swanlab.init

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

Parameter	Description
project	(str) Project name. If not specified, the name of the running directory is used.
workspace	(str) Workspace. By default, the experiment is synchronized to your personal space. If you want to upload to an organization, fill in the organization's username.
experiment_name	(str) Experiment name. If not specified, it defaults to "exp". The full experiment name is composed of experiment_name + "_" + suffix.
description	(str) Experiment description. If not specified, it defaults to None.
config	(dict, str) Experiment configuration. You can record some hyperparameters and other information here. Supports passing in configuration file paths, supports yaml and json files.
logdir	(str) Log file storage path. Defaults to swanlog.
suffix	(str, None, bool) Suffix for experiment_name. The full experiment name is composed of experiment_name and suffix. The default value is "default", which means the default suffix rule is '%b%d-%h-%m-%s', for example: Feb03_14-45-37. Setting it to None or False will not add a suffix.
mode	(str) Sets the mode for creating the SwanLab experiment. Options include "cloud", "local", "disabled". Defaults to "cloud". cloud: Uploads the experiment to the cloud. local: Does not upload to the cloud but records the experiment locally. disabled: Does not upload or record.
load	(str) Path to the configuration file to load. Supports yaml and json files.
public	(bool) Sets the visibility of the SwanLab project created directly by code. Defaults to False, i.e., private.

Introduction

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

• swanlab.init() generates a new background process to record data into the experiment. By default, it also synchronizes the data to swanlab.pro so that you can see the visualization results online in real-time.

• Before using swanlab.log() to record data, you need to 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, we will automatically call swanlab.finish to end the SwanLab experiment. However, if swanlab.init() is called from a subprocess, such as in a Jupyter notebook, you must explicitly call swanlab.finish at the end of the subprocess.

import swanlab

swanlab.init()
swanlab.finish()

More Usage

Setting Project, Experiment Name, and Description

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

Setting the Log File Save Location

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

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

Adding Experiment-related Metadata to the Experiment Configuration

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

Uploading to an Organization

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

Deprecated Parameters

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