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 wait(self):
"""
Wait for the run to finish, returning True if the run succeeded and false otherwise. Note
that in some cases (e.g. remote execution on Databricks), we may wait until the remote job
completes rather than until the MLflow run completes.
""" |
Wait for the run to finish, returning True if the run succeeded and false otherwise. Note
that in some cases (e.g. remote execution on Databricks), we may wait until the remote job
completes rather than until the MLflow run completes.
| wait | python | mlflow/mlflow | mlflow/projects/submitted_run.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/submitted_run.py | Apache-2.0 |
def cancel(self):
"""
Cancel the run (interrupts the command subprocess, cancels the Databricks run, etc) and
waits for it to terminate. The MLflow run status may not be set correctly
upon run cancellation.
""" |
Cancel the run (interrupts the command subprocess, cancels the Databricks run, etc) and
waits for it to terminate. The MLflow run status may not be set correctly
upon run cancellation.
| cancel | python | mlflow/mlflow | mlflow/projects/submitted_run.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/submitted_run.py | Apache-2.0 |
def _is_git_repo(path) -> bool:
"""Returns True if passed-in path is a valid git repository"""
import git
try:
repo = git.Repo(path)
if len(repo.branches) > 0:
return True
except git.exc.InvalidGitRepositoryError:
pass
return False | Returns True if passed-in path is a valid git repository | _is_git_repo | python | mlflow/mlflow | mlflow/projects/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/utils.py | Apache-2.0 |
def _parse_file_uri(uri: str) -> str:
"""Converts file URIs to filesystem paths"""
if _is_file_uri(uri):
parsed_file_uri = urllib.parse.urlparse(uri)
return str(
pathlib.Path(parsed_file_uri.netloc, parsed_file_uri.path, parsed_file_uri.fragment)
)
return uri | Converts file URIs to filesystem paths | _parse_file_uri | python | mlflow/mlflow | mlflow/projects/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/utils.py | Apache-2.0 |
def _is_valid_branch_name(work_dir, version):
"""
Returns True if the ``version`` is the name of a branch in a Git project.
``work_dir`` must be the working directory in a git repo.
"""
if version is not None:
from git import Repo
from git.exc import GitCommandError
repo = R... |
Returns True if the ``version`` is the name of a branch in a Git project.
``work_dir`` must be the working directory in a git repo.
| _is_valid_branch_name | python | mlflow/mlflow | mlflow/projects/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/utils.py | Apache-2.0 |
def _fetch_project(uri, version=None):
"""
Fetch a project into a local directory, returning the path to the local project directory.
"""
parsed_uri, subdirectory = _parse_subdirectory(uri)
use_temp_dst_dir = _is_zip_uri(parsed_uri) or not _is_local_uri(parsed_uri)
dst_dir = tempfile.mkdtemp() i... |
Fetch a project into a local directory, returning the path to the local project directory.
| _fetch_project | python | mlflow/mlflow | mlflow/projects/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/utils.py | Apache-2.0 |
def _create_run(uri, experiment_id, work_dir, version, entry_point, parameters):
"""
Create a ``Run`` against the current MLflow tracking server, logging metadata (e.g. the URI,
entry point, and parameters of the project) about the run. Return an ``ActiveRun`` that can be
used to report additional data ... |
Create a ``Run`` against the current MLflow tracking server, logging metadata (e.g. the URI,
entry point, and parameters of the project) about the run. Return an ``ActiveRun`` that can be
used to report additional data about the run (metrics/params) to the tracking server.
| _create_run | python | mlflow/mlflow | mlflow/projects/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/utils.py | Apache-2.0 |
def get_entry_point_command(project, entry_point, parameters, storage_dir):
"""
Returns the shell command to execute in order to run the specified entry point.
Args:
project: Project containing the target entry point.
entry_point: Entry point to run.
parameters: Parameters (dictiona... |
Returns the shell command to execute in order to run the specified entry point.
Args:
project: Project containing the target entry point.
entry_point: Entry point to run.
parameters: Parameters (dictionary) for the entry point command.
storage_dir: Base local directory to use f... | get_entry_point_command | python | mlflow/mlflow | mlflow/projects/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/utils.py | Apache-2.0 |
def get_run_env_vars(run_id, experiment_id):
"""
Returns a dictionary of environment variable key-value pairs to set in subprocess launched
to run MLflow projects.
"""
return {
MLFLOW_RUN_ID.name: run_id,
MLFLOW_TRACKING_URI.name: tracking.get_tracking_uri(),
MLFLOW_EXPERIMEN... |
Returns a dictionary of environment variable key-value pairs to set in subprocess launched
to run MLflow projects.
| get_run_env_vars | python | mlflow/mlflow | mlflow/projects/utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/utils.py | Apache-2.0 |
def compute_parameters(self, user_parameters, storage_dir):
"""
Given a dict mapping user-specified param names to values, computes parameters to
substitute into the command for this entry point. Returns a tuple (params, extra_params)
where `params` contains key-value pairs for parameter... |
Given a dict mapping user-specified param names to values, computes parameters to
substitute into the command for this entry point. Returns a tuple (params, extra_params)
where `params` contains key-value pairs for parameters specified in the entry point
definition, and `extra_params` c... | compute_parameters | python | mlflow/mlflow | mlflow/projects/_project_spec.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/_project_spec.py | Apache-2.0 |
def _resolve_experiment_id(experiment_name=None, experiment_id=None):
"""
Resolve experiment.
Verifies either one or other is specified - cannot be both selected.
If ``experiment_name`` is provided and does not exist, an experiment
of that name is created and its id is returned.
Args:
... |
Resolve experiment.
Verifies either one or other is specified - cannot be both selected.
If ``experiment_name`` is provided and does not exist, an experiment
of that name is created and its id is returned.
Args:
experiment_name: Name of experiment under which to launch the run.
e... | _resolve_experiment_id | python | mlflow/mlflow | mlflow/projects/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/__init__.py | Apache-2.0 |
def _maybe_set_run_terminated(active_run, status):
"""
If the passed-in active run is defined and still running (i.e. hasn't already been terminated
within user code), mark it as terminated with the passed-in status.
"""
if active_run is None:
return
run_id = active_run.info.run_id
c... |
If the passed-in active run is defined and still running (i.e. hasn't already been terminated
within user code), mark it as terminated with the passed-in status.
| _maybe_set_run_terminated | python | mlflow/mlflow | mlflow/projects/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/__init__.py | Apache-2.0 |
def _build_mlflow_run_cmd(
uri, entry_point, docker_args, storage_dir, env_manager, run_id, parameters
):
"""
Build and return an array containing an ``mlflow run`` command that can be invoked to locally
run the project at the specified URI.
"""
mlflow_run_arr = ["mlflow", "run", uri, "-e", entr... |
Build and return an array containing an ``mlflow run`` command that can be invoked to locally
run the project at the specified URI.
| _build_mlflow_run_cmd | python | mlflow/mlflow | mlflow/projects/backend/local.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/backend/local.py | Apache-2.0 |
def _run_mlflow_run_cmd(mlflow_run_arr, env_map):
"""
Invoke ``mlflow run`` in a subprocess, which in turn runs the entry point in a child process.
Returns a handle to the subprocess. Popen launched to invoke ``mlflow run``.
"""
final_env = os.environ.copy()
final_env.update(env_map)
# Launc... |
Invoke ``mlflow run`` in a subprocess, which in turn runs the entry point in a child process.
Returns a handle to the subprocess. Popen launched to invoke ``mlflow run``.
| _run_mlflow_run_cmd | python | mlflow/mlflow | mlflow/projects/backend/local.py | https://github.com/mlflow/mlflow/blob/master/mlflow/projects/backend/local.py | Apache-2.0 |
def model_version_to_prompt_version(
model_version: ModelVersion, prompt_tags: Optional[dict[str, str]] = None
) -> PromptVersion:
"""
Create a PromptVersion object from a ModelVersion object.
Args:
model_version: The ModelVersion object to convert to a PromptVersion.
prompt_tags: The p... |
Create a PromptVersion object from a ModelVersion object.
Args:
model_version: The ModelVersion object to convert to a PromptVersion.
prompt_tags: The prompt-level tags. Optional.
Returns:
PromptVersion: The converted PromptVersion object.
| model_version_to_prompt_version | python | mlflow/mlflow | mlflow/prompt/registry_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/prompt/registry_utils.py | Apache-2.0 |
def add_prompt_filter_string(
filter_string: Optional[str], is_prompt: bool = False
) -> Optional[str]:
"""
Additional filter string to include/exclude prompts from the result.
By default, exclude prompts from the result.
"""
if IS_PROMPT_TAG_KEY not in (filter_string or ""):
prompt_filt... |
Additional filter string to include/exclude prompts from the result.
By default, exclude prompts from the result.
| add_prompt_filter_string | python | mlflow/mlflow | mlflow/prompt/registry_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/prompt/registry_utils.py | Apache-2.0 |
def has_prompt_tag(tags: Optional[Union[list[RegisteredModelTag], dict[str, str]]]) -> bool:
"""Check if the given tags contain the prompt tag."""
if isinstance(tags, dict):
return IS_PROMPT_TAG_KEY in tags if tags else False
if not tags:
return
return any(tag.key == IS_PROMPT_TAG_KEY fo... | Check if the given tags contain the prompt tag. | has_prompt_tag | python | mlflow/mlflow | mlflow/prompt/registry_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/prompt/registry_utils.py | Apache-2.0 |
def is_prompt_supported_registry(registry_uri: Optional[str] = None) -> bool:
"""
Check if the current registry supports prompts.
Prompts registration is supported in:
- OSS MLflow Tracking Server (always)
- Unity Catalog
- Not supported in legacy Databricks workspace registry or Unity Catalog ... |
Check if the current registry supports prompts.
Prompts registration is supported in:
- OSS MLflow Tracking Server (always)
- Unity Catalog
- Not supported in legacy Databricks workspace registry or Unity Catalog OSS
| is_prompt_supported_registry | python | mlflow/mlflow | mlflow/prompt/registry_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/prompt/registry_utils.py | Apache-2.0 |
def require_prompt_registry(func):
"""Ensure that the current registry supports prompts."""
@functools.wraps(func)
def wrapper(*args, **kwargs):
if args and isinstance(args[0], mlflow.MlflowClient):
registry_uri = args[0]._registry_uri
else:
registry_uri = mlflow.get... | Ensure that the current registry supports prompts. | require_prompt_registry | python | mlflow/mlflow | mlflow/prompt/registry_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/prompt/registry_utils.py | Apache-2.0 |
def translate_prompt_exception(func):
"""
Translate MlflowException message related to RegisteredModel / ModelVersion into
prompt-specific message.
"""
MODEL_PATTERN = re.compile(r"(registered model|model version)", re.IGNORECASE)
@functools.wraps(func)
def wrapper(*args, **kwargs):
... |
Translate MlflowException message related to RegisteredModel / ModelVersion into
prompt-specific message.
| translate_prompt_exception | python | mlflow/mlflow | mlflow/prompt/registry_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/prompt/registry_utils.py | Apache-2.0 |
def validate_prompt_name(name: Any):
"""Validate the prompt name against the prompt specific rule"""
if not isinstance(name, str) or not name:
raise MlflowException.invalid_parameter_value(
"Prompt name must be a non-empty string.",
)
if PROMPT_NAME_RULE.match(name) is None:
... | Validate the prompt name against the prompt specific rule | validate_prompt_name | python | mlflow/mlflow | mlflow/prompt/registry_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/prompt/registry_utils.py | Apache-2.0 |
def handle_resource_already_exist_error(
name: str,
is_existing_entity_prompt: bool,
is_new_entity_prompt: bool,
):
"""
Show a more specific error message for name conflict in Model Registry.
1. When creating a model with the same name as an existing model, say "model already exists".
2. Wh... |
Show a more specific error message for name conflict in Model Registry.
1. When creating a model with the same name as an existing model, say "model already exists".
2. When creating a prompt with the same name as an existing prompt, say "prompt already exists".
3. Otherwise, explain that a prompt and... | handle_resource_already_exist_error | python | mlflow/mlflow | mlflow/prompt/registry_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/prompt/registry_utils.py | Apache-2.0 |
def parse_prompt_name_or_uri(
name_or_uri: str, version: Optional[Union[str, int]] = None
) -> tuple[str, Optional[Union[str, int]]]:
"""
Parse prompt name or URI into (name, version) tuple.
Handles two cases:
1. URI format: "prompts:/name/version" or "prompts:/name@alias"
- Returns (name, p... |
Parse prompt name or URI into (name, version) tuple.
Handles two cases:
1. URI format: "prompts:/name/version" or "prompts:/name@alias"
- Returns (name, parsed_version)
- Raises error if version parameter is also provided
2. Name format: "my_prompt"
- Returns (name, version)
... | parse_prompt_name_or_uri | python | mlflow/mlflow | mlflow/prompt/registry_utils.py | https://github.com/mlflow/mlflow/blob/master/mlflow/prompt/registry_utils.py | Apache-2.0 |
def log_model(
model,
artifact_path: Optional[str] = None,
conda_env=None,
code_paths=None,
registered_model_name=None,
signature=None,
input_example=None,
await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
pip_requirements=None,
extra_pip_requirements=None,
metadata=Non... |
Log a Promptflow model as an MLflow artifact for the current run.
Args:
model: A promptflow model loaded by `promptflow.load_flow()`.
artifact_path: Deprecated. Use `name` instead.
conda_env: {{ conda_env }}
code_paths: {{ code_paths }}
registered_model_name: If given, ... | log_model | python | mlflow/mlflow | mlflow/promptflow/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/promptflow/__init__.py | Apache-2.0 |
def save_model(
model,
path,
conda_env=None,
code_paths=None,
mlflow_model=None,
signature: ModelSignature = None,
input_example: ModelInputExample = None,
pip_requirements=None,
extra_pip_requirements=None,
metadata=None,
model_config: Optional[dict[str, Any]] = None,
):
... |
Save a Promptflow model to a path on the local file system.
Args:
model: A promptflow model loaded by `promptflow.load_flow()`.
path: Local path where the serialized model (as YAML) is to be saved.
conda_env: {{ conda_env }}
code_paths: {{ code_paths }}
mlflow_model: :p... | save_model | python | mlflow/mlflow | mlflow/promptflow/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/promptflow/__init__.py | Apache-2.0 |
def predict( # pylint: disable=unused-argument
self,
data: Union[pd.DataFrame, list[Union[str, dict[str, Any]]]],
params: Optional[dict[str, Any]] = None, # pylint: disable=unused-argument
) -> Union[dict, list]:
"""
Args:
data: Model input data. Either a pandas... |
Args:
data: Model input data. Either a pandas DataFrame with only 1 row or a dictionary.
.. code-block:: python
loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri)
# Predict on a flow input dictionary.
... | predict | python | mlflow/mlflow | mlflow/promptflow/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/promptflow/__init__.py | Apache-2.0 |
def _load_pyfunc(path, model_config: Optional[dict[str, Any]] = None): # noqa: D417
"""
Load PyFunc implementation for Promptflow. Called by ``pyfunc.load_model``.
Args
path: Local filesystem path to the MLflow Model with the ``promptflow`` flavor.
"""
from promptflow import load_flow
... |
Load PyFunc implementation for Promptflow. Called by ``pyfunc.load_model``.
Args
path: Local filesystem path to the MLflow Model with the ``promptflow`` flavor.
| _load_pyfunc | python | mlflow/mlflow | mlflow/promptflow/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/promptflow/__init__.py | Apache-2.0 |
def load_model(model_uri, dst_path=None):
"""
Load a Promptflow model 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_b... |
Load a Promptflow model 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:/<mlfl... | load_model | python | mlflow/mlflow | mlflow/promptflow/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/promptflow/__init__.py | Apache-2.0 |
def load_model(model_uri, dst_path=None):
"""
Load a Prophet model 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_buck... |
Load a Prophet model 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:/<mlflow_... | load_model | python | mlflow/mlflow | mlflow/prophet/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/prophet/__init__.py | Apache-2.0 |
def autolog(log_traces: bool = True, disable: bool = False, silent: bool = False):
"""
Enable (or disable) autologging for Pydantic_AI.
Args:
log_traces: If True, capture spans for agent + model calls.
disable: If True, disable the autologging patches.
silent: If True, suppress... |
Enable (or disable) autologging for Pydantic_AI.
Args:
log_traces: If True, capture spans for agent + model calls.
disable: If True, disable the autologging patches.
silent: If True, suppress MLflow warnings/info.
| autolog | python | mlflow/mlflow | mlflow/pydantic_ai/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pydantic_ai/__init__.py | Apache-2.0 |
def __init__( # noqa: D417
self,
config,
env_manager,
workers=1,
install_mlflow=False,
create_env_root_dir=False,
env_root_dir=None,
**kwargs,
):
"""
Args:
env_manager: Environment manager to use for preparing the environme... |
Args:
env_manager: Environment manager to use for preparing the environment. If None,
MLflow will automatically pick the env manager based on the model's flavor
configuration for generate_dockerfile. It can't be None for other methods.
env_root_dir: Root ... | __init__ | python | mlflow/mlflow | mlflow/pyfunc/backend.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/backend.py | Apache-2.0 |
def predict(
self,
model_uri,
input_path,
output_path,
content_type,
pip_requirements_override=None,
extra_envs=None,
):
"""
Generate predictions using generic python model saved with MLflow. The expected format of
the input JSON is the... |
Generate predictions using generic python model saved with MLflow. The expected format of
the input JSON is the MLflow scoring format.
Return the prediction results as a JSON.
| predict | python | mlflow/mlflow | mlflow/pyfunc/backend.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/backend.py | Apache-2.0 |
def setup_sigterm_on_parent_death():
"""
Uses prctl to automatically send SIGTERM to the command process when its parent is
dead.
This handles the case when the parent is a PySpark worker process.
If a user cancels the PySpark job, the wor... |
Uses prctl to automatically send SIGTERM to the command process when its parent is
dead.
This handles the case when the parent is a PySpark worker process.
If a user cancels the PySpark job, the worker process gets killed, regardless of
P... | setup_sigterm_on_parent_death | python | mlflow/mlflow | mlflow/pyfunc/backend.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/backend.py | Apache-2.0 |
def _get_base_image(self, model_path: str, install_java: bool) -> str:
"""
Determine the base image to use for the Dockerfile.
We use Python slim base image when all the following conditions are met:
1. Model URI is specified by the user
2. Model flavor does not require Java... |
Determine the base image to use for the Dockerfile.
We use Python slim base image when all the following conditions are met:
1. Model URI is specified by the user
2. Model flavor does not require Java
3. Python version is specified in the model
Returns:
... | _get_base_image | python | mlflow/mlflow | mlflow/pyfunc/backend.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/backend.py | Apache-2.0 |
def set_prediction_context(context: Optional[Context]):
"""
Set the context for the current prediction request. The context will be set as a thread-local
variable and will be accessible globally within the same thread.
Args:
context: The context for the current prediction request.
"""
i... |
Set the context for the current prediction request. The context will be set as a thread-local
variable and will be accessible globally within the same thread.
Args:
context: The context for the current prediction request.
| set_prediction_context | python | mlflow/mlflow | mlflow/pyfunc/context.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/context.py | Apache-2.0 |
def __getstate__(self):
"""
The `DBConnectArtifactCache` instance is created in Databricks Connect client side,
and it will be pickled to Databricks Connect UDF sandbox
(see `get_unpacked_artifact_dir` method), but Spark Connect client object is
not pickle-able, we need to skip t... |
The `DBConnectArtifactCache` instance is created in Databricks Connect client side,
and it will be pickled to Databricks Connect UDF sandbox
(see `get_unpacked_artifact_dir` method), but Spark Connect client object is
not pickle-able, we need to skip this field.
| __getstate__ | python | mlflow/mlflow | mlflow/pyfunc/dbconnect_artifact_cache.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/dbconnect_artifact_cache.py | Apache-2.0 |
def add_artifact_archive(self, cache_key, artifact_archive_path):
"""
Add an artifact archive file to Databricks connect cache.
The archive file must be 'tar.gz' format.
You can only call this method in Databricks Connect client side.
"""
if not artifact_archive_path.ends... |
Add an artifact archive file to Databricks connect cache.
The archive file must be 'tar.gz' format.
You can only call this method in Databricks Connect client side.
| add_artifact_archive | python | mlflow/mlflow | mlflow/pyfunc/dbconnect_artifact_cache.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/dbconnect_artifact_cache.py | Apache-2.0 |
def get_unpacked_artifact_dir(self, cache_key):
"""
Get unpacked artifact directory path, you can only call this method
inside Databricks Connect spark UDF sandbox.
"""
if cache_key not in self._cache:
raise RuntimeError(f"The artifact '{cache_key}' does not exist.")
... |
Get unpacked artifact directory path, you can only call this method
inside Databricks Connect spark UDF sandbox.
| get_unpacked_artifact_dir | python | mlflow/mlflow | mlflow/pyfunc/dbconnect_artifact_cache.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/dbconnect_artifact_cache.py | Apache-2.0 |
def archive_directory(input_dir, archive_file_path):
"""
Archive the `input_dir` directory, save the archive file to `archive_file_path`,
the generated archive file is 'tar.gz' format.
Note: all symlink files in the input directory are kept as it is in the archive file.
"""
archive_file_path = ... |
Archive the `input_dir` directory, save the archive file to `archive_file_path`,
the generated archive file is 'tar.gz' format.
Note: all symlink files in the input directory are kept as it is in the archive file.
| archive_directory | python | mlflow/mlflow | mlflow/pyfunc/dbconnect_artifact_cache.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/dbconnect_artifact_cache.py | Apache-2.0 |
def load_context(self, context):
"""
Loads artifacts from the specified :class:`~PythonModelContext` that can be used by
:func:`~PythonModel.predict` when evaluating inputs. When loading an MLflow model with
:func:`~load_model`, this method is called as soon as the :class:`~PythonModel` ... |
Loads artifacts from the specified :class:`~PythonModelContext` that can be used by
:func:`~PythonModel.predict` when evaluating inputs. When loading an MLflow model with
:func:`~load_model`, this method is called as soon as the :class:`~PythonModel` is
constructed.
The same :c... | load_context | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def predict_type_hints(self) -> _TypeHints:
"""
Internal method to get type hints from the predict function signature.
"""
if hasattr(self, "_predict_type_hints"):
return self._predict_type_hints
if _is_context_in_predict_function_signature(func=self.predict):
... |
Internal method to get type hints from the predict function signature.
| predict_type_hints | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def predict(self, context, model_input, params: Optional[dict[str, Any]] = None):
"""
Evaluates a pyfunc-compatible input and produces a pyfunc-compatible output.
For more information about the pyfunc input/output API, see the :ref:`pyfunc-inference-api`.
Args:
context: A :c... |
Evaluates a pyfunc-compatible input and produces a pyfunc-compatible output.
For more information about the pyfunc input/output API, see the :ref:`pyfunc-inference-api`.
Args:
context: A :class:`~PythonModelContext` instance containing artifacts that the model
... | predict | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def predict(
self,
model_input,
params: Optional[dict[str, Any]] = None,
):
"""
Args:
model_input: A pyfunc-compatible input for the model to evaluate.
params: Additional parameters to pass to the model for inference.
Returns:
Mode... |
Args:
model_input: A pyfunc-compatible input for the model to evaluate.
params: Additional parameters to pass to the model for inference.
Returns:
Model predictions.
| predict | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def __init__(self, artifacts, model_config):
"""
Args:
artifacts: A dictionary of ``<name, artifact_path>`` entries, where ``artifact_path``
is an absolute filesystem path to a given artifact.
model_config: The model configuration to make available to the model at... |
Args:
artifacts: A dictionary of ``<name, artifact_path>`` entries, where ``artifact_path``
is an absolute filesystem path to a given artifact.
model_config: The model configuration to make available to the model at
loading time.
| __init__ | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def predict(
self, context, messages: list[ChatMessage], params: ChatParams
) -> ChatCompletionResponse:
"""
Evaluates a chat input and produces a chat output.
Args:
context: A :class:`~PythonModelContext` instance containing artifacts that the model
can ... |
Evaluates a chat input and produces a chat output.
Args:
context: A :class:`~PythonModelContext` instance containing artifacts that the model
can use to perform inference.
messages (List[:py:class:`ChatMessage <mlflow.types.llm.ChatMessage>`]):
A... | predict | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def predict_stream(
self, context, messages: list[ChatMessage], params: ChatParams
) -> Generator[ChatCompletionChunk, None, None]:
"""
Evaluates a chat input and produces a chat output.
Override this function to implement a real stream prediction.
Args:
context:... |
Evaluates a chat input and produces a chat output.
Override this function to implement a real stream prediction.
Args:
context: A :class:`~PythonModelContext` instance containing artifacts that the model
can use to perform inference.
messages (List[:py:c... | predict_stream | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def predict(
self,
messages: list[ChatAgentMessage],
context: Optional[ChatContext] = None,
custom_inputs: Optional[dict[str, Any]] = None,
) -> ChatAgentResponse:
"""
Given a ChatAgent input, returns a ChatAgent output. In addition to calling ``predict``
with... |
Given a ChatAgent input, returns a ChatAgent output. In addition to calling ``predict``
with an input matching the type hints, you can also pass a single input dict that matches
the :py:class:`ChatAgentRequest <mlflow.types.agent.ChatAgentRequest>` schema for ease
of testing.
.... | predict | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def predict_stream(
self,
messages: list[ChatAgentMessage],
context: Optional[ChatContext] = None,
custom_inputs: Optional[dict[str, Any]] = None,
) -> Generator[ChatAgentChunk, None, None]:
"""
Given a ChatAgent input, returns a generator containing streaming ChatAge... |
Given a ChatAgent input, returns a generator containing streaming ChatAgent output chunks.
In addition to calling ``predict_stream`` with an input matching the type hints, you can
also pass a single input dict that matches the
:py:class:`ChatAgentRequest <mlflow.types.agent.ChatAgentReq... | predict_stream | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def predict(self, request: ResponsesAgentRequest) -> ResponsesAgentResponse:
"""
Given a ResponsesAgentRequest, returns a ResponsesAgentResponse.
You can see example implementations at
https://www.mlflow.org/docs/latest/llms/responses-agent-intro#simple-chat-example and
... |
Given a ResponsesAgentRequest, returns a ResponsesAgentResponse.
You can see example implementations at
https://www.mlflow.org/docs/latest/llms/responses-agent-intro#simple-chat-example and
https://www.mlflow.org/docs/latest/llms/responses-agent-intro#tool-calling-examp... | predict | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def predict_stream(
self, request: ResponsesAgentRequest
) -> Generator[ResponsesAgentStreamEvent, None, None]:
"""
Given a ResponsesAgentRequest, returns a generator of ResponsesAgentStreamEvent objects.
See more details at
https://www.mlflow.org/doc... |
Given a ResponsesAgentRequest, returns a generator of ResponsesAgentStreamEvent objects.
See more details at
https://www.mlflow.org/docs/latest/llms/responses-agent-intro#streaming-agent-output.
You can see example implementations at
https://www.mlflow.org/... | predict_stream | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def create_text_delta(self, delta: str, item_id: str) -> dict[str, Any]:
"""Helper method to create a dictionary conforming to the text delta schema for
streaming.
Read more at https://www.mlflow.org/docs/latest/llms/responses-agent-intro/#streaming-agent-output.
"""
... | Helper method to create a dictionary conforming to the text delta schema for
streaming.
Read more at https://www.mlflow.org/docs/latest/llms/responses-agent-intro/#streaming-agent-output.
| create_text_delta | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def create_text_output_item(self, text: str, id: str) -> dict[str, Any]:
"""Helper method to create a dictionary conforming to the text output item schema.
Read more at https://www.mlflow.org/docs/latest/llms/responses-agent-intro/#creating-agent-output.
Args:
text ... | Helper method to create a dictionary conforming to the text output item schema.
Read more at https://www.mlflow.org/docs/latest/llms/responses-agent-intro/#creating-agent-output.
Args:
text (str): The text to be outputted.
id (str): The id of the output item.
... | create_text_output_item | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def create_function_call_item(
self, id: str, call_id: str, name: str, arguments: str
) -> dict[str, Any]:
"""Helper method to create a dictionary conforming to the function call item schema.
Read more at https://www.mlflow.org/docs/latest/llms/responses-agent-intro/#creatin... | Helper method to create a dictionary conforming to the function call item schema.
Read more at https://www.mlflow.org/docs/latest/llms/responses-agent-intro/#creating-agent-output.
Args:
id (str): The id of the output item.
call_id (str): The id of the function ... | create_function_call_item | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def create_function_call_output_item(self, call_id: str, output: str) -> dict[str, Any]:
"""Helper method to create a dictionary conforming to the function call output item
schema.
Read more at https://www.mlflow.org/docs/latest/llms/responses-agent-intro/#creating-agent-output.
... | Helper method to create a dictionary conforming to the function call output item
schema.
Read more at https://www.mlflow.org/docs/latest/llms/responses-agent-intro/#creating-agent-output.
Args:
call_id (str): The id of the function call.
output (str)... | create_function_call_output_item | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def _save_model_with_class_artifacts_params( # noqa: D417
path,
python_model,
signature=None,
artifacts=None,
conda_env=None,
code_paths=None,
mlflow_model=None,
pip_requirements=None,
extra_pip_requirements=None,
model_config=None,
streamable=None,
model_code_path=None,... |
Args:
path: The path to which to save the Python model.
python_model: An instance of a subclass of :class:`~PythonModel`. ``python_model``
defines how the model loads artifacts and how it performs inference.
artifacts: A dictionary containing ``<name, artifact_uri>`` entries. Re... | _save_model_with_class_artifacts_params | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def __init__(self, python_model: PythonModel, context, signature):
"""
Args:
python_model: An instance of a subclass of :class:`~PythonModel`.
context: A :class:`~PythonModelContext` instance containing artifacts that
``python_model`` may use when performing ... |
Args:
python_model: An instance of a subclass of :class:`~PythonModel`.
context: A :class:`~PythonModelContext` instance containing artifacts that
``python_model`` may use when performing inference.
signature: :class:`~ModelSignature` instance describing... | __init__ | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def predict(self, model_input, params: Optional[dict[str, Any]] = None):
"""
Args:
model_input: Model input data as one of dict, str, bool, bytes, float, int, str type.
params: Additional parameters to pass to the model for inference.
Returns:
Model predictio... |
Args:
model_input: Model input data as one of dict, str, bool, bytes, float, int, str type.
params: Additional parameters to pass to the model for inference.
Returns:
Model predictions as an iterator of chunks. The chunks in the iterator must be type of
... | predict | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def predict_stream(self, model_input, params: Optional[dict[str, Any]] = None):
"""
Args:
model_input: LLM Model single input.
params: Additional parameters to pass to the model for inference.
Returns:
Streaming predictions.
"""
parameters = i... |
Args:
model_input: LLM Model single input.
params: Additional parameters to pass to the model for inference.
Returns:
Streaming predictions.
| predict_stream | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def predict(
self, context, model_input: Union[pd.DataFrame, dict[str, Any], list[dict[str, Any]]]
):
"""
Run prediction on the input data.
Args:
context: A :class:`~PythonModelContext` instance containing artifacts that the model
can use to perform infer... |
Run prediction on the input data.
Args:
context: A :class:`~PythonModelContext` instance containing artifacts that the model
can use to perform inference.
model_input: The input data for prediction, either of the following:
- Pandas DataFrame: If... | predict | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def _predict_single(self, data: Union[str, dict[str, Any]]) -> dict[str, Any]:
"""
Send a single prediction request to the MLflow Deployments endpoint.
Args:
data: The single input data for prediction. If the input data is a string, we will
construct the request payl... |
Send a single prediction request to the MLflow Deployments endpoint.
Args:
data: The single input data for prediction. If the input data is a string, we will
construct the request payload from it. If the input data is a dictionary, we
will directly use it as... | _predict_single | python | mlflow/mlflow | mlflow/pyfunc/model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/model.py | Apache-2.0 |
def get_or_load(archive_path):
"""Given a path returned by add_local_model(), this method will return a tuple of
(loaded_model, local_model_path).
If this Python process ever loaded the model before, we will reuse that copy.
"""
if archive_path in SparkModelCache._models:
... | Given a path returned by add_local_model(), this method will return a tuple of
(loaded_model, local_model_path).
If this Python process ever loaded the model before, we will reuse that copy.
| get_or_load | python | mlflow/mlflow | mlflow/pyfunc/spark_model_cache.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/spark_model_cache.py | Apache-2.0 |
def add_to_model(
model,
loader_module,
data=None,
code=None,
conda_env=None,
python_env=None,
model_config=None,
model_code_path=None,
**kwargs,
):
"""
Add a ``pyfunc`` spec to the model configuration.
Defines ``pyfunc`` configuration schema. Caller can use this to crea... |
Add a ``pyfunc`` spec to the model configuration.
Defines ``pyfunc`` configuration schema. Caller can use this to create a valid ``pyfunc`` model
flavor out of an existing directory structure. For example, other model flavors can use this to
specify how to use their output as a ``pyfunc``.
NOTE:
... | add_to_model | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def _validate_prediction_input(data: PyFuncInput, params, input_schema, params_schema, flavor=None):
"""
Internal helper function to transform and validate input data and params for prediction.
Any additional transformation logics related to input data and params should be added here.
"""
if input_s... |
Internal helper function to transform and validate input data and params for prediction.
Any additional transformation logics related to input data and params should be added here.
| _validate_prediction_input | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def _predict(self, data: PyFuncInput, params: Optional[dict[str, Any]] = None) -> PyFuncOutput:
"""
Generates model predictions.
If the model contains signature, enforce the input schema first before calling the model
implementation with the sanitized input. If the pyfunc model does not... |
Generates model predictions.
If the model contains signature, enforce the input schema first before calling the model
implementation with the sanitized input. If the pyfunc model does not include model schema,
the input is passed to the model implementation as is. See `Model Signature ... | _predict | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def _predict_stream(
self, data: PyFuncLLMSingleInput, params: Optional[dict[str, Any]] = None
) -> Iterator[PyFuncLLMOutputChunk]:
"""
Generates streaming model predictions. Only LLM supports this method.
If the model contains signature, enforce the input schema first before callin... |
Generates streaming model predictions. Only LLM supports this method.
If the model contains signature, enforce the input schema first before calling the model
implementation with the sanitized input. If the pyfunc model does not include model schema,
the input is passed to the model im... | _predict_stream | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def unwrap_python_model(self):
"""
Unwrap the underlying Python model object.
This method is useful for accessing custom model functions, while still being able to
leverage the MLflow designed workflow through the `predict()` method.
Returns:
The underlying wrapped ... |
Unwrap the underlying Python model object.
This method is useful for accessing custom model functions, while still being able to
leverage the MLflow designed workflow through the `predict()` method.
Returns:
The underlying wrapped model object
.. code-block:: pyth... | unwrap_python_model | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def get_raw_model(self):
"""
Get the underlying raw model if the model wrapper implemented `get_raw_model` function.
"""
if hasattr(self._model_impl, "get_raw_model"):
return self._model_impl.get_raw_model()
raise NotImplementedError("`get_raw_model` is not implemente... |
Get the underlying raw model if the model wrapper implemented `get_raw_model` function.
| get_raw_model | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def _load_model_or_server(
model_uri: str, env_manager: str, model_config: Optional[dict[str, Any]] = None
):
"""
Load a model with env restoration. If a non-local ``env_manager`` is specified, prepare an
independent Python environment with the training time dependencies of the specified model
insta... |
Load a model with env restoration. If a non-local ``env_manager`` is specified, prepare an
independent Python environment with the training time dependencies of the specified model
installed and start a MLflow Model Scoring Server process with that model in that environment.
Return a _ServedPyFuncModel... | _load_model_or_server | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def get_model_dependencies(model_uri, format="pip"):
"""
Downloads the model dependencies and returns the path to requirements.txt or conda.yaml file.
.. warning::
This API downloads all the model artifacts to the local filesystem. This may take
a long time for large models. To avoid this o... |
Downloads the model dependencies and returns the path to requirements.txt or conda.yaml file.
.. warning::
This API downloads all the model artifacts to the local filesystem. This may take
a long time for large models. To avoid this overhead, use
``mlflow.artifacts.download_artifacts("... | get_model_dependencies | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def _warn_potentially_incompatible_py_version_if_necessary(model_py_version=None):
"""
Compares the version of Python that was used to save a given model with the version
of Python that is currently running. If a major or minor version difference is detected,
logs an appropriate warning.
"""
if ... |
Compares the version of Python that was used to save a given model with the version
of Python that is currently running. If a major or minor version difference is detected,
logs an appropriate warning.
| _warn_potentially_incompatible_py_version_if_necessary | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def _convert_array_values(values, result_type):
"""
Convert list or numpy array values to spark dataframe column values.
"""
from pyspark.sql.types import ArrayType, StructType
if not isinstance(result_type, ArrayType):
raise MlflowException.invalid_parameter_value(
f"result_typ... |
Convert list or numpy array values to spark dataframe column values.
| _convert_array_values | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def _convert_struct_values(
result: Union[pandas.DataFrame, dict[str, Any]],
result_type,
):
"""
Convert spark StructType values to spark dataframe column values.
"""
from pyspark.sql.types import ArrayType, MapType, StructType
if not isinstance(result_type, StructType):
raise Mlfl... |
Convert spark StructType values to spark dataframe column values.
| _convert_struct_values | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def _convert_value_based_on_spark_type(value, spark_type):
"""
Convert value to python types based on the given spark type.
"""
from pyspark.sql.types import ArrayType, MapType, StructType
spark_primitive_type_to_python_type = _get_spark_primitive_type_to_python_type()
if type(spark_type) in ... |
Convert value to python types based on the given spark type.
| _convert_value_based_on_spark_type | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def _gen_prebuilt_env_archive_name(spark, local_model_path):
"""
Generate prebuilt env archive file name.
The format is:
'mlflow-{sha of python env config and dependencies}-{runtime version}-{platform machine}'
Note: The runtime version and platform machine information are included in the
archi... |
Generate prebuilt env archive file name.
The format is:
'mlflow-{sha of python env config and dependencies}-{runtime version}-{platform machine}'
Note: The runtime version and platform machine information are included in the
archive name because the prebuilt env might not be compatible across diff... | _gen_prebuilt_env_archive_name | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def build_model_env(model_uri, save_path):
"""
Prebuild model python environment and generate an archive file saved to provided
`save_path`.
Typical usages:
- Pre-build a model's environment in Databricks Runtime and then download the prebuilt
python environment archive file. This pre-built... |
Prebuild model python environment and generate an archive file saved to provided
`save_path`.
Typical usages:
- Pre-build a model's environment in Databricks Runtime and then download the prebuilt
python environment archive file. This pre-built environment archive can then be used
in `m... | build_model_env | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def _save_model_with_loader_module_and_data_path( # noqa: D417
path,
loader_module,
data_path=None,
code_paths=None,
conda_env=None,
mlflow_model=None,
pip_requirements=None,
extra_pip_requirements=None,
model_config=None,
streamable=None,
infer_code_paths=False,
):
"""
... |
Export model as a generic Python function model.
Args:
path: The path to which to save the Python model.
loader_module: The name of the Python module that is used to load the model
from ``data_path``. This module must define a method with the prototype
``_load_pyfunc(da... | _save_model_with_loader_module_and_data_path | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def _save_model_chat_agent_helper(python_model, mlflow_model, signature, input_example):
"""Helper method for save_model for ChatAgent models
Returns: a dict input_example
"""
if signature is not None:
raise MlflowException(
"ChatAgent subclasses have a standard signature that is se... | Helper method for save_model for ChatAgent models
Returns: a dict input_example
| _save_model_chat_agent_helper | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def _save_model_responses_agent_helper(python_model, mlflow_model, signature, input_example):
"""Helper method for save_model for ResponsesAgent models
Returns: a dictionary input example
"""
from mlflow.types.responses import (
RESPONSES_AGENT_INPUT_EXAMPLE,
RESPONSES_AGENT_INPUT_SCHEM... | Helper method for save_model for ResponsesAgent models
Returns: a dictionary input example
| _save_model_responses_agent_helper | python | mlflow/mlflow | mlflow/pyfunc/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/__init__.py | Apache-2.0 |
def predict(self, model_input: dict[str, Any], params=None) -> dict[str, Any]:
"""
Args:
model_input: A dict with the
:py:class:`ChatAgentRequest <mlflow.types.agent.ChatAgentRequest>` schema.
params: Unused in this function, but required in the signature because
... |
Args:
model_input: A dict with the
:py:class:`ChatAgentRequest <mlflow.types.agent.ChatAgentRequest>` schema.
params: Unused in this function, but required in the signature because
`load_model_and_predict` in `utils/_capture_modules.py` expects a params f... | predict | python | mlflow/mlflow | mlflow/pyfunc/loaders/chat_agent.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/loaders/chat_agent.py | Apache-2.0 |
def predict_stream(
self, model_input: dict[str, Any], params=None
) -> Generator[dict[str, Any], None, None]:
"""
Args:
model_input: A dict with the
:py:class:`ChatAgentRequest <mlflow.types.agent.ChatAgentRequest>` schema.
params: Unused in this func... |
Args:
model_input: A dict with the
:py:class:`ChatAgentRequest <mlflow.types.agent.ChatAgentRequest>` schema.
params: Unused in this function, but required in the signature because
`load_model_and_predict` in `utils/_capture_modules.py` expects a params f... | predict_stream | python | mlflow/mlflow | mlflow/pyfunc/loaders/chat_agent.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/loaders/chat_agent.py | Apache-2.0 |
def __init__(self, chat_model, context, signature):
"""
Args:
chat_model: An instance of a subclass of :class:`~ChatModel`.
context: A :class:`~PythonModelContext` instance containing artifacts that
``chat_model`` may use when performing inference.
... |
Args:
chat_model: An instance of a subclass of :class:`~ChatModel`.
context: A :class:`~PythonModelContext` instance containing artifacts that
``chat_model`` may use when performing inference.
signature: :class:`~ModelSignature` instance describing mo... | __init__ | python | mlflow/mlflow | mlflow/pyfunc/loaders/chat_model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/loaders/chat_model.py | Apache-2.0 |
def predict(
self, model_input: dict[str, Any], params: Optional[dict[str, Any]] = None
) -> dict[str, Any]:
"""
Args:
model_input: Model input data in the form of a chat request.
params: Additional parameters to pass to the model for inference.
... |
Args:
model_input: Model input data in the form of a chat request.
params: Additional parameters to pass to the model for inference.
Unused in this implementation, as the params are handled
via ``self._convert_input()``.
Returns:
... | predict | python | mlflow/mlflow | mlflow/pyfunc/loaders/chat_model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/loaders/chat_model.py | Apache-2.0 |
def predict_stream(
self, model_input: dict[str, Any], params: Optional[dict[str, Any]] = None
) -> Generator[dict[str, Any], None, None]:
"""
Args:
model_input: Model input data in the form of a chat request.
params: Additional parameters to pass to the model for inf... |
Args:
model_input: Model input data in the form of a chat request.
params: Additional parameters to pass to the model for inference.
Unused in this implementation, as the params are handled
via ``self._convert_input()``.
Returns:
... | predict_stream | python | mlflow/mlflow | mlflow/pyfunc/loaders/chat_model.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/loaders/chat_model.py | Apache-2.0 |
def predict(self, model_input: dict[str, Any], params=None) -> dict[str, Any]:
"""
Args:
model_input: A dict with the
:py:class:`ResponsesRequest <mlflow.types.responses.ResponsesRequest>` schema.
params: Unused in this function, but required in the signature beca... |
Args:
model_input: A dict with the
:py:class:`ResponsesRequest <mlflow.types.responses.ResponsesRequest>` schema.
params: Unused in this function, but required in the signature because
`load_model_and_predict` in `utils/_capture_modules.py` expects a para... | predict | python | mlflow/mlflow | mlflow/pyfunc/loaders/responses_agent.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/loaders/responses_agent.py | Apache-2.0 |
def predict_stream(
self, model_input: dict[str, Any], params=None
) -> Generator[dict[str, Any], None, None]:
"""
Args:
model_input: A dict with the
:py:class:`ResponsesRequest <mlflow.types.responses.ResponsesRequest>` schema.
params: Unused in this ... |
Args:
model_input: A dict with the
:py:class:`ResponsesRequest <mlflow.types.responses.ResponsesRequest>` schema.
params: Unused in this function, but required in the signature because
`load_model_and_predict` in `utils/_capture_modules.py` expects a para... | predict_stream | python | mlflow/mlflow | mlflow/pyfunc/loaders/responses_agent.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/loaders/responses_agent.py | Apache-2.0 |
def wait_server_ready(self, timeout=30, scoring_server_proc=None):
"""
Wait until the scoring server is ready to accept requests.
""" |
Wait until the scoring server is ready to accept requests.
| wait_server_ready | python | mlflow/mlflow | mlflow/pyfunc/scoring_server/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/scoring_server/client.py | Apache-2.0 |
def invoke(self, data, params: Optional[dict[str, Any]] = None):
"""
Invoke inference on input data. The input data must be pandas dataframe or numpy array or
a dict of numpy arrays.
Args:
data: Model input data.
params: Additional parameters to pass to the model... |
Invoke inference on input data. The input data must be pandas dataframe or numpy array or
a dict of numpy arrays.
Args:
data: Model input data.
params: Additional parameters to pass to the model for inference.
Returns:
Prediction result.
| invoke | python | mlflow/mlflow | mlflow/pyfunc/scoring_server/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/scoring_server/client.py | Apache-2.0 |
def invoke(self, data, params: Optional[dict[str, Any]] = None):
"""
Args:
data: Model input data.
params: Additional parameters to pass to the model for inference.
Returns:
:py:class:`PredictionsResponse <mlflow.deployments.PredictionsResponse>` result.
... |
Args:
data: Model input data.
params: Additional parameters to pass to the model for inference.
Returns:
:py:class:`PredictionsResponse <mlflow.deployments.PredictionsResponse>` result.
| invoke | python | mlflow/mlflow | mlflow/pyfunc/scoring_server/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/scoring_server/client.py | Apache-2.0 |
def invoke(self, data, params: Optional[dict[str, Any]] = None):
"""
Invoke inference on input data. The input data must be pandas dataframe or numpy array or
a dict of numpy arrays.
Args:
data: Model input data.
params: Additional parameters to pass to the model... |
Invoke inference on input data. The input data must be pandas dataframe or numpy array or
a dict of numpy arrays.
Args:
data: Model input data.
params: Additional parameters to pass to the model for inference.
Returns:
:py:class:`PredictionsResponse... | invoke | python | mlflow/mlflow | mlflow/pyfunc/scoring_server/client.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/scoring_server/client.py | Apache-2.0 |
def infer_and_parse_json_input(json_input, schema: Schema = None):
"""
Args:
json_input: A JSON-formatted string representation of TF serving input or a Pandas
DataFrame, or a stream containing such a string representation.
schema: Optional schema specification to be used dur... |
Args:
json_input: A JSON-formatted string representation of TF serving input or a Pandas
DataFrame, or a stream containing such a string representation.
schema: Optional schema specification to be used during parsing.
| infer_and_parse_json_input | python | mlflow/mlflow | mlflow/pyfunc/scoring_server/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/scoring_server/__init__.py | Apache-2.0 |
def _decode_json_input(json_input):
"""
Args:
json_input: A JSON-formatted string representation of TF serving input or a Pandas
DataFrame, or a stream containing such a string representation.
Returns:
A dictionary representation of the JSON input.
"""
if isinsta... |
Args:
json_input: A JSON-formatted string representation of TF serving input or a Pandas
DataFrame, or a stream containing such a string representation.
Returns:
A dictionary representation of the JSON input.
| _decode_json_input | python | mlflow/mlflow | mlflow/pyfunc/scoring_server/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/scoring_server/__init__.py | Apache-2.0 |
def infer_and_parse_data(data, schema: Schema = None):
"""
Args:
data: A dictionary representation of TF serving input or a Pandas
DataFrame, or a stream containing such a string representation.
schema: Optional schema specification to be used during parsing.
"""
format_keys... |
Args:
data: A dictionary representation of TF serving input or a Pandas
DataFrame, or a stream containing such a string representation.
schema: Optional schema specification to be used during parsing.
| infer_and_parse_data | python | mlflow/mlflow | mlflow/pyfunc/scoring_server/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/scoring_server/__init__.py | Apache-2.0 |
def parse_csv_input(csv_input, schema: Schema = None):
"""
Args:
csv_input: A CSV-formatted string representation of a Pandas DataFrame, or a stream
containing such a string representation.
schema: Optional schema specification to be used during parsing.
"""
import pan... |
Args:
csv_input: A CSV-formatted string representation of a Pandas DataFrame, or a stream
containing such a string representation.
schema: Optional schema specification to be used during parsing.
| parse_csv_input | python | mlflow/mlflow | mlflow/pyfunc/scoring_server/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/scoring_server/__init__.py | Apache-2.0 |
def _handle_serving_error(error_message, error_code, include_traceback=True):
"""
Logs information about an exception thrown by model inference code that is currently being
handled and reraises it with the specified error message. The exception stack trace
is also included in the reraised error message.... |
Logs information about an exception thrown by model inference code that is currently being
handled and reraises it with the specified error message. The exception stack trace
is also included in the reraised error message.
Args:
error_message: A message for the reraised exception.
erro... | _handle_serving_error | python | mlflow/mlflow | mlflow/pyfunc/scoring_server/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/scoring_server/__init__.py | Apache-2.0 |
def init(model: PyFuncModel):
"""
Initialize the server. Loads pyfunc model from the path.
"""
from fastapi import FastAPI, Request
from fastapi.responses import Response
app = FastAPI()
input_schema = model.metadata.get_input_schema()
# set the environment variable to indicate that we ... |
Initialize the server. Loads pyfunc model from the path.
| init | python | mlflow/mlflow | mlflow/pyfunc/scoring_server/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/scoring_server/__init__.py | Apache-2.0 |
async def ping(request: Request):
"""
Determine if the container is working and healthy.
We declare it healthy if we can load the model successfully.
"""
health = model is not None
status = 200 if health else 404
return Response(content="\n", status_code=status, m... |
Determine if the container is working and healthy.
We declare it healthy if we can load the model successfully.
| ping | python | mlflow/mlflow | mlflow/pyfunc/scoring_server/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/scoring_server/__init__.py | Apache-2.0 |
async def transformation(request: Request):
"""
Do an inference on a single batch of data. In this sample server,
we take data as CSV or json, convert it to a Pandas DataFrame or Numpy,
generate predictions and convert them back to json.
"""
data = await request.body()
... |
Do an inference on a single batch of data. In this sample server,
we take data as CSV or json, convert it to a Pandas DataFrame or Numpy,
generate predictions and convert them back to json.
| transformation | python | mlflow/mlflow | mlflow/pyfunc/scoring_server/__init__.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/scoring_server/__init__.py | Apache-2.0 |
def pyfunc(func):
"""
A decorator that forces data validation against type hint of the input data
in the wrapped method. It is no-op if the type hint is not supported by MLflow.
.. note::
The function that applies this decorator must be a valid `predict` function
of `mlflow.pyfunc.Pytho... |
A decorator that forces data validation against type hint of the input data
in the wrapped method. It is no-op if the type hint is not supported by MLflow.
.. note::
The function that applies this decorator must be a valid `predict` function
of `mlflow.pyfunc.PythonModel`, or a callable th... | pyfunc | python | mlflow/mlflow | mlflow/pyfunc/utils/data_validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/utils/data_validation.py | Apache-2.0 |
def wrap_non_list_predict_pydantic(func, input_pydantic_model, validation_error_msg, unpack=False):
"""
Used by MLflow defined subclasses of PythonModel that have non-list a pydantic model as input.
Takes in a dict input, validates it against `input_pydantic_model`, and then creates
the pydantic model.
... |
Used by MLflow defined subclasses of PythonModel that have non-list a pydantic model as input.
Takes in a dict input, validates it against `input_pydantic_model`, and then creates
the pydantic model.
If `unpack` is True, the validated dict is parsed into the function arguments.
Otherwise, the whol... | wrap_non_list_predict_pydantic | python | mlflow/mlflow | mlflow/pyfunc/utils/data_validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/utils/data_validation.py | Apache-2.0 |
def _get_func_info_if_type_hint_supported(func) -> Optional[FuncInfo]:
"""
Internal method to check if the predict function has type hints and if they are supported
by MLflow.
For PythonModel, the signature must be one of below:
- predict(self, context, model_input, params=None)
- predic... |
Internal method to check if the predict function has type hints and if they are supported
by MLflow.
For PythonModel, the signature must be one of below:
- predict(self, context, model_input, params=None)
- predict(self, model_input, params=None)
For callables, the function must contain... | _get_func_info_if_type_hint_supported | python | mlflow/mlflow | mlflow/pyfunc/utils/data_validation.py | https://github.com/mlflow/mlflow/blob/master/mlflow/pyfunc/utils/data_validation.py | Apache-2.0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.