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The previous commit was meant to remove .github/workflows and removed every tracked path instead — 892 files, the whole repo. This puts back the 874 that were never in scope; the eighteen workflow files stay gone, for the reasons that commit gives. Content is byte-identical to 9d2aec9 outside .github/workflows.
76 lines
1.9 KiB
Python
76 lines
1.9 KiB
Python
# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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"""Example showing how to use advanced logging features in Anomalib.
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This example demonstrates how to configure different loggers (TensorBoard,
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WandB, MLflow, Comet) and customize logging behavior.
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"""
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from pathlib import Path
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from anomalib.data import MVTecAD
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from anomalib.engine import Engine
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from anomalib.loggers import AnomalibMLFlowLogger, AnomalibTensorBoardLogger, AnomalibWandbLogger
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from anomalib.models import Patchcore
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# 1. Basic TensorBoard Logging
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# This is the default logger
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engine = Engine(
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logger=AnomalibTensorBoardLogger(save_dir="logs/tensorboard"),
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max_epochs=1,
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)
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# 2. Weights & Biases (WandB) Logging
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# Track experiments with WandB
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engine = Engine(
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logger=AnomalibWandbLogger(
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project="anomalib",
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name="patchcore_experiment",
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save_dir="logs/wandb",
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),
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max_epochs=1,
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)
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# 3. MLflow Logging
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# Track experiments with MLflow
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engine = Engine(
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logger=AnomalibMLFlowLogger(
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experiment_name="anomalib",
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tracking_uri="logs/mlflow",
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),
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max_epochs=1,
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)
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# 4. Multiple Loggers
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# Use multiple loggers simultaneously
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engine = Engine(
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logger=[
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AnomalibTensorBoardLogger(save_dir="logs/tensorboard"),
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AnomalibWandbLogger(project="anomalib", save_dir="logs/wandb"),
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],
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max_epochs=1,
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)
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# 5. Complete Training Example with Logging
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model = Patchcore()
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datamodule = MVTecAD(
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root=Path("./datasets/MVTecAD"),
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category="bottle",
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)
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# Configure engine with logging
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engine = Engine(
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logger=AnomalibTensorBoardLogger(save_dir="logs/tensorboard"),
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max_epochs=1,
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log_graph=True, # Log model graph
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enable_checkpointing=True, # Save model checkpoints
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default_root_dir="results", # Root directory for all outputs
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)
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# Train with logging enabled
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engine.fit(
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model=model,
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datamodule=datamodule,
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)
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