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 for recording and visualizing experimental results, helping data scientists and researchers better manage and share their work.

You can synchronize projects from Wandb to SwanLab in two ways:

1. Synchronized Tracking: If your current project uses wandb for experiment tracking, you can use the swanlab.sync_wandb() command to synchronize metrics to SwanLab while running the training script.
2. Convert Existing Projects: If you want to copy a project from wandb to SwanLab, you can use swanlab convert to convert an existing project on Wandb to a SwanLab project.

INFO

The current version only supports converting scalar charts.

• 1. Synchronized Tracking
  • 1.1 Add the sync_wandb Command
  • 1.2 Alternative Approach
  • 1.3 Test Code
• 2. Convert Existing Projects
  • 2.1 Locate Your Project, Entity, and Run ID on wandb.ai
  • 2.2 Method 1: Command Line Conversion
  • 2.3 Method 2: Conversion Within Code

1. Synchronized Tracking

1.1 Add the sync_wandb Command

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

import swanlab

swanlab.sync_wandb()

...

wandb.init()

In the above code, wandb.init() will simultaneously initialize swanlab, with the project name, experiment name, and configuration matching the project, name, and config in wandb.init(). Therefore, you do not need to manually initialize swanlab.

INFO

sync_wandb supports two parameters:

• mode: The recording mode of swanlab, supports cloud, local, and disabled.
• wandb_run: If this parameter is set to False, the data will not be uploaded to wandb, equivalent to setting wandb.init(mode="offline").

1.2 Alternative Approach

Another approach is to manually initialize swanlab first, then run the wandb code.

import swanlab

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

...

wandb.init()

In this approach, the project name, experiment name, and configuration will match the project, experiment_name, and config in swanlab.init(). The project and name in the subsequent wandb.init() will be ignored, and the config will be updated in swanlab.config.

1.3 Test Code

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})

2. Convert Existing Projects

2.1 Locate Your Project, Entity, and Run ID on wandb.ai

The project, entity, and run ID are required for conversion (run ID is optional).
The location of the project and entity:

The location of the run ID:

2.2 Method 1: Command Line Conversion

First, ensure that you are logged into wandb in the current environment and have access to the target project.

Conversion command:

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

Supported parameters:

• -t: Conversion type, options are wandb and tensorboard.
• --wb-project: The name of the wandb project to be converted.
• --wb-entity: The space name where the wandb project is located.
• --wb-runid: The ID of the wandb Run (a specific experiment under the project).

If --wb-runid is not provided, all Runs under the specified project will be converted; if provided, only the specified Run will be converted.

2.3 Method 2: Conversion Within Code

from swanlab.converter import WandbConverter

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

The effect is the same as command line conversion.