Use sphinx-autodoc-typehints

adaptive_scheduler/_mock_scheduler.py

@@ -43,15 +43,15 @@ class MockScheduler:
 
     Parameters
     ----------
-    startup_delay : int
+    startup_delay
         The waiting before starting the process.
-    max_running_jobs : int
+    max_running_jobs
         Maximum number of simultaneously running jobs.
-    refresh_interval : int
+    refresh_interval
         Refresh interval of checking whether proccesses are still running.
-    bash : str, default: "bash"
+    bash
         ``bash`` executable.
-    url : str, optional
+    url
         The URL of the socket. Defaults to {DEFAULT_URL}.
     """
 

adaptive_scheduler/_scheduler/base_scheduler.py

@@ -27,33 +27,33 @@ class BaseScheduler(abc.ABC):
 
     Parameters
     ----------
-    cores : int
+    cores
         Number of cores per job (so per learner.)
-    python_executable : str, default: `sys.executable`
+    python_executable
         The Python executable that should run adaptive-scheduler. By default
         it uses the same Python as where this function is called.
-    log_folder : str, default: ""
+    log_folder
         The folder in which to put the log-files.
-    mpiexec_executable : str, optional
+    mpiexec_executable
         ``mpiexec`` executable. By default `mpiexec` will be
         used (so probably from ``conda``).
-    executor_type : str
+    executor_type
         The executor that is used, by default `mpi4py.futures.MPIPoolExecutor` is used.
         One can use ``"ipyparallel"``, ``"dask-mpi"``, ``"mpi4py"``,
         ``"loky"``, or ``"process-pool"``.
-    num_threads : int, default 1
+    num_threads
         ``MKL_NUM_THREADS``, ``OPENBLAS_NUM_THREADS``, ``OMP_NUM_THREADS``, and
         ``NUMEXPR_NUM_THREADS`` will be set to this number.
-    extra_scheduler : list, optional
+    extra_scheduler
         Extra ``#SLURM`` (depending on scheduler type)
         arguments, e.g. ``["--exclusive=user", "--time=1"]``.
-    extra_env_vars : list, optional
+    extra_env_vars
         Extra environment variables that are exported in the job
         script. e.g. ``["TMPDIR='/scratch'", "PYTHONPATH='my_dir:$PYTHONPATH'"]``.
-    extra_script : str, optional
+    extra_script
         Extra script that will be executed after any environment variables are set,
         but before the main scheduler is run.
-    batch_folder : str, default: ""
+    batch_folder
         The folder in which to put the batch files.
 
     Returns
@@ -99,12 +99,12 @@ def queue(self, *, me_only: bool = True) -> dict[str, dict]:
 
         Parameters
         ----------
-        me_only : bool, default: True
+        me_only
             Only see your jobs.
 
         Returns
         -------
-        queue : dict
+        queue
             Mapping of ``job_id`` -> `dict` with ``name`` and ``state``, for
             example ``{job_id: {"job_name": "TEST_JOB-1", "state": "R" or "Q"}}``.
 
@@ -135,7 +135,7 @@ def job_script(self, options: dict[str, Any]) -> str:
 
         Returns
         -------
-        job_script : str
+        job_script
             A job script that can be submitted to the scheduler.
         """
 
@@ -174,11 +174,11 @@ def cancel(
 
         Parameters
         ----------
-        job_names : list
+        job_names
             List of job names.
-        with_progress_bar : bool, default: True
+        with_progress_bar
             Display a progress bar using `tqdm`.
-        max_tries : int, default: 5
+        max_tries
             Maximum number of attempts to cancel a job.
         """
 

adaptive_scheduler/_scheduler/local.py

@@ -77,7 +77,7 @@ def job_script(self, options: dict[str, Any]) -> str:
 
         Returns
         -------
-        job_script : str
+        job_script
             A job script that can be submitted to PBS.
 
         Notes

adaptive_scheduler/_scheduler/pbs.py

@@ -128,7 +128,7 @@ def job_script(self, options: dict[str, Any]) -> str:
 
         Returns
         -------
-        job_script : str
+        job_script
             A job script that can be submitted to PBS.
         """
         job_script = textwrap.dedent(

adaptive_scheduler/_scheduler/slurm.py

@@ -23,38 +23,38 @@ class SLURM(BaseScheduler):
 
     Parameters
     ----------
-    cores : int | None
+    cores
         Number of cores per job (so per learner.)
         Either use `cores` or `nodes` and `cores_per_node`.
-    nodes : int | None
+    nodes
         Number of nodes per job (so per learner.)
         Either `nodes` and `cores_per_node` or use `cores`.
     cores_per_node: int | None
         Number of cores per node.
         Either `nodes` and `cores_per_node` or use `cores`.
     partition: str | None
         The SLURM partition to submit the job to.
-    exclusive : bool
+    exclusive
         Whether to use exclusive nodes (e.g., if SLURM it adds ``--exclusive`` as option).
-    log_folder : str, default: ""
+    log_folder
         The folder in which to put the log-files.
-    mpiexec_executable : str, optional
+    mpiexec_executable
         ``mpiexec`` executable. By default `mpiexec` will be
         used (so probably from ``conda``).
-    executor_type : str
+    executor_type
         The executor that is used, by default `mpi4py.futures.MPIPoolExecutor` is used.
         One can use ``"ipyparallel"``, ``"dask-mpi"``, ``"mpi4py"``,
         ``"loky"``, or ``"process-pool"``.
-    num_threads : int, default 1
+    num_threads
         ``MKL_NUM_THREADS``, ``OPENBLAS_NUM_THREADS``, ``OMP_NUM_THREADS``, and
         ``NUMEXPR_NUM_THREADS`` will be set to this number.
-    extra_scheduler : list, optional
+    extra_scheduler
         Extra ``#SLURM`` (depending on scheduler type)
         arguments, e.g. ``["--exclusive=user", "--time=1"]``.
-    extra_env_vars : list, optional
+    extra_env_vars
         Extra environment variables that are exported in the job
         script. e.g. ``["TMPDIR='/scratch'", "PYTHONPATH='my_dir:$PYTHONPATH'"]``.
-    extra_script : str, optional
+    extra_script
         Extra script that will be executed after any environment variables are set,
         but before the main scheduler is run.
     """
@@ -183,7 +183,7 @@ def job_script(self, options: dict[str, Any]) -> str:
 
         Returns
         -------
-        job_script : str
+        job_script
             A job script that can be submitted to SLURM.
         """
         job_script = textwrap.dedent(

adaptive_scheduler/_server_support/common.py

@@ -44,7 +44,7 @@ def get_allowed_url() -> str:
 
     Returns
     -------
-    url : str
+    url
         An url that can be used for the database manager, with the format
         ``tcp://ip_of_this_machine:allowed_port.``.
     """
@@ -87,16 +87,16 @@ def cleanup_scheduler_files(
 
     Parameters
     ----------
-    job_names : list
+    job_names
         List of job names.
-    scheduler : `~adaptive_scheduler.scheduler.BaseScheduler`
+    scheduler
         A scheduler instance from `adaptive_scheduler.scheduler`.
-    with_progress_bar : bool, default: True
+    with_progress_bar
         Display a progress bar using `tqdm`.
-    move_to : str, default: None
+    move_to
         Move the file to a different directory.
         If None the file is removed.
-    log_file_folder : str, default: ''
+    log_file_folder
         The folder in which to delete the log-files.
     """
     to_rm = _get_all_files(job_names, scheduler)
@@ -154,9 +154,9 @@ def periodically_clean_ipython_profiles(
 
     Parameters
     ----------
-    scheduler : `~adaptive_scheduler.scheduler.BaseScheduler`
+    scheduler
         A scheduler instance from `adaptive_scheduler.scheduler`.
-    interval : int, default: 600
+    interval
         The interval at which to remove old profiles.
 
     Returns

adaptive_scheduler/_server_support/database_manager.py

@@ -129,19 +129,19 @@ class DatabaseManager(BaseManager):
 
     Parameters
     ----------
-    url : str
+    url
         The url of the database manager, with the format
         ``tcp://ip_of_this_machine:allowed_port.``. Use `get_allowed_url`
         to get a `url` that will work.
-    scheduler : `~adaptive_scheduler.scheduler.BaseScheduler`
+    scheduler
         A scheduler instance from `adaptive_scheduler.scheduler`.
-    db_fname : str
+    db_fname
         Filename of the database, e.g. 'running.json'.
-    learners : list of `adaptive.BaseLearner` isinstances
+    learners
         List of `learners` corresponding to `fnames`.
-    fnames : list
+    fnames
         List of `fnames` corresponding to `learners`.
-    overwrite_db : bool, default: True
+    overwrite_db
         Overwrite the existing database upon starting.
 
     Attributes

adaptive_scheduler/_server_support/job_manager.py

@@ -36,28 +36,28 @@ def command_line_options(
 
     Parameters
     ----------
-    scheduler : `~adaptive_scheduler.scheduler.BaseScheduler`
+    scheduler
         A scheduler instance from `adaptive_scheduler.scheduler`.
     database_manager
         A database manager instance.
-    runner_kwargs : dict, default: None
+    runner_kwargs
         Extra keyword argument to pass to the `adaptive.Runner`. Note that this dict
         will be serialized and pasted in the ``job_script``.
-    goal : callable, default: None
+    goal
         The goal passed to the `adaptive.Runner`. Note that this function will
         be serialized and pasted in the ``job_script``. Can be a smart-goal
         that accepts
         ``Callable[[adaptive.BaseLearner], bool] | int | float | datetime | timedelta | None``.
         See `adaptive_scheduler.utils.smart_goal` for more information.
-    log_interval : int, default: 300
+    log_interval
         Time in seconds between log entries.
-    save_interval : int, default: 300
+    save_interval
         Time in seconds between saving of the learners.
-    save_dataframe : bool
+    save_dataframe
         Whether to periodically save the learner's data as a `pandas.DataFame`.
-    dataframe_format : str
+    dataframe_format
         The format in which to save the `pandas.DataFame`. See the type hint for the options.
-    loky_start_method : str
+    loky_start_method
         Loky start method, by default "loky".
 
     Returns
@@ -94,40 +94,40 @@ class JobManager(BaseManager):
 
     Parameters
     ----------
-    job_names : list
+    job_names
         List of unique names used for the jobs with the same length as
         `learners`. Note that a job name does not correspond to a certain
         specific learner.
-    database_manager : `DatabaseManager`
+    database_manager
         A `DatabaseManager` instance.
-    scheduler : `~adaptive_scheduler.scheduler.BaseScheduler`
+    scheduler
         A scheduler instance from `adaptive_scheduler.scheduler`.
-    interval : int, default: 30
+    interval
         Time in seconds between checking and starting jobs.
-    max_simultaneous_jobs : int, default: 500
+    max_simultaneous_jobs
         Maximum number of simultaneously running jobs. By default no more than 500
         jobs will be running. Keep in mind that if you do not specify a ``runner.goal``,
         jobs will run forever, resulting in the jobs that were not initially started
         (because of this `max_simultaneous_jobs` condition) to not ever start.
-    max_fails_per_job : int, default: 40
+    max_fails_per_job
         Maximum number of times that a job can fail. This is here as a fail switch
         because a job might fail instantly because of a bug inside your code.
         The job manager will stop when
         ``n_jobs * total_number_of_jobs_failed > max_fails_per_job`` is true.
-    save_dataframe : bool
+    save_dataframe
         Whether to periodically save the learner's data as a `pandas.DataFame`.
-    dataframe_format : str
+    dataframe_format
         The format in which to save the `pandas.DataFame`. See the type hint for the options.
-    loky_start_method : str
+    loky_start_method
         Loky start method, by default "loky".
-    log_interval : int, default: 300
+    log_interval
         Time in seconds between log entries.
-    save_interval : int, default: 300
+    save_interval
         Time in seconds between saving of the learners.
-    runner_kwargs : dict, default: None
+    runner_kwargs
         Extra keyword argument to pass to the `adaptive.Runner`. Note that this dict
         will be serialized and pasted in the ``job_script``.
-    goal : callable, default: None
+    goal
         The goal passed to the `adaptive.Runner`. Note that this function will
         be serialized and pasted in the ``job_script``. Can be a smart-goal
         that accepts

adaptive_scheduler/_server_support/kill_manager.py

@@ -28,17 +28,17 @@ def logs_with_string_or_condition(
 
     Parameters
     ----------
-    error : str or callable
+    error
         String that is searched for or callable that is applied
         to the log text. Must take a single argument, a list of
         strings, and return True if the job has to be killed, or
         False if not.
-    database_manager : `DatabaseManager`
+    database_manager
         A `DatabaseManager` instance.
 
     Returns
     -------
-    has_string : dict
+    has_string
         A list ``(job_name, fnames)``, which have the string inside their log-file.
     """
     if isinstance(error, str):
@@ -73,21 +73,21 @@ class KillManager(BaseManager):
 
     Parameters
     ----------
-    scheduler : `~adaptive_scheduler.scheduler.BaseScheduler`
+    scheduler
         A scheduler instance from `adaptive_scheduler.scheduler`.
-    database_manager : `DatabaseManager`
+    database_manager
         A `DatabaseManager` instance.
-    error : str or callable, default: "srun: error:"
+    error
         If ``error`` is a string and is found in the log files, the job will
         be cancelled and restarted. If it is a callable, it is applied
         to the log text. Must take a single argument, a list of
         strings, and return True if the job has to be killed, or
         False if not.
-    interval : int, default: 600
+    interval
         Time in seconds between checking for the condition.
-    max_cancel_tries : int, default: 5
+    max_cancel_tries
         Try maximum `max_cancel_tries` times to cancel a job.
-    move_to : str, optional
+    move_to
         If a job is cancelled the log is either removed (if ``move_to=None``)
         or moved to a folder (e.g. if ``move_to='old_logs'``).
     """