如何在Python中获取并打印Wandb Sweep的URL？

已知在Wandb中可以通过wandb.run.get_url()获取单个Run实例的URL，若已持有sweep_id，可通过以下两种方式在Python中获取并打印对应的Sweep URL：

方法一：通过Sweep对象调用API获取 #

创建Sweep后，通过wandb.Sweep类实例化得到对应Sweep对象，再调用其get_url()方法即可，逻辑和获取Run URL一致。

方法二：手动拼接URL #

根据Wandb的URL规则，直接用已知的entity、project和sweep_id拼接出完整URL，格式为：
https://wandb.ai/{entity}/{project}/sweeps/{sweep_id}

完整示例代码 #

"""
Main Idea:
- create sweep with a sweep config & get sweep_id for the agents (note, this creates a sweep in wandb's website)
- create agent to run a setting of hps by giving it the sweep_id (that matches the sweep in the wandb website)
- keep running agents with sweep_id until you're done

note:
    - Each individual training session with a specific set of hyperparameters in a sweep is considered a wandb run.

ref:
    - read: https://docs.wandb.ai/guides/sweeps
"""

import wandb
from pprint import pprint
import torch

sweep_config: dict = {
    "project": "playground",
    "entity": "your_wanbd_username",
    "name": "my-ultimate-sweep",
    "metric":
        {"name": "train_loss",
         "goal": "minimize"}
    ,
    "method": "random",
    "parameters": None,  # not set yet
}

parameters = {
    'optimizer': {
        'values': ['adam', 'adafactor']}
    ,
    'scheduler': {
        'values': ['cosine', 'none']}
    ,
    'lr': {
        "distribution": "log_uniform_values",
        "min": 1e-6,
        "max": 0.2}
    ,
    'batch_size': {
        'distribution': 'q_log_uniform_values',
        'q': 8,
        'min': 32,
        'max': 256,
    }
    ,
    'num_its': {'value': 5}
}
sweep_config['parameters'] = parameters
pprint(sweep_config)

# 创建sweep并获取sweep_id
sweep_id = wandb.sweep(sweep_config)
print(f'{sweep_id=}')

# 方法1：通过Sweep对象获取URL
sweep = wandb.Sweep(sweep_id, entity=sweep_config["entity"], project=sweep_config["project"])
sweep_url = sweep.get_url()
print(f"Sweep URL: {sweep_url}")

# 方法2：手动拼接URL
manual_sweep_url = f"https://wandb.ai/{sweep_config['entity']}/{sweep_config['project']}/sweeps/{sweep_id}"
print(f"手动拼接的Sweep URL: {manual_sweep_url}")


def my_train_func():
    run = wandb.init(config=sweep_config)
    print(f'{run=}')
    pprint(f'{wandb.config=}')
    lr = wandb.config.lr
    num_its = wandb.config.num_its

    train_loss: float = 8.0 + torch.rand(1).item()
    for i in range(num_its):
        update_step: float = lr * torch.rand(1).item()
        wandb.log({"lr": lr, "train_loss": train_loss - update_step})
    run.finish()


# 启动sweep agent，执行5次训练
wandb.agent(sweep_id, function=my_train_func, count=5)

内容的提问来源于stack exchange，提问作者Charlie Parker