Logging to stdout and file

This guide will teach you how to configure logging in AutoIntent. By default, it is fully disabled.

It will be demonstrated on toy search_space example:

from pathlib import Path

from autointent import Dataset, Pipeline
from autointent.configs import LoggingConfig

search_space = [
    {
        "node_type": "scoring",
        "target_metric": "scoring_roc_auc",
        "search_space": [
            {
                "module_name": "knn",
                "k": [1],
                "weights": ["uniform"],
                "embedder_config": ["avsolatorio/GIST-small-Embedding-v0"],
            },
        ],
    },
    {
        "node_type": "decision",
        "target_metric": "decision_accuracy",
        "search_space": [
            {"module_name": "threshold", "thresh": [0.5]},
            {"module_name": "argmax"},
        ],
    },
]

log_config = LoggingConfig(project_dir=Path("logging_tutorial"))
pipeline_optimizer = Pipeline.from_search_space(search_space)
pipeline_optimizer.set_config(log_config)

dataset = Dataset.from_hub("DeepPavlov/clinc150_subset")

/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Fully Custom Logging

One can fully customize logging via python’s standard module `logging <https://docs.python.org/3/library/logging.html>`__. Everything you need to do is configure it before AutoIntent execution:

import logging

logging.basicConfig(level="INFO")
pipeline_optimizer.fit(dataset)

INFO:autointent._pipeline._pipeline:starting pipeline optimization...
WARNING:autointent._pipeline._pipeline:Memory storage is not compatible with resuming optimization. Modules from previous runs won't be available. Set dump_modules=True in LoggingConfig to enable proper resuming.
INFO:autointent.nodes._node_optimizer:Starting scoring node optimization...
WARNING:autointent.nodes._node_optimizer:Storage directory must be provided for study persistence.
[I 2025-08-01 06:19:38,993] A new study created in memory with name: scoring
INFO:autointent.nodes._node_optimizer:NodeType.scoring node optimization is finished!
INFO:autointent.nodes._node_optimizer:Starting decision node optimization...
WARNING:autointent.nodes._node_optimizer:Storage directory must be provided for study persistence.
WARNING:autointent.modules.base._base:"argmax" is NOT designed to handle OOS samples, but your data contains it. So, using this method reduces the power of classification.
INFO:autointent.nodes._node_optimizer:NodeType.decision node optimization is finished!
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0])

<autointent.context._context.Context at 0x7f89baa23e20>

See external tutorials and guides about logging module.

Export from AutoIntent

If you don’t have to customize logging, you can export our configuration. Everything you need to do is setup it before AutoIntent execution:

from autointent import setup_logging

setup_logging("INFO", log_filename="tests/logs/my_exp")

The first parameter affects the logs to the standard output stream. The second parameter is optional. If it is specified, then the “DEBUG” messages are logged to the file, regardless of what is specified by the first parameter.