run

Run(
    settings: Settings,
    config: (dict[str, Any] | None) = None,
    sweep_config: (dict[str, Any] | None) = None,
    launch_config: (dict[str, Any] | None) = None
) -> None

import wandb

run = wandb.init()

import wandb

assert wandb.run is None

wandb.init()

assert wandb.run is not None

import wandb

wandb.init()
wandb.finish()

assert wandb.run is None

with wandb.init() as run:
    pass  # データをここでログに記録

assert wandb.run is None

alert(
    title: str,
    text: str,
    level: (str | AlertLevel | None) = None,
    wait_duration: (int | float | timedelta | None) = None
) -> None

define_metric(
    name: str,
    step_metric: (str | wandb_metric.Metric | None) = None,
    step_sync: (bool | None) = None,
    hidden: (bool | None) = None,
    summary: (str | None) = None,
    goal: (str | None) = None,
    overwrite: (bool | None) = None
) -> wandb_metric.Metric

detach() -> None

display(
    height: int = 420,
    hidden: bool = (False)
) -> bool

finish(
    exit_code: (int | None) = None,
    quiet: (bool | None) = None
) -> None

finish_artifact(
    artifact_or_path: (Artifact | str),
    name: (str | None) = None,
    type: (str | None) = None,
    aliases: (list[str] | None) = None,
    distributed_id: (str | None) = None
) -> Artifact

get_project_url() -> (str | None)

get_sweep_url() -> (str | None)

get_url() -> (str | None)

join(
    exit_code: (int | None) = None
) -> None

link_artifact(
    artifact: Artifact,
    target_path: str,
    aliases: (list[str] | None) = None
) -> None

link_model(
    path: StrPath,
    registered_model_name: str,
    name: (str | None) = None,
    aliases: (list[str] | None) = None
) -> None

run.link_model(
    path="/local/directory",
    registered_model_name="my_reg_model",
    name="my_model_artifact",
    aliases=["production"],
)

run.link_model(
    path="/local/directory",
    registered_model_name="my_entity/my_project/my_reg_model",
    name="my_model_artifact",
    aliases=["production"],
)

run.link_model(
    path="/local/directory",
    registered_model_name="my_reg_model",
    name="my_entity/my_project/my_model_artifact",
    aliases=["production"],
)

log(
    data: dict[str, Any],
    step: (int | None) = None,
    commit: (bool | None) = None,
    sync: (bool | None) = None
) -> None

run.log(
    {
        "train/accuracy": 0.9,
        "train/loss": 30,
        "validate/accuracy": 0.8,
        "validate/loss": 20,
    }
)

# 例: X軸として使用する "epoch" メトリクスをログする方法。
run.log({"epoch": 40, "train-loss": 0.5})

# 通常の使用法：
run.log({"train-loss": 0.5, "accuracy": 0.8})
run.log({"train-loss": 0.4, "accuracy": 0.9})

# 自動インクリメントなしの暗黙のステップ：
run.log({"train-loss": 0.5}, commit=False)
run.log({"accuracy": 0.8})
run.log({"train-loss": 0.4}, commit=False)
run.log({"accuracy": 0.9})

# 明示的なステップ：
run.log({"train-loss": 0.5}, step=current_step)
run.log({"accuracy": 0.8}, step=current_step)
current_step += 1
run.log({"train-loss": 0.4}, step=current_step)
run.log({"accuracy": 0.9}, step=current_step)

import wandb

run = wandb.init()
run.log({"accuracy": 0.9, "epoch": 5})

import wandb

run = wandb.init()
run.log({"loss": 0.2}, commit=False)
# 別の場所でこのステップを報告する準備ができたとき：
run.log({"accuracy": 0.8})

import numpy as np
import wandb

# グラデーションをランダムに正規分布からサンプリング
gradients = np.random.randn(100, 100)
run = wandb.init()
run.log({"gradients": wandb.Histogram(gradients)})

import numpy as np
import wandb

run = wandb.init()
examples = []
for i in range(3):
    pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
    image = wandb.Image(pixels, caption=f"random field {i}")
    examples.append(image)
run.log({"examples": examples})

import numpy as np
from PIL import Image as PILImage
import wandb

run = wandb.init()
examples = []
for i in range(3):
    pixels = np.random.randint(
        low=0,
        high=256,
        size=(100, 100, 3),
        dtype=np.uint8,
    )
    pil_image = PILImage.fromarray(pixels, mode="RGB")
    image = wandb.Image(pil_image, caption=f"random field {i}")
    examples.append(image)
run.log({"examples": examples})

import numpy as np
import wandb

run = wandb.init()
# 軸 (time, channel, height, width)
frames = np.random.randint(
    low=0,
    high=256,
    size=(10, 3, 100, 100),
    dtype=np.uint8,
)
run.log({"video": wandb.Video(frames, fps=4)})

from matplotlib import pyplot as plt
import numpy as np
import wandb

run = wandb.init()
fig, ax = plt.subplots()
x = np.linspace(0, 10)
y = x * x
ax.plot(x, y)  # プロットy = x^2
run.log({"chart": fig})

import wandb

run = wandb.init()
run.log({"pr": wandb.plot.pr_curve(y_test, y_probas, labels)})

import wandb

run = wandb.init()
run.log(
    {
        "generated_samples": [
            wandb.Object3D(open("sample.obj")),
            wandb.Object3D(open("sample.gltf")),
            wandb.Object3D(open("sample.glb")),
        ]
    }
)

log_artifact(
    artifact_or_path: (Artifact | StrPath),
    name: (str | None) = None,
    type: (str | None) = None,
    aliases: (list[str] | None) = None,
    tags: (list[str] | None) = None
) -> Artifact

log_code(
    root: (str | None) = ".",
    name: (str | None) = None,
    include_fn: (Callable[[str, str], bool] | Callable[[str], bool]) = _is_py_requirements_or_dockerfile,
    exclude_fn: (Callable[[str, str], bool] | Callable[[str], bool]) = filenames.exclude_wandb_fn
) -> (Artifact | None)

run.log_code()

run.log_code(
    "../",
    include_fn=lambda path: path.endswith(".py") or path.endswith(".ipynb"),
    exclude_fn=lambda path, root: os.path.relpath(path, root).startswith(
        "cache/"
    ),
)

log_model(
    path: StrPath,
    name: (str | None) = None,
    aliases: (list[str] | None) = None
) -> None

run.log_model(
    path="/local/directory",
    name="my_model_artifact",
    aliases=["production"],
)

run.log_model(
    path="/local/directory",
    name="my_entity/my_project/my_model_artifact",
    aliases=["production"],
)

mark_preempting() -> None

project_name() -> str

restore(
    name: str,
    run_path: (str | None) = None,
    replace: bool = (False),
    root: (str | None) = None
) -> (None | TextIO)

save(
    glob_str: (str | os.PathLike | None) = None,
    base_path: (str | os.PathLike | None) = None,
    policy: PolicyName = "live"
) -> (bool | list[str])

wandb.save("these/are/myfiles/*")
# => run内の"these/are/myfiles/"フォルダにファイルを保存します。

wandb.save("these/are/myfiles/*", base_path="these")
# => run内の"are/myfiles/"フォルダにファイルを保存します。

wandb.save("/User/username/Documents/run123/*.txt")
# => run内の"run123/"フォルダにファイルを保存します。以下の注意を参照してください。

wandb.save("/User/username/Documents/run123/*.txt", base_path="/User")
# => run内の"username/Documents/run123/"フォルダにファイルを保存します。

wandb.save("files/*/saveme.txt")
# => 各 "saveme.txt" ファイルを "files/" の適切なサブディレクトリに保存します。

status() -> RunStatus

to_html(
    height: int = 420,
    hidden: bool = (False)
) -> str

unwatch(
    models: (torch.nn.Module | Sequence[torch.nn.Module] | None) = None
) -> None

upsert_artifact(
    artifact_or_path: (Artifact | str),
    name: (str | None) = None,
    type: (str | None) = None,
    aliases: (list[str] | None) = None,
    distributed_id: (str | None) = None
) -> Artifact

use_artifact(
    artifact_or_name: (str | Artifact),
    type: (str | None) = None,
    aliases: (list[str] | None) = None,
    use_as: (str | None) = None
) -> Artifact

use_model(
    name: str
) -> FilePathStr

run.use_model(
    name="my_model_artifact:latest",
)

run.use_model(
    name="my_project/my_model_artifact:v0",
)

run.use_model(
    name="my_entity/my_project/my_model_artifact:<digest>",
)

run.use_model(
    name="my_entity/my_project/my_model_artifact",
)

watch(
    models: (torch.nn.Module | Sequence[torch.nn.Module]),
    criterion: (torch.F | None) = None,
    log: (Literal['gradients', 'parameters', 'all'] | None) = "gradients",
    log_freq: int = 1000,
    idx: (int | None) = None,
    log_graph: bool = (False)
) -> None

__enter__() -> Run

__exit__(
    exc_type: type[BaseException],
    exc_val: BaseException,
    exc_tb: TracebackType
) -> bool