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Weights & Biases ​

Weights & Biases (Wandb) is a platform for experiment tracking, model optimization, and collaboration in machine learning and deep learning projects. W&B provides powerful tools to log and visualize experimental results, helping data scientists and researchers better manage and share their work.

wandb

Synchronization Tutorials for Other Tools

You can sync Wandb projects to SwanLab in two ways:

  1. Live Synchronization: If your current project uses Wandb for experiment tracking, you can use the swanlab.sync_wandb() command to log metrics to SwanLab simultaneously while running the training script.
  2. Convert Existing Projects: If you want to replicate a Wandb project on SwanLab, you can use swanlab convert to migrate an existing Wandb project to SwanLab.

INFO

The current version only supports converting scalar charts.

1. Live Synchronization ​

1.1 Add the sync_wandb Command ​

Add the swanlab.sync_wandb() command anywhere in your code before wandb.init() to synchronize Wandb metrics to SwanLab during training.

python
import swanlab  

swanlab.sync_wandb()  

...  

wandb.init()

With this implementation, wandb.init() will simultaneously initialize SwanLab, using the same project, name, and config parameters from wandb.init(). Therefore, you don’t need to manually initialize SwanLab.

INFO

sync_wandb supports two parameters:

  • mode: SwanLab logging mode, supporting cloud, local, and disabled.
  • wandb_run: If set to False, data will not be uploaded to Wandb (equivalent to wandb.init(mode="offline")).

1.2 Alternative Implementation ​

Another approach is to manually initialize SwanLab first before running Wandb code.

python
import swanlab  

swanlab.init(...)  
swanlab.sync_wandb()  

...  

wandb.init()

In this implementation, the project name, experiment name, and configuration will follow the project, experiment_name, and config parameters from swanlab.init(). Subsequent wandb.init() parameters for project and name will be ignored, while config will update swanlab.config.

1.3 Test Code ​

python
import wandb  
import random  
import swanlab  

swanlab.sync_wandb()  
# swanlab.init(project="sync_wandb")  

wandb.init(  
  project="test",  
  config={"a": 1, "b": 2},  
  name="test",  
)  

epochs = 10  
offset = random.random() / 5  
for epoch in range(2, epochs):  
  acc = 1 - 2 ** -epoch - random.random() / epoch - offset  
  loss = 2 ** -epoch + random.random() / epoch + offset  

  wandb.log({"acc": acc, "loss": loss})

alt text

2. Convert Existing Projects ​

2.1 Locate Your project, entity, and runid on wandb.ai ​

The conversion requires project, entity, and optionally runid.
Locations of project and entity:
alt text

Location of runid:
alt text

2.2 Method 1: Command-Line Conversion ​

First, ensure you are logged into Wandb and have access to the target project.

Conversion command:

bash
swanlab convert -t wandb --wb-project [WANDB_PROJECT_NAME] --wb-entity [WANDB_ENTITY]

Supported parameters:

  • -t: Conversion type (wandb or tensorboard).
  • -p: SwanLab project name.
  • -w: SwanLab workspace name.
  • --mode: (str) Logging mode (default: "cloud"), options: ["cloud", "local", "offline", "disabled"].
  • -l: Log directory path.
  • --wb-project: Wandb project name to convert.
  • --wb-entity: Wandb entity (username/team) where the project resides.
  • --wb-runid: Wandb Run ID (specific experiment under the project).

If --wb-runid is omitted, all Runs under the project will be converted. If specified, only the selected Run will be converted.

Asynchronous Conversion (Download Data Locally First, Then Upload to SwanLab)

  1. Download data locally:
bash
swanlab convert --mode 'offline' -t wandb --wb-project [WANDB_PROJECT_NAME] --wb-entity [WANDB_ENTITY]
  1. Upload to SwanLab:
bash
swanlab sync [LOG_DIRECTORY_PATH]

SwanLab Sync Documentation

2.3 Method 2: In-Code Conversion ​

python
from swanlab.converter import WandbConverter  

wb_converter = WandbConverter()  
# wb_runid is optional  
wb_converter.run(wb_project="WANDB_PROJECT_NAME", wb_entity="WANDB_USERNAME")

This achieves the same result as command-line conversion.

WandbConverter parameters:

  • project: SwanLab project name.
  • workspace: SwanLab workspace name.
  • mode: (str) Logging mode (default: "cloud"), options: ["cloud", "local", "offline", "disabled"].
  • logdir: Log directory path.

WandbConverter.run parameters:

  • wb_project: Wandb project name.
  • wb_entity: Wandb entity (username/team).
  • wb_runid: Wandb Run ID (specific experiment).

Asynchronous Conversion (Download Data Locally First, Then Upload to SwanLab)

  1. Download data locally:
python
from swanlab.converter import WandbConverter  

wb_converter = WandbConverter(mode="offline")  
# wb_runid is optional  
wb_converter.run(wb_project="WANDB_PROJECT_NAME", wb_entity="WANDB_USERNAME")
  1. Upload to SwanLab:
bash
swanlab sync [LOG_DIRECTORY_PATH]

SwanLab Sync Documentation