wehub-resource-sync
124 lines
3.8 KiB
Python
124 lines
3.8 KiB
Python
import os
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import platform
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import click
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import importlib_metadata
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import yaml
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from packaging.requirements import Requirement
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import mlflow
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from mlflow.utils.databricks_utils import get_databricks_runtime_version
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def doctor(mask_envs=False):
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"""Prints out useful information for debugging issues with MLflow.
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Args:
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mask_envs: If True, mask the MLflow environment variable values
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(e.g. `"MLFLOW_ENV_VAR": "***"`) in the output to prevent leaking sensitive
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information.
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.. warning::
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- This API should only be used for debugging purposes.
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- The output may contain sensitive information such as a database URI containing a password.
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.. code-block:: python
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:caption: Example
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import mlflow
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with mlflow.start_run():
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mlflow.doctor()
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.. code-block:: text
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:caption: Output
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System information: Linux #58~20.04.1-Ubuntu SMP Thu Oct 13 13:09:46 UTC 2022
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Python version: 3.8.13
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MLflow version: 2.0.1
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MLflow module location: /usr/local/lib/python3.8/site-packages/mlflow/__init__.py
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Tracking URI: sqlite:///mlflow.db
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Registry URI: sqlite:///mlflow.db
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MLflow environment variables:
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MLFLOW_TRACKING_URI: sqlite:///mlflow.db
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MLflow dependencies:
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Flask: 2.2.2
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Jinja2: 3.0.3
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alembic: 1.8.1
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click: 8.1.3
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cloudpickle: 2.2.0
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databricks-cli: 0.17.4.dev0
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docker: 6.0.0
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entrypoints: 0.4
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gitpython: 3.1.29
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gunicorn: 20.1.0
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importlib-metadata: 5.0.0
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markdown: 3.4.1
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matplotlib: 3.6.1
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numpy: 1.23.4
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packaging: 21.3
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pandas: 1.5.1
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protobuf: 3.19.6
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pyarrow: 9.0.0
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pytz: 2022.6
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pyyaml: 6.0
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querystring-parser: 1.2.4
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requests: 2.28.1
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scikit-learn: 1.1.3
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scipy: 1.9.3
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shap: 0.41.0
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sqlalchemy: 1.4.42
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sqlparse: 0.4.3
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"""
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items = [
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("System information", " ".join((platform.system(), platform.version()))),
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("Python version", platform.python_version()),
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("MLflow version", mlflow.__version__),
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("MLflow module location", mlflow.__file__),
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("Tracking URI", mlflow.get_tracking_uri()),
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("Registry URI", mlflow.get_registry_uri()),
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]
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if (runtime := get_databricks_runtime_version()) is not None:
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items.append(("Databricks runtime version", runtime))
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if active_run := mlflow.active_run():
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items.extend([
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("Active experiment ID", active_run.info.experiment_id),
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("Active run ID", active_run.info.run_id),
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("Active run artifact URI", active_run.info.artifact_uri),
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])
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mlflow_envs = {
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k: ("***" if mask_envs else v) for k, v in os.environ.items() if k.startswith("MLFLOW_")
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}
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if mlflow_envs:
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items.append((
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"MLflow environment variables",
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yaml.dump({"_": mlflow_envs}, indent=2).replace("'", "").lstrip("_:").rstrip("\n"),
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))
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try:
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requires = importlib_metadata.requires("mlflow")
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except importlib_metadata.PackageNotFoundError:
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requires = importlib_metadata.requires("mlflow-skinny")
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mlflow_dependencies = {}
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for req in requires:
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req = Requirement(req)
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try:
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dist = importlib_metadata.distribution(req.name)
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except importlib_metadata.PackageNotFoundError:
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continue
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else:
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mlflow_dependencies[req.name] = dist.version
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items.append((
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"MLflow dependencies",
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yaml.dump({"_": mlflow_dependencies}, indent=2).replace("'", "").lstrip("_:").rstrip("\n"),
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))
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for key, val in items:
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click.secho(key, fg="blue", nl=False)
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click.echo(f": {val}")
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