[Integration] add swanlab logger

[Zeyi-Lin]

Pull Request Description

This PR introduces SwanLab, a lightweight open-source experiment tracking tool, as a new logging option for the training framework. The integration provides both online and offline tracking capabilities, along with a local dashboard for visualizing results.

We have officially integrated with excellent open-source projects such as transformers, LLaMA Factory, and veRL. We are also very eager to integrate with the outstanding 🌟Ultralytics to provide developers with a better training experience.

🎬Here is a onlinedemo of the integrated effect:

https://swanlab.cn/@ZeyiLin/Qwen2.5-0.5B-SFT-paddlenlp/runs/myyyg5lbbikbnvdtw11zr/chart

Below is a detailed overview of the changes and usage instructions:

Key Features of SwanLab Integration

1. Online and Offline Tracking:

• Online Mode: Track experiments remotely and store data on SwanLab's cloud platform.
• Offline Mode: Use a local dashboard to visualize training logs without an internet connection.

2. Hardware Monitoring:

• Automatically tracks GPU usage, power consumption, temperature, and other hardware metrics.
• Supports NVIDIA GPUs, Huawei Ascend NPUs and Kunlunxin XPUs.

3. Remote Access:

• View training progress remotely via the SwanLab web interface or mobile app.

4. Local Dashboard:

• Includes an open-source local dashboard for offline visualization of training logs.

Usage Instructions

Step 1: Set Up Online Tracking (Optional)

Install:

pip install swanlab

To use SwanLab's online tracking, log in to the SwanLab website and obtain your API key from the Settings page. Then, authenticate using the following command:

swanlab login

If you prefer offline mode, skip this step.

Step 2: Configure SwanLab as the Logger

To enable SwanLab as the experiment tracker, Please execute the following command to enable swanlab:

"""
Tested on:
pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
pip install paddlenlp==3.0.0b4
"""
from paddlenlp.trl import SFTConfig, SFTTrainer
from datasets import load_dataset

dataset = load_dataset("ZHUI/alpaca_demo", split="train")

training_args = SFTConfig(
    output_dir="Qwen/Qwen2.5-0.5B-SFT",
    device="gpu",
    per_device_train_batch_size=1,
    logging_steps=20,
    report_to="swanlab",
    )

trainer = SFTTrainer(
    args=training_args,
    model="Qwen/Qwen2.5-0.5B-Instruct",
    train_dataset=dataset,
)

trainer.train()

Then, You can now happily use SwanLab for experiment tracking!

Step 3: View Training Logs

After logging in, you will see a confirmation message:

• Online Tracking: View logs on the SwanLab website.

For more details, refer to the SwanLab Cloud Documentation.

• Offline Tracking: Use the local dashboard to visualize logs:

swanlab watch

For advanced configurations, such as setting a custom port, refer to the Offline Dashboard Documentation and CLI Documentation.

Impact

• Provides a lightweight, flexible, and user-friendly experiment tracking solution.
• Supports both online and offline use cases, making it suitable for environments with restricted internet access.
• Enhances hardware monitoring capabilities for better resource utilization.

[paddle-bot]

Thanks for your contribution!

[CLAassistant]

All committers have signed the CLA.

[Zeyi-Lin]

@ZHUI Hey, 🤔it seems that the test failure was not caused by my commits. Could you please run the test again? 😄Thank you!

[ZHUI]

Ok, i'll re-run those tests.