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e378de1e41
clearml/trains/task.py
allegroai e378de1e41 Add multi configuration section support (hyperparams and configurations) 
Support setting offline mode API version using TRAINS_OFFLINE_MODE env var
2020-08-08 12:35:03 +03:00

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import atexit
import json
import os
import shutil
import signal
import sys
import threading
import time
from argparse import ArgumentParser
from tempfile import mkstemp, mkdtemp
from zipfile import ZipFile, ZIP_DEFLATED
try:
# noinspection PyCompatibility
from collections.abc import Callable, Sequence as CollectionsSequence
except ImportError:
from collections import Callable, Sequence as CollectionsSequence
from typing import Optional, Union, Mapping, Sequence, Any, Dict, Iterable, TYPE_CHECKING
import psutil
import six
from pathlib2 import Path
from .backend_api.services import tasks, projects, queues
from .backend_api.session.session import Session, ENV_ACCESS_KEY, ENV_SECRET_KEY
from .backend_interface.metrics import Metrics
from .backend_interface.model import Model as BackendModel
from .backend_interface.task import Task as _Task
from .backend_interface.task.log import TaskHandler
from .backend_interface.task.development.worker import DevWorker
from .backend_interface.task.repo import ScriptInfo
from .backend_interface.util import get_single_result, exact_match_regex, make_message, mutually_exclusive
from .binding.absl_bind import PatchAbsl
from .binding.artifacts import Artifacts, Artifact
from .binding.environ_bind import EnvironmentBind, PatchOsFork
from .binding.frameworks.fastai_bind import PatchFastai
from .binding.frameworks.pytorch_bind import PatchPyTorchModelIO
from .binding.frameworks.tensorflow_bind import TensorflowBinding
from .binding.frameworks.xgboost_bind import PatchXGBoostModelIO
from .binding.joblib_bind import PatchedJoblib
from .binding.matplotlib_bind import PatchedMatplotlib
from .config import config, DEV_TASK_NO_REUSE, get_is_master_node
from .config import running_remotely, get_remote_task_id
from .config.cache import SessionCache
from .debugging.log import LoggerRoot
from .errors import UsageError
from .logger import Logger
from .model import Model, InputModel, OutputModel, ARCHIVED_TAG
from .task_parameters import TaskParameters
from .utilities.args import argparser_parseargs_called, get_argparser_last_args, \
argparser_update_currenttask
from .utilities.dicts import ReadOnlyDict, merge_dicts
from .utilities.proxy_object import ProxyDictPreWrite, ProxyDictPostWrite, flatten_dictionary, \
nested_from_flat_dictionary, naive_nested_from_flat_dictionary
from .utilities.resource_monitor import ResourceMonitor
from .utilities.seed import make_deterministic
# noinspection PyProtectedMember
from .backend_interface.task.args import _Arguments
if TYPE_CHECKING:
import pandas
import numpy
from PIL import Image
class Task(_Task):
"""
The ``Task`` class is a code template for a Task object which, together with its connected experiment components,
represents the current running experiment. These connected components include hyperparameters, loggers,
configuration, label enumeration, models, and other artifacts.
The term "main execution Task" refers to the Task context for current running experiment. Python experiment scripts
can create one, and only one, main execution Task. It is a traceable, and after a script runs and Trains stores
the Task in the **Trains Server** (backend), it is modifiable, reproducible, executable by a worker, and you
can duplicate it for further experimentation.
The ``Task`` class and its methods allow you to create and manage experiments, as well as perform
advanced experimentation functions, such as autoML.
.. warning::
Do not construct Task objects directly. Use one of the methods listed below to create experiments or
reference existing experiments.
For detailed information about creating Task objects, see the following methods:
- Create a new reproducible Task - :meth:`Task.init`
.. important::
In some cases, ``Task.init`` may return a Task object which is already stored in **Trains Server** (already
initialized), instead of creating a new Task. For a detailed explanation of those cases, see the ``Task.init``
method.
- Create a new non-reproducible Task - :meth:`Task.create`
- Get the current running Task - :meth:`Task.current_task`
- Get another (different) Task - :meth:`Task.get_task`
.. note::
The **Trains** documentation often refers to a Task as, "Task (experiment)".
"Task" refers to the class in the Trains Python Client Package, the object in your Python experiment script,
and the entity with which **Trains Server** and **Trains Agent** work.
"Experiment" refers to your deep learning solution, including its connected components, inputs, and outputs,
and is the experiment you can view, analyze, compare, modify, duplicate, and manage using the Trains
**Web-App** (UI).
Therefore, a "Task" is effectively an "experiment", and "Task (experiment)" encompasses its usage throughout
the Trains.
The exception to this Task behavior is sub-tasks (non-reproducible Tasks), which do not use the main execution
Task. Creating a sub-task always creates a new Task with a new Task ID.
"""
TaskTypes = _Task.TaskTypes
NotSet = object()
__create_protection = object()
__main_task = None # type: Optional[Task]
__exit_hook = None
__forked_proc_main_pid = None
__task_id_reuse_time_window_in_hours = float(config.get('development.task_reuse_time_window_in_hours', 24.0))
__detect_repo_async = config.get('development.vcs_repo_detect_async', False)
__default_output_uri = config.get('development.default_output_uri', None)
__default_configuration_name = 'General'
class _ConnectedParametersType(object):
argparse = "argument_parser"
dictionary = "dictionary"
task_parameters = "task_parameters"
@classmethod
def _options(cls):
return {
var for var, val in vars(cls).items()
if isinstance(val, six.string_types)
}
def __init__(self, private=None, **kwargs):
"""
.. warning::
**Do not construct Task manually!**
Please use :meth:`Task.init` or :meth:`Task.get_task`
"""
if private is not Task.__create_protection:
raise UsageError(
'Task object cannot be instantiated externally, use Task.current_task() or Task.get_task(...)')
self._repo_detect_lock = threading.RLock()
super(Task, self).__init__(**kwargs)
self._arguments = _Arguments(self)
self._logger = None
self._last_input_model_id = None
self._connected_output_model = None
self._dev_worker = None
self._connected_parameter_type = None
self._detect_repo_async_thread = None
self._resource_monitor = None
self._calling_filename = None
# register atexit, so that we mark the task as stopped
self._at_exit_called = False
@classmethod
def current_task(cls):
# type: () -> Task
"""
Get the current running Task (experiment). This is the main execution Task (task context) returned as a Task
object.
:return: The current running Task (experiment).
"""
return cls.__main_task
@classmethod
def init(
cls,
project_name=None, # type: Optional[str]
task_name=None, # type: Optional[str]
task_type=TaskTypes.training, # type: Task.TaskTypes
reuse_last_task_id=True, # type: Union[bool, str]
continue_last_task=False, # type: Union[bool, str]
output_uri=None, # type: Optional[str]
auto_connect_arg_parser=True, # type: Union[bool, Mapping[str, bool]]
auto_connect_frameworks=True, # type: Union[bool, Mapping[str, bool]]
auto_resource_monitoring=True, # type: bool
):
# type: (...) -> Task
"""
Creates a new Task (experiment) if:
- The Task never ran before. No Task with the same ``task_name`` and ``project_name`` is stored in
**Trains Server**.
- The Task has run before (the same ``task_name`` and ``project_name``), and (a) it stored models and / or
artifacts, or (b) its status is Published , or (c) it is Archived.
- A new Task is forced by calling ``Task.init`` with ``reuse_last_task_id=False``.
Otherwise, the already initialized Task object for the same ``task_name`` and ``project_name`` is returned.
.. note::
To reference another Task, instead of initializing the same Task more than once, call
:meth:`Task.get_task`. For example, to "share" the same experiment in more than one script,
call ``Task.get_task``. See the ``Task.get_task`` method for an example.
For example:
The first time the following code runs, it will create a new Task. The status will be Completed.
.. code-block:: py
from trains import Task
task = Task.init('myProject', 'myTask')
If this code runs again, it will not create a new Task. It does not store a model or artifact,
it is not Published (its status Completed) , it was not Archived, and a new Task is not forced.
If the Task is Published or Archived, and run again, it will create a new Task with a new Task ID.
The following code will create a new Task every time it runs, because it stores an artifact.
.. code-block:: py
task = Task.init('myProject', 'myOtherTask')
d = {'a': '1'}
task.upload_artifact('myArtifact', d)
:param str project_name: The name of the project in which the experiment will be created. If the project does
not exist, it is created. If ``project_name`` is ``None``, the repository name is used. (Optional)
:param str task_name: The name of Task (experiment). If ``task_name`` is ``None``, the Python experiment
script's file name is used. (Optional)
:param TaskTypes task_type: The task type.
Valid task types:
- ``TaskTypes.training`` (default)
- ``TaskTypes.testing``
- ``TaskTypes.inference``
- ``TaskTypes.data_processing``
- ``TaskTypes.application``
- ``TaskTypes.monitor``
- ``TaskTypes.controller``
- ``TaskTypes.optimizer``
- ``TaskTypes.service``
- ``TaskTypes.qc``
- ``TaskTypes.custom``
:param bool reuse_last_task_id: Force a new Task (experiment) with a previously used Task ID,
and the same project and Task name.
.. note::
If the previously executed Task has artifacts or models, it will not be reused (overwritten)
and a new Task will be created.
When a Task is reused, the previous execution outputs are deleted, including console outputs and logs.
The values are:
- ``True`` - Reuse the last Task ID. (default)
- ``False`` - Force a new Task (experiment).
- A string - You can also specify a Task ID (string) to be reused,
instead of the cached ID based on the project/name combination.
:param bool continue_last_task: Continue the execution of a previously executed Task (experiment)
.. note::
When continuing the executing of a previously executed Task,
all previous artifacts / models/ logs are intact.
New logs will continue iteration/step based on the previous-execution maximum iteration value.
For example:
The last train/loss scalar reported was iteration 100, the next report will be iteration 101.
The values are:
- ``True`` - Continue the the last Task ID.
specified explicitly by reuse_last_task_id or implicitly with the same logic as reuse_last_task_id
- ``False`` - Overwrite the execution of previous Task (default).
- A string - You can also specify a Task ID (string) to be continued.
This is equivalent to `continue_last_task=True` and `reuse_last_task_id=a_task_id_string`.
:param str output_uri: The default location for output models and other artifacts. In the default location,
Trains creates a subfolder for the output. The subfolder structure is the following:
<output destination name> / <project name> / <task name>.< Task ID>
The following are examples of ``output_uri`` values for the supported locations:
- A shared folder: ``/mnt/share/folder``
- S3: ``s3://bucket/folder``
- Google Cloud Storage: ``gs://bucket-name/folder``
- Azure Storage: ``azure://company.blob.core.windows.net/folder/``
.. important::
For cloud storage, you must install the **Trains** package for your cloud storage type,
and then configure your storage credentials. For detailed information, see
`Trains Python Client Extras <./references/trains_extras_storage/>`_ in the "Trains Python Client
Reference" section.
:param auto_connect_arg_parser: Automatically connect an argparse object to the Task?
The values are:
- ``True`` - Automatically connect. (default)
- ``False`` - Do not automatically connect.
- A dictionary - In addition to a boolean, you can use a dictionary for fined grained control of connected
arguments. The dictionary keys are argparse variable names and the values are booleans.
The ``False`` value excludes the specified argument from the Task's parameter section.
Keys missing from the dictionary default to ``True``, and an empty dictionary defaults to ``False``.
For example:
.. code-block:: py
auto_connect_arg_parser={'do_not_include_me': False, }
.. note::
To manually connect an argparse, use :meth:`Task.connect`.
:param auto_connect_frameworks: Automatically connect frameworks? This includes patching MatplotLib, XGBoost,
scikit-learn, Keras callbacks, and TensorBoard/X to serialize plots, graphs, and the model location to
the **Trains Server** (backend), in addition to original output destination.
The values are:
- ``True`` - Automatically connect (default)
- ``False`` - Do not automatically connect
- A dictionary - In addition to a boolean, you can use a dictionary for fined grained control of connected
frameworks. The dictionary keys are frameworks and the values are booleans.
Keys missing from the dictionary default to ``True``, and an empty dictionary defaults to ``False``.
For example:
.. code-block:: py
auto_connect_frameworks={'matplotlib': True, 'tensorflow': True, 'pytorch': True,
'xgboost': True, 'scikit': True}
:param bool auto_resource_monitoring: Automatically create machine resource monitoring plots?
These plots appear in in the **Trains Web-App (UI)**, **RESULTS** tab, **SCALARS** sub-tab,
with a title of **:resource monitor:**.
The values are:
- ``True`` - Automatically create resource monitoring plots. (default)
- ``False`` - Do not automatically create.
:return: The main execution Task (Task context).
"""
def verify_defaults_match():
validate = [
('project name', project_name, cls.__main_task.get_project_name()),
('task name', task_name, cls.__main_task.name),
('task type', str(task_type), str(cls.__main_task.task_type)),
]
for field, default, current in validate:
if default is not None and default != current:
raise UsageError(
"Current task already created "
"and requested {field} '{default}' does not match current {field} '{current}'. "
"If you wish to create additional tasks use `Task.create`".format(
field=field,
default=default,
current=current,
)
)
if cls.__main_task is not None:
# if this is a subprocess, regardless of what the init was called for,
# we have to fix the main task hooks and stdout bindings
if cls.__forked_proc_main_pid != os.getpid() and cls.__is_subprocess():
if task_type is None:
task_type = cls.__main_task.task_type
# make sure we only do it once per process
cls.__forked_proc_main_pid = os.getpid()
# make sure we do not wait for the repo detect thread
cls.__main_task._detect_repo_async_thread = None
cls.__main_task._dev_worker = None
cls.__main_task._resource_monitor = None
# remove the logger from the previous process
logger = cls.__main_task.get_logger()
logger.set_flush_period(None)
# create a new logger (to catch stdout/err)
cls.__main_task._logger = None
cls.__main_task._reporter = None
cls.__main_task.get_logger()
cls.__main_task._artifacts_manager = Artifacts(cls.__main_task)
# unregister signal hooks, they cause subprocess to hang
# noinspection PyProtectedMember
cls.__main_task.__register_at_exit(cls.__main_task._at_exit)
# TODO: Check if the signal handler method is safe enough, for the time being, do not unhook
# cls.__main_task.__register_at_exit(None, only_remove_signal_and_exception_hooks=True)
if not running_remotely():
verify_defaults_match()
return cls.__main_task
is_sub_process_task_id = None
# check that we are not a child process, in that case do nothing.
# we should not get here unless this is Windows platform, all others support fork
if cls.__is_subprocess():
class _TaskStub(object):
def __call__(self, *args, **kwargs):
return self
def __getattr__(self, attr):
return self
def __setattr__(self, attr, val):
pass
is_sub_process_task_id = cls.__get_master_id_task_id()
# we could not find a task ID, revert to old stub behaviour
if not is_sub_process_task_id:
return _TaskStub()
elif running_remotely() and not get_is_master_node():
# make sure we only do it once per process
cls.__forked_proc_main_pid = os.getpid()
# make sure everyone understands we should act as if we are a subprocess (fake pid 1)
cls.__update_master_pid_task(pid=1, task=get_remote_task_id())
else:
# set us as master process (without task ID)
cls.__update_master_pid_task()
is_sub_process_task_id = None
if task_type is None:
# Backwards compatibility: if called from Task.current_task and task_type
# was not specified, keep legacy default value of TaskTypes.training
task_type = cls.TaskTypes.training
elif isinstance(task_type, six.string_types):
if task_type not in Task.TaskTypes.__members__:
raise ValueError("Task type '{}' not supported, options are: {}".format(
task_type, Task.TaskTypes.__members__.keys()))
task_type = Task.TaskTypes.__members__[str(task_type)]
try:
if not running_remotely():
# if this is the main process, create the task
if not is_sub_process_task_id:
task = cls._create_dev_task(
default_project_name=project_name,
default_task_name=task_name,
default_task_type=task_type,
reuse_last_task_id=reuse_last_task_id,
continue_last_task=continue_last_task,
detect_repo=False if (
isinstance(auto_connect_frameworks, dict) and
not auto_connect_frameworks.get('detect_repository', True)) else True
)
# set defaults
if cls._offline_mode:
task.output_uri = None
elif output_uri:
task.output_uri = output_uri
elif cls.__default_output_uri:
task.output_uri = cls.__default_output_uri
# store new task ID
cls.__update_master_pid_task(task=task)
else:
# subprocess should get back the task info
task = Task.get_task(task_id=is_sub_process_task_id)
else:
# if this is the main process, create the task
if not is_sub_process_task_id:
task = cls(
private=cls.__create_protection,
task_id=get_remote_task_id(),
log_to_backend=False,
)
if cls.__default_output_uri and not task.output_uri:
task.output_uri = cls.__default_output_uri
# store new task ID
cls.__update_master_pid_task(task=task)
# make sure we are started
task.started(ignore_errors=True)
else:
# subprocess should get back the task info
task = Task.get_task(task_id=is_sub_process_task_id)
except Exception:
raise
else:
Task.__main_task = task
# register the main task for at exit hooks (there should only be one)
task.__register_at_exit(task._at_exit)
# patch OS forking
PatchOsFork.patch_fork()
if auto_connect_frameworks:
is_auto_connect_frameworks_bool = not isinstance(auto_connect_frameworks, dict)
if is_auto_connect_frameworks_bool or auto_connect_frameworks.get('scikit', True):
PatchedJoblib.update_current_task(task)
if is_auto_connect_frameworks_bool or auto_connect_frameworks.get('matplotlib', True):
PatchedMatplotlib.update_current_task(Task.__main_task)
if is_auto_connect_frameworks_bool or auto_connect_frameworks.get('tensorflow', True):
PatchAbsl.update_current_task(Task.__main_task)
TensorflowBinding.update_current_task(task)
if is_auto_connect_frameworks_bool or auto_connect_frameworks.get('pytorch', True):
PatchPyTorchModelIO.update_current_task(task)
if is_auto_connect_frameworks_bool or auto_connect_frameworks.get('xgboost', True):
PatchXGBoostModelIO.update_current_task(task)
if is_auto_connect_frameworks_bool or auto_connect_frameworks.get('fastai', True):
PatchFastai.update_current_task(task)
if auto_resource_monitoring and not is_sub_process_task_id:
task._resource_monitor = ResourceMonitor(
task, report_mem_used_per_process=not config.get(
'development.worker.report_global_mem_used', False))
task._resource_monitor.start()
# make sure all random generators are initialized with new seed
make_deterministic(task.get_random_seed())
if auto_connect_arg_parser:
EnvironmentBind.update_current_task(Task.__main_task)
# Patch ArgParser to be aware of the current task
argparser_update_currenttask(Task.__main_task)
# set excluded arguments
if isinstance(auto_connect_arg_parser, dict):
task._arguments.exclude_parser_args(auto_connect_arg_parser)
# Check if parse args already called. If so, sync task parameters with parser
if argparser_parseargs_called():
for parser, parsed_args in get_argparser_last_args():
task._connect_argparse(parser=parser, parsed_args=parsed_args)
elif argparser_parseargs_called():
# actually we have nothing to do, in remote running, the argparser will ignore
# all non argparser parameters, only caveat if parameter connected with the same name
# as the argparser this will be solved once sections are introduced to parameters
pass
# Make sure we start the logger, it will patch the main logging object and pipe all output
# if we are running locally and using development mode worker, we will pipe all stdout to logger.
# The logger will automatically take care of all patching (we just need to make sure to initialize it)
logger = task.get_logger()
# show the debug metrics page in the log, it is very convenient
if not is_sub_process_task_id:
if cls._offline_mode:
logger.report_text('TRAINS running in offline mode, session stored in {}'.format(
task.get_offline_mode_folder()))
else:
logger.report_text('TRAINS results page: {}'.format(task.get_output_log_web_page()))
# Make sure we start the dev worker if required, otherwise it will only be started when we write
# something to the log.
task._dev_mode_task_start()
return task
@classmethod
def create(cls, project_name=None, task_name=None, task_type=TaskTypes.training):
# type: (Optional[str], Optional[str], Task.TaskTypes) -> Task
"""
Create a new, non-reproducible Task (experiment). This is called a sub-task.
.. note::
This method always creates a new, non-reproducible Task. To create a reproducible Task, call the
:meth:`Task.init` method. To reference another Task, call the :meth:`Task.get_task` method .
:param str project_name: The name of the project in which the experiment will be created.
If ``project_name`` is ``None``, and the main execution Task is initialized (see :meth:`Task.init`),
then the main execution Task's project is used. Otherwise, if the project does
not exist, it is created. (Optional)
:param str task_name: The name of Task (experiment).
:param TaskTypes task_type: The task type.
Valid task types:
- ``TaskTypes.training`` (default)
- ``TaskTypes.testing``
- ``TaskTypes.inference``
- ``TaskTypes.data_processing``
- ``TaskTypes.application``
- ``TaskTypes.monitor``
- ``TaskTypes.controller``
- ``TaskTypes.optimizer``
- ``TaskTypes.service``
- ``TaskTypes.qc``
- ``TaskTypes.custom``
:return: A new experiment.
"""
if not project_name:
if not cls.__main_task:
raise ValueError("Please provide project_name, no global task context found "
"(Task.current_task hasn't been called)")
project_name = cls.__main_task.get_project_name()
try:
task = cls(
private=cls.__create_protection,
project_name=project_name,
task_name=task_name,
task_type=task_type,
log_to_backend=False,
force_create=True,
)
except Exception:
raise
return task
@classmethod
def get_task(cls, task_id=None, project_name=None, task_name=None):
# type: (Optional[str], Optional[str], Optional[str]) -> Task
"""
Get a Task by Id, or project name / task name combination.
For example:
The following code demonstrates calling ``Task.get_task`` to report a scalar to another Task. The output
of :meth:`.Logger.report_scalar` from testing is associated with the Task named ``training``. It allows
training and testing to run concurrently, because they initialized different Tasks (see :meth:`Task.init`
for information about initializing Tasks).
The training script:
.. code-block:: py
# initialize the training Task
task = Task.init('myProject', 'training')
# do some training
The testing script:
.. code-block:: py
# initialize the testing Task
task = Task.init('myProject', 'testing')
# get the training Task
train_task = Task.get_task(project_name='myProject', task_name='training')
# report metrics in the training Task
for x in range(10):
train_task.get_logger().report_scalar('title', 'series', value=x * 2, iteration=x)
:param str task_id: The Id (system UUID) of the experiment to get.
If specified, ``project_name`` and ``task_name`` are ignored.
:param str project_name: The project name of the Task to get.
:param str task_name: The name of the Task within ``project_name`` to get.
:return: The Task specified by ID, or project name / experiment name combination.
"""
return cls.__get_task(task_id=task_id, project_name=project_name, task_name=task_name)
@classmethod
def get_tasks(cls, task_ids=None, project_name=None, task_name=None, task_filter=None):
# type: (Optional[Sequence[str]], Optional[str], Optional[str], Optional[Dict]) -> Sequence[Task]
"""
Get a list of Tasks by one of the following:
- A list of specific Task IDs.
- All Tasks in a project matching a full or partial Task name.
- All Tasks in any project matching a full or partial Task name.
:param list(str) task_ids: The Ids (system UUID) of experiments to get.
If ``task_ids`` specified, then ``project_name`` and ``task_name`` are ignored.
:param str project_name: The project name of the Tasks to get. To get the experiment
in all projects, use the default value of ``None``. (Optional)
:param str task_name: The full name or partial name of the Tasks to match within the specified
``project_name`` (or all projects if ``project_name`` is ``None``).
This method supports regular expressions for name matching. (Optional)
:param list(str) task_ids: list of unique task id string (if exists other parameters are ignored)
:param str project_name: project name (str) the task belongs to (use None for all projects)
:param str task_name: task name (str) in within the selected project
Return any partial match of task_name, regular expressions matching is also supported
If None is passed, returns all tasks within the project
:param dict task_filter: filter and order Tasks. See service.tasks.GetAllRequest for details
:return: The Tasks specified by the parameter combinations (see the parameters).
"""
return cls.__get_tasks(task_ids=task_ids, project_name=project_name,
task_name=task_name, **(task_filter or {}))
@property
def output_uri(self):
# type: () -> str
return self.storage_uri
@output_uri.setter
def output_uri(self, value):
# type: (str) -> None
# check if we have the correct packages / configuration
if value and value != self.storage_uri:
from .storage.helper import StorageHelper
helper = StorageHelper.get(value)
if not helper:
raise ValueError("Could not get access credentials for '{}' "
", check configuration file ~/trains.conf".format(value))
helper.check_write_permissions(value)
self.storage_uri = value
@property
def artifacts(self):
# type: () -> Dict[str, Artifact]
"""
A read-only dictionary of Task artifacts (name, artifact).
:return: The artifacts.
"""
if not Session.check_min_api_version('2.3'):
return ReadOnlyDict()
artifacts_pairs = []
if self.data.execution and self.data.execution.artifacts:
artifacts_pairs = [(a.key, Artifact(a)) for a in self.data.execution.artifacts]
if self._artifacts_manager:
artifacts_pairs += list(self._artifacts_manager.registered_artifacts.items())
return ReadOnlyDict(artifacts_pairs)
@property
def models(self):
# type: () -> Dict[str, Sequence[Model]]
"""
Read-only dictionary of the Task's loaded/stored models
:return: A dictionary of models loaded/stored {'input': list(Model), 'output': list(Model)}.
"""
return self.get_models()
@classmethod
def clone(
cls,
source_task=None, # type: Optional[Union[Task, str]]
name=None, # type: Optional[str]
comment=None, # type: Optional[str]
parent=None, # type: Optional[str]
project=None, # type: Optional[str]
):
# type: (...) -> Task
"""
Create a duplicate (a clone) of a Task (experiment). The status of the cloned Task is ``Draft``
and modifiable.
Use this method to manage experiments and for autoML.
:param str source_task: The Task to clone. Specify a Task object or a Task ID. (Optional)
:param str name: The name of the new cloned Task. (Optional)
:param str comment: A comment / description for the new cloned Task. (Optional)
:param str parent: The Id of the parent Task of the new Task.
- If ``parent`` is not specified, then ``parent`` is set to ``source_task.parent``.
- If ``parent`` is not specified and ``source_task.parent`` is not available, then
``parent`` set to to ``source_task``.
:param str project: The Id of the project in which to create the new Task.
If ``None``, the new task inherits the original Task's project. (Optional)
:return: The new cloned Task (experiment).
"""
assert isinstance(source_task, (six.string_types, Task))
if not Session.check_min_api_version('2.4'):
raise ValueError("Trains-server does not support DevOps features, "
"upgrade trains-server to 0.12.0 or above")
task_id = source_task if isinstance(source_task, six.string_types) else source_task.id
if not parent:
if isinstance(source_task, six.string_types):
source_task = cls.get_task(task_id=source_task)
parent = source_task.id if not source_task.parent else source_task.parent
elif isinstance(parent, Task):
parent = parent.id
cloned_task_id = cls._clone_task(cloned_task_id=task_id, name=name, comment=comment,
parent=parent, project=project)
cloned_task = cls.get_task(task_id=cloned_task_id)
return cloned_task
@classmethod
def enqueue(cls, task, queue_name=None, queue_id=None):
# type: (Union[Task, str], Optional[str], Optional[str]) -> Any
"""
Enqueue a Task for execution, by adding it to an execution queue.
.. note::
A worker daemon must be listening at the queue for the worker to fetch the Task and execute it,
see `Use Case Examples <../trains_agent_ref/#use-case-examples>`_ on the "Trains Agent
Reference page.
:param Task/str task: The Task to enqueue. Specify a Task object or Task ID.
:param str queue_name: The name of the queue. If not specified, then ``queue_id`` must be specified.
:param str queue_id: The Id of the queue. If not specified, then ``queue_name`` must be specified.
:return: An enqueue JSON response.
.. code-block:: javascript
{
"queued": 1,
"updated": 1,
"fields": {
"status": "queued",
"status_reason": "",
"status_message": "",
"status_changed": "2020-02-24T15:05:35.426770+00:00",
"last_update": "2020-02-24T15:05:35.426770+00:00",
"execution.queue": "2bd96ab2d9e54b578cc2fb195e52c7cf"
}
}
- ``queued`` - The number of Tasks enqueued (an integer or ``null``).
- ``updated`` - The number of Tasks updated (an integer or ``null``).
- ``fields``
- ``status`` - The status of the experiment.
- ``status_reason`` - The reason for the last status change.
- ``status_message`` - Information about the status.
- ``status_changed`` - The last status change date and time (ISO 8601 format).
- ``last_update`` - The last Task update time, including Task creation, update, change, or events for
this task (ISO 8601 format).
- ``execution.queue`` - The Id of the queue where the Task is enqueued. ``null`` indicates not enqueued.
"""
assert isinstance(task, (six.string_types, Task))
if not Session.check_min_api_version('2.4'):
raise ValueError("Trains-server does not support DevOps features, "
"upgrade trains-server to 0.12.0 or above")
# make sure we have wither name ot id
mutually_exclusive(queue_name=queue_name, queue_id=queue_id)
task_id = task if isinstance(task, six.string_types) else task.id
session = cls._get_default_session()
if not queue_id:
req = queues.GetAllRequest(name=exact_match_regex(queue_name), only_fields=["id"])
res = cls._send(session=session, req=req)
if not res.response.queues:
raise ValueError('Could not find queue named "{}"'.format(queue_name))
queue_id = res.response.queues[0].id
if len(res.response.queues) > 1:
LoggerRoot.get_base_logger().info("Multiple queues with name={}, selecting queue id={}".format(
queue_name, queue_id))
req = tasks.EnqueueRequest(task=task_id, queue=queue_id)
res = cls._send(session=session, req=req)
resp = res.response
return resp
@classmethod
def dequeue(cls, task):
# type: (Union[Task, str]) -> Any
"""
Dequeue (remove) a Task from an execution queue.
:param Task/str task: The Task to dequeue. Specify a Task object or Task ID.
:return: A dequeue JSON response.
.. code-block:: javascript
{
"dequeued": 1,
"updated": 1,
"fields": {
"status": "created",
"status_reason": "",
"status_message": "",
"status_changed": "2020-02-24T16:43:43.057320+00:00",
"last_update": "2020-02-24T16:43:43.057320+00:00",
"execution.queue": null
}
}
- ``dequeued`` - The number of Tasks enqueued (an integer or ``null``).
- ``fields``
- ``status`` - The status of the experiment.
- ``status_reason`` - The reason for the last status change.
- ``status_message`` - Information about the status.
- ``status_changed`` - The last status change date and time in ISO 8601 format.
- ``last_update`` - The last time the Task was created, updated,
changed or events for this task were reported.
- ``execution.queue`` - The Id of the queue where the Task is enqueued. ``null`` indicates not enqueued.
- ``updated`` - The number of Tasks updated (an integer or ``null``).
"""
assert isinstance(task, (six.string_types, Task))
if not Session.check_min_api_version('2.4'):
raise ValueError("Trains-server does not support DevOps features, "
"upgrade trains-server to 0.12.0 or above")
task_id = task if isinstance(task, six.string_types) else task.id
session = cls._get_default_session()
req = tasks.DequeueRequest(task=task_id)
res = cls._send(session=session, req=req)
resp = res.response
return resp
def add_tags(self, tags):
# type: (Union[Sequence[str], str]) -> None
"""
Add Tags to this task. Old tags are not deleted. When executing a Task (experiment) remotely,
this method has no effect).
:param tags: A list of tags which describe the Task to add.
"""
if not running_remotely() or not self.is_main_task():
if isinstance(tags, six.string_types):
tags = tags.split(" ")
self.data.tags.extend(tags)
self._edit(tags=list(set(self.data.tags)))
def connect(self, mutable, name=None):
# type: (Any, Optional[str]) -> Any
"""
Connect an object to a Task object. This connects an experiment component (part of an experiment) to the
experiment. For example, connect hyperparameters or models.
:param object mutable: The experiment component to connect. The object can be any object Task supports
integrating, including:
- argparse - An argparse object for parameters.
- dict - A dictionary for parameters.
- TaskParameters - A TaskParameters object.
- model - A model object for initial model warmup, or for model update/snapshot uploading.
:param str name: A section name associated with the connected object. Default: 'General'
Currently only supported for `dict` / `TaskParameter` objects
Examples:
name='General' will put the connected dictionary under the General section in the hyper-parameters
name='Train' will put the connected dictionary under the Train section in the hyper-parameters
:return: The result returned when connecting the object, if supported.
:raise: Raise an exception on unsupported objects.
"""
dispatch = (
(OutputModel, self._connect_output_model),
(InputModel, self._connect_input_model),
(ArgumentParser, self._connect_argparse),
(dict, self._connect_dictionary),
(TaskParameters, self._connect_task_parameters),
)
multi_config_support = Session.check_min_api_version('2.9')
if multi_config_support and not name:
name = self.__default_configuration_name
if not multi_config_support and name and name != self.__default_configuration_name:
raise ValueError("Multiple configurations are not supported with the current 'trains-server', "
"please upgrade to the latest version")
for mutable_type, method in dispatch:
if isinstance(mutable, mutable_type):
return method(mutable, name=name)
raise Exception('Unsupported mutable type %s: no connect function found' % type(mutable).__name__)
def connect_configuration(self, configuration, name=None, description=None):
# type: (Union[Mapping, Path, str], Optional[str], Optional[str]) -> Union[Mapping, Path, str]
"""
Connect a configuration dictionary or configuration file (pathlib.Path / str) to a Task object.
This method should be called before reading the configuration file.
Later, when creating an output model, the model will include the contents of the configuration dictionary
or file.
For example, a local file:
.. code-block:: py
config_file = task.connect_configuration(config_file)
my_params = json.load(open(config_file,'rt'))
A parameter dictionary:
.. code-block:: py
my_params = task.connect_configuration(my_params)
:param configuration: The configuration. This is usually the configuration used in the model training process.
Specify one of the following:
- A dictionary - A dictionary containing the configuration. Trains stores the configuration in
the **Trains Server** (backend), in a HOCON format (JSON-like format) which is editable.
- A ``pathlib2.Path`` string - A path to the configuration file. Trains stores the content of the file.
A local path must be relative path. When executing a Task remotely in a worker, the contents brought
from the **Trains Server** (backend) overwrites the contents of the file.
:param str name: Configuration section name. default: 'General'
Allowing users to store multiple configuration dicts/files
:param str description: Configuration section description (text). default: None
:return: If a dictionary is specified, then a dictionary is returned. If pathlib2.Path / string is
specified, then a path to a local configuration file is returned. Configuration object.
"""
pathlib_Path = None
if not isinstance(configuration, (dict, Path, six.string_types)):
try:
from pathlib import Path as pathlib_Path
except ImportError:
pass
if not pathlib_Path or not isinstance(configuration, pathlib_Path):
raise ValueError("connect_configuration supports `dict`, `str` and 'Path' types, "
"{} is not supported".format(type(configuration)))
multi_config_support = Session.check_min_api_version('2.9')
if multi_config_support and not name:
name = self.__default_configuration_name
if not multi_config_support and name and name != self.__default_configuration_name:
raise ValueError("Multiple configurations are not supported with the current 'trains-server', "
"please upgrade to the latest version")
# parameter dictionary
if isinstance(configuration, dict):
def _update_config_dict(task, config_dict):
if multi_config_support:
# noinspection PyProtectedMember
task._set_configuration(
name=name, description=description, config_type='dictionary', config_dict=config_dict)
else:
# noinspection PyProtectedMember
task._set_model_config(config_dict=config_dict)
if not running_remotely() or not self.is_main_task():
if multi_config_support:
self._set_configuration(
name=name, description=description, config_type='dictionary', config_dict=configuration)
else:
self._set_model_config(config_dict=configuration)
configuration = ProxyDictPostWrite(self, _update_config_dict, **configuration)
else:
configuration.clear()
configuration.update(self._get_configuration_dict(name=name) if multi_config_support
else self._get_model_config_dict())
configuration = ProxyDictPreWrite(False, False, **configuration)
return configuration
# it is a path to a local file
if not running_remotely() or not self.is_main_task():
# check if not absolute path
configuration_path = Path(configuration)
if not configuration_path.is_file():
ValueError("Configuration file does not exist")
try:
with open(configuration_path.as_posix(), 'rt') as f:
configuration_text = f.read()
except Exception:
raise ValueError("Could not connect configuration file {}, file could not be read".format(
configuration_path.as_posix()))
if multi_config_support:
self._set_configuration(
name=name, description=description,
config_type=configuration_path.suffixes[-1].lstrip('.')
if configuration_path.suffixes and configuration_path.suffixes[-1] else 'file',
config_text=configuration_text)
else:
self._set_model_config(config_text=configuration_text)
return configuration
else:
configuration_text = self._get_configuration_text(name=name) if multi_config_support \
else self._get_model_config_text()
configuration_path = Path(configuration)
fd, local_filename = mkstemp(prefix='trains_task_config_',
suffix=configuration_path.suffixes[-1] if
configuration_path.suffixes else '.txt')
os.write(fd, configuration_text.encode('utf-8'))
os.close(fd)
if pathlib_Path:
return pathlib_Path(local_filename)
return Path(local_filename) if isinstance(configuration, Path) else local_filename
def connect_label_enumeration(self, enumeration):
# type: (Dict[str, int]) -> Dict[str, int]
"""
Connect a label enumeration dictionary to a Task (experiment) object.
Later, when creating an output model, the model will include the label enumeration dictionary.
:param dict enumeration: A label enumeration dictionary of string (label) to integer (value) pairs.
For example:
.. code-block:: javascript
{
'background': 0,
'person': 1
}
:return: The label enumeration dictionary (JSON).
"""
if not isinstance(enumeration, dict):
raise ValueError("connect_label_enumeration supports only `dict` type, "
"{} is not supported".format(type(enumeration)))
if not running_remotely() or not self.is_main_task():
self.set_model_label_enumeration(enumeration)
else:
# pop everything
enumeration.clear()
enumeration.update(self.get_labels_enumeration())
return enumeration
def get_logger(self):
# type: () -> Logger
"""
Get a Logger object for reporting, for this task context. You can view all Logger report output associated with
the Task for which this method is called, including metrics, plots, text, tables, and images, in the
**Trains Web-App (UI)**.
:return: The Logger for the Task (experiment).
"""
return self._get_logger()
def mark_started(self, force=False):
# type: (bool) -> ()
"""
Manually mark a Task as started (happens automatically)
:param bool force: If True the task status will be changed to `started` regardless of the current Task state.
"""
# UI won't let us see metrics if we're not started
self.started(force=force)
self.reload()
def mark_stopped(self, force=False):
# type: (bool) -> ()
"""
Manually mark a Task as stopped (also used in :meth:`_at_exit`)
:param bool force: If True the task status will be changed to `stopped` regardless of the current Task state.
"""
# flush any outstanding logs
self.flush(wait_for_uploads=True)
# mark task as stopped
self.stopped(force=force)
def flush(self, wait_for_uploads=False):
# type: (bool) -> bool
"""
Flush any outstanding reports or console logs.
:param bool wait_for_uploads: Wait for all outstanding uploads to complete before existing the flush?
- ``True`` - Wait
- ``False`` - Do not wait (default)
"""
# make sure model upload is done
if BackendModel.get_num_results() > 0 and wait_for_uploads:
BackendModel.wait_for_results()
# flush any outstanding logs
if self._logger:
# noinspection PyProtectedMember
self._logger._flush_stdout_handler()
if self._reporter:
self.reporter.flush()
LoggerRoot.flush()
return True
def reset(self, set_started_on_success=False, force=False):
# type: (bool, bool) -> None
"""
Reset a Task. Trains reloads a Task after a successful reset.
When a worker executes a Task remotely, the Task does not reset unless
the ``force`` parameter is set to ``True`` (this avoids accidentally clearing logs and metrics).
:param bool set_started_on_success: If successful, automatically set the Task to started?
- ``True`` - If successful, set to started.
- ``False`` - If successful, do not set to started. (default)
:param bool force: Force a Task reset, even when executing the Task (experiment) remotely in a worker?
- ``True`` - Force
- ``False`` - Do not force (default)
"""
if not running_remotely() or not self.is_main_task() or force:
super(Task, self).reset(set_started_on_success=set_started_on_success)
def close(self):
"""
Close the current Task. Enables you to manually shutdown the task.
.. warning::
Only call :meth:`Task.close` if you are certain the Task is not needed.
"""
if self._at_exit_called:
return
# store is main before we call at_exit, because will will Null it
is_main = self.is_main_task()
# wait for repository detection (5 minutes should be reasonable time to detect all packages)
if self._logger and not self.__is_subprocess():
self._wait_for_repo_detection(timeout=300.)
self.__shutdown()
# unregister atexit callbacks and signal hooks, if we are the main task
if is_main:
self.__register_at_exit(None)
def register_artifact(self, name, artifact, metadata=None, uniqueness_columns=True):
# type: (str, pandas.DataFrame, Dict, Union[bool, Sequence[str]]) -> None
"""
Register (add) an artifact for the current Task. Registered artifacts are dynamically sychronized with the
**Trains Server** (backend). If a registered artifact is updated, the update is stored in the
**Trains Server** (backend). Registered artifacts are primarily used for Data Audition.
The currently supported registered artifact object type is a pandas.DataFrame.
See also :meth:`Task.unregister_artifact` and :meth:`Task.get_registered_artifacts`.
.. note::
Trains also supports uploaded artifacts which are one-time uploads of static artifacts that are not
dynamically sychronized with the **Trains Server** (backend). These static artifacts include
additional object types. For more information, see :meth:`Task.upload_artifact`.
:param str name: The name of the artifact.
.. warning::
If an artifact with the same name was previously registered, it is overwritten.
:param object artifact: The artifact object.
:param dict metadata: A dictionary of key-value pairs for any metadata. This dictionary appears with the
experiment in the **Trains Web-App (UI)**, **ARTIFACTS** tab.
:param uniqueness_columns: A Sequence of columns for artifact uniqueness comparison criteria, or the default
value of ``True``. If ``True``, the artifact uniqueness comparison criteria is all the columns,
which is the same as ``artifact.columns``.
"""
if not isinstance(uniqueness_columns, CollectionsSequence) and uniqueness_columns is not True:
raise ValueError('uniqueness_columns should be a List (sequence) or True')
if isinstance(uniqueness_columns, str):
uniqueness_columns = [uniqueness_columns]
self._artifacts_manager.register_artifact(
name=name, artifact=artifact, metadata=metadata, uniqueness_columns=uniqueness_columns)
def unregister_artifact(self, name):
# type: (str) -> None
"""
Unregister (remove) a registered artifact. This removes the artifact from the watch list that Trains uses
to synchronize artifacts with the **Trains Server** (backend).
.. important::
- Calling this method does not remove the artifact from a Task. It only stops Trains from
monitoring the artifact.
- When this method is called, Trains immediately takes the last snapshot of the artifact.
"""
self._artifacts_manager.unregister_artifact(name=name)
def get_registered_artifacts(self):
# type: () -> Dict[str, Artifact]
"""
Get a dictionary containing the Task's registered (dynamically synchronized) artifacts (name, artifact object).
.. note::
After calling ``get_registered_artifacts``, you can still modify the registered artifacts.
:return: The registered (dynamically synchronized) artifacts.
"""
return self._artifacts_manager.registered_artifacts
def upload_artifact(
self,
name, # type: str
artifact_object, # type: Union[str, Mapping, pandas.DataFrame, numpy.ndarray, Image.Image, Any]
metadata=None, # type: Optional[Mapping]
delete_after_upload=False, # type: bool
auto_pickle=True, # type: bool
):
# type: (...) -> bool
"""
Upload (add) a static artifact to a Task object. The artifact is uploaded in the background.
The currently supported upload (static) artifact types include:
- string / pathlib2.Path - A path to artifact file. If a wildcard or a folder is specified, then Trains
creates and uploads a ZIP file.
- dict - Trains stores a dictionary as ``.json`` file and uploads it.
- pandas.DataFrame - Trains stores a pandas.DataFrame as ``.csv.gz`` (compressed CSV) file and uploads it.
- numpy.ndarray - Trains stores a numpy.ndarray as ``.npz`` file and uploads it.
- PIL.Image - Trains stores a PIL.Image as ``.png`` file and uploads it.
- Any - If called with auto_pickle=True, the object will be pickled and uploaded.
:param str name: The artifact name.
.. warning::
If an artifact with the same name was previously uploaded, then it is overwritten.
:param object artifact_object: The artifact object.
:param dict metadata: A dictionary of key-value pairs for any metadata. This dictionary appears with the
experiment in the **Trains Web-App (UI)**, **ARTIFACTS** tab.
:param bool delete_after_upload: After the upload, delete the local copy of the artifact?
- ``True`` - Delete the local copy of the artifact.
- ``False`` - Do not delete. (default)
:param bool auto_pickle: If True (default) and the artifact_object is not one of the following types:
pathlib2.Path, dict, pandas.DataFrame, numpy.ndarray, PIL.Image, url (string), local_file (string)
the artifact_object will be pickled and uploaded as pickle file artifact (with file extension .pkl)
:return: The status of the upload.
- ``True`` - Upload succeeded.
- ``False`` - Upload failed.
:raise: If the artifact object type is not supported, raise a ``ValueError``.
"""
return self._artifacts_manager.upload_artifact(
name=name, artifact_object=artifact_object, metadata=metadata,
delete_after_upload=delete_after_upload, auto_pickle=auto_pickle)
def get_models(self):
# type: () -> Dict[str, Sequence[Model]]
"""
Return a dictionary with {'input': [], 'output': []} loaded/stored models of the current Task
Input models are files loaded in the task, either manually or automatically logged
Output models are files stored in the task, either manually or automatically logged
Automatically logged frameworks are for example: TensorFlow, Keras, PyTorch, ScikitLearn(joblib) etc.
:return: A dictionary with keys input/output, each is list of Model objects.
Example:
.. code-block:: py
{'input': [trains.Model()], 'output': [trains.Model()]}
"""
task_models = {'input': self._get_models(model_type='input'),
'output': self._get_models(model_type='output')}
return task_models
def is_current_task(self):
# type: () -> bool
"""
.. deprecated:: 0.13.0
This method is deprecated. Use :meth:`Task.is_main_task` instead.
Is this Task object the main execution Task (initially returned by :meth:`Task.init`)?
:return: Is this Task object the main execution Task?
- ``True`` - Is the main execution Task.
- ``False`` - Is not the main execution Task.
"""
return self.is_main_task()
def is_main_task(self):
# type: () -> bool
"""
Is this Task object the main execution Task (initially returned by :meth:`Task.init`)?
.. note::
If :meth:`Task.init` was never called, this method will *not* create
it, making this test more efficient than:
.. code-block:: py
Task.init() == task
:return: Is this Task object the main execution Task?
- ``True`` - Is the main execution Task.
- ``False`` - Is not the main execution Task.
"""
return self is self.__main_task
def set_model_config(self, config_text=None, config_dict=None):
# type: (Optional[str], Optional[Mapping]) -> None
"""
.. deprecated:: 0.14.1
Use :meth:`Task.connect_configuration` instead.
"""
self._set_model_config(config_text=config_text, config_dict=config_dict)
def get_model_config_text(self):
# type: () -> str
"""
.. deprecated:: 0.14.1
Use :meth:`Task.connect_configuration` instead.
"""
return self._get_model_config_text()
def get_model_config_dict(self):
# type: () -> Dict
"""
.. deprecated:: 0.14.1
Use :meth:`Task.connect_configuration` instead.
"""
return self._get_model_config_dict()
def set_model_label_enumeration(self, enumeration=None):
# type: (Optional[Mapping[str, int]]) -> ()
"""
Set the label enumeration for the Task object before creating an output model.
Later, when creating an output model, the model will inherit these properties.
:param dict enumeration: A label enumeration dictionary of string (label) to integer (value) pairs.
For example:
.. code-block:: javascript
{
'background': 0,
'person': 1
}
"""
super(Task, self).set_model_label_enumeration(enumeration=enumeration)
def get_last_iteration(self):
# type: () -> int
"""
Get the last reported iteration, which is the last iteration for which the Task reported a metric.
.. note::
The maximum reported iteration is not in the local cache. This method
sends a request to the **Trains Server** (backend).
:return: The last reported iteration number.
"""
self._reload_last_iteration()
return max(self.data.last_iteration or 0, self._reporter.max_iteration if self._reporter else 0)
def set_initial_iteration(self, offset=0):
# type: (int) -> int
"""
Set initial iteration, instead of zero. Useful when continuing training from previous checkpoints
:param int offset: Initial iteration (at starting point)
:return: Newly set initial offset.
"""
return super(Task, self).set_initial_iteration(offset=offset)
def get_initial_iteration(self):
# type: () -> int
"""
Return the initial iteration offset, default is 0
Useful when continuing training from previous checkpoints
:return: Initial iteration offset.
"""
return super(Task, self).get_initial_iteration()
def get_last_scalar_metrics(self):
# type: () -> Dict[str, Dict[str, Dict[str, float]]]
"""
Get the last scalar metrics which the Task reported. This is a nested dictionary, ordered by title and series.
For example:
.. code-block:: javascript
{
'title': {
'series': {
'last': 0.5,
'min': 0.1,
'max': 0.9
}
}
}
:return: The last scalar metrics.
"""
self.reload()
metrics = self.data.last_metrics
scalar_metrics = dict()
for i in metrics.values():
for j in i.values():
scalar_metrics.setdefault(j['metric'], {}).setdefault(
j['variant'], {'last': j['value'], 'min': j['min_value'], 'max': j['max_value']})
return scalar_metrics
def get_parameters_as_dict(self):
# type: () -> Dict
"""
Get the Task parameters as a raw nested dictionary.
.. note::
The values are not parsed. They are returned as is.
"""
return naive_nested_from_flat_dictionary(self.get_parameters())
def set_parameters_as_dict(self, dictionary):
# type: (Dict) -> None
"""
Set the parameters for the Task object from a dictionary. The dictionary can be nested.
This does not link the dictionary to the Task object. It does a one-time update. This
is the same behavior as the :meth:`Task.connect` method.
"""
self._arguments.copy_from_dict(flatten_dictionary(dictionary))
def set_base_docker(self, docker_cmd):
# type: (str) -> ()
"""
Set the base docker image for this experiment
If provided, this value will be used by trains-agent to execute this experiment
inside the provided docker image.
"""
if not self.running_locally() and self.is_main_task():
return
super(Task, self).set_base_docker(docker_cmd)
def execute_remotely(self, queue_name=None, clone=False, exit_process=True):
# type: (Optional[str], bool, bool) -> ()
"""
If task is running locally (i.e., not by ``trains-agent``), then clone the Task and enqueue it for remote
execution; or, stop the execution of the current Task, reset its state, and enqueue it. If ``exit==True``,
*exit* this process.
.. note::
If the task is running remotely (i.e., ``trains-agent`` is executing it), this call is a no-op
(i.e., does nothing).
:param queue_name: The queue name used for enqueueing the task. If ``None``, this call exits the process
without enqueuing the task.
:param clone: Clone the Task and execute the newly cloned Task?
The values are:
- ``True`` - A cloned copy of the Task will be created, and enqueued, instead of this Task.
- ``False`` - The Task will be enqueued.
:param exit_process: The function call will leave the calling process at the end?
- ``True`` - Exit the process (exit(0)).
- ``False`` - Do not exit the process.
.. warning::
If ``clone==False``, then ``exit_process`` must be ``True``.
"""
# do nothing, we are running remotely
if running_remotely():
return
if not clone and not exit_process:
raise ValueError(
"clone==False and exit_process==False is not supported. "
"Task enqueuing itself must exit the process afterwards.")
# make sure we analyze the process
if self.status in (Task.TaskStatusEnum.in_progress, ):
if clone:
# wait for repository detection (5 minutes should be reasonable time to detect all packages)
self.flush(wait_for_uploads=True)
if self._logger and not self.__is_subprocess():
self._wait_for_repo_detection(timeout=300.)
else:
# close ourselves (it will make sure the repo is updated)
self.close()
# clone / reset Task
if clone:
task = Task.clone(self)
else:
task = self
self.reset()
# enqueue ourselves
if queue_name:
Task.enqueue(task, queue_name=queue_name)
LoggerRoot.get_base_logger().warning(
'Switching to remote execution, output log page {}'.format(task.get_output_log_web_page()))
# leave this process.
if exit_process:
LoggerRoot.get_base_logger().warning('Terminating local execution process')
exit(0)
return
def wait_for_status(
self,
status=(_Task.TaskStatusEnum.completed, _Task.TaskStatusEnum.stopped, _Task.TaskStatusEnum.closed),
raise_on_status=(tasks.TaskStatusEnum.failed,),
check_interval_sec=60.,
):
# type: (Iterable[Task.TaskStatusEnum], Optional[Iterable[Task.TaskStatusEnum]], float) -> ()
"""
Wait for a task to reach a defined status.
:param status: Status to wait for. Defaults to ('completed', 'stopped', 'closed', )
:param raise_on_status: Raise RuntimeError if the status of the tasks matches one of these values.
Defaults to ('failed').
:param check_interval_sec: Interval in seconds between two checks. Defaults to 60 seconds.
:raise: RuntimeError if the status is one of {raise_on_status}.
"""
stopped_status = list(status) + (list(raise_on_status) if raise_on_status else [])
while self.status not in stopped_status:
time.sleep(check_interval_sec)
if raise_on_status and self.status in raise_on_status:
raise RuntimeError("Task {} has status: {}.".format(self.task_id, self.status))
def export_task(self):
# type: () -> dict
"""
Export Task's configuration into a dictionary (for serialization purposes).
A Task can be copied/modified by calling Task.import_task()
Notice: Export task does not include the tasks outputs, such as results
(scalar/plots etc.) or Task artifacts/models
:return: dictionary of the Task's configuration.
"""
self.reload()
export_data = self.data.to_dict()
export_data.pop('last_metrics', None)
export_data.pop('last_iteration', None)
export_data.pop('status_changed', None)
export_data.pop('status_reason', None)
export_data.pop('status_message', None)
export_data.get('execution', {}).pop('artifacts', None)
export_data.get('execution', {}).pop('model', None)
export_data['project_name'] = self.get_project_name()
return export_data
def update_task(self, task_data):
# type: (dict) -> bool
"""
Update current task with configuration found on the task_data dictionary.
See also export_task() for retrieving Task configuration.
:param task_data: dictionary with full Task configuration
:return: return True if Task update was successful
"""
return bool(self.import_task(task_data=task_data, target_task=self, update=True))
@classmethod
def import_task(cls, task_data, target_task=None, update=False):
# type: (dict, Optional[Union[str, Task]], bool) -> Optional[Task]
"""
Import (create) Task from previously exported Task configuration (see Task.export_task)
Can also be used to edit/update an existing Task (by passing `target_task` and `update=True`).
:param task_data: dictionary of a Task's configuration
:param target_task: Import task_data into an existing Task. Can be either task_id (str) or Task object.
:param update: If True, merge task_data with current Task configuration.
:return: return True if Task was imported/updated
"""
if not target_task:
project_name = task_data.get('project_name') or Task._get_project_name(task_data.get('project', ''))
target_task = Task.create(project_name=project_name, task_name=task_data.get('name', None))
elif isinstance(target_task, six.string_types):
target_task = Task.get_task(task_id=target_task)
elif not isinstance(target_task, Task):
raise ValueError(
"`target_task` must be either Task id (str) or Task object, "
"received `target_task` type {}".format(type(target_task)))
target_task.reload()
cur_data = target_task.data.to_dict()
cur_data = merge_dicts(cur_data, task_data) if update else dict(**task_data)
cur_data.pop('id', None)
cur_data.pop('project', None)
# noinspection PyProtectedMember
valid_fields = list(tasks.EditRequest._get_data_props().keys())
cur_data = dict((k, cur_data[k]) for k in valid_fields if k in cur_data)
res = target_task._edit(**cur_data)
if res and res.ok():
target_task.reload()
return target_task
return None
@classmethod
def import_offline_session(cls, session_folder_zip):
# type: (str) -> (Optional[str])
"""
Upload an off line session (execution) of a Task.
Full Task execution includes repository details, installed packages, artifacts, logs, metric and debug samples.
:param session_folder_zip: Path to a folder containing the session, or zip-file of the session folder.
:return: Newly created task ID (str)
"""
print('TRAINS: Importing offline session from {}'.format(session_folder_zip))
temp_folder = None
if Path(session_folder_zip).is_file():
# unzip the file:
temp_folder = mkdtemp(prefix='trains-offline-')
ZipFile(session_folder_zip).extractall(path=temp_folder)
session_folder_zip = temp_folder
session_folder = Path(session_folder_zip)
if not session_folder.is_dir():
raise ValueError("Could not find the session folder / zip-file {}".format(session_folder))
try:
with open(session_folder / cls._offline_filename, 'rt') as f:
export_data = json.load(f)
except Exception as ex:
raise ValueError(
"Could not read Task object {}: Exception {}".format(session_folder / cls._offline_filename, ex))
task = cls.import_task(export_data)
task.mark_started(force=True)
# fix artifacts
if task.data.execution.artifacts:
from . import StorageManager
# noinspection PyProtectedMember
offline_folder = os.path.join(export_data.get('offline_folder', ''), 'data/')
# noinspection PyProtectedMember
remote_url = task._get_default_report_storage_uri()
if remote_url and remote_url.endswith('/'):
remote_url = remote_url[:-1]
for artifact in task.data.execution.artifacts:
local_path = artifact.uri.replace(offline_folder, '', 1)
local_file = session_folder / 'data' / local_path
if local_file.is_file():
remote_path = local_path.replace(
'.{}{}'.format(export_data['id'], os.sep), '.{}{}'.format(task.id, os.sep), 1)
artifact.uri = '{}/{}'.format(remote_url, remote_path)
StorageManager.upload_file(local_file=local_file.as_posix(), remote_url=artifact.uri)
# noinspection PyProtectedMember
task._edit(execution=task.data.execution)
# logs
TaskHandler.report_offline_session(task, session_folder)
# metrics
Metrics.report_offline_session(task, session_folder)
# print imported results page
print('TRAINS results page: {}'.format(task.get_output_log_web_page()))
task.completed()
# close task
task.close()
# cleanup
if temp_folder:
# noinspection PyBroadException
try:
shutil.rmtree(temp_folder)
except Exception:
pass
return task.id
@classmethod
def set_credentials(cls, api_host=None, web_host=None, files_host=None, key=None, secret=None, host=None):
# type: (Optional[str], Optional[str], Optional[str], Optional[str], Optional[str], Optional[str]) -> ()
"""
Set new default **Trains Server** (backend) host and credentials.
These credentials will be overridden by either OS environment variables, or the Trains configuration
file, ``trains.conf``.
.. warning::
Credentials must be set before initializing a Task object.
For example, to set credentials for a remote computer:
.. code-block:: py
Task.set_credentials(api_host='http://localhost:8008', web_host='http://localhost:8080',
files_host='http://localhost:8081', key='optional_credentials', secret='optional_credentials')
task = Task.init('project name', 'experiment name')
:param str api_host: The API server url. For example, ``host='http://localhost:8008'``
:param str web_host: The Web server url. For example, ``host='http://localhost:8080'``
:param str files_host: The file server url. For example, ``host='http://localhost:8081'``
:param str key: The user key (in the key/secret pair). For example, ``key='thisisakey123'``
:param str secret: The user secret (in the key/secret pair). For example, ``secret='thisisseceret123'``
:param str host: The host URL (overrides api_host). For example, ``host='http://localhost:8008'``
"""
if api_host:
Session.default_host = api_host
if web_host:
Session.default_web = web_host
if files_host:
Session.default_files = files_host
if key:
Session.default_key = key
if not running_remotely():
ENV_ACCESS_KEY.set(key)
if secret:
Session.default_secret = secret
if not running_remotely():
ENV_SECRET_KEY.set(secret)
if host:
Session.default_host = host
Session.default_web = web_host or ''
Session.default_files = files_host or ''
def _set_model_config(self, config_text=None, config_dict=None):
# type: (Optional[str], Optional[Mapping]) -> None
"""
Set Task model configuration text/dict
:param config_text: model configuration (unconstrained text string). usually the content
of a configuration file. If `config_text` is not None, `config_dict` must not be provided.
:param config_dict: model configuration parameters dictionary.
If `config_dict` is not None, `config_text` must not be provided.
"""
# noinspection PyProtectedMember
design = OutputModel._resolve_config(config_text=config_text, config_dict=config_dict)
super(Task, self)._set_model_design(design=design)
def _get_model_config_text(self):
# type: () -> str
"""
Get Task model configuration text (before creating an output model)
When an output model is created it will inherit these properties
:return: The model config_text (unconstrained text string).
"""
return super(Task, self).get_model_design()
def _get_model_config_dict(self):
# type: () -> Dict
"""
Get Task model configuration dictionary (before creating an output model)
When an output model is created it will inherit these properties
:return: config_dict: model configuration parameters dictionary.
"""
config_text = self._get_model_config_text()
# noinspection PyProtectedMember
return OutputModel._text_to_config_dict(config_text)
@classmethod
def _reset_current_task_obj(cls):
if not cls.__main_task:
return
task = cls.__main_task
cls.__main_task = None
if task._dev_worker:
task._dev_worker.unregister()
task._dev_worker = None
@classmethod
def _create_dev_task(
cls, default_project_name, default_task_name, default_task_type,
reuse_last_task_id, continue_last_task=False, detect_repo=True,
):
if not default_project_name or not default_task_name:
# get project name and task name from repository name and entry_point
result, _ = ScriptInfo.get(create_requirements=False, check_uncommitted=False)
if not default_project_name:
# noinspection PyBroadException
try:
parts = result.script['repository'].split('/')
default_project_name = (parts[-1] or parts[-2]).replace('.git', '') or 'Untitled'
except Exception:
default_project_name = 'Untitled'
if not default_task_name:
# noinspection PyBroadException
try:
default_task_name = os.path.splitext(os.path.basename(result.script['entry_point']))[0]
except Exception:
pass
# conform reuse_last_task_id and continue_last_task
if continue_last_task and isinstance(continue_last_task, str):
reuse_last_task_id = continue_last_task
continue_last_task = True
# if we force no task reuse from os environment
if DEV_TASK_NO_REUSE.get() or not reuse_last_task_id or isinstance(reuse_last_task_id, str):
default_task = None
else:
# if we have a previous session to use, get the task id from it
default_task = cls.__get_last_used_task_id(
default_project_name,
default_task_name,
default_task_type.value,
)
closed_old_task = False
default_task_id = None
task = None
in_dev_mode = not running_remotely()
if in_dev_mode:
if isinstance(reuse_last_task_id, str) and reuse_last_task_id:
default_task_id = reuse_last_task_id
elif not reuse_last_task_id or not cls.__task_is_relevant(default_task):
default_task_id = None
else:
default_task_id = default_task.get('id') if default_task else None
if default_task_id:
try:
task = cls(
private=cls.__create_protection,
task_id=default_task_id,
log_to_backend=True,
)
# instead of resting the previously used task we are continuing the training with it.
if task and continue_last_task:
task.reload()
task.mark_started(force=True)
task.set_initial_iteration(task.get_last_iteration()+1)
else:
task_tags = task.data.system_tags if hasattr(task.data, 'system_tags') else task.data.tags
task_artifacts = task.data.execution.artifacts \
if hasattr(task.data.execution, 'artifacts') else None
if ((str(task._status) in (
str(tasks.TaskStatusEnum.published), str(tasks.TaskStatusEnum.closed)))
or task.output_model_id or (ARCHIVED_TAG in task_tags)
or (cls._development_tag not in task_tags)
or task_artifacts):
# If the task is published or closed, we shouldn't reset it so we can't use it in dev mode
# If the task is archived, or already has an output model,
# we shouldn't use it in development mode either
default_task_id = None
task = None
else:
with task._edit_lock:
# from now on, there is no need to reload, we just clear stuff,
# this flag will be cleared off once we actually refresh at the end of the function
task._reload_skip_flag = True
# reset the task, so we can update it
task.reset(set_started_on_success=False, force=False)
# clear the heaviest stuff first
task._clear_task(
system_tags=[cls._development_tag],
comment=make_message('Auto-generated at %(time)s by %(user)s@%(host)s'))
except (Exception, ValueError):
# we failed reusing task, create a new one
default_task_id = None
# create a new task
if not default_task_id:
task = cls(
private=cls.__create_protection,
project_name=default_project_name,
task_name=default_task_name,
task_type=default_task_type,
log_to_backend=True,
)
# no need to reload yet, we clear this before the end of the function
task._reload_skip_flag = True
if in_dev_mode:
# update this session, for later use
cls.__update_last_used_task_id(default_project_name, default_task_name, default_task_type.value, task.id)
# set default docker image from env.
task._set_default_docker_image()
# mark us as the main Task, there should only be one dev Task at a time.
if not Task.__main_task:
Task.__main_task = task
# mark the task as started
task.started()
# reload, making sure we are synced
task._reload_skip_flag = False
task.reload()
# force update of base logger to this current task (this is the main logger task)
task._setup_log(replace_existing=True)
logger = task.get_logger()
if closed_old_task:
logger.report_text('TRAINS Task: Closing old development task id={}'.format(default_task.get('id')))
# print warning, reusing/creating a task
if default_task_id and not continue_last_task:
logger.report_text('TRAINS Task: overwriting (reusing) task id=%s' % task.id)
elif default_task_id and continue_last_task:
logger.report_text('TRAINS Task: continuing previous task id=%s '
'Notice this run will not be reproducible!' % task.id)
else:
logger.report_text('TRAINS Task: created new task id=%s' % task.id)
# update current repository and put warning into logs
if detect_repo:
# noinspection PyBroadException
try:
import traceback
stack = traceback.extract_stack(limit=10)
# NOTICE WE ARE ALWAYS 3 down from caller in stack!
for i in range(len(stack)-1, 0, -1):
# look for the Task.init call, then the one above it is the callee module
if stack[i].name == 'init':
task._calling_filename = os.path.abspath(stack[i-1].filename)
break
except Exception:
pass
if in_dev_mode and cls.__detect_repo_async:
task._detect_repo_async_thread = threading.Thread(target=task._update_repository)
task._detect_repo_async_thread.daemon = True
task._detect_repo_async_thread.start()
else:
task._update_repository()
# make sure we see something in the UI
thread = threading.Thread(target=LoggerRoot.flush)
thread.daemon = True
thread.start()
return task
def _get_logger(self, flush_period=NotSet):
# type: (Optional[float]) -> Logger
"""
get a logger object for reporting based on the task
:param flush_period: The period of the logger flush.
If None of any other False value, will not flush periodically.
If a logger was created before, this will be the new period and
the old one will be discarded.
:return: Logger object
"""
if not self._logger:
# do not recreate logger after task was closed/quit
if self._at_exit_called:
raise ValueError("Cannot use Task Logger after task was closed")
# force update of base logger to this current task (this is the main logger task)
self._setup_log(replace_existing=self.is_main_task())
# Get a logger object
self._logger = Logger(private_task=self)
# make sure we set our reported to async mode
# we make sure we flush it in self._at_exit
self.reporter.async_enable = True
# if we just created the logger, set default flush period
if not flush_period or flush_period is self.NotSet:
flush_period = DevWorker.report_period
if isinstance(flush_period, (int, float)):
flush_period = int(abs(flush_period))
if flush_period is None or isinstance(flush_period, int):
self._logger.set_flush_period(flush_period)
return self._logger
def _connect_output_model(self, model, name=None):
assert isinstance(model, OutputModel)
model.connect(self)
return model
def _save_output_model(self, model):
"""
Save a reference to the connected output model.
:param model: The connected output model
"""
self._connected_output_model = model
def _reconnect_output_model(self):
"""
If there is a saved connected output model, connect it again.
This is needed if the input model is connected after the output model
is connected, an then we will have to get the model design from the
input model by reconnecting.
"""
if self._connected_output_model:
self.connect(self._connected_output_model)
def _connect_input_model(self, model, name=None):
assert isinstance(model, InputModel)
# we only allow for an input model to be connected once
# at least until we support multiple input models
# notice that we do not check the task's input model because we allow task reuse and overwrite
# add into comment that we are using this model
comment = self.comment or ''
if not comment.endswith('\n'):
comment += '\n'
comment += 'Using model id: {}'.format(model.id)
self.set_comment(comment)
if self._last_input_model_id and self._last_input_model_id != model.id:
self.log.info('Task connect, second input model is not supported, adding into comment section')
return
self._last_input_model_id = model.id
model.connect(self)
return model
def _try_set_connected_parameter_type(self, option):
# """ Raise an error if current value is not None and not equal to the provided option value """
# value = self._connected_parameter_type
# if not value or value == option:
# self._connected_parameter_type = option
# return option
#
# def title(option):
# return " ".join(map(str.capitalize, option.split("_")))
#
# raise ValueError(
# "Task already connected to {}. "
# "Task can be connected to only one the following argument options: {}".format(
# title(value),
# ' / '.join(map(title, self._ConnectedParametersType._options())))
# )
# added support for multiple type connections through _Arguments
return option
def _connect_argparse(self, parser, args=None, namespace=None, parsed_args=None, name=None):
# do not allow argparser to connect to jupyter notebook
# noinspection PyBroadException
try:
if 'IPython' in sys.modules:
# noinspection PyPackageRequirements
from IPython import get_ipython
ip = get_ipython()
if ip is not None and 'IPKernelApp' in ip.config:
return parser
except Exception:
pass
self._try_set_connected_parameter_type(self._ConnectedParametersType.argparse)
if self.is_main_task():
argparser_update_currenttask(self)
if (parser is None or parsed_args is None) and argparser_parseargs_called():
# if we have a parser but nor parsed_args, we need to find the parser
if parser and not parsed_args:
for _parser, _parsed_args in get_argparser_last_args():
if _parser == parser:
parsed_args = _parsed_args
break
else:
# prefer the first argparser (hopefully it is more relevant?!
for _parser, _parsed_args in get_argparser_last_args():
if parser is None:
parser = _parser
if parsed_args is None and parser == _parser:
parsed_args = _parsed_args
if running_remotely() and self.is_main_task():
self._arguments.copy_to_parser(parser, parsed_args)
else:
self._arguments.copy_defaults_from_argparse(
parser, args=args, namespace=namespace, parsed_args=parsed_args)
return parser
def _connect_dictionary(self, dictionary, name=None):
def _update_args_dict(task, config_dict):
# noinspection PyProtectedMember
task._arguments.copy_from_dict(flatten_dictionary(config_dict), prefix=name)
def _refresh_args_dict(task, config_dict):
# reread from task including newly added keys
# noinspection PyProtectedMember
a_flat_dict = task._arguments.copy_to_dict(flatten_dictionary(config_dict), prefix=name)
# noinspection PyProtectedMember
nested_dict = config_dict._to_dict()
config_dict.clear()
config_dict.update(nested_from_flat_dictionary(nested_dict, a_flat_dict))
self._try_set_connected_parameter_type(self._ConnectedParametersType.dictionary)
if not running_remotely() or not self.is_main_task():
self._arguments.copy_from_dict(flatten_dictionary(dictionary), prefix=name)
dictionary = ProxyDictPostWrite(self, _update_args_dict, **dictionary)
else:
flat_dict = flatten_dictionary(dictionary)
flat_dict = self._arguments.copy_to_dict(flat_dict, prefix=name)
dictionary = nested_from_flat_dictionary(dictionary, flat_dict)
dictionary = ProxyDictPostWrite(self, _refresh_args_dict, **dictionary)
return dictionary
def _connect_task_parameters(self, attr_class, name=None):
self._try_set_connected_parameter_type(self._ConnectedParametersType.task_parameters)
if running_remotely() and self.is_main_task():
parameters = self.get_parameters()
if not name:
attr_class.update_from_dict(parameters)
else:
attr_class.update_from_dict(
dict((k[len(name)+1:], v) for k, v in parameters.items() if k.startswith('{}/'.format(name))))
else:
self.set_parameters(attr_class.to_dict(), __parameters_prefix=name)
return attr_class
def _validate(self, check_output_dest_credentials=False):
if running_remotely():
super(Task, self)._validate(check_output_dest_credentials=False)
def _output_model_updated(self):
""" Called when a connected output model is updated """
if running_remotely() or not self.is_main_task():
return
# Make sure we know we've started, just in case we didn't so far
self._dev_mode_task_start(model_updated=True)
def _dev_mode_task_start(self, model_updated=False):
""" Called when we suspect the task has started running """
self._dev_mode_setup_worker(model_updated=model_updated)
def _dev_mode_stop_task(self, stop_reason):
# make sure we do not get called (by a daemon thread) after at_exit
if self._at_exit_called:
return
self.log.warning(
"### TASK STOPPED - USER ABORTED - {} ###".format(
stop_reason.upper().replace('_', ' ')
)
)
self.flush(wait_for_uploads=True)
self.stopped()
if self._dev_worker:
self._dev_worker.unregister()
# NOTICE! This will end the entire execution tree!
if self.__exit_hook:
self.__exit_hook.remote_user_aborted = True
self._kill_all_child_processes(send_kill=False)
time.sleep(2.0)
self._kill_all_child_processes(send_kill=True)
# noinspection PyProtectedMember
os._exit(1)
@staticmethod
def _kill_all_child_processes(send_kill=False):
# get current process if pid not provided
pid = os.getpid()
try:
parent = psutil.Process(pid)
except psutil.Error:
# could not find parent process id
return
for child in parent.children(recursive=True):
if send_kill:
child.kill()
else:
child.terminate()
# kill ourselves
if send_kill:
parent.kill()
else:
parent.terminate()
def _dev_mode_setup_worker(self, model_updated=False):
if running_remotely() or not self.is_main_task() or self._at_exit_called or self._offline_mode:
return
if self._dev_worker:
return self._dev_worker
self._dev_worker = DevWorker()
self._dev_worker.register(self)
logger = self.get_logger()
flush_period = logger.get_flush_period()
if not flush_period or flush_period > self._dev_worker.report_period:
logger.set_flush_period(self._dev_worker.report_period)
def _wait_for_repo_detection(self, timeout=None):
# wait for detection repo sync
if not self._detect_repo_async_thread:
return
with self._repo_detect_lock:
if not self._detect_repo_async_thread:
return
# noinspection PyBroadException
try:
if self._detect_repo_async_thread.is_alive():
# if negative timeout, just kill the thread:
if timeout is not None and timeout < 0:
from .utilities.lowlevel.threads import kill_thread
kill_thread(self._detect_repo_async_thread)
else:
self.log.info('Waiting for repository detection and full package requirement analysis')
self._detect_repo_async_thread.join(timeout=timeout)
# because join has no return value
if self._detect_repo_async_thread.is_alive():
self.log.info('Repository and package analysis timed out ({} sec), '
'giving up'.format(timeout))
# done waiting, kill the thread
from .utilities.lowlevel.threads import kill_thread
kill_thread(self._detect_repo_async_thread)
else:
self.log.info('Finished repository detection and package analysis')
self._detect_repo_async_thread = None
except Exception:
pass
def _summary_artifacts(self):
# signal artifacts upload, and stop daemon
self._artifacts_manager.stop(wait=True)
# print artifacts summary (if not empty)
if self._artifacts_manager.summary:
self.get_logger().report_text(self._artifacts_manager.summary)
def _at_exit(self):
# protect sub-process at_exit (should never happen)
if self._at_exit_called:
return
# shutdown will clear the main, so we have to store it before.
# is_main = self.is_main_task()
self.__shutdown()
# In rare cases we might need to forcefully shutdown the process, currently we should avoid it.
# if is_main:
# # we have to forcefully shutdown if we have forked processes, sometimes they will get stuck
# os._exit(self.__exit_hook.exit_code if self.__exit_hook and self.__exit_hook.exit_code else 0)
def __shutdown(self):
"""
Will happen automatically once we exit code, i.e. atexit
:return:
"""
# protect sub-process at_exit
if self._at_exit_called:
return
is_sub_process = self.__is_subprocess()
# noinspection PyBroadException
try:
# from here do not get into watch dog
self._at_exit_called = True
wait_for_uploads = True
# first thing mark task as stopped, so we will not end up with "running" on lost tasks
# if we are running remotely, the daemon will take care of it
task_status = None
wait_for_std_log = True
if not running_remotely() and self.is_main_task() and not is_sub_process:
# check if we crashed, ot the signal is not interrupt (manual break)
task_status = ('stopped', )
if self.__exit_hook:
is_exception = self.__exit_hook.exception
# check if we are running inside a debugger
if not is_exception and sys.modules.get('pydevd'):
# noinspection PyBroadException
try:
is_exception = sys.last_type
except Exception:
pass
if (is_exception and not isinstance(self.__exit_hook.exception, KeyboardInterrupt)) \
or (not self.__exit_hook.remote_user_aborted and self.__exit_hook.signal not in (None, 2)):
task_status = ('failed', 'Exception')
wait_for_uploads = False
else:
wait_for_uploads = (self.__exit_hook.remote_user_aborted or self.__exit_hook.signal is None)
if not self.__exit_hook.remote_user_aborted and self.__exit_hook.signal is None and \
not is_exception:
task_status = ('completed', )
else:
task_status = ('stopped', )
# user aborted. do not bother flushing the stdout logs
wait_for_std_log = self.__exit_hook.signal is not None
# wait for repository detection (if we didn't crash)
if wait_for_uploads and self._logger:
# we should print summary here
self._summary_artifacts()
# make sure that if we crashed the thread we are not waiting forever
if not is_sub_process:
self._wait_for_repo_detection(timeout=10.)
# kill the repo thread (negative timeout, do not wait), if it hasn't finished yet.
self._wait_for_repo_detection(timeout=-1)
# wait for uploads
print_done_waiting = False
if wait_for_uploads and (BackendModel.get_num_results() > 0 or
(self._reporter and self.reporter.get_num_results() > 0)):
self.log.info('Waiting to finish uploads')
print_done_waiting = True
# from here, do not send log in background thread
if wait_for_uploads:
self.flush(wait_for_uploads=True)
# wait until the reporter flush everything
if self._reporter:
self.reporter.stop()
if self.is_main_task():
# notice: this will close the reporting for all the Tasks in the system
Metrics.close_async_threads()
# notice: this will close the jupyter monitoring
ScriptInfo.close()
if self.is_main_task():
# noinspection PyBroadException
try:
from .storage.helper import StorageHelper
StorageHelper.close_async_threads()
except Exception:
pass
if print_done_waiting:
self.log.info('Finished uploading')
elif self._logger:
# noinspection PyProtectedMember
self._logger._flush_stdout_handler()
# from here, do not check worker status
if self._dev_worker:
self._dev_worker.unregister()
self._dev_worker = None
# stop resource monitoring
if self._resource_monitor:
self._resource_monitor.stop()
self._resource_monitor = None
if not is_sub_process:
# change task status
if not task_status:
pass
elif task_status[0] == 'failed':
self.mark_failed(status_reason=task_status[1])
elif task_status[0] == 'completed':
self.completed()
elif task_status[0] == 'stopped':
self.stopped()
if self._logger:
self._logger.set_flush_period(None)
# noinspection PyProtectedMember
self._logger._close_stdout_handler(wait=wait_for_uploads or wait_for_std_log)
# this is so in theory we can close a main task and start a new one
if self.is_main_task():
Task.__main_task = None
except Exception:
# make sure we do not interrupt the exit process
pass
# make sure we store last task state
if self._offline_mode and not is_sub_process:
# noinspection PyBroadException
try:
# create zip file
offline_folder = self.get_offline_mode_folder()
zip_file = offline_folder.as_posix() + '.zip'
with ZipFile(zip_file, 'w', allowZip64=True, compression=ZIP_DEFLATED) as zf:
for filename in offline_folder.rglob('*'):
if filename.is_file():
relative_file_name = filename.relative_to(offline_folder).as_posix()
zf.write(filename.as_posix(), arcname=relative_file_name)
print('TRAINS Task: Offline session stored in {}'.format(zip_file))
except Exception:
pass
# delete locking object (lock file)
if self._edit_lock:
# noinspection PyBroadException
try:
del self.__edit_lock
except Exception:
pass
self._edit_lock = None
@classmethod
def __register_at_exit(cls, exit_callback, only_remove_signal_and_exception_hooks=False):
class ExitHooks(object):
_orig_exit = None
_orig_exc_handler = None
remote_user_aborted = False
def __init__(self, callback):
self.exit_code = None
self.exception = None
self.signal = None
self._exit_callback = callback
self._org_handlers = {}
self._signal_recursion_protection_flag = False
self._except_recursion_protection_flag = False
def update_callback(self, callback):
if self._exit_callback and not six.PY2:
# noinspection PyBroadException
try:
atexit.unregister(self._exit_callback)
except Exception:
pass
self._exit_callback = callback
if callback:
self.hook()
else:
# un register int hook
if self._orig_exc_handler:
sys.excepthook = self._orig_exc_handler
self._orig_exc_handler = None
for h in self._org_handlers:
# noinspection PyBroadException
try:
signal.signal(h, self._org_handlers[h])
except Exception:
pass
self._org_handlers = {}
def hook(self):
if self._orig_exit is None:
self._orig_exit = sys.exit
sys.exit = self.exit
if self._orig_exc_handler is None:
self._orig_exc_handler = sys.excepthook
sys.excepthook = self.exc_handler
if self._exit_callback:
atexit.register(self._exit_callback)
# TODO: check if sub-process hooks are safe enough, for the time being allow it
if not self._org_handlers: # ## and not Task._Task__is_subprocess():
if sys.platform == 'win32':
catch_signals = [signal.SIGINT, signal.SIGTERM, signal.SIGSEGV, signal.SIGABRT,
signal.SIGILL, signal.SIGFPE]
else:
catch_signals = [signal.SIGINT, signal.SIGTERM, signal.SIGSEGV, signal.SIGABRT,
signal.SIGILL, signal.SIGFPE, signal.SIGQUIT]
for c in catch_signals:
# noinspection PyBroadException
try:
self._org_handlers[c] = signal.getsignal(c)
signal.signal(c, self.signal_handler)
except Exception:
pass
def exit(self, code=0):
self.exit_code = code
self._orig_exit(code)
def exc_handler(self, exctype, value, traceback, *args, **kwargs):
if self._except_recursion_protection_flag:
# noinspection PyArgumentList
return sys.__excepthook__(exctype, value, traceback, *args, **kwargs)
self._except_recursion_protection_flag = True
self.exception = value
if self._orig_exc_handler:
# noinspection PyArgumentList
ret = self._orig_exc_handler(exctype, value, traceback, *args, **kwargs)
else:
# noinspection PyNoneFunctionAssignment, PyArgumentList
ret = sys.__excepthook__(exctype, value, traceback, *args, **kwargs)
self._except_recursion_protection_flag = False
return ret
def signal_handler(self, sig, frame):
if self._signal_recursion_protection_flag:
# call original
org_handler = self._org_handlers.get(sig)
if isinstance(org_handler, Callable):
org_handler = org_handler(sig, frame)
return org_handler
self._signal_recursion_protection_flag = True
# call exit callback
self.signal = sig
if self._exit_callback:
# noinspection PyBroadException
try:
self._exit_callback()
except Exception:
pass
# call original signal handler
org_handler = self._org_handlers.get(sig)
if isinstance(org_handler, Callable):
# noinspection PyBroadException
try:
org_handler = org_handler(sig, frame)
except Exception:
org_handler = signal.SIG_DFL
# remove stdout logger, just in case
# noinspection PyBroadException
try:
# noinspection PyProtectedMember
Logger._remove_std_logger()
except Exception:
pass
self._signal_recursion_protection_flag = False
# return handler result
return org_handler
# we only remove the signals since this will hang subprocesses
if only_remove_signal_and_exception_hooks:
if not cls.__exit_hook:
return
if cls.__exit_hook._orig_exc_handler:
sys.excepthook = cls.__exit_hook._orig_exc_handler
cls.__exit_hook._orig_exc_handler = None
for s in cls.__exit_hook._org_handlers:
# noinspection PyBroadException
try:
signal.signal(s, cls.__exit_hook._org_handlers[s])
except Exception:
pass
cls.__exit_hook._org_handlers = {}
return
if cls.__exit_hook is None:
# noinspection PyBroadException
try:
cls.__exit_hook = ExitHooks(exit_callback)
cls.__exit_hook.hook()
except Exception:
cls.__exit_hook = None
else:
cls.__exit_hook.update_callback(exit_callback)
@classmethod
def __get_task(cls, task_id=None, project_name=None, task_name=None):
if task_id:
return cls(private=cls.__create_protection, task_id=task_id, log_to_backend=False)
if project_name:
res = cls._send(
cls._get_default_session(),
projects.GetAllRequest(
name=exact_match_regex(project_name)
)
)
project = get_single_result(entity='project', query=project_name, results=res.response.projects)
else:
project = None
system_tags = 'system_tags' if hasattr(tasks.Task, 'system_tags') else 'tags'
res = cls._send(
cls._get_default_session(),
tasks.GetAllRequest(
project=[project.id] if project else None,
name=exact_match_regex(task_name) if task_name else None,
only_fields=['id', 'name', 'last_update', system_tags]
)
)
res_tasks = res.response.tasks
# if we have more than one result, first filter 'archived' results:
if len(res_tasks) > 1:
filtered_tasks = [t for t in res_tasks if not getattr(t, system_tags, None) or
'archived' not in getattr(t, system_tags, None)]
if filtered_tasks:
res_tasks = filtered_tasks
task = get_single_result(entity='task', query=task_name, results=res_tasks, raise_on_error=False)
if not task:
return None
return cls(
private=cls.__create_protection,
task_id=task.id,
log_to_backend=False,
)
@classmethod
def __get_tasks(cls, task_ids=None, project_name=None, task_name=None, **kwargs):
if task_ids:
if isinstance(task_ids, six.string_types):
task_ids = [task_ids]
return [cls(private=cls.__create_protection, task_id=task_id, log_to_backend=False)
for task_id in task_ids]
return [cls(private=cls.__create_protection, task_id=task.id, log_to_backend=False)
for task in cls._query_tasks(project_name=project_name, task_name=task_name, **kwargs)]
@classmethod
def _query_tasks(cls, task_ids=None, project_name=None, task_name=None, **kwargs):
if not task_ids:
task_ids = None
elif isinstance(task_ids, six.string_types):
task_ids = [task_ids]
if project_name:
res = cls._send(
cls._get_default_session(),
projects.GetAllRequest(
name=exact_match_regex(project_name)
)
)
project = get_single_result(entity='project', query=project_name, results=res.response.projects)
else:
project = None
system_tags = 'system_tags' if hasattr(tasks.Task, 'system_tags') else 'tags'
only_fields = ['id', 'name', 'last_update', system_tags]
if kwargs and kwargs.get('only_fields'):
only_fields = list(set(kwargs.pop('only_fields')) | set(only_fields))
res = cls._send(
cls._get_default_session(),
tasks.GetAllRequest(
id=task_ids,
project=[project.id] if project else kwargs.pop('project', None),
name=task_name if task_name else None,
only_fields=only_fields,
**kwargs
)
)
return res.response.tasks
@classmethod
def __get_hash_key(cls, *args):
def normalize(x):
return "<{}>".format(x) if x is not None else ""
return ":".join(map(normalize, args))
@classmethod
def __get_last_used_task_id(cls, default_project_name, default_task_name, default_task_type):
hash_key = cls.__get_hash_key(
cls._get_api_server(), default_project_name, default_task_name, default_task_type)
# check if we have a cached task_id we can reuse
# it must be from within the last 24h and with the same project/name/type
task_sessions = SessionCache.load_dict(str(cls))
task_data = task_sessions.get(hash_key)
if task_data is None:
return None
try:
task_data['type'] = cls.TaskTypes(task_data['type'])
except (ValueError, KeyError):
LoggerRoot.get_base_logger().warning(
"Corrupted session cache entry: {}. "
"Unsupported task type: {}"
"Creating a new task.".format(hash_key, task_data['type']),
)
return None
return task_data
@classmethod
def __update_last_used_task_id(cls, default_project_name, default_task_name, default_task_type, task_id):
hash_key = cls.__get_hash_key(
cls._get_api_server(), default_project_name, default_task_name, default_task_type)
task_id = str(task_id)
# update task session cache
task_sessions = SessionCache.load_dict(str(cls))
last_task_session = {'time': time.time(), 'project': default_project_name, 'name': default_task_name,
'type': default_task_type, 'id': task_id}
# remove stale sessions
for k in list(task_sessions.keys()):
if ((time.time() - task_sessions[k].get('time', 0)) >
60 * 60 * cls.__task_id_reuse_time_window_in_hours):
task_sessions.pop(k)
# update current session
task_sessions[hash_key] = last_task_session
# store
SessionCache.store_dict(str(cls), task_sessions)
@classmethod
def __task_timed_out(cls, task_data):
return \
task_data and \
task_data.get('id') and \
task_data.get('time') and \
(time.time() - task_data.get('time')) > (60 * 60 * cls.__task_id_reuse_time_window_in_hours)
@classmethod
def __get_task_api_obj(cls, task_id, only_fields=None):
if not task_id or cls._offline_mode:
return None
all_tasks = cls._send(
cls._get_default_session(),
tasks.GetAllRequest(id=[task_id], only_fields=only_fields),
).response.tasks
# The task may not exist in environment changes
if not all_tasks:
return None
return all_tasks[0]
@classmethod
def __task_is_relevant(cls, task_data):
"""
Check that a cached task is relevant for reuse.
A task is relevant for reuse if:
1. It is not timed out i.e it was last use in the previous 24 hours.
2. It's name, project and type match the data in the server, so not
to override user changes made by using the UI.
:param task_data: A mapping from 'id', 'name', 'project', 'type' keys
to the task's values, as saved in the cache.
:return: True, if the task is relevant for reuse. False, if not.
"""
if not task_data:
return False
if cls.__task_timed_out(task_data):
return False
task_id = task_data.get('id')
if not task_id:
return False
task = cls.__get_task_api_obj(task_id, ('id', 'name', 'project', 'type'))
if task is None:
return False
project_name = None
if task.project:
project = cls._send(
cls._get_default_session(),
projects.GetByIdRequest(project=task.project)
).response.project
if project:
project_name = project.name
if task_data.get('type') and \
task_data.get('type') not in (cls.TaskTypes.training, cls.TaskTypes.testing) and \
not Session.check_min_api_version(2.8):
print('WARNING: Changing task type to "{}" : '
'trains-server does not support task type "{}", '
'please upgrade trains-server.'.format(cls.TaskTypes.training, task_data['type'].value))
task_data['type'] = cls.TaskTypes.training
compares = (
(task.name, 'name'),
(project_name, 'project'),
(task.type, 'type'),
)
# compare after casting to string to avoid enum instance issues
# remember we might have replaced the api version by now, so enums are different
return all(six.text_type(server_data) == six.text_type(task_data.get(task_data_key))
for server_data, task_data_key in compares)
@classmethod
def __close_timed_out_task(cls, task_data):
if not task_data:
return False
task = cls.__get_task_api_obj(task_data.get('id'), ('id', 'status'))
if task is None:
return False
stopped_statuses = (
str(tasks.TaskStatusEnum.stopped),
str(tasks.TaskStatusEnum.published),
str(tasks.TaskStatusEnum.publishing),
str(tasks.TaskStatusEnum.closed),
str(tasks.TaskStatusEnum.failed),
str(tasks.TaskStatusEnum.completed),
)
if str(task.status) not in stopped_statuses:
cls._send(
cls._get_default_session(),
tasks.StoppedRequest(
task=task.id,
force=True,
status_message="Stopped timed out development task"
),
)
return True
return False
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