syne_tune.backend.local_backend module

class syne_tune.backend.local_backend.LocalBackend(entry_point, delete_checkpoints=False, pass_args_as_json=False, rotate_gpus=True, num_gpus_per_trial=1, gpus_to_use=None)

Bases: TrialBackend

A backend running locally by spawning sub-process concurrently. Note that no resource management is done so the concurrent number of trials should be adjusted to the machine capacity.

Additional arguments on top of parent class TrialBackend:

Parameters:

• entry_point (str) – Path to Python main file to be tuned

• rotate_gpus (bool) – In case several GPUs are present, each trial is scheduled on a different GPU. A new trial is preferentially scheduled on a free GPU, and otherwise the GPU with least prior assignments is chosen. If False, then all GPUs are used at the same time for all trials. Defaults to True.

• num_gpus_per_trial (int) – Number of GPUs to be allocated to each trial. Must be not larger than the total number of GPUs available. Defaults to 1

• gpus_to_use (Optional[List[int]]) – If this is given, the backend only uses GPUs in this lists (non-negative ints). Entries must be in range(get_num_gpus()). Defaults to using all GPUs.

trial_path(trial_id)

Parameters:

trial_id (int) – ID of trial

Return type:

Path

Returns:

Directory where files related to trial are written to

checkpoint_trial_path(trial_id)

Parameters:

trial_id (int) – ID of trial

Return type:

Path

Returns:

Directory where checkpoints for trial are written to and read from

copy_checkpoint(src_trial_id, tgt_trial_id)

Copy the checkpoint folder from one trial to the other.

Parameters:

• src_trial_id (int) – Source trial ID (copy from)

• tgt_trial_id (int) – Target trial ID (copy to)

delete_checkpoint(trial_id)

Removes checkpoint folder for a trial. It is OK for the folder not to exist.

Parameters:

trial_id (int) – ID of trial for which checkpoint files are deleted

busy_trial_ids()

Returns list of ids for currently busy trials

A trial is busy if its status is in_progress or stopping. If the execution setup is able to run n_workers jobs in parallel, then if this method returns a list of size n, the tuner may start n_workers - n new jobs.

Return type:

List[Tuple[int, str]]

Returns:

List of (trial_id, status)

stdout(trial_id)

Fetch stdout log for trial

Parameters:

trial_id (int) – ID of trial

Return type:

List[str]

Returns:

Lines of the log of the trial (stdout)

stderr(trial_id)

Fetch stderr log for trial

Parameters:

trial_id (int) – ID of trial

Return type:

List[str]

Returns:

Lines of the log of the trial (stderr)

set_path(results_root=None, tuner_name=None)

Parameters:

• results_root (Optional[str]) – The local folder that should contain the results of the tuning experiment. Used by Tuner to indicate a desired path where the results should be written to. This is used to unify the location of backend files and Tuner results when possible (in the local backend). By default, the backend does not do anything since not all backends may be able to unify their file locations.

• tuner_name (Optional[str]) – Name of the tuner, can be used for instance to save checkpoints on remote storage.

entrypoint_path()

Return type:

Path

Returns:

Entrypoint path of script to be executed

set_entrypoint(entry_point)

Update the entrypoint.

Parameters:

entry_point (str) – New path of the entrypoint.