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,
    settings: Settings = None,
    id: str = None,
    resume: Union[Literal['must', 'allow', 'never'], bool] = None,
    reinit: bool = 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).
tags	(list) Tags for the experiment. Can pass a list of strings, and the tags will be displayed in the tag bar at the top of the experiment.
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", "offline", or "disabled". Default is "cloud". cloud: Uploads the experiment to the cloud (public or private deployment). offline: Only records experiment data locally. 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.
tags	(list) Tags for the experiment.
settings	(dict) Settings for the experiment. Supports passing a swanlab.Settings object.
id	(str) The ID of the last experiment. Used to resume the last experiment. Must be a 21-character string.
resume	(str) Resume mode. Can be "must", "allow", "never", or True/False. Default is None. must: You must pass the id parameter, and the experiment must exist. allow: If an experiment exists, it will be resumed. Otherwise, a new experiment will be created. never: The id parameter cannot be passed, and a new experiment will be created. (This is equivalent to not enabling resume.)
reinit	(bool) Whether to reinitialize the experiment. If True, the last experiment will be finished every time swanlab.init() is called; default is None.

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]
)

Resume Training

"Resume Training" refers to the ability to resume training from a previous state. If you previously had an experiment with the status of completed or interrupted, and you need to add more experimental data, you can use the resume and id parameters to restore this experiment.

swanlab.init(
    resume=True,
    id="14pk4qbyav4toobziszli",  # id must be a 21-character string
)

The experiment ID can be found in the experiment's "Environment" tab or in the URL. It must be a 21-character string.

Use Cases for resume

1. The previous training process was interrupted. You want to continue training based on a checkpoint and have the experiment chart continue from the previous swanlab experiment, rather than creating a new one.
2. Training and evaluation are split into two separate processes, but you want both the evaluation and training records to appear in the same swanlab experiment.
3. Some configuration parameters were incorrectly filled out, and you want to update them.

⚠️ Notes

1. Experiments generated by cloning a project cannot be resumed.

Breakpoint continuation supports three modes:

1. allow: If an experiment corresponding to the provided id exists under the project, it will be resumed. Otherwise, a new experiment will be created.
2. must: If an experiment corresponding to the provided id exists under the project, it will be resumed. Otherwise, an error will be thrown.
3. never: The id parameter cannot be passed, and a new experiment will be created instead. (This is equivalent to not enabling resume.)

INFO

Setting resume=True has the same effect as resume="allow".
Setting resume=False has the same effect as resume="never".

Test code:

import swanlab

run = swanlab.init()
swanlab.log({"loss": 2, "acc": 0.4})
run.finish()

run = swanlab.init(resume=True, id=run.id)
swanlab.log({"loss": 0.2, "acc": 0.9})

Deprecated Parameters

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