M) 
 2   |       RTX 3090       |   89 %   |  100 %   |   37 %   |   80 C   |  13.9MB/s  |  4.8MB/s   |  P2 314.48/350.00  |      21415/24576       | user1(21392M) gdm(4M) 
 3   |       RTX 3090       |  100 %   |  100 %   |   32 %   |   71 C   |  27.6MB/s  |  7.7MB/s   |  P2 260.81/350.00  |      21035/24576       | user1(21012M) gdm(4M) 
 4   |       RTX 3090       |   79 %   |  100 %   |   31 %   |   75 C   |  13.1MB/s  |  7.4MB/s   |  P2 321.92/350.00  |      20975/24576       | user1(20952M) gdm(4M) 
 5   |       RTX 3090       |   90 %   |  100 %   |   28 %   |   84 C   |  35.0MB/s  |  9.8MB/s   |  P2 283.55/350.00  |      21035/24576       | user1(21012M) gdm(4M) 
 6   |       RTX 3090       |   78 %   |  100 %   |   56 %   |   75 C   |  28.8MB/s  |  8.3MB/s   |  P2 349.30/350.00  |      21135/24576       | user1(21112M) gdm(4M) 
 7   |       RTX 3090       |   84 %   |  100 %   |   82 %   |   80 C   |  13.9MB/s  |  4.0MB/s   |  P2 362.74/350.00  |      21235/24576       | user1(21212M) gdm(4M) 

One sample output for a remote server might look like:
```bash
⏰ Time: 09:41:00

Server 2
Driver: 575.57.08 | CUDA: 12.9 | GPUs: 7
GPU  |         name         |   fan    |   util   | mem_util |   temp   |     rx     |     tx     |       power        |   memory[used/total]   |      processes      
-----+----------------------+----------+----------+----------+----------+------------+------------+--------------------+------------------------+---------------------
 0   |       RTX 3090       |   41 %   |   0 %    |   0 %    |   30 C   |  400KB/s   |  500KB/s   |  P8 22.33/350.00   |        18/24576        |       gdm(4M)       
 1   |       RTX 3090       |   30 %   |   0 %    |   0 %    |   33 C   |  400KB/s   |  450KB/s   |  P8 15.31/350.00   |        18/24576        |       gdm(4M)       
 2   |       RTX 3090       |   30 %   |   0 %    |   0 %    |   29 C   |  450KB/s   |  500KB/s   |   P8 7.20/350.00   |        18/24576        |       gdm(4M)       
 3   |       RTX 3090       |   30 %   |   0 %    |   0 %    |   29 C   |  500KB/s   |  500KB/s   |   P8 4.42/350.00   |        18/24576        |       gdm(4M)       
 4   |       RTX 3090       |   30 %   |   0 %    |   0 %    |   26 C   |  800KB/s   |  950KB/s   |   P8 5.04/350.00   |        18/24576        |       gdm(4M)       
 5   |       RTX 3090       |   30 %   |   0 %    |   0 %    |   24 C   |  500KB/s   |  550KB/s   |   P8 5.13/350.00   |        18/24576        |       gdm(4M)       
 6   |       RTX 3090       |   30 %   |   0 %    |   0 %    |   25 C   |  450KB/s   |  550KB/s   |   P8 8.03/350.00   |        18/24576        |       gdm(4M)       

3.System Requirements

The hosts should install the NVIDIA driver and be able to use nvidia-smi in terminal.

4.Tips

nvidia-smi query options: use nvidia-smi --help-query-gpu to get the query options.

5.Acknowledgements

Thanks to NVIDIA for providing the nvidia-smi tool, which is used to query GPU information.