대규모 로깅과 성능

import wandb

with wandb.init() as run:
    run.log({"loss": 0.42}, commit=True)

import wandb

with wandb.init() as run:
    run.log(
        {
            "a": 1,
            "b": {
                "c": 2,
                "d": {
                    "e": 3,
                    "f": 4,
                },
            },
        }
    )

import wandb

with wandb.init() as run:
    run.log({"a": 1, "b": 2, "c": 3})

import wandb

with wandb.init() as run:
    run.log({"a": 1})
    run.log({"a": 2})
    run.log({"a": 3})

로그 빈도 = 분당 wandb.Run.log() 호출 수

처리량 = 분당 로깅된 포인트 수

처리량 = 로깅된 포인트 수 × 로그 빈도

import wandb

with wandb.init() as run:
    run.log(
        {
            "a": 1,
            "b": {
                "c": "hello",
                "d": [1, 2, 3],
            },
        }
    )

import json
import wandb

with wandb.init(project="wide-values") as run:
    # 권장하지 않음
    run.log({"wide_key": list(range(10000000))})

    # 권장하지 않음
    with open("large_file.json", "r") as f:
        large_data = json.load(f)
        run.log(large_data)

import wandb

with wandb.init(project="metric-frequency") as run:
    # 권장: 관련 스칼라 메트릭을 한 번에 묶어서 로깅하세요
    run.log(
        {
            "loss": 0.12,
            "accuracy": 0.98,
            "lr": 1e-4,
        },
        commit=True,
    )

import json
import wandb

# 권장
with wandb.init(
    project="config-size",
    config={
        "lr": 0.1,
        "batch_size": 32,
        "epochs": 4,
    },
) as run:
    pass

# 권장하지 않음
with wandb.init(
    project="config-size",
    config={
        "large_list": list(range(10000000)),
        "large_string": "a" * 10000000,
    },
) as run:
    pass

# 권장하지 않음
with open("large_config.json", "r") as f:
    large_config = json.load(f)
    wandb.init(config=large_config)

import random
import wandb

with wandb.init(project="basic-intro") as run:
    for epoch in range(10):
        accuracy = 1 - 2 ** -epoch - random.random() / (epoch + 1)
        loss = 2 ** -epoch + random.random() / (epoch + 1)

        if epoch % 5 == 0:
            run.log({"acc": accuracy, "loss": loss})