repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
mlflow | mlflow/prophet/__init__.py | .py | """
The ``mlflow.prophet`` module provides an API for logging and loading Prophet models.
This module exports univariate Prophet models in the following flavors:
Prophet (native) format
This is the main flavor that can be accessed with Prophet APIs.
:py:mod:`mlflow.pyfunc`
Produced for use by generic pyfunc-ba... | 405 | 14,768 |
mlflow | mlflow/agno/utils.py | .py | import importlib
import logging
import pkgutil
from agno.models.base import Model
from agno.storage.base import Storage
_logger = logging.getLogger(__name__)
def discover_storage_backends():
# 1. Import all storage modules
import agno.storage as pkg
for _, modname, _ in pkgutil.iter_modules(pkg.__path_... | 52 | 1,455 |
mlflow | mlflow/agno/__init__.py | .py | import inspect
import logging
from mlflow.telemetry.events import AutologgingEvent
from mlflow.telemetry.track import _record_event
from mlflow.utils.annotations import experimental as experimental
from mlflow.utils.autologging_utils import autologging_integration, safe_patch
FLAVOR_NAME = "agno"
_logger = logging.ge... | 113 | 4,117 |
mlflow | mlflow/agno/autolog_v2.py | .py | """
Autologging logic for Agno V2 (>= 2.0.0) using OpenTelemetry instrumentation.
"""
import importlib.metadata as _meta
import logging
from opentelemetry import trace
from opentelemetry.context import Context
from opentelemetry.trace import Tracer, TracerProvider
from mlflow.exceptions import MlflowException
from m... | 127 | 4,527 |
mlflow | mlflow/agno/autolog_v1.py | .py | """
Autologging logic for Agno V1 using MLflow's tracing API.
"""
import logging
from typing import Any
import mlflow
from mlflow.entities import SpanType
from mlflow.entities.span import LiveSpan
from mlflow.tracing.constant import SpanAttributeKey, TokenUsageKey
from mlflow.tracing.utils import construct_full_input... | 194 | 6,385 |
mlflow | mlflow/onnx/__init__.py | .py | """
The ``mlflow.onnx`` module provides APIs for logging and loading ONNX models in the MLflow Model
format. This module exports MLflow Models with the following flavors:
ONNX (native) format
This is the main flavor that can be loaded back as an ONNX model object.
:py:mod:`mlflow.pyfunc`
Produced for use by ge... | 613 | 25,827 |
mlflow | mlflow/models/wheeled_model.py | .py | import os
import platform
import shutil
import subprocess
import sys
import yaml
import mlflow
from mlflow import MlflowClient
from mlflow.environment_variables import MLFLOW_WHEELED_MODEL_PIP_DOWNLOAD_OPTIONS
from mlflow.exceptions import MlflowException
from mlflow.protos.databricks_pb2 import BAD_REQUEST
from mlfl... | 318 | 13,312 |
mlflow | mlflow/models/flavor_backend.py | .py | from abc import ABCMeta, abstractmethod
from mlflow.utils.annotations import developer_stable
@developer_stable
class FlavorBackend:
"""
Abstract class for Flavor Backend.
This class defines the API interface for local model deployment of MLflow model flavors.
"""
__metaclass__ = ABCMeta
de... | 104 | 3,321 |
mlflow | mlflow/models/utils.py | .py | import base64
import datetime as dt
import decimal
import importlib
import json
import logging
import os
import re
import shutil
import sys
import tempfile
import uuid
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Union
import numpy as np
i... | 2,077 | 84,552 |
mlflow | mlflow/models/model_config.py | .py | import os
from typing import Any
import yaml
from mlflow.exceptions import MlflowException
from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
__mlflow_model_config__ = None
class ModelConfig:
"""
ModelConfig used in code to read a YAML configuration file or a dictionary.
Args:
de... | 151 | 5,072 |
mlflow | mlflow/models/python_api.py | .py | import logging
import os
import shutil
from io import StringIO
from typing import ForwardRef, get_args, get_origin
from mlflow.exceptions import MlflowException
from mlflow.models.flavor_backend_registry import get_flavor_backend
from mlflow.utils import env_manager as _EnvManager
from mlflow.utils.databricks_utils im... | 379 | 16,191 |
mlflow | mlflow/models/model.py | .py | import json
import logging
import os
import shutil
import uuid
from datetime import datetime, timezone
from pathlib import Path
from pprint import pformat
from typing import Any, Callable, Literal, NamedTuple
from urllib.parse import urlparse
import yaml
from packaging.requirements import InvalidRequirement, Requireme... | 1,661 | 68,804 |
mlflow | mlflow/models/dependencies_schemas.py | .py | import json
import logging
import warnings
from abc import ABC, abstractmethod
from contextlib import contextmanager
from dataclasses import dataclass, field
from enum import Enum
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from mlflow.models.model import Model
_logger = logging.getLogger(__name__)
... | 298 | 10,208 |
mlflow | mlflow/models/flavor_backend_registry.py | .py | """
Registry of supported flavor backends. Contains a mapping of flavors to flavor backends. This
mapping is used to select suitable flavor when deploying generic MLflow models.
Flavor backend can deploy particular flavor locally to generate predictions, deploy as a local
REST api endpoint, or build a docker image for... | 54 | 2,094 |
mlflow | mlflow/models/docker_utils.py | .py | import logging
import os
import subprocess
from subprocess import Popen
from typing import Literal
from urllib.parse import urlparse
from packaging.version import Version
from mlflow.environment_variables import MLFLOW_DOCKER_OPENJDK_VERSION
from mlflow.utils import env_manager as em
from mlflow.utils.file_utils impo... | 230 | 8,519 |
mlflow | mlflow/models/__init__.py | .py | """
The ``mlflow.models`` module provides an API for saving machine learning models in
"flavors" that can be understood by different downstream tools.
The built-in flavors are:
- :py:mod:`mlflow.catboost`
- :py:mod:`mlflow.dspy`
- :py:mod:`mlflow.h2o`
- :py:mod:`mlflow.langchain`
- :py:mod:`mlflow.lightgbm`
- :py:mod... | 97 | 2,702 |
mlflow | mlflow/models/signature.py | .py | """
The :py:mod:`mlflow.models.signature` module provides an API for specification of model signature.
Model signature defines schema of model input and output. See :py:class:`mlflow.types.schema.Schema`
for more details on Schema and data types.
"""
import inspect
import logging
import re
import warnings
from copy i... | 650 | 25,444 |
mlflow | mlflow/models/auth_policy.py | .py | from mlflow.models.resources import Resource, _ResourceBuilder
class UserAuthPolicy:
"""
A minimal list of scopes that the user should have access to
in order to invoke this model
Note: This is only compatible with Databricks Environment currently.
TODO: Add Databricks Documentation for User Auth... | 80 | 2,305 |
mlflow | mlflow/models/rag_signatures.py | .py | from dataclasses import dataclass, field
from mlflow.models import ModelSignature
from mlflow.types.schema import (
Array,
ColSpec,
DataType,
Object,
Property,
Schema,
)
from mlflow.utils.annotations import deprecated
@deprecated("mlflow.types.llm.ChatMessage")
@dataclass
class Message:
r... | 118 | 3,091 |
mlflow | mlflow/models/display_utils.py | .py | import html
from pathlib import Path
from mlflow.models.model import ModelInfo
from mlflow.models.signature import ModelSignature
from mlflow.types import schema
from mlflow.utils import databricks_utils
def _is_input_string(inputs: schema.Schema) -> bool:
return (
not inputs.has_input_names()
an... | 159 | 5,346 |
mlflow | mlflow/models/cli.py | .py | import logging
import click
from mlflow.mcp.decorator import mlflow_mcp
from mlflow.models import python_api
from mlflow.models.flavor_backend_registry import get_flavor_backend
from mlflow.models.model import update_model_requirements
from mlflow.utils import cli_args
from mlflow.utils import env_manager as _EnvMana... | 354 | 12,849 |
mlflow | mlflow/models/resources.py | .py | import os
from abc import ABC, abstractmethod
from enum import Enum
from typing import Any
import yaml
DEFAULT_API_VERSION = "1"
class ResourceType(Enum):
"""
Enum to define the different types of resources needed to serve a model.
"""
UC_CONNECTION = "uc_connection"
VECTOR_SEARCH_INDEX = "vect... | 342 | 12,220 |
mlflow | mlflow/models/notebook_resources/eval_with_dataset_example.py | .py | # ruff: noqa: F821, I001
{{pipInstall}}
import pandas as pd
import mlflow
evals = [
{
"request": {
"messages": [
{"role": "user", "content": "How do I convert a Spark DataFrame to Pandas?"}
],
},
# Optional, needed for judging correctness.
"e... | 23 | 574 |
mlflow | mlflow/models/notebook_resources/eval_with_synthetic_example.py | .py | # ruff: noqa: F821, I001
{{pipInstall}}
from databricks.agents.evals import generate_evals_df
import mlflow
agent_description = "A chatbot that answers questions about Databricks."
question_guidelines = """
# User personas
- A developer new to the Databricks platform
# Example questions
- What API lets me parallelize... | 23 | 730 |
mlflow | mlflow/models/container/__init__.py | .py | """
Initialize the environment and start model serving in a Docker container.
To be executed only during the model deployment.
"""
import logging
import multiprocessing
import os
import shlex
import shutil
import signal
import sys
from pathlib import Path
from subprocess import Popen, check_call
import mlflow
from ... | 256 | 9,006 |
mlflow | mlflow/models/evaluation/default_evaluator.py | .py | import copy
import inspect
import json
import logging
import pathlib
import pickle
import shutil
import tempfile
import traceback
from abc import abstractmethod
from typing import Any, Callable, NamedTuple, Optional
import numpy as np
import pandas as pd
import mlflow
from mlflow import MlflowClient, MlflowException
... | 984 | 41,785 |
mlflow | mlflow/models/evaluation/evaluator_registry.py | .py | import warnings
from mlflow.exceptions import MlflowException
from mlflow.utils.import_hooks import register_post_import_hook
from mlflow.utils.plugins import get_entry_points
class ModelEvaluatorRegistry:
"""
Scheme-based registry for model evaluator implementations
"""
def __init__(self):
... | 81 | 3,014 |
mlflow | mlflow/models/evaluation/__init__.py | .py | from mlflow.data.evaluation_dataset import EvaluationDataset
from mlflow.models.evaluation.base import (
EvaluationArtifact,
EvaluationMetric,
EvaluationResult,
ModelEvaluator,
evaluate,
list_evaluators,
make_metric,
)
from mlflow.models.evaluation.validation import MetricThreshold
__all__ ... | 24 | 528 |
mlflow | mlflow/models/evaluation/lift_curve.py | .py | import matplotlib.pyplot as plt
import numpy as np
def _cumulative_gain_curve(y_true, y_score, pos_label=None):
"""
This method is copied from scikit-plot package.
See https://github.com/reiinakano/scikit-plot/blob/2dd3e6a76df77edcbd724c4db25575f70abb57cb/scikitplot/helpers.py#L157
This function gene... | 179 | 6,165 |
mlflow | mlflow/models/evaluation/calibration_curve.py | .py | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.figure import Figure
from sklearn.calibration import CalibrationDisplay, calibration_curve
def make_multi_class_calibration_plot(
n_classes, y_true, y_probs, calibration_config, label_list
) -> Figure:
"""Generate one calibration plot for all ... | 113 | 4,277 |
mlflow | mlflow/models/evaluation/artifacts.py | .py | import json
import pathlib
import pickle
from json import JSONDecodeError
from typing import NamedTuple
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from mlflow.environment_variables import MLFLOW_ALLOW_PICKLE_DESERIALIZATION
from mlflow.exceptions import MlflowException
from mlflow.models.e... | 204 | 7,197 |
mlflow | mlflow/models/evaluation/deprecated.py | .py | import functools
import warnings
from mlflow.models.evaluation import evaluate as model_evaluate
@functools.wraps(model_evaluate)
def evaluate(*args, **kwargs):
warnings.warn(
"The `mlflow.evaluate` API has been deprecated as of MLflow 3.0.0. "
"Please use these new alternatives:\n\n"
" -... | 20 | 767 |
mlflow | mlflow/models/evaluation/base.py | .py | import inspect
import json
import keyword
import logging
import os
import pathlib
import signal
import urllib.parse
from abc import ABCMeta, abstractmethod
from contextlib import contextmanager, nullcontext
from dataclasses import dataclass
from inspect import Parameter, Signature
from types import FunctionType
from ty... | 1,812 | 81,237 |
mlflow | mlflow/models/evaluation/_shap_patch.py | .py | import pickle
import shap
from shap._serializable import Deserializer, Serializable, Serializer
class _PatchedKernelExplainer(shap.KernelExplainer):
def save(self, out_file, model_saver=None, masker_saver=None):
"""
This patched `save` method fix `KernelExplainer.save`.
Issues in original... | 52 | 2,548 |
mlflow | mlflow/models/evaluation/validation.py | .py | import logging
import operator
import os
from decimal import Decimal
from mlflow.exceptions import MlflowException
from mlflow.models.evaluation import EvaluationResult
from mlflow.protos.databricks_pb2 import BAD_REQUEST, INVALID_PARAMETER_VALUE
_logger = logging.getLogger(__name__)
class MetricThreshold:
"""
... | 434 | 18,147 |
mlflow | mlflow/models/evaluation/evaluators/default.py | .py | import logging
import os
import time
from typing import Optional
import numpy as np
import pandas as pd
import mlflow
from mlflow.entities.metric import Metric
from mlflow.exceptions import MlflowException
from mlflow.metrics import (
MetricValue,
ari_grade_level,
exact_match,
flesch_kincaid_grade_lev... | 237 | 8,493 |
mlflow | mlflow/models/evaluation/evaluators/classifier.py | .py | import logging
import math
from contextlib import contextmanager
from typing import Any, Callable, NamedTuple, Optional
import numpy as np
import pandas as pd
from sklearn import metrics as sk_metrics
import mlflow
from mlflow import MlflowException
from mlflow.environment_variables import _MLFLOW_EVALUATE_SUPPRESS_C... | 712 | 26,949 |
mlflow | mlflow/models/evaluation/evaluators/regressor.py | .py | from typing import Optional
import numpy as np
from sklearn import metrics as sk_metrics
import mlflow
from mlflow.models.evaluation.base import EvaluationMetric, EvaluationResult, _ModelType
from mlflow.models.evaluation.default_evaluator import (
BuiltInEvaluator,
_extract_output_and_other_columns,
_ext... | 97 | 3,342 |
mlflow | mlflow/models/evaluation/evaluators/shap.py | .py | import functools
import logging
from typing import Optional
import numpy as np
from packaging.version import Version
from sklearn.pipeline import Pipeline as sk_Pipeline
import mlflow
from mlflow import MlflowException
from mlflow.models.evaluation.base import EvaluationMetric, EvaluationResult, _ModelType
from mlflo... | 292 | 11,879 |
mlflow | mlflow/models/evaluation/utils/metric.py | .py | import logging
from dataclasses import dataclass
from typing import Any, Callable
import numpy as np
from mlflow.metrics.base import MetricValue
from mlflow.models.evaluation.base import EvaluationMetric
_logger = logging.getLogger(__name__)
@dataclass
class MetricDefinition:
"""
A dataclass representing a... | 129 | 4,532 |
mlflow | mlflow/models/evaluation/utils/trace.py | .py | import contextlib
import inspect
import logging
from typing import Any, Callable
from mlflow.ml_package_versions import FLAVOR_TO_MODULE_NAME
from mlflow.utils.autologging_utils import (
AUTOLOGGING_INTEGRATIONS,
autologging_conf_lock,
get_autolog_function,
is_autolog_supported,
)
from mlflow.utils.aut... | 180 | 8,278 |
mlflow | mlflow/azure/client.py | .py | """
This module provides utilities for performing Azure Blob Storage operations without requiring
the heavyweight azure-storage-blob library dependency
"""
import logging
import urllib
from copy import deepcopy
from mlflow.utils import rest_utils
from mlflow.utils.file_utils import read_chunk
_logger = logging.getLo... | 320 | 11,509 |
mlflow | mlflow/projects/env_type.py | .py | DOCKER = "docker_env"
PYTHON = "python_env"
CONDA = "conda_env"
ALL = [DOCKER, PYTHON, CONDA]
| 5 | 94 |
mlflow | mlflow/projects/utils.py | .py | import logging
import os
import pathlib
import re
import shutil
import tempfile
import urllib.parse
import zipfile
from io import BytesIO
from mlflow import tracking
from mlflow.entities import Param, SourceType
from mlflow.environment_variables import MLFLOW_EXPERIMENT_ID, MLFLOW_RUN_ID, MLFLOW_TRACKING_URI
from mlfl... | 351 | 12,649 |
mlflow | mlflow/projects/databricks.py | .py | import hashlib
import json
import logging
import os
import posixpath
import re
import tempfile
import textwrap
import time
import uuid
from pathlib import Path
from shlex import quote
from mlflow import tracking
from mlflow.entities import RunStatus
from mlflow.environment_variables import MLFLOW_EXPERIMENT_ID, MLFLOW... | 612 | 24,796 |
mlflow | mlflow/projects/_project_spec.py | .py | """Internal utilities for parsing MLproject YAML files."""
import os
import yaml
from mlflow.exceptions import ExecutionException, MlflowException
from mlflow.projects import env_type
from mlflow.tracking import artifact_utils
from mlflow.utils import data_utils
from mlflow.utils.environment import _PYTHON_ENV_FILE_... | 374 | 15,108 |
mlflow | mlflow/projects/submitted_run.py | .py | import logging
import os
import signal
from abc import abstractmethod
from mlflow.entities import RunStatus
from mlflow.utils.annotations import developer_stable
_logger = logging.getLogger(__name__)
@developer_stable
class SubmittedRun:
"""
Wrapper around an MLflow project run (e.g. a subprocess running an... | 107 | 3,529 |
mlflow | mlflow/projects/kubernetes.py | .py | import logging
import os
import time
from datetime import datetime
from shlex import quote, split
from threading import RLock
import docker
import kubernetes
from kubernetes.config.config_exception import ConfigException
from mlflow.entities import RunStatus
from mlflow.exceptions import ExecutionException
from mlflo... | 166 | 6,363 |
mlflow | mlflow/projects/__init__.py | .py | """
The ``mlflow.projects`` module provides an API for running MLflow projects locally or remotely.
"""
import json
import logging
import os
import yaml
import mlflow.projects.databricks
from mlflow import tracking
from mlflow.entities import RunStatus
from mlflow.exceptions import ExecutionException, MlflowExceptio... | 448 | 17,350 |
mlflow | mlflow/projects/docker.py | .py | import logging
import os
import posixpath
import shutil
import subprocess
import tempfile
import urllib.parse
import urllib.request
import docker
from mlflow import tracking
from mlflow.environment_variables import MLFLOW_TRACKING_URI
from mlflow.exceptions import ExecutionException
from mlflow.projects.utils import ... | 168 | 6,305 |
mlflow | mlflow/projects/backend/abstract_backend.py | .py | from abc import ABCMeta, abstractmethod
from mlflow.utils.annotations import developer_stable
@developer_stable
class AbstractBackend:
"""
Abstract plugin class defining the interface needed to execute MLflow projects. You can define
subclasses of ``AbstractBackend`` and expose them as third-party plugin... | 51 | 2,113 |
mlflow | mlflow/projects/backend/__init__.py | .py | """
This module defines developer APIs for defining pluggable execution backends
for MLflow projects. See `MLflow Plugins <../../plugins.html>`_ for more information.
"""
from mlflow.projects.backend.abstract_backend import AbstractBackend
__all__ = ["AbstractBackend"]
| 9 | 272 |
mlflow | mlflow/projects/backend/loader.py | .py | import logging
from mlflow.projects.backend.local import LocalBackend
from mlflow.utils.plugins import get_entry_points
ENTRYPOINT_GROUP_NAME = "mlflow.project_backend"
_logger = logging.getLogger(__name__)
# Statically register backend defined in mlflow
MLFLOW_BACKENDS = {
"local": LocalBackend,
}
def load_... | 36 | 932 |
mlflow | mlflow/projects/backend/local.py | .py | import logging
import os
import platform
import posixpath
import subprocess
import sys
from pathlib import Path
import mlflow
from mlflow import tracking
from mlflow.environment_variables import (
MLFLOW_KERBEROS_TICKET_CACHE,
MLFLOW_KERBEROS_USER,
MLFLOW_PYARROW_EXTRA_CONF,
)
from mlflow.exceptions import... | 429 | 17,192 |
mlflow | mlflow/types/utils.py | .py | import logging
import warnings
from collections import defaultdict
from copy import deepcopy
from typing import Any, Dict, List
import numpy as np
import pandas as pd
import pydantic
from mlflow.exceptions import MlflowException
from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
from mlflow.types import... | 742 | 27,468 |
mlflow | mlflow/types/responses.py | .py | import json
from collections.abc import Sequence
from itertools import tee
from typing import Any, Generator, Iterator
from uuid import uuid4
from pydantic import BaseModel, ConfigDict, model_validator
from mlflow.types.agent import ChatContext
from mlflow.types.responses_helpers import (
BaseRequestPayload,
... | 567 | 21,457 |
mlflow | mlflow/types/__init__.py | .py | """
The :py:mod:`mlflow.types` module defines data types and utilities to be used by other mlflow
components to describe interface independent of other frameworks or languages.
"""
from mlflow.version import IS_TRACING_SDK_ONLY
if not IS_TRACING_SDK_ONLY:
try:
import numpy as _np # noqa: F401
_H... | 38 | 949 |
mlflow | mlflow/types/chat.py | .py | from __future__ import annotations
import warnings
from typing import Annotated, Any, Literal
from uuid import uuid4
from pydantic import BaseModel, ConfigDict, Field, model_serializer
class TextContentPart(BaseModel):
type: Literal["text"]
text: str
class ImageUrl(BaseModel):
"""
Represents an im... | 369 | 11,530 |
mlflow | mlflow/types/llm.py | .py | from __future__ import annotations
import time
import uuid
from dataclasses import asdict, dataclass, field, fields
from typing import Any, Literal
from mlflow.types.schema import AnyType, Array, ColSpec, DataType, Map, Object, Property, Schema
# TODO: Switch to pydantic in a future version of MLflow.
# For no... | 952 | 37,735 |
mlflow | mlflow/types/type_hints.py | .py | import base64
import logging
from datetime import datetime
from functools import lru_cache
from types import UnionType
from typing import Any, NamedTuple, Optional, TypeVar, Union, get_args, get_origin
import pydantic
import pydantic.fields
from mlflow.environment_variables import _MLFLOW_IS_IN_SERVING_ENVIRONMENT
fr... | 633 | 24,613 |
mlflow | mlflow/types/schema.py | .py | from __future__ import annotations
import builtins
import datetime as dt
import json
import string
from abc import ABC, abstractmethod
from copy import deepcopy
from dataclasses import is_dataclass
from enum import Enum
from types import UnionType
from typing import Any, TypedDict, Union, get_args, get_origin
import ... | 1,518 | 56,238 |
mlflow | mlflow/types/agent.py | .py | from typing import Any
from pydantic import ConfigDict, model_validator
from mlflow.types.chat import BaseModel, ChatUsage, ToolCall
from mlflow.types.llm import (
_custom_inputs_col_spec,
_custom_outputs_col_spec,
_token_usage_stats_col_spec,
)
from mlflow.types.schema import (
Array,
ColSpec,
... | 236 | 9,118 |
mlflow | mlflow/types/responses_helpers.py | .py | import warnings
from typing import Any
from pydantic import BaseModel, ConfigDict, Field, model_validator
"""
Classes are inspired by classes for Response and ResponseStreamEvent in openai-python
https://github.com/openai/openai-python/blob/ed53107e10e6c86754866b48f8bd862659134ca8/src/openai/types/responses/response... | 430 | 12,574 |
mlflow | mlflow/claude_code/__init__.py | .py | """Claude Code integration for MLflow.
This module provides automatic tracing of Claude Code conversations to MLflow.
Usage:
mlflow autolog claude [directory] [options]
After setup, use the regular 'claude' command and traces will be automatically captured.
To enable tracing for the Claude Agent SDK, use `mlflo... | 27 | 644 |
mlflow | mlflow/claude_code/hooks.py | .py | """Legacy compatibility helpers for the retired Python Claude hook runtime."""
import json
import sys
from mlflow.claude_code.tracing import get_hook_response
def stop_hook_handler() -> None:
"""No-op shim for repositories still wired to the old Python hook."""
print(json.dumps(get_hook_response())) # noqa... | 17 | 531 |
mlflow | mlflow/claude_code/tracing.py | .py | """MLflow tracing integration for Claude Code interactions."""
import dataclasses
import json
import logging
import os
import sys
from datetime import datetime
from pathlib import Path
from typing import Any
import dateutil.parser
import mlflow
from mlflow.claude_code.config import (
MLFLOW_TRACING_ENABLED,
... | 882 | 32,646 |
mlflow | mlflow/claude_code/cli.py | .py | """MLflow CLI commands for Claude Code integration."""
import os
import sys
from pathlib import Path
import click
from mlflow.claude_code.config import get_tracing_status, setup_environment_config
from mlflow.claude_code.hooks import stop_hook_handler
from mlflow.claude_code.plugin import (
disable_tracing_plugi... | 353 | 11,286 |
mlflow | mlflow/claude_code/config.py | .py | """Configuration management for Claude Code integration with MLflow."""
import json
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Any
from mlflow.environment_variables import (
MLFLOW_EXPERIMENT_ID,
MLFLOW_EXPERIMENT_NAME,
MLFLOW_TRACKING_URI,
)
# Configuration f... | 193 | 6,366 |
mlflow | mlflow/claude_code/plugin.py | .py | """Plugin bootstrap helpers for Claude Code tracing."""
from __future__ import annotations
import shutil
import subprocess
from pathlib import Path
from typing import Any
import click
from mlflow.claude_code.config import (
ENVIRONMENT_FIELD,
MLFLOW_EXPERIMENT_ID,
MLFLOW_EXPERIMENT_NAME,
MLFLOW_TRAC... | 109 | 2,768 |
mlflow | mlflow/pmdarima/__init__.py | .py | """
The ``mlflow.pmdarima`` module provides an API for logging and loading ``pmdarima`` models.
This module exports univariate ``pmdarima`` models in the following formats:
Pmdarima format
Serialized instance of a ``pmdarima`` model using pickle.
:py:mod:`mlflow.pyfunc`
Produced for use by generic pyfunc-based... | 651 | 23,878 |
mlflow | mlflow/crewai/__init__.py | .py | """
The ``mlflow.crewai`` module provides an API for tracing CrewAI AI agents.
"""
import importlib
import logging
from packaging.version import Version
from mlflow.crewai.autolog import (
patched_class_call,
patched_native_tool_call,
patched_standalone_call,
)
from mlflow.telemetry.events import Autolog... | 126 | 4,681 |
mlflow | mlflow/crewai/autolog.py | .py | import inspect
import json
import logging
import warnings
from contextlib import contextmanager, nullcontext
from typing import Any
from packaging.version import Version
import mlflow
from mlflow.entities import SpanType
from mlflow.entities.span import LiveSpan
from mlflow.tracing.constant import SpanAttributeKey, T... | 481 | 16,629 |
mlflow | mlflow/agent/agents.py | .py | """Registry of coding agent CLIs supported by ``mlflow agent setup``.
To support a new agent, append an :class:`AgentTool` entry to :data:`AGENTS`.
That is the only place per-agent variation lives.
"""
from __future__ import annotations
import shutil
from dataclasses import dataclass
from typing import Literal
Agen... | 62 | 1,618 |
mlflow | mlflow/agent/cli.py | .py | """`mlflow agent` CLI group.
Wires per-subcommand modules under :mod:`mlflow.agent`. To add a new
subcommand, drop a package under ``mlflow/agent/<name>/`` and register it
here with ``commands.add_command``.
"""
from __future__ import annotations
import click
from mlflow.agent.setup.cli import setup
@click.group(... | 21 | 435 |
mlflow | mlflow/agent/setup/select.py | .py | from __future__ import annotations
import os
import select
import sys
import click
if sys.platform != "win32":
import termios
import tty
def _read_key() -> str:
"""Read a single keystroke (or escape sequence) from stdin in raw mode."""
fd = sys.stdin.fileno()
old = termios.tcgetattr(fd)
try... | 95 | 3,034 |
mlflow | mlflow/agent/setup/prompt.py | .py | from __future__ import annotations
import re
from importlib import resources
from pathlib import Path
import mlflow.assistant.skills as _skills_pkg
from mlflow.agent.agents import AgentTool
_PLACEHOLDER = re.compile(r"\{\{\s*(\w+)\s*\}\}")
def _read_template(filename: str) -> str:
return resources.files("mlflo... | 113 | 4,022 |
mlflow | mlflow/agent/setup/cli.py | .py | from __future__ import annotations
import socket
import subprocess
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Any
from urllib.parse import urlparse
import click
from mlflow.agent.agents import AGENTS, AgentName, AgentTool, detect_installed, get_agent
from mlflow.agent.se... | 365 | 13,229 |
mlflow | mlflow/shap/__init__.py | .py | import os
import tempfile
import types
import warnings
from contextlib import contextmanager
from typing import Any
import numpy as np
import yaml
import mlflow
import mlflow.utils.autologging_utils
from mlflow import pyfunc
from mlflow.models import Model, ModelInputExample, ModelSignature
from mlflow.models.model i... | 692 | 25,527 |
mlflow | mlflow/llama_index/pyfunc_wrapper.py | .py | import asyncio
import threading
import uuid
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from llama_index.core import QueryBundle
from mlflow.models.utils import _convert_llm_input_data
CHAT_ENGINE_NAME = "chat"
QUERY_ENGINE_NAME = "query"
RETRIEVER_ENGINE_NAME = "retriever"
SUPPORTED_ENGINES = {CHAT_... | 331 | 12,676 |
mlflow | mlflow/llama_index/tracer.py | .py | import inspect
import json
import logging
from functools import singledispatchmethod
from typing import Any, Generator
import llama_index.core
import pydantic
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.base.llms.b... | 749 | 32,309 |
mlflow | mlflow/llama_index/model.py | .py | import logging
import os
import tempfile
from typing import Any
import yaml
import mlflow
from mlflow import pyfunc
from mlflow.entities.model_registry.prompt import Prompt
from mlflow.exceptions import MlflowException
from mlflow.llama_index.constant import FLAVOR_NAME
from mlflow.llama_index.pyfunc_wrapper import c... | 574 | 23,901 |
mlflow | mlflow/llama_index/__init__.py | .py | from mlflow.llama_index.autolog import autolog
from mlflow.llama_index.constant import FLAVOR_NAME
from mlflow.version import IS_TRACING_SDK_ONLY
__all__ = ["autolog", "FLAVOR_NAME"]
# Import model logging APIs only if mlflow skinny or full package is installed,
# i.e., skip if only mlflow-tracing package is installe... | 23 | 594 |
mlflow | mlflow/llama_index/autolog.py | .py | from mlflow.llama_index.constant import FLAVOR_NAME
from mlflow.telemetry.events import AutologgingEvent
from mlflow.telemetry.track import _record_event
from mlflow.utils.autologging_utils import autologging_integration
def autolog(
log_traces: bool = True,
disable: bool = False,
silent: bool = False,
):... | 59 | 2,196 |
mlflow | mlflow/llama_index/serialize_objects.py | .py | import importlib
import inspect
import json
import logging
from typing import Any, Callable
from llama_index.core import PromptTemplate
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.schema import BaseComponent
_logger ... | 193 | 7,152 |
mlflow | fs2db/src/generate_synthetic_data.py | .py | # ruff: noqa: T201
"""
Generate synthetic MLflow FileStore data for testing the fs2db migration tool.
Usage:
uv run --with mlflow==3.6.0 --no-project python -I \
fs2db/src/generate_synthetic_data.py --output /tmp/fs2db/v3.6.0/ --size small
This script uses the MLflow public API to create realistic on-disk... | 435 | 14,337 |
mlflow | dev/check_actions.py | .py | """Validate GitHub Actions workflow and action files.
Complements `.github/policy.rego` with checks that need cross-file or remote
context.
"""
import json
import re
import subprocess
import sys
from collections import defaultdict
from collections.abc import Iterator
from dataclasses import dataclass
from pathlib imp... | 281 | 8,951 |
mlflow | dev/run_dev_server.py | .py | """Launch the MLflow dev backend and the React dev server for local development.
Cleans up child process groups on exit/SIGINT/SIGTERM so we don't leave zombies.
"""
from __future__ import annotations
import argparse
import atexit
import os
import shlex
import shutil
import signal
import socket
import subprocess
imp... | 183 | 6,450 |
mlflow | dev/check_function_signatures.py | .py | from __future__ import annotations
import argparse
import ast
import os
import subprocess
import sys
from dataclasses import dataclass
from pathlib import Path
def is_github_actions() -> bool:
return os.environ.get("GITHUB_ACTIONS") == "true"
@dataclass
class Error:
file_path: Path
line: int
column... | 383 | 13,140 |
mlflow | dev/format.py | .py | import os
import re
import subprocess
import sys
RUFF_FORMAT = [sys.executable, "-m", "ruff", "format"]
MESSAGE_REGEX = re.compile(r"^Would reformat: (.+)$")
def transform(stdout: str, is_maintainer: bool) -> str:
if not stdout:
return stdout
transformed = []
for line in stdout.splitlines():
... | 56 | 1,621 |
mlflow | dev/check_init_py.py | .py | """
Pre-commit hook to check for missing `__init__.py` files in mlflow and tests directories.
This script ensures that all directories under the mlflow package and tests directory that contain
Python files also have an `__init__.py` file. This prevents `setuptools` from excluding these
directories during package build... | 55 | 1,989 |
mlflow | dev/check_skills.py | .py | import re
import sys
from pathlib import Path
from typing import Any
import yaml
# https://agentskills.io/specification#frontmatter
NAME_RE = re.compile(r"^[a-z0-9]+(-[a-z0-9]+)*$")
NAME_MAX = 64
DESCRIPTION_MAX = 1024
def parse_frontmatter(text: str) -> dict[str, Any] | None:
if not text.startswith("---\n"):
... | 69 | 2,015 |
mlflow | dev/show_package_release_dates.py | .py | import asyncio
import json
import re
import subprocess
import sys
from collections.abc import Sequence
from datetime import datetime, timedelta, timezone
from pathlib import Path
from pypi import Package, get_packages
def get_cooldown_days() -> int:
pyproject = Path(__file__).resolve().parent.parent / "pyproject... | 72 | 2,619 |
mlflow | dev/check_patch_prs.py | .py | import argparse
import concurrent.futures
import itertools
import os
import re
import subprocess
import sys
import tempfile
from dataclasses import dataclass
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any
import requests
from packaging.version import Version
MAX_COM... | 291 | 9,832 |
mlflow | dev/update_requirements.py | .py | """
This script updates the `max_major_version` attribute of each package in a YAML dependencies
specification (e.g. requirements/core-requirements.yaml) to the maximum available version on PyPI.
"""
import asyncio
import os
import re
import urllib.error
import urllib.request
from datetime import datetime, timedelta, ... | 109 | 3,973 |
mlflow | dev/check_whitespace_only.py | .py | """
Detect files where all changes are whitespace-only.
This helps avoid unnecessary commit history noise from whitespace-only changes.
"""
import argparse
import json
import os
import sys
import time
import urllib.error
import urllib.request
from typing import cast
BYPASS_LABEL = "allow-whitespace-only"
_MAX_ATTEM... | 149 | 4,404 |
mlflow | dev/ruff.py | .py | import os
import re
import subprocess
import sys
RUFF = [sys.executable, "-m", "ruff", "check", "--output-format=concise"]
MESSAGE_REGEX = re.compile(r"^.+:\d+:\d+: ([A-Z0-9]+) (\[\*\] )?.+$")
def transform(stdout: str, is_maintainer: bool) -> str:
transformed = []
for line in stdout.splitlines():
if... | 57 | 1,691 |
mlflow | dev/classify_flaky_tests.py | .py | """Classify detected flaky tests and decide which to annotate @pytest.mark.flaky.
Second stage of the flaky-test pipeline. `detect_flaky_tests.py` produces the
*deterministic* signal (a test that failed on one run attempt and passed on the next
attempt of the same commit). This stage adds *judgment*: given each test's... | 200 | 8,014 |
mlflow | dev/update_changelog.py | .py | import argparse
import os
import re
import subprocess
from collections import defaultdict
from datetime import datetime
from pathlib import Path
from typing import Any, NamedTuple
import requests
from packaging.version import Version
def get_header_for_version(version: str) -> str:
return "## {} ({})".format(ver... | 281 | 8,573 |
mlflow | dev/detect_flaky_tests.py | .py | """Detect flaky tests from user-triggered CI re-runs.
Ground-truth flake signal: a job that **failed on one run attempt and passed on the
next attempt of the same commit** flaked by definition (same code, different outcome),
and a human already judged it worth re-running by hitting "Re-run failed jobs".
This script m... | 260 | 10,892 |
mlflow | dev/create_release_tag.py | .py | """
How to test this script
-----------------------
# Ensure origin points to your fork
git remote -v | grep origin
# Pretend we're releasing MLflow 9.0.0
git checkout -b branch-9.0
# First, test the dry run mode
python dev/create_release_tag.py --new-version 9.0.0 --dry-run
git tag -d v9.0.0
# Open https://github.c... | 59 | 1,806 |
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