code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def load_model(
model_uri: str, dst_path: Optional[str] = None, return_type="pipeline", device=None, **kwargs
):
"""
Load a ``transformers`` object from a local file or a run.
Args:
model_uri: The location, in URI format, of the MLflow model. For example:
- ``/Users/me/path/to/loca... |
Load a ``transformers`` object from a local file or a run.
Args:
model_uri: The location, in URI format, of the MLflow model. For example:
- ``/Users/me/path/to/local/model``
- ``relative/path/to/local/model``
- ``s3://my_bucket/path/to/model``
- ``runs... | load_model | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def persist_pretrained_model(model_uri: str) -> None:
"""
Persist Transformers pretrained model weights to the artifacts directory of the specified
model_uri. This API is primary used for updating an MLflow Model that was logged or saved
with setting save_pretrained=False. Such models cannot be register... |
Persist Transformers pretrained model weights to the artifacts directory of the specified
model_uri. This API is primary used for updating an MLflow Model that was logged or saved
with setting save_pretrained=False. Such models cannot be registered to Databricks Workspace
Model Registry, due to the ful... | persist_pretrained_model | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _is_model_distributed_in_memory(transformers_model):
"""Check if the model is distributed across multiple devices in memory."""
# Check if the model attribute exists. If not, accelerate was not used and the model can
# be safely saved
if not hasattr(transformers_model, "hf_device_map"):
ret... | Check if the model is distributed across multiple devices in memory. | _is_model_distributed_in_memory | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _load_model(path: str, flavor_config, return_type: str, device=None, **kwargs):
"""
Loads components from a locally serialized ``Pipeline`` object.
"""
import transformers
conf = {
"task": flavor_config[FlavorKey.TASK],
}
if framework := flavor_config.get(FlavorKey.FRAMEWORK):
... |
Loads components from a locally serialized ``Pipeline`` object.
| _load_model | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _fetch_model_card(model_name):
"""
Attempts to retrieve the model card for the specified model architecture iff the
`huggingface_hub` library is installed. If a card cannot be found in the registry or
the library is not installed, returns None.
"""
try:
import huggingface_hub as hub
... |
Attempts to retrieve the model card for the specified model architecture iff the
`huggingface_hub` library is installed. If a card cannot be found in the registry or
the library is not installed, returns None.
| _fetch_model_card | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _write_card_data(card_data, path):
"""
Writes the card data, if specified or available, to the provided path in two separate files
"""
if card_data:
try:
path.joinpath(_CARD_TEXT_FILE_NAME).write_text(card_data.text, encoding="utf-8")
except UnicodeError as e:
... |
Writes the card data, if specified or available, to the provided path in two separate files
| _write_card_data | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _extract_license_file_from_repository(model_name):
"""Returns the top-level file inventory of `RepoFile` objects from the huggingface hub"""
try:
import huggingface_hub as hub
except ImportError:
_logger.debug(
f"Unable to list repository contents for the model repo {model_na... | Returns the top-level file inventory of `RepoFile` objects from the huggingface hub | _extract_license_file_from_repository | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _get_supported_pretrained_model_types():
"""
Users might not have all the necessary libraries installed to determine the supported model
"""
supported_model_types = ()
try:
from transformers import FlaxPreTrainedModel
supported_model_types += (FlaxPreTrainedModel,)
except ... |
Users might not have all the necessary libraries installed to determine the supported model
| _get_supported_pretrained_model_types | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _build_pipeline_from_model_input(model_dict: dict[str, Any], task: Optional[str]) -> Pipeline:
"""
Utility for generating a pipeline from component parts. If required components are not
specified, use the transformers library pipeline component validation to force raising an
exception. The underlyin... |
Utility for generating a pipeline from component parts. If required components are not
specified, use the transformers library pipeline component validation to force raising an
exception. The underlying Exception thrown in transformers is verbose enough for diagnosis.
| _build_pipeline_from_model_input | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _get_task_for_model(model_name_or_path: str, default_task=None) -> str:
"""
Get the Transformers pipeline task type fro the model instance.
NB: The get_task() function only works for remote models available in the Hugging
Face hub, so the default task should be supplied when using a custom local mo... |
Get the Transformers pipeline task type fro the model instance.
NB: The get_task() function only works for remote models available in the Hugging
Face hub, so the default task should be supplied when using a custom local model.
| _get_task_for_model | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _validate_llm_inference_task_type(llm_inference_task: str, pipeline_task: str) -> None:
"""
Validates that an ``inference_task`` type is supported by ``transformers`` pipeline type.
"""
supported_llm_inference_tasks = _SUPPORTED_LLM_INFERENCE_TASK_TYPES_BY_PIPELINE_TASK.get(
pipeline_task, [... |
Validates that an ``inference_task`` type is supported by ``transformers`` pipeline type.
| _validate_llm_inference_task_type | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _get_engine_type(model):
"""
Determines the underlying execution engine for the model based on the 3 currently supported
deep learning framework backends: ``tensorflow``, ``torch``, or ``flax``.
"""
from transformers import FlaxPreTrainedModel, PreTrainedModel, TFPreTrainedModel
from transfo... |
Determines the underlying execution engine for the model based on the 3 currently supported
deep learning framework backends: ``tensorflow``, ``torch``, or ``flax``.
| _get_engine_type | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _get_model_config(local_path, pyfunc_config):
"""
Load the model configuration if it was provided for use in the `_TransformersWrapper` pyfunc
Model wrapper.
"""
config_path = local_path.joinpath("inference_config.txt")
if config_path.exists():
_logger.warning(
"Inference... |
Load the model configuration if it was provided for use in the `_TransformersWrapper` pyfunc
Model wrapper.
| _get_model_config | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _load_pyfunc(path, model_config: Optional[dict[str, Any]] = None):
"""
Loads the model as pyfunc model
"""
local_path = pathlib.Path(path)
flavor_configuration = _get_flavor_configuration(local_path, FLAVOR_NAME)
model_config = _get_model_config(local_path.joinpath(_COMPONENTS_BINARY_DIR_NAM... |
Loads the model as pyfunc model
| _load_pyfunc | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _is_conversational_pipeline(pipeline):
"""
Checks if the pipeline is a ConversationalPipeline.
"""
if cp := _try_import_conversational_pipeline():
return isinstance(pipeline, cp)
return False |
Checks if the pipeline is a ConversationalPipeline.
| _is_conversational_pipeline | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _try_import_conversational_pipeline():
"""
Try importing ConversationalPipeline because for version > 4.41.2
it is removed from the transformers package.
"""
try:
from transformers import ConversationalPipeline
return ConversationalPipeline
except ImportError:
return |
Try importing ConversationalPipeline because for version > 4.41.2
it is removed from the transformers package.
| _try_import_conversational_pipeline | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _parse_raw_pipeline_input(self, data):
"""
Converts inputs to the expected types for specific Pipeline types.
Specific logic for individual pipeline types are called via their respective methods if
the input isn't a basic str or List[str] input type of Pipeline.
These parsers... |
Converts inputs to the expected types for specific Pipeline types.
Specific logic for individual pipeline types are called via their respective methods if
the input isn't a basic str or List[str] input type of Pipeline.
These parsers are required due to the conversion that occurs within... | _parse_raw_pipeline_input | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _validate_text_classification_input(data):
"""
Perform input type validation for TextClassification pipelines and casting of data
that is manipulated internally by the MLflow model server back to a structure that
can be used for pipeline inference.
To illustrate the input an... |
Perform input type validation for TextClassification pipelines and casting of data
that is manipulated internally by the MLflow model server back to a structure that
can be used for pipeline inference.
To illustrate the input and outputs of this function, for the following inputs to
... | _validate_text_classification_input | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _check_keys(payload):
"""Check if a dictionary contains only allowable keys."""
allowable_str_keys = {"text", "text_pair"}
if set(payload) - allowable_str_keys and not all(
isinstance(key, int) for key in payload.keys()
):
raise MlflowE... | Check if a dictionary contains only allowable keys. | _check_keys | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _coerce_exploded_dict_to_single_dict(
self, data: list[dict[str, Any]]
) -> dict[str, list[Any]]:
"""
Parses the result of Pandas DataFrame.to_dict(orient="records") from pyfunc
signature validation to coerce the output to the required format for a
Pipeline that requires ... |
Parses the result of Pandas DataFrame.to_dict(orient="records") from pyfunc
signature validation to coerce the output to the required format for a
Pipeline that requires a single dict with list elements such as
TableQuestionAnsweringPipeline.
Example input:
[
... | _coerce_exploded_dict_to_single_dict | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _flatten_zero_shot_text_classifier_output_to_df(self, data):
"""
Converts the output of sequences, labels, and scores to a Pandas DataFrame output.
Example input:
[{'sequence': 'My dog loves to eat spaghetti',
'labels': ['happy', 'sad'],
'scores': [0.98969709873... |
Converts the output of sequences, labels, and scores to a Pandas DataFrame output.
Example input:
[{'sequence': 'My dog loves to eat spaghetti',
'labels': ['happy', 'sad'],
'scores': [0.9896970987319946, 0.010302911512553692]},
{'sequence': 'My dog hates going to ... | _flatten_zero_shot_text_classifier_output_to_df | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _strip_input_from_response_in_instruction_pipelines(
self,
input_data,
output,
output_key,
flavor_config,
include_prompt=True,
collapse_whitespace=False,
):
"""
Parse the output from instruction pipelines to conform with other text generato... |
Parse the output from instruction pipelines to conform with other text generator
pipeline types and remove line feed characters and other confusing outputs
| _strip_input_from_response_in_instruction_pipelines | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _wrap_strings_as_list_if_scalar(output_data):
"""
Wraps single string outputs in a list to support batch processing logic in serving.
Scalar values are not supported for processing in batch logic as they cannot be coerced
to DataFrame representations.
"""
if isinstanc... |
Wraps single string outputs in a list to support batch processing logic in serving.
Scalar values are not supported for processing in batch logic as they cannot be coerced
to DataFrame representations.
| _wrap_strings_as_list_if_scalar | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _parse_lists_of_dict_to_list_of_str(self, output_data, target_dict_key) -> list[str]:
"""
Parses the output results from select Pipeline types to extract specific values from a
target key.
Examples (with "a" as the `target_dict_key`):
Input: [{"a": "valid", "b": "invalid"}, ... |
Parses the output results from select Pipeline types to extract specific values from a
target key.
Examples (with "a" as the `target_dict_key`):
Input: [{"a": "valid", "b": "invalid"}, {"a": "another valid", "c": invalid"}]
Output: ["valid", "another_valid"]
Input: [{"... | _parse_lists_of_dict_to_list_of_str | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _parse_feature_extraction_output(output_data):
"""
Parse the return type from a FeatureExtractionPipeline output. The mixed types for
input are present depending on how the pyfunc is instantiated. For model serving usage,
the returned type from MLServer will be a numpy.ndarray type, ... |
Parse the return type from a FeatureExtractionPipeline output. The mixed types for
input are present depending on how the pyfunc is instantiated. For model serving usage,
the returned type from MLServer will be a numpy.ndarray type, otherwise, the return
within a manually executed pyfun... | _parse_feature_extraction_output | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _parse_tokenizer_output(self, output_data, target_set):
"""
Parses the tokenizer pipeline output.
Examples:
Input: [{"entity": "PRON", "score": 0.95}, {"entity": "NOUN", "score": 0.998}]
Output: "PRON,NOUN"
Input: [[{"entity": "PRON", "score": 0.95}, {"entity": "NO... |
Parses the tokenizer pipeline output.
Examples:
Input: [{"entity": "PRON", "score": 0.95}, {"entity": "NOUN", "score": 0.998}]
Output: "PRON,NOUN"
Input: [[{"entity": "PRON", "score": 0.95}, {"entity": "NOUN", "score": 0.998}],
[{"entity": "PRON", "score": 0.9... | _parse_tokenizer_output | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _parse_list_of_multiple_dicts(output_data, target_dict_key):
"""
Returns the first value of the `target_dict_key` that matches in the first dictionary in a
list of dictionaries.
"""
def fetch_target_key_value(data, key):
if isinstance(data[0], dict):
... |
Returns the first value of the `target_dict_key` that matches in the first dictionary in a
list of dictionaries.
| _parse_list_of_multiple_dicts | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _parse_question_answer_input(self, data):
"""
Parses the single string input representation for a question answer pipeline into the
required dict format for a `question-answering` pipeline.
"""
if isinstance(data, list):
return [self._parse_question_answer_input(e... |
Parses the single string input representation for a question answer pipeline into the
required dict format for a `question-answering` pipeline.
| _parse_question_answer_input | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _parse_json_encoded_list(self, data, key_to_unpack):
"""
Parses the complex input types for pipelines such as ZeroShotClassification in which
the required input type is Dict[str, Union[str, List[str]]] wherein the list
provided is encoded as JSON. This method unpacks that string to t... |
Parses the complex input types for pipelines such as ZeroShotClassification in which
the required input type is Dict[str, Union[str, List[str]]] wherein the list
provided is encoded as JSON. This method unpacks that string to the required
elements.
| _parse_json_encoded_list | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _parse_json_encoded_dict_payload_to_dict(data, key_to_unpack):
"""
Parses complex dict input types that have been json encoded. Pipelines like
TableQuestionAnswering require such input types.
"""
if isinstance(data, list):
return [
{
... |
Parses complex dict input types that have been json encoded. Pipelines like
TableQuestionAnswering require such input types.
| _parse_json_encoded_dict_payload_to_dict | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _convert_cast_lists_from_np_back_to_list(data):
"""
This handles the casting of dicts within lists from Pandas DF conversion within model
serving back into the required Dict[str, List[str]] if this type matching occurs.
Otherwise, it's a noop.
"""
if not isinstance(da... |
This handles the casting of dicts within lists from Pandas DF conversion within model
serving back into the required Dict[str, List[str]] if this type matching occurs.
Otherwise, it's a noop.
| _convert_cast_lists_from_np_back_to_list | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def is_base64_image(image):
"""Check whether input image is a base64 encoded"""
try:
b64_decoded_image = base64.b64decode(image)
return (
base64.b64encode(b64_decoded_image).decode("utf-8") == image
or base64.encodebytes(b64_decoded_image).decode(... | Check whether input image is a base64 encoded | is_base64_image | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _convert_image_input(self, input_data):
"""
Conversion utility for decoding the base64 encoded bytes data of a raw image file when
parsed through model serving, if applicable. Direct usage of the pyfunc implementation
outside of model serving will treat this utility as a noop.
... |
Conversion utility for decoding the base64 encoded bytes data of a raw image file when
parsed through model serving, if applicable. Direct usage of the pyfunc implementation
outside of model serving will treat this utility as a noop.
For reference, the expected encoding for input to Mo... | _convert_image_input | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _convert_audio_input(
self, data: Union[AudioInput, list[dict[int, list[AudioInput]]]]
) -> Union[AudioInput, list[AudioInput]]:
"""
Convert the input data into the format that the Transformers pipeline expects.
Args:
data: The input data to be converted. This can be... |
Convert the input data into the format that the Transformers pipeline expects.
Args:
data: The input data to be converted. This can be one of the following:
1. A single input audio data (bytes, numpy array, or a path or URI to an audio file)
2. List of dicti... | _convert_audio_input | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _decode_audio(self, audio: AudioInput) -> AudioInput:
"""
Decode the audio data if it is base64 encoded bytes, otherwise no-op.
"""
if isinstance(audio, str):
# Input is an URI to the audio file to be processed.
self._validate_str_input_uri_or_file(audio)
... |
Decode the audio data if it is base64 encoded bytes, otherwise no-op.
| _decode_audio | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def is_base64_audio(audio: bytes) -> bool:
"""Check whether input audio is a base64 encoded"""
try:
return base64.b64encode(base64.b64decode(audio)) == audio
except binascii.Error:
return False | Check whether input audio is a base64 encoded | is_base64_audio | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _validate_str_input_uri_or_file(input_str):
"""
Validation of blob references to either audio or image files,
if a string is input to the ``predict``
method, perform validation of the string contents by checking for a valid uri or
filesystem reference instead of surfacing the... |
Validation of blob references to either audio or image files,
if a string is input to the ``predict``
method, perform validation of the string contents by checking for a valid uri or
filesystem reference instead of surfacing the cryptic stack trace that is otherwise raised
for a... | _validate_str_input_uri_or_file | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def _format_prompt_template(self, input_data):
"""
Wraps the input data in the specified prompt template. If no template is
specified, or if the pipeline is an unsupported type, or if the input type
is not a string or list of strings, then the input data is returned unchanged.
""... |
Wraps the input data in the specified prompt template. If no template is
specified, or if the pipeline is an unsupported type, or if the input type
is not a string or list of strings, then the input data is returned unchanged.
| _format_prompt_template | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def autolog(
log_input_examples=False,
log_model_signatures=False,
log_models=False,
log_datasets=False,
disable=False,
exclusive=False,
disable_for_unsupported_versions=False,
silent=False,
extra_tags=None,
):
"""
This autologging integration is solely used for disabling spu... |
This autologging integration is solely used for disabling spurious autologging of irrelevant
sub-models that are created during the training and evaluation of transformers-based models.
Autologging functionality is not implemented fully for the transformers flavor.
| autolog | python | mlflow/mlflow | mlflow/transformers/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/transformers/__init__.py | Apache-2.0 |
def check_content_and_tool_calls(cls, values):
"""
Ensure at least one of 'content' or 'tool_calls' is set.
"""
if IS_PYDANTIC_V2_OR_NEWER:
content = values.content
tool_calls = values.tool_calls
else:
content = values.get("content")
... |
Ensure at least one of 'content' or 'tool_calls' is set.
| check_content_and_tool_calls | python | mlflow/mlflow | mlflow/types/agent.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/agent.py | Apache-2.0 |
def check_tool_messages(cls, values):
"""
Ensure that the 'name' and 'tool_call_id' fields are set for tool messages.
"""
if IS_PYDANTIC_V2_OR_NEWER:
name = values.name
role = values.role
tool_call_id = values.tool_call_id
else:
nam... |
Ensure that the 'name' and 'tool_call_id' fields are set for tool messages.
| check_tool_messages | python | mlflow/mlflow | mlflow/types/agent.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/agent.py | Apache-2.0 |
def check_message_ids(cls, values):
"""
Ensure that all messages have an ID and it is unique.
"""
if IS_PYDANTIC_V2_OR_NEWER:
message_ids = [msg.id for msg in values.messages]
else:
message_ids = [msg.get("id") for msg in values.get("messages")]
i... |
Ensure that all messages have an ID and it is unique.
| check_message_ids | python | mlflow/mlflow | mlflow/types/agent.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/agent.py | Apache-2.0 |
def check_message_id(cls, values):
"""
Ensure that the message ID is unique.
"""
message_id = values.delta.id if IS_PYDANTIC_V2_OR_NEWER else values.get("delta").get("id")
if message_id is None:
raise ValueError(
"The field `delta` of ChatAgentChunk m... |
Ensure that the message ID is unique.
| check_message_id | python | mlflow/mlflow | mlflow/types/agent.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/agent.py | Apache-2.0 |
def from_dict(cls, data):
"""
Create an instance of the class from a dict, ignoring any undefined fields.
This is useful when the dict contains extra fields, causing cls(**data) to fail.
"""
field_names = [field.name for field in fields(cls)]
filtered_data = {k: v for k, ... |
Create an instance of the class from a dict, ignoring any undefined fields.
This is useful when the dict contains extra fields, causing cls(**data) to fail.
| from_dict | python | mlflow/mlflow | mlflow/types/llm.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/llm.py | Apache-2.0 |
def output_text(self) -> str:
"""Convenience property that aggregates all `output_text` items from the `output`
list.
If no `output_text` content blocks exist, then an empty string is returned.
"""
texts: list[str] = []
for output in self.output:
if output.ty... | Convenience property that aggregates all `output_text` items from the `output`
list.
If no `output_text` content blocks exist, then an empty string is returned.
| output_text | python | mlflow/mlflow | mlflow/types/responses_helpers.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/responses_helpers.py | Apache-2.0 |
def __eq__(self, other) -> bool:
"""
Determine if two objects are equal.
""" |
Determine if two objects are equal.
| __eq__ | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def __repr__(self) -> str:
"""
The string representation of the object.
""" |
The string representation of the object.
| __repr__ | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def _merge(self, other: BaseType) -> BaseType:
"""
Merge two objects and return the updated object if they're compatible.
""" |
Merge two objects and return the updated object if they're compatible.
| _merge | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def __init__(
self,
name: str,
dtype: ALLOWED_DTYPES,
required: bool = True,
) -> None:
"""
Args:
name: The name of the property
dtype: The data type of the property
required: Whether this property is required
"""
if... |
Args:
name: The name of the property
dtype: The data type of the property
required: Whether this property is required
| __init__ | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def from_json_dict(cls, **kwargs):
"""
Deserialize from a json loaded dictionary.
The dictionary is expected to contain only one key as `name`, and
the value should be a dictionary containing `type` and
optional `required` keys.
Example: {"property_name": {"type": "string... |
Deserialize from a json loaded dictionary.
The dictionary is expected to contain only one key as `name`, and
the value should be a dictionary containing `type` and
optional `required` keys.
Example: {"property_name": {"type": "string", "required": True}}
| from_json_dict | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def _merge(self, other: BaseType) -> Property:
"""
Check if current property is compatible with another property and return
the updated property.
When two properties have the same name, we need to check if their dtypes
are compatible or not.
An example of two compatible p... |
Check if current property is compatible with another property and return
the updated property.
When two properties have the same name, we need to check if their dtypes
are compatible or not.
An example of two compatible properties:
.. code-block:: python
... | _merge | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def from_json_dict(cls, **kwargs):
"""
Deserialize from a json loaded dictionary.
The dictionary is expected to contain `type` and
`properties` keys.
Example: {"type": "object", "properties": {"property_name": {"type": "string"}}}
"""
if not {"properties", "type"}... |
Deserialize from a json loaded dictionary.
The dictionary is expected to contain `type` and
`properties` keys.
Example: {"type": "object", "properties": {"property_name": {"type": "string"}}}
| from_json_dict | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def _merge(self, other: BaseType) -> Object:
"""
Check if the current object is compatible with another object and return
the updated object.
When we infer the signature from a list of objects, it is possible
that one object has more properties than the other. In this case,
... |
Check if the current object is compatible with another object and return
the updated object.
When we infer the signature from a list of objects, it is possible
that one object has more properties than the other. In this case,
we should mark those optional properties as required=... | _merge | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def from_json_dict(cls, **kwargs):
"""
Deserialize from a json loaded dictionary.
The dictionary is expected to contain `type` and
`items` keys.
Example: {"type": "array", "items": "string"}
"""
if not {"items", "type"} <= set(kwargs.keys()):
raise Mlf... |
Deserialize from a json loaded dictionary.
The dictionary is expected to contain `type` and
`items` keys.
Example: {"type": "array", "items": "string"}
| from_json_dict | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def from_json_dict(cls, **kwargs):
"""
Deserialize from a json loaded dictionary.
The dictionary is expected to contain `type` and
`values` keys.
Example: {"type": "map", "values": "string"}
"""
if not {"values", "type"} <= set(kwargs.keys()):
raise Ml... |
Deserialize from a json loaded dictionary.
The dictionary is expected to contain `type` and
`values` keys.
Example: {"type": "map", "values": "string"}
| from_json_dict | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def __init__(self):
"""
AnyType can store any json-serializable data including None values.
For example:
.. code-block::python
from mlflow.types.schema import AnyType, Schema, ColSpec
schema = Schema([ColSpec(type=AnyType(), name="id")])
.. Note::
... |
AnyType can store any json-serializable data including None values.
For example:
.. code-block::python
from mlflow.types.schema import AnyType, Schema, ColSpec
schema = Schema([ColSpec(type=AnyType(), name="id")])
.. Note::
AnyType should be used ... | __init__ | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def from_json_dict(cls, **kwargs):
"""
Deserialize from a json loaded dictionary.
The dictionary is expected to contain `type` and
optional `name` and `required` keys.
"""
if not {"type"} <= set(kwargs.keys()):
raise MlflowException("Missing keys in ColSpec JS... |
Deserialize from a json loaded dictionary.
The dictionary is expected to contain `type` and
optional `name` and `required` keys.
| from_json_dict | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def from_json_dict(cls, **kwargs):
"""
Deserialize from a json loaded dictionary.
The dictionary is expected to contain `dtype` and `shape` keys.
"""
if not {"dtype", "shape"} <= set(kwargs.keys()):
raise MlflowException(
"Missing keys in TensorSpec JS... |
Deserialize from a json loaded dictionary.
The dictionary is expected to contain `dtype` and `shape` keys.
| from_json_dict | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def from_json_dict(cls, **kwargs):
"""
Deserialize from a json loaded dictionary.
The dictionary is expected to contain `type` and `tensor-spec` keys.
"""
if not {"tensor-spec", "type"} <= set(kwargs.keys()):
raise MlflowException(
"Missing keys in Ten... |
Deserialize from a json loaded dictionary.
The dictionary is expected to contain `type` and `tensor-spec` keys.
| from_json_dict | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def input_types_dict(self) -> dict[str, Union[DataType, np.dtype, Array, Object]]:
"""Maps column names to types, iff this schema declares names."""
if not self.has_input_names():
raise MlflowException("Cannot get input types as a dict for schema without names.")
return {x.name: x.ty... | Maps column names to types, iff this schema declares names. | input_types_dict | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def input_dict(self) -> dict[str, Union[ColSpec, TensorSpec]]:
"""Maps column names to inputs, iff this schema declares names."""
if not self.has_input_names():
raise MlflowException("Cannot get input dict for schema without names.")
return {x.name: x for x in self.inputs} | Maps column names to inputs, iff this schema declares names. | input_dict | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def numpy_types(self) -> list[np.dtype]:
"""Convenience shortcut to get the datatypes as numpy types."""
if self.is_tensor_spec():
return [x.type for x in self.inputs]
if all(isinstance(x.type, DataType) for x in self.inputs):
return [x.type.to_numpy() for x in self.input... | Convenience shortcut to get the datatypes as numpy types. | numpy_types | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def pandas_types(self) -> list[np.dtype]:
"""Convenience shortcut to get the datatypes as pandas types. Unsupported by TensorSpec."""
if self.is_tensor_spec():
raise MlflowException("TensorSpec only supports numpy types, use numpy_types() instead")
if all(isinstance(x.type, DataType)... | Convenience shortcut to get the datatypes as pandas types. Unsupported by TensorSpec. | pandas_types | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def as_spark_schema(self):
"""Convert to Spark schema. If this schema is a single unnamed column, it is converted
directly the corresponding spark data type, otherwise it's returned as a struct (missing
column names are filled with an integer sequence).
Unsupported by TensorSpec.
... | Convert to Spark schema. If this schema is a single unnamed column, it is converted
directly the corresponding spark data type, otherwise it's returned as a struct (missing
column names are filled with an integer sequence).
Unsupported by TensorSpec.
| as_spark_schema | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def validate_type_and_shape(
cls,
spec: str,
value: Any,
value_type: Union[DataType, Object],
shape: Optional[tuple[int, ...]],
):
"""
Validate that the value has the expected type and shape.
"""
from mlflow.models.utils import _enforce_object,... |
Validate that the value has the expected type and shape.
| validate_type_and_shape | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def from_json_dict(cls, **kwargs):
"""
Deserialize from a json loaded dictionary.
The dictionary is expected to contain `name`, `type` and `default` keys.
"""
# For backward compatibility, we accept both `type` and `dtype` keys
required_keys1 = {"name", "dtype", "default"... |
Deserialize from a json loaded dictionary.
The dictionary is expected to contain `name`, `type` and `default` keys.
| from_json_dict | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def _get_dataclass_annotations(cls) -> dict[str, Any]:
"""
Given a dataclass or an instance of one, collect annotations from it and all its parent
dataclasses.
"""
if not is_dataclass(cls):
raise TypeError(f"{cls.__name__} is not a dataclass.")
annotations = {}
effective_class = cls... |
Given a dataclass or an instance of one, collect annotations from it and all its parent
dataclasses.
| _get_dataclass_annotations | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def convert_dataclass_to_schema(dataclass):
"""
Converts a given dataclass into a Schema object. The dataclass must include type hints
for all its fields. Fields can be of basic types, other dataclasses, or Lists/Optional of
these types. Union types are not supported. Only the top-level fields are direc... |
Converts a given dataclass into a Schema object. The dataclass must include type hints
for all its fields. Fields can be of basic types, other dataclasses, or Lists/Optional of
these types. Union types are not supported. Only the top-level fields are directly converted
to ColSpecs, while nested fields ... | convert_dataclass_to_schema | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def _convert_dataclass_to_nested_object(dataclass):
"""
Convert a nested dataclass to an Object type used within a ColSpec.
"""
properties = []
for field_name, field_type in dataclass.__annotations__.items():
properties.append(_convert_field_to_property(field_name, field_type))
return Ob... |
Convert a nested dataclass to an Object type used within a ColSpec.
| _convert_dataclass_to_nested_object | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def _convert_field_to_property(field_name, field_type):
"""
Helper function to convert a single field to a Property object suitable for inclusion in an
Object.
"""
is_optional = False
effective_type = field_type
if get_origin(field_type) == Union and type(None) in get_args(field_type):
... |
Helper function to convert a single field to a Property object suitable for inclusion in an
Object.
| _convert_field_to_property | python | mlflow/mlflow | mlflow/types/schema.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/schema.py | Apache-2.0 |
def type_hints_no_signature_inference():
"""
This function returns a tuple of types that can be used
as type hints, but no schema can be inferred from them.
..note::
These types can not be used as nested types in other type hints.
"""
type_hints = ()
try:
import pandas as pd... |
This function returns a tuple of types that can be used
as type hints, but no schema can be inferred from them.
..note::
These types can not be used as nested types in other type hints.
| type_hints_no_signature_inference | python | mlflow/mlflow | mlflow/types/type_hints.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/type_hints.py | Apache-2.0 |
def _infer_colspec_type_from_type_hint(type_hint: type[Any]) -> ColSpecType:
"""
Infer the ColSpec type from a type hint.
The inferred dtype should be one of the supported data types in COLSPEC_TYPES.
"""
if type_hint == Any:
color_warning(
message="Any type hint is inferred as A... |
Infer the ColSpec type from a type hint.
The inferred dtype should be one of the supported data types in COLSPEC_TYPES.
| _infer_colspec_type_from_type_hint | python | mlflow/mlflow | mlflow/types/type_hints.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/type_hints.py | Apache-2.0 |
def _infer_type_from_pydantic_model(model: pydantic.BaseModel) -> Object:
"""
Infer the object schema from a pydantic model.
"""
if _is_pydantic_type_hint(model):
fields = model_fields(model)
else:
raise TypeError(f"model must be a Pydantic model class, but got {type(model)}")
p... |
Infer the object schema from a pydantic model.
| _infer_type_from_pydantic_model | python | mlflow/mlflow | mlflow/types/type_hints.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/type_hints.py | Apache-2.0 |
def _get_element_type_of_list_type_hint(type_hint: type[list[Any]]) -> Any:
"""
Get the element type of list[...] type hint
"""
args = get_args(type_hint)
# Optional[list[...]]
if type(None) in args:
raise MlflowException.invalid_parameter_value(OPTIONAL_INPUT_MSG)
# a valid list[...... |
Get the element type of list[...] type hint
| _get_element_type_of_list_type_hint | python | mlflow/mlflow | mlflow/types/type_hints.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/type_hints.py | Apache-2.0 |
def _infer_schema_from_list_type_hint(type_hint: type[list[Any]]) -> Schema:
"""
Infer schema from a list type hint.
The type hint must be list[...], and the inferred schema contains a
single ColSpec, where the type is based on the element type of the list type hint,
since ColSpec represents a colum... |
Infer schema from a list type hint.
The type hint must be list[...], and the inferred schema contains a
single ColSpec, where the type is based on the element type of the list type hint,
since ColSpec represents a column's data type of the dataset.
e.g. list[int] -> Schema([ColSpec(type=DataType.lo... | _infer_schema_from_list_type_hint | python | mlflow/mlflow | mlflow/types/type_hints.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/type_hints.py | Apache-2.0 |
def _validate_data_against_type_hint(data: Any, type_hint: type[Any]) -> Any:
"""
Validate the data against provided type hint.
The allowed conversions are:
dictionary data with Pydantic model type hint -> Pydantic model instance
Args:
data: The data to validate
type_hint: The t... |
Validate the data against provided type hint.
The allowed conversions are:
dictionary data with Pydantic model type hint -> Pydantic model instance
Args:
data: The data to validate
type_hint: The type hint to validate against
| _validate_data_against_type_hint | python | mlflow/mlflow | mlflow/types/type_hints.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/type_hints.py | Apache-2.0 |
def _parse_data_for_datatype_hint(data: Any, type_hint: type[Any]) -> Any:
"""
Parse the data based on the type hint.
This should only be used in MLflow serving environment to convert
json data to the expected format.
Allowed conversions:
- string data with datetime type hint -> datetime obj... |
Parse the data based on the type hint.
This should only be used in MLflow serving environment to convert
json data to the expected format.
Allowed conversions:
- string data with datetime type hint -> datetime object
- string data with bytes type hint -> bytes object
| _parse_data_for_datatype_hint | python | mlflow/mlflow | mlflow/types/type_hints.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/type_hints.py | Apache-2.0 |
def _get_origin_type(type_hint: type[Any]) -> Any:
"""
Get the origin type of a type hint.
If the type hint is Union type, return the origin type of the effective type.
If the type hint is Union type with multiple effective types, return Any.
"""
origin_type = get_origin(type_hint)
if origin... |
Get the origin type of a type hint.
If the type hint is Union type, return the origin type of the effective type.
If the type hint is Union type with multiple effective types, return Any.
| _get_origin_type | python | mlflow/mlflow | mlflow/types/type_hints.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/type_hints.py | Apache-2.0 |
def _convert_data_to_type_hint(data: Any, type_hint: type[Any]) -> Any:
"""
Convert data to the expected format based on the type hint.
This function is used in data validation of @pyfunc to support compatibility with
functions such as mlflow.evaluate and spark_udf since they accept pandas DF as input.
... |
Convert data to the expected format based on the type hint.
This function is used in data validation of @pyfunc to support compatibility with
functions such as mlflow.evaluate and spark_udf since they accept pandas DF as input.
NB: the input pandas DataFrame must contain a single column with the same t... | _convert_data_to_type_hint | python | mlflow/mlflow | mlflow/types/type_hints.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/type_hints.py | Apache-2.0 |
def _sanitize_data(data: Any) -> Any:
"""
Sanitize the data by converting any numpy lists to Python lists.
This is needed because spark_udf (pandas_udf) implicitly converts lists into numpy arrays.
For example, below udf demonstrates the behavior:
df = spark.createDataFrame(pd.DataFrame({"input... |
Sanitize the data by converting any numpy lists to Python lists.
This is needed because spark_udf (pandas_udf) implicitly converts lists into numpy arrays.
For example, below udf demonstrates the behavior:
df = spark.createDataFrame(pd.DataFrame({"input": [["a", "b"], ["c", "d"]]}))
@panda... | _sanitize_data | python | mlflow/mlflow | mlflow/types/type_hints.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/type_hints.py | Apache-2.0 |
def _get_tensor_shape(data, variable_dimension: Optional[int] = 0) -> tuple:
"""Infer the shape of the inputted data.
This method creates the shape of the tensor to store in the TensorSpec. The variable dimension
is assumed to be the first dimension by default. This assumption can be overridden by inputtin... | Infer the shape of the inputted data.
This method creates the shape of the tensor to store in the TensorSpec. The variable dimension
is assumed to be the first dimension by default. This assumption can be overridden by inputting
a different variable dimension or `None` to represent that the input tensor do... | _get_tensor_shape | python | mlflow/mlflow | mlflow/types/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/utils.py | Apache-2.0 |
def clean_tensor_type(dtype: np.dtype):
"""
This method strips away the size information stored in flexible datatypes such as np.str_ and
np.bytes_. Other numpy dtypes are returned unchanged.
Args:
dtype: Numpy dtype of a tensor
Returns:
dtype: Cleaned numpy dtype
"""
if no... |
This method strips away the size information stored in flexible datatypes such as np.str_ and
np.bytes_. Other numpy dtypes are returned unchanged.
Args:
dtype: Numpy dtype of a tensor
Returns:
dtype: Cleaned numpy dtype
| clean_tensor_type | python | mlflow/mlflow | mlflow/types/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/utils.py | Apache-2.0 |
def _infer_colspec_type(data: Any) -> Union[DataType, Array, Object, AnyType]:
"""
Infer an MLflow Colspec type from the dataset.
Args:
data: data to infer from.
Returns:
Object
"""
dtype = _infer_datatype(data)
if dtype is None:
raise MlflowException(
... |
Infer an MLflow Colspec type from the dataset.
Args:
data: data to infer from.
Returns:
Object
| _infer_colspec_type | python | mlflow/mlflow | mlflow/types/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/utils.py | Apache-2.0 |
def _infer_datatype(data: Any) -> Optional[Union[DataType, Array, Object, AnyType]]:
"""
Infer the datatype of input data.
Data type and inferred schema type mapping:
- dict -> Object
- list -> Array
- numpy.ndarray -> Array
- scalar -> DataType
- None, empty dictiona... |
Infer the datatype of input data.
Data type and inferred schema type mapping:
- dict -> Object
- list -> Array
- numpy.ndarray -> Array
- scalar -> DataType
- None, empty dictionary/list -> AnyType
.. Note::
Empty numpy arrays are inferred as None to keep th... | _infer_datatype | python | mlflow/mlflow | mlflow/types/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/utils.py | Apache-2.0 |
def _infer_array_datatype(data: Union[list, np.ndarray]) -> Optional[Array]:
"""Infer schema from an array. This tries to infer type if there is at least one
non-null item in the list, assuming the list has a homogeneous type. However,
if the list is empty or all items are null, returns None as a sign of un... | Infer schema from an array. This tries to infer type if there is at least one
non-null item in the list, assuming the list has a homogeneous type. However,
if the list is empty or all items are null, returns None as a sign of undetermined.
E.g.
["a", "b"] => Array(string)
["a", None] => Arr... | _infer_array_datatype | python | mlflow/mlflow | mlflow/types/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/utils.py | Apache-2.0 |
def _infer_schema(data: Any) -> Schema:
"""
Infer an MLflow schema from a dataset.
Data inputted as a numpy array or a dictionary is represented by :py:class:`TensorSpec`.
All other inputted data types are specified by :py:class:`ColSpec`.
A `TensorSpec` captures the data shape (default variable a... |
Infer an MLflow schema from a dataset.
Data inputted as a numpy array or a dictionary is represented by :py:class:`TensorSpec`.
All other inputted data types are specified by :py:class:`ColSpec`.
A `TensorSpec` captures the data shape (default variable axis is 0), the data type (numpy.dtype)
and ... | _infer_schema | python | mlflow/mlflow | mlflow/types/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/types/utils.py | Apache-2.0 |
def _get_min_indent_of_docstring(docstring_str: str) -> str:
"""
Get the minimum indentation string of a docstring, based on the assumption
that the closing triple quote for multiline comments must be on a new line.
Note that based on ruff rule D209, the closing triple quote for multiline
comments m... |
Get the minimum indentation string of a docstring, based on the assumption
that the closing triple quote for multiline comments must be on a new line.
Note that based on ruff rule D209, the closing triple quote for multiline
comments must be on a new line.
Args:
docstring_str: string with ... | _get_min_indent_of_docstring | python | mlflow/mlflow | mlflow/utils/annotations.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/annotations.py | Apache-2.0 |
def experimental(api_or_type: Union[C, str]) -> C:
"""Decorator / decorator creator for marking APIs experimental in the docstring.
Args:
api_or_type: An API to mark, or an API typestring for which to generate a decorator.
Returns:
Decorated API (if a ``api_or_type`` is an API) or a functi... | Decorator / decorator creator for marking APIs experimental in the docstring.
Args:
api_or_type: An API to mark, or an API typestring for which to generate a decorator.
Returns:
Decorated API (if a ``api_or_type`` is an API) or a function that decorates
the specified API type (if ``api... | experimental | python | mlflow/mlflow | mlflow/utils/annotations.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/annotations.py | Apache-2.0 |
def deprecated(
alternative: Optional[str] = None, since: Optional[str] = None, impact: Optional[str] = None
):
"""Annotation decorator for marking APIs as deprecated in docstrings and raising a warning if
called.
Args:
alternative: The name of a superseded replacement function, method,
... | Annotation decorator for marking APIs as deprecated in docstrings and raising a warning if
called.
Args:
alternative: The name of a superseded replacement function, method,
or class to use in place of the deprecated one.
since: A version designator defining during which release the ... | deprecated | python | mlflow/mlflow | mlflow/utils/annotations.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/annotations.py | Apache-2.0 |
def keyword_only(func):
"""A decorator that forces keyword arguments in the wrapped method."""
@wraps(func)
def wrapper(*args, **kwargs):
if len(args) > 0:
raise TypeError(f"Method {func.__name__} only takes keyword arguments.")
return func(**kwargs)
indent = _get_min_inden... | A decorator that forces keyword arguments in the wrapped method. | keyword_only | python | mlflow/mlflow | mlflow/utils/annotations.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/annotations.py | Apache-2.0 |
def filter_user_warnings_once(func):
"""A decorator that filter user warnings to only show once in the wrapped method."""
@wraps(func)
def wrapper(*args, **kwargs):
with warnings.catch_warnings():
warnings.simplefilter("once", category=UserWarning)
return func(*args, **kwarg... | A decorator that filter user warnings to only show once in the wrapped method. | filter_user_warnings_once | python | mlflow/mlflow | mlflow/utils/annotations.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/annotations.py | Apache-2.0 |
def _get_arg_names(f):
"""Get the argument names of a function.
Args:
f: A function.
Returns:
A list of argument names.
"""
# `inspect.getargspec` or `inspect.getfullargspec` doesn't work properly for a wrapped function.
# See https://hynek.me/articles/decorators#mangled-signa... | Get the argument names of a function.
Args:
f: A function.
Returns:
A list of argument names.
| _get_arg_names | python | mlflow/mlflow | mlflow/utils/arguments_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/arguments_utils.py | Apache-2.0 |
def get_conda_bin_executable(executable_name):
"""
Return path to the specified executable, assumed to be discoverable within the 'bin'
subdirectory of a conda installation.
The conda home directory (expected to contain a 'bin' subdirectory) is configurable via the
``mlflow.projects.MLFLOW_CONDA_HO... |
Return path to the specified executable, assumed to be discoverable within the 'bin'
subdirectory of a conda installation.
The conda home directory (expected to contain a 'bin' subdirectory) is configurable via the
``mlflow.projects.MLFLOW_CONDA_HOME`` environment variable. If
``mlflow.projects.ML... | get_conda_bin_executable | python | mlflow/mlflow | mlflow/utils/conda.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/conda.py | Apache-2.0 |
def _list_conda_environments(extra_env=None):
"""Return a list of names of conda environments.
Args:
extra_env: Extra environment variables for running "conda env list" command.
"""
prc = process._exec_cmd(
[get_conda_bin_executable("conda"), "env", "list", "--json"], extra_env=extra_e... | Return a list of names of conda environments.
Args:
extra_env: Extra environment variables for running "conda env list" command.
| _list_conda_environments | python | mlflow/mlflow | mlflow/utils/conda.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/conda.py | Apache-2.0 |
def _create_conda_env_retry(
conda_env_path, conda_env_create_path, project_env_name, conda_extra_env_vars, _capture_output
):
"""
`conda env create` command can fail due to network issues such as `ConnectionResetError`
while collecting package metadata. This function retries the command up to 3 times.
... |
`conda env create` command can fail due to network issues such as `ConnectionResetError`
while collecting package metadata. This function retries the command up to 3 times.
| _create_conda_env_retry | python | mlflow/mlflow | mlflow/utils/conda.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/conda.py | Apache-2.0 |
def _get_conda_extra_env_vars(env_root_dir=None):
"""
Given the `env_root_dir` (See doc of PyFuncBackend constructor argument `env_root_dir`),
return a dict of environment variables which are used to config conda to generate envs
under the expected `env_root_dir`.
"""
if env_root_dir is None:
... |
Given the `env_root_dir` (See doc of PyFuncBackend constructor argument `env_root_dir`),
return a dict of environment variables which are used to config conda to generate envs
under the expected `env_root_dir`.
| _get_conda_extra_env_vars | python | mlflow/mlflow | mlflow/utils/conda.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/conda.py | Apache-2.0 |
def get_or_create_conda_env(
conda_env_path,
env_id=None,
capture_output=False,
env_root_dir=None,
pip_requirements_override=None,
extra_envs=None,
):
"""Given a `Project`, creates a conda environment containing the project's dependencies if such
a conda environment doesn't already exist... | Given a `Project`, creates a conda environment containing the project's dependencies if such
a conda environment doesn't already exist. Returns the name of the conda environment.
Args:
conda_env_path: Path to a conda yaml file.
env_id: Optional string that is added to the contents of the yaml f... | get_or_create_conda_env | python | mlflow/mlflow | mlflow/utils/conda.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/conda.py | Apache-2.0 |
def _get_conda_dependencies(conda_yaml_path):
"""Extracts conda dependencies from a conda yaml file.
Args:
conda_yaml_path: Conda yaml file path.
"""
with open(conda_yaml_path) as f:
conda_yaml = yaml.safe_load(f)
return [d for d in conda_yaml.get("dependencies", []) if isinstan... | Extracts conda dependencies from a conda yaml file.
Args:
conda_yaml_path: Conda yaml file path.
| _get_conda_dependencies | python | mlflow/mlflow | mlflow/utils/conda.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/conda.py | Apache-2.0 |
def login(backend: str = "databricks", interactive: bool = True) -> None:
"""Configure MLflow server authentication and connect MLflow to tracking server.
This method provides a simple way to connect MLflow to its tracking server. Currently only
Databricks tracking server is supported. Users will be prompt... | Configure MLflow server authentication and connect MLflow to tracking server.
This method provides a simple way to connect MLflow to its tracking server. Currently only
Databricks tracking server is supported. Users will be prompted to enter the credentials if no
existing Databricks profile is found, and t... | login | python | mlflow/mlflow | mlflow/utils/credentials.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/credentials.py | Apache-2.0 |
def _overwrite_or_create_databricks_profile(
file_name,
profile,
profile_name="DEFAULT",
):
"""Overwrite or create a profile in the databricks config file.
Args:
file_name: string, the file name of the databricks config file, usually `~/.databrickscfg`.
profile: dict, contains the a... | Overwrite or create a profile in the databricks config file.
Args:
file_name: string, the file name of the databricks config file, usually `~/.databrickscfg`.
profile: dict, contains the authentiacation profile information.
profile_name: string, the name of the profile to be overwritten or ... | _overwrite_or_create_databricks_profile | python | mlflow/mlflow | mlflow/utils/credentials.py | https://github.com/mlflow/mlflow/blob/master/mlflow/utils/credentials.py | Apache-2.0 |
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