swanlab.init

init(
    project: str = None,
    workspace: str = None,
    experiment_name: str = None,
    description: str = None,
    config: Union[dict, str] = None,
    logdir: str = None,
    mode: str = "cloud",
    load: str = None,
    public: bool = None,
    callbacks: list = None,
    **kwargs,
)

Parameter	Description
project	(str) The name of the project. If not specified, the name of the current working directory will be used.
workspace	(str) The workspace. By default, experiments are synchronized to your personal space. If you want to upload to an organization, specify the organization's username.
experiment_name	(str) The name of the experiment. If not specified, it will default to a format like "swan-1" (animal name + sequence number).
description	(str) A description of the experiment. If not specified, it defaults to None.
config	(dict, str) Configuration for the experiment. You can record hyperparameters and other information here. Supports passing a configuration file path (yaml or json).
logdir	(str) The path to store offline dashboard log files. Defaults to swanlog.
mode	(str) Sets the mode for creating SwanLab experiments. Options are "cloud", "local", or "disabled". Default is "cloud". cloud: Uploads the experiment to the cloud (public or private deployment). local: Does not upload to the cloud but records experiment information locally. disabled: Neither uploads nor records.
load	(str) The path to a configuration file to load. Supports yaml and json files.
public	(bool) Sets the visibility of the SwanLab project created directly via code. Default is False (private).
callbacks	(list) Sets experiment callback functions. Supports subclasses of swankit.callback.SwanKitCallback.
name	(str) Same effect as experiment_name. Lower priority than experiment_name.
notes	(str) Same effect as description. Lower priority than description.

Introduction

• In machine learning workflows, you can add swandb.init() at the beginning of training and testing scripts. SwanLab will track every step of the machine learning process.

• swanlab.init() spawns a new background process to log data to the experiment. By default, it also synchronizes the data to swanlab.cn, allowing you to view real-time visualizations online.

• Before using swanlab.log() to record data, you must call swanlab.init():

import swanlab

swanlab.init()
swanlab.log({"loss": 0.1846})

• Calling swanlab.init() returns an object of type SwanLabRun, which can also perform log operations:

import swanlab

run = swanlab.init()
run.log({"loss": 0.1846})

• At the end of the script, swanlab.finish will be automatically called to conclude the SwanLab experiment. However, if swanlab.init() is called from a subprocess (e.g., in a Jupyter notebook), you must explicitly call swanlab.finish at the end of the subprocess.

import swanlab

swanlab.init()
swanlab.finish()

Additional Usage

Setting Project, Experiment Name, and Description

swanlab.init(
    project="cats-detection",
    experiment_name="YoloX-baseline",
    description="Baseline experiment for the YoloX detection model, primarily for subsequent comparisons.",
)

Setting the Log File Save Location

Only valid when mode="local"

The following code demonstrates how to save log files to a custom directory:

swanlab.init(
    logdir="path/to/my_custom_dir",
    mode="local",
)

Adding Experiment Metadata to the Configuration

swanlab.init(
    config={
        "learning-rate": 1e-4,
        "model": "CNN",
    }
)

Uploading to an Organization

swanlab.init(
    workspace="[organization's username]"
)

Plugins

For more information about plugins, refer to the Plugins documentation.

from swanlab.plugin.notification import EmailCallback

email_callback = EmailCallback(...)

swanlab.init(
    callbacks=[email_callback]
)

Deprecated Parameters

• cloud: Replaced by the mode parameter in v0.3.4. The parameter is still available and will override the mode setting.