nvidb 1.4.2

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

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 install directly from PyPI:

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 Configuration

Option A: Interactive Setup (Recommended)

Use the interactive command to add servers:

nvidb add

This will guide you through adding a new server with prompts for host, port, username, authentication method, etc.

Option B: Manual Configuration

To manually configure remote servers, create or edit the configuration file at ~/.nvidb/config.yml:

mkdir -p ~/.nvidb/
cp config.example.yml ~/.nvidb/config.yml
# Edit the file with your server details

Configuration file structure:

servers:
  - host: "example1.com"
    port: 22
    username: "user1"
    description: "Description of the first server"
    auth: "auto"  # auto | key | password
    
  - host: "example2.com"
    port: 22
    username: "user2"
    password: "password2"  # Optional, prompted if not set
    description: "Description of the second server"
    auth: "auto"

Configuration Options:

• host: Server hostname or IP address (required)
• port: SSH port, default is 22 (required)
• username: SSH username (required)
• description: Human-readable server description (optional)
• auth: Authentication method - auto, key, or password (optional, default: auto)
• password: SSH password (optional, will prompt if needed)

Environment Variables

You can customize the working directory by setting NVIDB_HOME:

export NVIDB_HOME=/path/to/custom/nvidb

Default working directory is ~/.nvidb/.

---

2. Usage

2.1 Basic Commands

nvidb                  # Monitor local GPU only (interactive TUI)
nvidb --remote         # Monitor local and remote servers
nvidb --once           # Print GPU stats once and exit
nvidb --once --remote  # Print all servers once and exit
nvidb --version        # Show version

2.2 Server Management

nvidb add              # Interactively add a new server
nvidb info             # Show configuration info and server list

2.3 GPU Logging

Continuously log GPU statistics to an SQLite database:

nvidb log                          # Log local GPU with default settings
nvidb log --remote                 # Log local and remote GPUs
nvidb log --interval 10            # Set logging interval to 10 seconds
nvidb log --db-path /path/to/db    # Specify custom database path

Press Ctrl+C to stop logging and save data.

2.4 Cleanup

Remove server configurations or delete log data:

nvidb clean              # Interactive cleanup menu
nvidb clean all          # Delete all data (requires double confirmation)

2.5 Interactive TUI Navigation

When viewing GPU stats, use these keyboard shortcuts:

Key	Action
j / ↓	Move selection down
k / ↑	Move selection up
Enter / Space	Toggle expand/collapse server
a	Expand all servers
c	Collapse all servers
q	Quit

2.6 GPU Monitor Decorator

Use the @nvidb.monitor decorator to track GPU usage during function execution:

import nvidb

@nvidb.monitor
def train_model():
    # Your training code here
    pass

# With custom options
@nvidb.monitor(sample_interval=0.05, gpu_indices=[0, 1])
def multi_gpu_training(epochs: int = 100):
    pass

# Async function support
@nvidb.monitor
async def async_training():
    pass

After function execution, it outputs:

======================================================================
[nvidb.monitor] Function completed: train_model
  Signature: train_model()
  Location: /path/to/file.py:14
----------------------------------------------------------------------
  Duration: 125.3s
----------------------------------------------------------------------
  GPU 0: NVIDIA GeForce RTX 3090 Ti
    Memory:
      Peak:    8192.00 MiB / 24.00 GiB
      Delta:   +6144.00 MiB
    Utilization:
      Avg:     85.0%
    Temperature:
      Peak:    72C
    Power:
      Peak:    320.5W
======================================================================

Decorator Options:

• sample_interval: Sampling interval in seconds (default: 0.1)
• gpu_indices: List of GPU indices to monitor (default: all GPUs)
• enabled: Enable/disable monitoring (default: True)

---

3. Sample Output

Time: 09:41:00 | Servers: 2 | [j/k] Navigate [Enter] Toggle [a] Expand All [c] Collapse All [q] Quit
--------------------------------------------------------------------------------
* v [1] Local Machine (l1ght@localhost)  1 GPUs | 1 idle | 0% avg | 0GB/24GB

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)   

  > [2] Server 1  8 GPUs | 0 idle | 78% avg | 156GB/192GB

---

4. System Requirements

• NVIDIA driver installed with nvidia-smi available in terminal
• Python 3.8+
• SSH access to remote servers (for remote monitoring)

5. Tips

• Use nvidia-smi --help-query-gpu to see available query options
• Database files are stored in ~/.nvidb/gpu_log.db by default
• Configuration and logs are stored in ~/.nvidb/ directory

6. Acknowledgements

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