image-match/python/py/Lib/site-packages/wandb/__init__.py

"""Use wandb to track machine learning work.

Train and fine-tune models, manage models from experimentation to production.

For guides and examples, see https://docs.wandb.ai.

For scripts and interactive notebooks, see https://github.com/wandb/examples.

For reference documentation, see https://docs.wandb.ai/models/ref/python.
"""
from __future__ import annotations

__version__ = "0.26.0"


from wandb.errors import Error

# This needs to be early as other modules call it.
from wandb.errors.term import termsetup, termlog, termerror, termwarn

# Configure the logger as early as possible for consistent behavior.
from wandb.sdk.lib import wb_logging as _wb_logging
_wb_logging.configure_wandb_logger()

from wandb import sdk as wandb_sdk

import wandb

wandb.wandb_lib = wandb_sdk.lib  # type: ignore

init = wandb_sdk.init
setup = wandb_sdk.setup
attach = _attach = wandb_sdk._attach
teardown = _teardown = wandb_sdk.teardown
finish = wandb_sdk.finish
join = finish
login = wandb_sdk.login
helper = wandb_sdk.helper
sweep = wandb_sdk.sweep
controller = wandb_sdk.controller
require = wandb_sdk.require
Artifact = wandb_sdk.Artifact
AlertLevel = wandb_sdk.AlertLevel
Settings = wandb_sdk.Settings
Config = wandb_sdk.Config

from wandb.apis import InternalApi, PublicApi
from wandb.errors import CommError, UsageError

from wandb.sdk.lib import preinit as _preinit
from wandb.sdk.lib import lazyloader as _lazyloader

from wandb.integration.torch import wandb_torch

from wandb.sdk.data_types._private import _cleanup_media_tmp_dir

_cleanup_media_tmp_dir()

from wandb.data_types import Graph
from wandb.data_types import Image
from wandb.data_types import Plotly
from wandb.data_types import Video
from wandb.data_types import Audio
from wandb.data_types import Table
from wandb.data_types import Html
from wandb.data_types import box3d
from wandb.data_types import Object3D
from wandb.data_types import Molecule
from wandb.data_types import Histogram
from wandb.data_types import Classes
from wandb.data_types import JoinedTable

from wandb.wandb_agent import agent

from wandb.plot import visualize, plot_table
from wandb.integration.sagemaker import sagemaker_auth
from wandb.sdk.internal import profiler
from wandb.sdk.wandb_run import Run

# Artifact import types
from wandb.sdk.artifacts.artifact_ttl import ArtifactTTL


# globals
Api = PublicApi
api = InternalApi()
run: Run | None = None
config = _preinit.PreInitObject("wandb.config", wandb_sdk.wandb_config.Config)
summary = _preinit.PreInitObject("wandb.summary", wandb_sdk.wandb_summary.Summary)
log = _preinit.PreInitCallable("wandb.log", Run.log)  # type: ignore
watch = _preinit.PreInitCallable("wandb.watch", Run.watch)  # type: ignore
unwatch = _preinit.PreInitCallable("wandb.unwatch", Run.unwatch)  # type: ignore
save = _preinit.PreInitCallable("wandb.save", Run.save)  # type: ignore
restore = wandb_sdk.wandb_run.restore
use_artifact = _preinit.PreInitCallable(
    "wandb.use_artifact", Run.use_artifact  # type: ignore
)
log_artifact = _preinit.PreInitCallable(
    "wandb.log_artifact", Run.log_artifact  # type: ignore
)
log_model = _preinit.PreInitCallable(
    "wandb.log_model", Run.log_model  # type: ignore
)
use_model = _preinit.PreInitCallable(
    "wandb.use_model", Run.use_model  # type: ignore
)
link_model = _preinit.PreInitCallable(
    "wandb.link_model", Run.link_model  # type: ignore
)
define_metric = _preinit.PreInitCallable(
    "wandb.define_metric", Run.define_metric  # type: ignore
)

mark_preempting = _preinit.PreInitCallable(
    "wandb.mark_preempting", Run.mark_preempting  # type: ignore
)

alert = _preinit.PreInitCallable("wandb.alert", Run.alert)  # type: ignore
pin_config_keys = _preinit.PreInitCallable(
    "wandb.pin_config_keys", Run.pin_config_keys  # type: ignore
)

# record of patched libraries
patched = {"tensorboard": [], "keras": [], "gym": []}  # type: ignore

keras = _lazyloader.LazyLoader("wandb.keras", globals(), "wandb.integration.keras")
sklearn = _lazyloader.LazyLoader("wandb.sklearn", globals(), "wandb.sklearn")
tensorflow = _lazyloader.LazyLoader(
    "wandb.tensorflow", globals(), "wandb.integration.tensorflow"
)
xgboost = _lazyloader.LazyLoader(
    "wandb.xgboost", globals(), "wandb.integration.xgboost"
)
catboost = _lazyloader.LazyLoader(
    "wandb.catboost", globals(), "wandb.integration.catboost"
)
tensorboard = _lazyloader.LazyLoader(
    "wandb.tensorboard", globals(), "wandb.integration.tensorboard"
)
gym = _lazyloader.LazyLoader("wandb.gym", globals(), "wandb.integration.gym")
lightgbm = _lazyloader.LazyLoader(
    "wandb.lightgbm", globals(), "wandb.integration.lightgbm"
)
jupyter = _lazyloader.LazyLoader("wandb.jupyter", globals(), "wandb.jupyter")
sacred = _lazyloader.LazyLoader("wandb.sacred", globals(), "wandb.integration.sacred")


def ensure_configured():
    global api
    api = InternalApi()


def set_trace():
    import pdb

    pdb.set_trace()


if wandb_sdk.lib.ipython.in_notebook():
    from IPython import get_ipython  # type: ignore[import-not-found]

    jupyter._load_ipython_extension(get_ipython())


if "dev" in __version__:
    import wandb.env
    import os

    # Disable error reporting in dev versions.
    os.environ[wandb.env.ERROR_REPORTING] = os.environ.get(
        wandb.env.ERROR_REPORTING,
        "false",
    )


__all__ = (
    "__version__",
    "init",
    "finish",
    "setup",
    "save",
    "sweep",
    "controller",
    "agent",
    "config",
    "log",
    "summary",
    "join",
    "Api",
    "Graph",
    "Image",
    "Plotly",
    "Video",
    "Audio",
    "Table",
    "Html",
    "box3d",
    "Object3D",
    "Molecule",
    "Histogram",
    "ArtifactTTL",
    "log_artifact",
    "use_artifact",
    "log_model",
    "use_model",
    "link_model",
    "define_metric",
    "watch",
    "unwatch",
    "plot_table",
    "Run",
)