Source code for kedro.framework.session.session

"""This module implements Kedro session responsible for project lifecycle."""
import getpass
import logging
import logging.config
import os
import subprocess
import traceback
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, Iterable, Union

import click

from kedro import __version__ as kedro_version
from kedro.config import ConfigLoader, MissingConfigException
from kedro.framework.context import KedroContext
from kedro.framework.context.context import _convert_paths_to_absolute_posix
from kedro.framework.hooks import _create_hook_manager
from kedro.framework.hooks.manager import _register_hooks, _register_hooks_setuptools
from kedro.framework.project import (
    configure_logging,
    pipelines,
    settings,
    validate_settings,
)
from kedro.framework.session.store import BaseSessionStore
from kedro.io.core import generate_timestamp
from kedro.runner import AbstractRunner, SequentialRunner


def _describe_git(project_path: Path) -> Dict[str, Dict[str, Any]]:
    project_path = str(project_path)
    try:
        res = subprocess.check_output(
            ["git", "rev-parse", "--short", "HEAD"],
            cwd=project_path,
            stderr=subprocess.STDOUT,
        )
        git_data = {"commit_sha": res.decode().strip()}  # type: Dict[str, Any]
        git_status_res = subprocess.check_output(
            ["git", "status", "--short"],
            cwd=project_path,
            stderr=subprocess.STDOUT,
        )
        git_data["dirty"] = bool(git_status_res.decode().strip())

    # `subprocess.check_output()` raises `NotADirectoryError` on Windows
    except (subprocess.CalledProcessError, FileNotFoundError, NotADirectoryError):
        logging.getLogger(__name__).debug("Unable to git describe %s", project_path)
        return {}

    return {"git": git_data}


def _jsonify_cli_context(ctx: click.core.Context) -> Dict[str, Any]:
    return {
        "args": ctx.args,
        "params": ctx.params,
        "command_name": ctx.command.name,
        "command_path": ctx.command_path,
    }


[docs]class KedroSessionError(Exception): """``KedroSessionError`` raised by ``KedroSession`` in the case that multiple runs are attempted in one session. """ pass
[docs]class KedroSession: """``KedroSession`` is the object that is responsible for managing the lifecycle of a Kedro run. Use `KedroSession.create()` as a context manager to construct a new KedroSession with session data provided (see the example below). Example: :: >>> from kedro.framework.session import KedroSession >>> from kedro.framework.startup import bootstrap_project >>> from pathlib import Path >>> # If you are creating a session outside of a Kedro project (i.e. not using >>> # `kedro run` or `kedro jupyter`), you need to run `bootstrap_project` to >>> # let Kedro find your configuration. >>> bootstrap_project(Path("<project_root>")) >>> with KedroSession.create() as session: >>> session.run() """ def __init__( self, session_id: str, package_name: str = None, project_path: Union[Path, str] = None, save_on_close: bool = False, ): self._project_path = Path(project_path or Path.cwd()).resolve() self.session_id = session_id self.save_on_close = save_on_close self._package_name = package_name self._store = self._init_store() self._run_called = False hook_manager = _create_hook_manager() _register_hooks(hook_manager, settings.HOOKS) _register_hooks_setuptools(hook_manager, settings.DISABLE_HOOKS_FOR_PLUGINS) self._hook_manager = hook_manager
[docs] @classmethod def create( # pylint: disable=too-many-arguments cls, package_name: str = None, project_path: Union[Path, str] = None, save_on_close: bool = True, env: str = None, extra_params: Dict[str, Any] = None, ) -> "KedroSession": """Create a new instance of ``KedroSession`` with the session data. Args: package_name: Package name for the Kedro project the session is created for. The package_name argument will be removed in Kedro `0.19.0`. project_path: Path to the project root directory. Default is current working directory Path.cwd(). save_on_close: Whether or not to save the session when it's closed. env: Environment for the KedroContext. extra_params: Optional dictionary containing extra project parameters for underlying KedroContext. If specified, will update (and therefore take precedence over) the parameters retrieved from the project configuration. Returns: A new ``KedroSession`` instance. """ validate_settings() session = cls( package_name=package_name, project_path=project_path, session_id=generate_timestamp(), save_on_close=save_on_close, ) # have to explicitly type session_data otherwise mypy will complain # possibly related to this: https://github.com/python/mypy/issues/1430 session_data: Dict[str, Any] = { "package_name": session._package_name, "project_path": session._project_path, "session_id": session.session_id, **_describe_git(session._project_path), } ctx = click.get_current_context(silent=True) if ctx: session_data["cli"] = _jsonify_cli_context(ctx) env = env or os.getenv("KEDRO_ENV") if env: session_data["env"] = env if extra_params: session_data["extra_params"] = extra_params try: session_data["username"] = getpass.getuser() except Exception as exc: # pylint: disable=broad-except logging.getLogger(__name__).debug( "Unable to get username. Full exception: %s", exc ) session._store.update(session_data) # we need a ConfigLoader registered in order to be able to set up logging session._setup_logging() return session
def _get_logging_config(self) -> Dict[str, Any]: logging_config = self._get_config_loader()["logging"] # turn relative paths in logging config into absolute path # before initialising loggers logging_config = _convert_paths_to_absolute_posix( project_path=self._project_path, conf_dictionary=logging_config ) return logging_config def _setup_logging(self) -> None: """Register logging specified in logging directory.""" try: logging_config = self._get_logging_config() except MissingConfigException: self._logger.debug( "No project logging configuration loaded; " "Kedro's default logging configuration will be used." ) else: configure_logging(logging_config) def _init_store(self) -> BaseSessionStore: store_class = settings.SESSION_STORE_CLASS classpath = f"{store_class.__module__}.{store_class.__qualname__}" store_args = deepcopy(settings.SESSION_STORE_ARGS) store_args.setdefault("path", (self._project_path / "sessions").as_posix()) store_args["session_id"] = self.session_id try: return store_class(**store_args) except TypeError as err: raise ValueError( f"\n{err}.\nStore config must only contain arguments valid " f"for the constructor of '{classpath}'." ) from err except Exception as err: raise ValueError( f"\n{err}.\nFailed to instantiate session store of type '{classpath}'." ) from err def _log_exception(self, exc_type, exc_value, exc_tb): type_ = [] if exc_type.__module__ == "builtins" else [exc_type.__module__] type_.append(exc_type.__qualname__) exc_data = { "type": ".".join(type_), "value": str(exc_value), "traceback": traceback.format_tb(exc_tb), } self._store["exception"] = exc_data @property def _logger(self) -> logging.Logger: return logging.getLogger(__name__) @property def store(self) -> Dict[str, Any]: """Return a copy of internal store.""" return dict(self._store)
[docs] def load_context(self) -> KedroContext: """An instance of the project context.""" env = self.store.get("env") extra_params = self.store.get("extra_params") config_loader = self._get_config_loader() context_class = settings.CONTEXT_CLASS context = context_class( package_name=self._package_name, project_path=self._project_path, config_loader=config_loader, env=env, extra_params=extra_params, hook_manager=self._hook_manager, ) self._hook_manager.hook.after_context_created( # pylint: disable=no-member context=context ) return context
def _get_config_loader(self) -> ConfigLoader: """An instance of the config loader.""" env = self.store.get("env") extra_params = self.store.get("extra_params") config_loader_class = settings.CONFIG_LOADER_CLASS return config_loader_class( conf_source=str(self._project_path / settings.CONF_SOURCE), env=env, runtime_params=extra_params, **settings.CONFIG_LOADER_ARGS, )
[docs] def close(self): """Close the current session and save its store to disk if `save_on_close` attribute is True. """ if self.save_on_close: self._store.save()
def __enter__(self): return self def __exit__(self, exc_type, exc_value, tb_): if exc_type: self._log_exception(exc_type, exc_value, tb_) self.close()
[docs] def run( # pylint: disable=too-many-arguments,too-many-locals self, pipeline_name: str = None, tags: Iterable[str] = None, runner: AbstractRunner = None, node_names: Iterable[str] = None, from_nodes: Iterable[str] = None, to_nodes: Iterable[str] = None, from_inputs: Iterable[str] = None, to_outputs: Iterable[str] = None, load_versions: Dict[str, str] = None, ) -> Dict[str, Any]: """Runs the pipeline with a specified runner. Args: pipeline_name: Name of the pipeline that is being run. tags: An optional list of node tags which should be used to filter the nodes of the ``Pipeline``. If specified, only the nodes containing *any* of these tags will be run. runner: An optional parameter specifying the runner that you want to run the pipeline with. node_names: An optional list of node names which should be used to filter the nodes of the ``Pipeline``. If specified, only the nodes with these names will be run. from_nodes: An optional list of node names which should be used as a starting point of the new ``Pipeline``. to_nodes: An optional list of node names which should be used as an end point of the new ``Pipeline``. from_inputs: An optional list of input datasets which should be used as a starting point of the new ``Pipeline``. to_outputs: An optional list of output datasets which should be used as an end point of the new ``Pipeline``. load_versions: An optional flag to specify a particular dataset version timestamp to load. Raises: ValueError: If the named or `__default__` pipeline is not defined by `register_pipelines`. Exception: Any uncaught exception during the run will be re-raised after being passed to ``on_pipeline_error`` hook. KedroSessionError: If more than one run is attempted to be executed during a single session. Returns: Any node outputs that cannot be processed by the ``DataCatalog``. These are returned in a dictionary, where the keys are defined by the node outputs. """ # pylint: disable=protected-access,no-member # Report project name self._logger.info("Kedro project %s", self._project_path.name) if self._run_called: raise KedroSessionError( "A run has already been completed as part of the" " active KedroSession. KedroSession has a 1-1 mapping with" " runs, and thus only one run should be executed per session." ) session_id = self.store["session_id"] save_version = session_id extra_params = self.store.get("extra_params") or {} context = self.load_context() name = pipeline_name or "__default__" try: pipeline = pipelines[name] except KeyError as exc: raise ValueError( f"Failed to find the pipeline named '{name}'. " f"It needs to be generated and returned " f"by the 'register_pipelines' function." ) from exc filtered_pipeline = pipeline.filter( tags=tags, from_nodes=from_nodes, to_nodes=to_nodes, node_names=node_names, from_inputs=from_inputs, to_outputs=to_outputs, ) record_data = { "session_id": session_id, "project_path": self._project_path.as_posix(), "env": context.env, "kedro_version": kedro_version, "tags": tags, "from_nodes": from_nodes, "to_nodes": to_nodes, "node_names": node_names, "from_inputs": from_inputs, "to_outputs": to_outputs, "load_versions": load_versions, "extra_params": extra_params, "pipeline_name": pipeline_name, "runner": getattr(runner, "__name__", str(runner)), } catalog = context._get_catalog( save_version=save_version, load_versions=load_versions, ) # Run the runner hook_manager = self._hook_manager runner = runner or SequentialRunner() hook_manager.hook.before_pipeline_run( # pylint: disable=no-member run_params=record_data, pipeline=filtered_pipeline, catalog=catalog ) try: run_result = runner.run( filtered_pipeline, catalog, hook_manager, session_id ) self._run_called = True except Exception as error: hook_manager.hook.on_pipeline_error( error=error, run_params=record_data, pipeline=filtered_pipeline, catalog=catalog, ) raise hook_manager.hook.after_pipeline_run( run_params=record_data, run_result=run_result, pipeline=filtered_pipeline, catalog=catalog, ) return run_result