Source code for torchx.schedulers.api

#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

import abc
import re
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from typing import Iterable, List, Mapping, Optional

from torchx.specs import (
from torchx.workspace.api import Workspace

class Stream(str, Enum):
    STDOUT = "stdout"
    STDERR = "stderr"
    COMBINED = "combined"

class DescribeAppResponse:
    Response object returned by ``Scheduler.describe(app)`` API. Contains
    the status and description of the application as known by the scheduler.
    For some schedulers implementations this response object has necessary
    and sufficient information to recreate an ``AppDef`` object. For these types
    of schedulers, the user can re-``run()`` the recreted application. Otherwise
    the user can only call non-creating methods (e.g. ``wait()``, ``status()``,

    Since this class is a data class and contains many member variables we
    keep the usage simple and provide a no-args constructor and chose to
    access the member vars directly rather than provide accessors.

    If scheduler returns arbitrary message, the ``msg`` field should be populated.
    If scheduler returns a structured json, the ``structured_error_msg`` field should be populated.

    app_id: str = "<NOT_SET>"
    state: AppState = AppState.UNSUBMITTED
    num_restarts: int = -1
    msg: str = NONE
    structured_error_msg: str = NONE
    ui_url: Optional[str] = None

    roles_statuses: List[RoleStatus] = field(default_factory=list)
    roles: List[Role] = field(default_factory=list)

[docs]class Scheduler(abc.ABC): """ An interface abstracting functionalities of a scheduler. Implementors need only implement those methods annotated with ``@abc.abstractmethod``. """ def __init__(self, backend: str, session_name: str) -> None: self.backend = backend self.session_name = session_name
[docs] def close(self) -> None: """ Only for schedulers that have local state! Closes the scheduler freeing any allocated resources. Once closed, the scheduler object is deemed to no longer be valid and any method called on the object results in undefined behavior. This method should not raise exceptions and is allowed to be called multiple times on the same object. .. note:: Override only for scheduler implementations that have local state (``torchx/schedulers/``). Schedulers simply wrapping a remote scheduler's client need not implement this method. """ pass
[docs] def submit( self, app: AppDef, cfg: Mapping[str, CfgVal], workspace: Optional[str] = None, ) -> str: """ Submits the application to be run by the scheduler. WARNING: Mostly used for tests. Users should prefer to use the TorchX runner instead. Returns: The application id that uniquely identifies the submitted app. """ if workspace: sched = self assert isinstance(sched, Workspace) role = app.roles[0] sched.build_workspace_and_update_role(role, workspace, cfg) dryrun_info = self.submit_dryrun(app, cfg) return self.schedule(dryrun_info)
[docs] @abc.abstractmethod def schedule(self, dryrun_info: AppDryRunInfo) -> str: """ Same as ``submit`` except that it takes an ``AppDryRunInfo``. Implementors are encouraged to implement this method rather than directly implementing ``submit`` since ``submit`` can be trivially implemented by: :: dryrun_info = self.submit_dryrun(app, cfg) return schedule(dryrun_info) """ raise NotImplementedError()
[docs] def submit_dryrun(self, app: AppDef, cfg: Mapping[str, CfgVal]) -> AppDryRunInfo: """ Rather than submitting the request to run the app, returns the request object that would have been submitted to the underlying service. The type of the request object is scheduler dependent. This method can be used to dry-run an application. Please refer to the scheduler implementation's documentation regarding the actual return type. """ resolved_cfg = self.run_opts().resolve(cfg) dryrun_info = self._submit_dryrun(app, resolved_cfg) for role in app.roles: dryrun_info = role.pre_proc(self.backend, dryrun_info) dryrun_info._app = app dryrun_info._cfg = resolved_cfg return dryrun_info
@abc.abstractmethod def _submit_dryrun(self, app: AppDef, cfg: Mapping[str, CfgVal]) -> AppDryRunInfo: raise NotImplementedError()
[docs] def run_opts(self) -> runopts: """ Returns the run configuration options expected by the scheduler. Basically a ``--help`` for the ``run`` API. """ return runopts()
[docs] @abc.abstractmethod def describe(self, app_id: str) -> Optional[DescribeAppResponse]: """ Describes the specified application. Returns: AppDef description or ``None`` if the app does not exist. """ raise NotImplementedError()
[docs] def exists(self, app_id: str) -> bool: """ Returns: ``True`` if the app exists (was submitted), ``False`` otherwise """ desc = self.describe(app_id) return desc is not None
@abc.abstractmethod def _cancel_existing(self, app_id: str) -> None: """ Kills the application. This method will only be called on an application that exists. """ raise NotImplementedError()
[docs] def cancel(self, app_id: str) -> None: """ Cancels/kills the application. This method is idempotent within the same thread and is safe to call on the same application multiple times. However when called from multiple threads/processes on the same app the exact semantics of this method depends on the idempotency guarantees of the underlying scheduler API. .. note:: This method does not block for the application to reach a cancelled state. To ensure that the application reaches a terminal state use the ``wait`` API. """ if self.exists(app_id): self._cancel_existing(app_id) else: # do nothing if the app does not exist return
[docs] def log_iter( self, app_id: str, role_name: str, k: int = 0, regex: Optional[str] = None, since: Optional[datetime] = None, until: Optional[datetime] = None, should_tail: bool = False, streams: Optional[Stream] = None, ) -> Iterable[str]: """ Returns an iterator to the log lines of the ``k``th replica of the ``role``. The iterator ends end all qualifying log lines have been read. If the scheduler supports time-based cursors fetching log lines for custom time ranges, then the ``since``, ``until`` fields are honored, otherwise they are ignored. Not specifying ``since`` and ``until`` is equivalent to getting all available log lines. If the ``until`` is empty, then the iterator behaves like ``tail -f``, following the log output until the job reaches a terminal state. The exact definition of what constitutes a log is scheduler specific. Some schedulers may consider stderr or stdout as the log, others may read the logs from a log file. Behaviors and assumptions: 1. Produces an undefined-behavior if called on an app that does not exist The caller should check that the app exists using ``exists(app_id)`` prior to calling this method. 2. Is not stateful, calling this method twice with same parameters returns a new iterator. Prior iteration progress is lost. 3. Does not always support log-tailing. Not all schedulers support live log iteration (e.g. tailing logs while the app is running). Refer to the specific scheduler's documentation for the iterator's behavior. 3.1 If the scheduler supports log-tailing, it should be controlled by``should_tail`` parameter. 4. Does not guarantee log retention. It is possible that by the time this method is called, the underlying scheduler may have purged the log records for this application. If so this method raises an arbitrary exception. 5. If ``should_tail`` is True, the method only raises a ``StopIteration`` exception when the accessible log lines have been fully exhausted and the app has reached a final state. For instance, if the app gets stuck and does not produce any log lines, then the iterator blocks until the app eventually gets killed (either via timeout or manually) at which point it raises a ``StopIteration``. If ``should_tail`` is False, the method raises ``StopIteration`` when there are no more logs. 6. Need not be supported by all schedulers. 7. Some schedulers may support line cursors by supporting ``__getitem__`` (e.g. ``iter[50]`` seeks to the 50th log line). 8. Whitespace is preserved, each new line should include ``\\n``. To support interactive progress bars the returned lines don't need to include ``\\n`` but should then be printed without a newline to correctly handle ``\\r`` carriage returns. Args: streams: The IO output streams to select. One of: combined, stdout, stderr. If the selected stream isn't supported by the scheduler it will throw an ValueError. Returns: An ``Iterator`` over log lines of the specified role replica Raises: NotImplementedError: if the scheduler does not support log iteration """ raise NotImplementedError( f"{self.__class__.__qualname__} does not support application log iteration" )
def _validate(self, app: AppDef, scheduler: str) -> None: """ Validates whether application is consistent with the scheduler. Raises: ValueError: if application is not compatible with scheduler """ for role in app.roles: if role.resource == NULL_RESOURCE: raise ValueError( f"No resource for role: {role.image}." f" Did you forget to attach resource to the role" )
def filter_regex(regex: str, data: Iterable[str]) -> Iterable[str]: """ filter_regex takes a string iterator and returns an iterator that only has values that match the regex. """ r = re.compile(regex) return filter(lambda datum:, data) def split_lines(text: str) -> List[str]: """ split_lines splits the string by new lines and keeps the new line characters. """ lines = [] while len(text) > 0: idx = text.find("\n") if idx >= 0: lines.append(text[: idx + 1]) text = text[idx + 1 :] else: lines.append(text) break return lines


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources