# -*- coding: utf-8 -*-
"""
:mod:`orion.core.io.experiment_builder` -- Create experiment from user options
==============================================================================
.. module:: experiment
:platform: Unix
:synopsis: Functions which build `Experiment` and `ExperimentView` objects
based on user configuration.
The instantiation of an `Experiment` is not a trivial process when the user request an experiment
with specific options. One can easily create a new experiment with
`ExperimentView('some_experiment_name')`, but the configuration of a _writable_ experiment is less
straighforward. This is because there is many sources of configuration and they have a strict
hierarchy. From the more global to the more specific, there is:
1. Global configuration:
Defined by `orion.core.DEF_CONFIG_FILES_PATHS`.
Can be scattered in user file system, defaults could look like:
- `/some/path/to/.virtualenvs/orion/share/orion.core`
- `/etc/xdg/xdg-ubuntu/orion.core`
- `/home/${USER}/.config/orion.core`
Note that some variables have default value even if user do not defined them in global
configuration:
- `max_trials = orion.core.io.resolve_config.DEF_CMD_MAX_TRIALS`
- `pool_size = orion.core.io.resolve_config.DEF_CMD_POOL_SIZE`
- `algorithms = random`
- Database specific:
* `database.name = 'orion'`
* `database.type = 'MongoDB'`
* `database.host = ${HOST}`
2. Oríon specific environment variables:
Environment variables which can override global configuration
- Database specific:
* `ORION_DB_NAME`
* `ORION_DB_TYPE`
* `ORION_DB_ADDRESS`
3. Experiment configuration inside the database
Configuration of the experiment if present in the database.
Making this part of the configuration of the experiment makes it possible
for the user to execute an experiment by only specifying partial configuration. The rest of the
configuration is fetched from the database.
For example, a user could:
1. Rerun the same experiment
Only providing the name is sufficient to rebuild the entire configuration of the
experiment.
2. Make a modification to an existing experiment
The user can provide the name of the existing experiment and only provide the changes to
apply on it. Here is an minimal example where we fully initialize a first experiment with a
config file and then branch from it with minimal information.
.. code-block:: bash
# Initialize root experiment
orion hunt --init-only --config previous_exeriment.yaml ./userscript -x~'uniform(0, 10)'
# Branch a new experiment
orion hunt -n previous_experiment ./userscript -x~'uniform(0, 100)'
4. Configuration file
This configuration file is meant to overwrite the configuration coming from the database.
If this configuration file was interpreted as part of the global configuration, a user could
only modify an experiment using command line arguments.
5. Command-line arguments
Those are the arguments provided to `orion` for any method (hunt, insert, etc). It includes the
argument to `orion` itself as well as the user's script name and its arguments.
"""
import copy
import datetime
import getpass
import logging
import sys
from orion.algo.space import Space
import orion.core
from orion.core.evc.adapters import Adapter
from orion.core.evc.conflicts import detect_conflicts, ExperimentNameConflict
from orion.core.io import resolve_config
from orion.core.io.database import DuplicateKeyError
from orion.core.io.experiment_branch_builder import ExperimentBranchBuilder
from orion.core.io.interactive_commands.branching_prompt import BranchingPrompt
from orion.core.io.space_builder import SpaceBuilder
import orion.core.utils.backward as backward
from orion.core.utils.exceptions import (
BranchingEvent, NoConfigurationError, NoNameError, RaceCondition)
from orion.core.worker.experiment import Experiment, ExperimentView
from orion.core.worker.primary_algo import PrimaryAlgo
from orion.core.worker.strategy import Strategy
from orion.storage.base import get_storage, setup_storage
log = logging.getLogger(__name__)
##
# Functions to build experiments
##
[docs]def build(name, version=None, branching=None, **config):
"""Build an experiment object
If new, `space` argument must be provided, else all arguments are fetched from the database
based on (name, version). If any argument given does not match the corresponding ones in the
database for given (name, version), than the version is incremented and the experiment will be a
child of the previous version.
Parameters
----------
name: str
Name of the experiment to build
version: int, optional
Version to select. If None, last version will be selected. If version given is larger than
largest version available, the largest version will be selected.
space: dict, optional
Optimization space of the algorithm. Should have the form `dict(name='<prior>(args)')`.
algorithms: str or dict, optional
Algorithm used for optimization.
strategy: str or dict, optional
Parallel strategy to use to parallelize the algorithm.
max_trials: int, optional
Maximum number or trials before the experiment is considered done.
storage: dict, optional
Configuration of the storage backend.
branching: dict, optional
Arguments to control the branching.
branch_from: str, optional
Name of the experiment to branch from.
manual_resolution: bool, optional
Starts the prompt to resolve manually the conflicts. Defaults to False.
non_monitored_arguments: list of str, optional
Will ignore these arguments while looking for differences. Defaults to [].
ignore_code_changes: bool, optional
Will ignore code changes while looking for differences. Defaults to False.
algorithm_change: bool, optional
Whether to automatically solve the algorithm conflict (change of algo config).
Defaults to True.
code_change_type: str, optional
How to resolve code change automatically. Must be one of 'noeffect', 'unsure' or
'break'. Defaults to 'break'.
cli_change_type: str, optional
How to resolve cli change automatically. Must be one of 'noeffect', 'unsure' or 'break'.
Defaults to 'break'.
config_change_type: str, optional
How to resolve config change automatically. Must be one of 'noeffect', 'unsure' or
'break'. Defaults to 'break'.
"""
config = copy.deepcopy(config)
for key, value in list(config.items()):
if key.startswith('_') or value is None:
config.pop(key)
if 'strategy' in config:
config['producer'] = {'strategy': config.pop('strategy')}
if branching is None:
branching = {}
if branching.get('branch_from'):
branching.setdefault('branch_to', name)
name = branching['branch_from']
db_config = fetch_config_from_db(name, version)
new_config = config
config = resolve_config.merge_configs(db_config, config)
metadata = resolve_config.fetch_metadata(config.get('user'), config.get('user_args'))
config = resolve_config.merge_configs(db_config, config, {'metadata': metadata})
# TODO: Find a better solution
if isinstance(config.get('algorithms'), dict) and len(config['algorithms']) > 1:
config['algorithms'] = new_config['algorithms']
config.setdefault('name', name)
config.setdefault('version', version)
if 'space' not in config:
raise NoConfigurationError(
'Experiment {} does not exist in DB and space was not defined.'.format(name))
if len(config['space']) == 0:
raise NoConfigurationError("No prior found. Please include at least one.")
experiment = create_experiment(**copy.deepcopy(config))
if experiment.id is None:
try:
_register_experiment(experiment)
except DuplicateKeyError:
experiment = build(branching=branching, **config)
return experiment
conflicts = _get_conflicts(experiment, branching)
must_branch = len(conflicts.get()) > 1 or branching.get('branch_to')
if must_branch:
branched_experiment = _branch_experiment(experiment, conflicts, version, branching)
try:
_register_experiment(branched_experiment)
except DuplicateKeyError as e:
raise RaceCondition('There was a race condition during branching.') from e
return branched_experiment
_update_experiment(experiment)
return experiment
[docs]def build_view(name, version=None):
"""Build experiment view
An experiment view provides all reading operations of standard experiment but prevents the
modification of the experiment and its trials.
Parameters
----------
name: str
Name of the experiment to build
version: int, optional
Version to select. If None, last version will be selected. If version given is larger than
largest version available, the largest version will be selected.
"""
db_config = fetch_config_from_db(name, version)
if not db_config:
message = ("No experiment with given name '%s' and version '%s' inside database, "
"no view can be created." % (name, version if version else '*'))
raise NoConfigurationError(message)
db_config.setdefault('version', 1)
experiment = create_experiment(**db_config)
return ExperimentView(experiment)
[docs]def create_experiment(name, version, space, **kwargs):
"""Instantiate the experiment and its attribute objects
All unspecified arguments will be replaced by system's defaults (orion.core.config.*).
Parameters
----------
name: str
Name of the experiment.
version: int
Version of the experiment.
space: dict or Space object
Optimization space of the algorithm. If dict, should have the form
`dict(name='<prior>(args)')`.
algorithms: str or dict, optional
Algorithm used for optimization.
strategy: str or dict, optional
Parallel strategy to use to parallelize the algorithm.
max_trials: int, optional
Maximum number or trials before the experiment is considered done.
storage: dict, optional
Configuration of the storage backend.
"""
experiment = Experiment(name=name, version=version)
experiment._id = kwargs.get('_id', None) # pylint:disable=protected-access
experiment.pool_size = kwargs.get('pool_size')
if experiment.pool_size is None:
experiment.pool_size = orion.core.config.experiment.get(
'pool_size', deprecated='ignore')
experiment.max_trials = kwargs.get('max_trials', orion.core.config.experiment.max_trials)
experiment.space = _instantiate_space(space)
experiment.algorithms = _instantiate_algo(experiment.space, kwargs.get('algorithms'))
experiment.producer = kwargs.get('producer', {})
experiment.producer['strategy'] = _instantiate_strategy(experiment.producer.get('strategy'))
experiment.working_dir = kwargs.get('working_dir', orion.core.config.experiment.working_dir)
experiment.metadata = kwargs.get('metadata', {'user': kwargs.get('user', getpass.getuser())})
experiment.refers = kwargs.get('refers', {'parent_id': None, 'root_id': None, 'adapter': []})
experiment.refers['adapter'] = _instantiate_adapters(experiment.refers.get('adapter', []))
return experiment
[docs]def fetch_config_from_db(name, version=None):
"""Fetch configuration from database
Parameters
----------
name: str
Name of the experiment to fetch
version: int, optional
Version to select. If None, last version will be selected. If version given is larger than
largest version available, the largest version will be selected.
"""
configs = get_storage().fetch_experiments({'name': name})
if not configs:
return {}
config = _fetch_config_version(configs, version)
if len(configs) > 1:
log.info("Many versions for experiment %s have been found. Using latest "
"version %s.", name, config['version'])
backward.populate_space(config, force_update=False)
return config
##
# Private helper functions to build experiments
##
def _instantiate_adapters(config):
"""Instantiate the adapter object
Parameters
----------
config: list
List of adapter configurations to build a CompositeAdapter for the EVC.
"""
return Adapter.build(config)
def _instantiate_space(config):
"""Instantiate the space object
Build the Space object if argument is a dictionary, else return the Space object as is.
Parameters
----------
config: dict or Space object
Dictionary of priors or already built Space object.
"""
if isinstance(config, Space):
return config
return SpaceBuilder().build(config)
def _instantiate_algo(space, config):
"""Instantiate the algorithm object
Parameters
----------
config: dict, optional
Configuration of the strategy. If None of empty, system's defaults are used
(orion.core.config.experiment.algorithms).
"""
if not config:
config = orion.core.config.experiment.algorithms
return PrimaryAlgo(space, config)
def _instantiate_strategy(config=None):
"""Instantiate the strategy object
Parameters
----------
config: dict, optional
Configuration of the strategy. If None of empty, system's defaults are used
(orion.core.config.producer.strategy).
"""
if not config:
config = orion.core.config.experiment.strategy
if isinstance(config, str):
strategy_type = config
config = {}
else:
strategy_type, config = next(iter(config.items()))
return Strategy(of_type=strategy_type, **config)
def _register_experiment(experiment):
"""Register a new experiment in the database"""
experiment.metadata['datetime'] = datetime.datetime.utcnow()
config = experiment.configuration
# This will raise DuplicateKeyError if a concurrent experiment with
# identical (name, metadata.user) is written first in the database.
get_storage().create_experiment(config)
# XXX: Reminder for future DB implementations:
# MongoDB, updates an inserted dict with _id, so should you :P
experiment._id = config['_id'] # pylint:disable=protected-access
# Update refers in db if experiment is root
if experiment.refers.get('parent_id') is None:
log.debug('update refers (name: %s)', experiment.name)
experiment.refers['root_id'] = experiment.id
get_storage().update_experiment(experiment, refers=experiment.configuration['refers'])
def _update_experiment(experiment):
"""Update experiment configuration in database"""
log.debug('updating experiment (name: %s)', experiment.name)
config = experiment.configuration
# TODO: Remove since this should not occur anymore without metadata.user in the indices?
# Writing the final config to an already existing experiment raises
# a DuplicatKeyError because of the embedding id `metadata.user`.
# To avoid this `final_config["name"]` is popped out before
# `db.write()`, thus seamingly breaking the compound index
# `(name, metadata.user)`
config.pop("name")
get_storage().update_experiment(experiment, **config)
def _branch_experiment(experiment, conflicts, version, branching_arguments):
"""Create a new branch experiment with adapters for the given conflicts"""
experiment_brancher = ExperimentBranchBuilder(conflicts, **branching_arguments)
needs_manual_resolution = (not experiment_brancher.is_resolved or
experiment_brancher.manual_resolution)
if not experiment_brancher.is_resolved:
name_conflict = conflicts.get([ExperimentNameConflict])[0]
if not name_conflict.is_resolved and not version:
raise RaceCondition('There was likely a race condition during version increment.')
if needs_manual_resolution:
# TODO: This should only be possible when using cmdline API
branching_prompt = BranchingPrompt(experiment_brancher)
if not sys.__stdin__.isatty():
raise BranchingEvent(branching_prompt.get_status())
branching_prompt.cmdloop()
if branching_prompt.abort or not experiment_brancher.is_resolved:
sys.exit()
config = experiment_brancher.conflicting_config
config['refers']['adapter'] = experiment_brancher.create_adapters().configuration
config['refers']['parent_id'] = experiment.id
config.pop('_id')
return create_experiment(**config)
def _get_conflicts(experiment, branching):
"""Get conflicts between current experiment and corresponding configuration in database"""
db_experiment = build_view(experiment.name, experiment.version)
conflicts = detect_conflicts(db_experiment.configuration, experiment.configuration,
branching)
# elif must_branch and not enable_branching:
# raise ValueError("Configuration is different and generate a branching event")
return conflicts
def _fetch_config_version(configs, version=None):
"""Fetch the experiment configuration corresponding to the given version
Parameters
----------
configs: list
List of configurations fetched from storoge.
version: int, optional
Version to select. If None, last version will be selected. If version given is larger than
largest version available, the largest version will be selected.
"""
max_version = max(configs, key=lambda exp: exp.get('version', 1)).get('version', 1)
if version is None:
version = max_version
else:
version = version
if version > max_version:
log.warning("Version %s was specified but most recent version is only %s. "
"Using %s.", version, max_version, max_version)
version = min(version, max_version)
configs = filter(lambda exp: exp.get('version', 1) == version, configs)
return next(iter(configs))
###
# Functions for commandline API
###
[docs]def build_from_args(cmdargs):
"""Build an experiment based on commandline arguments.
Options provided in commandline and configuration file (--config) will overwrite system's
default values and configuration from database if experiment already exits.
Commandline arguments have precedence over configuration file options.
.. seealso::
:func:`orion.core.io.experiment_builder.build` for more information on experiment creation.
"""
cmd_config = get_cmd_config(cmdargs)
if 'name' not in cmd_config:
raise NoNameError()
setup_storage(cmd_config['storage'], debug=cmd_config.get('debug'))
return build(**cmd_config)
[docs]def build_view_from_args(cmdargs):
"""Build an experiment view based on commandline arguments
.. seealso::
:func:`orion.core.io.experiment_builder.build_view` for more information on experiment view
creation.
"""
cmd_config = get_cmd_config(cmdargs)
if 'name' not in cmd_config:
raise NoNameError()
setup_storage(cmd_config['storage'], debug=cmd_config.get('debug'))
name = cmd_config.get('name')
version = cmd_config.get('version')
return build_view(name, version)
[docs]def get_cmd_config(cmdargs):
"""Fetch configuration defined by commandline and local configuration file.
Arguments of commandline have priority over options in configuration file.
"""
cmdargs = resolve_config.fetch_config_from_cmdargs(cmdargs)
cmd_config = resolve_config.fetch_config(cmdargs)
cmd_config = resolve_config.merge_configs(cmd_config, cmdargs)
cmd_config.update(cmd_config.pop('experiment', {}))
cmd_config['branching'] = cmd_config.pop('evc', {})
# TODO: user_args won't be defined if reading from DB only (`orion hunt -n <exp> ` alone)
metadata = resolve_config.fetch_metadata(cmd_config.get('user'), cmd_config.get('user_args'))
cmd_config['metadata'] = metadata
cmd_config.pop('config', None)
backward.populate_space(cmd_config)
backward.update_db_config(cmd_config)
return cmd_config