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This may refer to the name of a built-on algorithm (e.g. RLlib's DQN or PPO), a user-defined trainable function or class, or the string identifier of a trainable function or class registered in the tune registry. If Experiment, then Tune will execute training based on Experiment.spec. If you want to pass in a Python lambda, you will need to first register the function: ``tune.register_trainable("lambda_id", lambda x: ...)``. You can then use ``tune.run("lambda_id")``. metric: Metric to optimize. This metric should be reported with `tune.report()`. If set, will be passed to the search algorithm and scheduler. mode: Must be one of [min, max]. Determines whether objective is minimizing or maximizing the metric attribute. If set, will be passed to the search algorithm and scheduler. name: Name of experiment. stop: Stopping criteria. If dict, the keys may be any field in the return result of 'train()', whichever is reached first. If function, it must take (trial_id, result) as arguments and return a boolean (True if trial should be stopped, False otherwise). This can also be a subclass of ``ray.tune.Stopper``, which allows users to implement custom experiment-wide stopping (i.e., stopping an entire Tune run based on some time constraint). time_budget_s: Global time budget in seconds after which all trials are stopped. Can also be a ``datetime.timedelta`` object. config: Algorithm-specific configuration for Tune variant generation (e.g. env, hyperparams). Defaults to empty dict. Custom search algorithms may ignore this. resources_per_trial: Machine resources to allocate per trial, e.g. ``{"cpu": 64, "gpu": 8}``. Note that GPUs will not be assigned unless you specify them here. Defaults to 1 CPU and 0 GPUs in ``Trainable.default_resource_request()``. This can also be a PlacementGroupFactory object wrapping arguments to create a per-trial placement group. num_samples: Number of times to sample from the hyperparameter space. Defaults to 1. If `grid_search` is provided as an argument, the grid will be repeated `num_samples` of times. If this is -1, (virtually) infinite samples are generated until a stopping condition is met. storage_path: Path to store results at. Can be a local directory or a destination on cloud storage. Defaults to the local ``~/ray_results`` directory. search_alg: Search algorithm for optimization. You can also use the name of the algorithm. scheduler: Scheduler for executing the experiment. Choose among FIFO (default), MedianStopping, AsyncHyperBand, HyperBand and PopulationBasedTraining. Refer to ray.tune.schedulers for more options. You can also use the name of the scheduler. verbose: 0, 1, or 2. Verbosity mode. 0 = silent, 1 = default, 2 = verbose. Defaults to 1. If the ``RAY_AIR_NEW_OUTPUT=1`` environment variable is set, uses the old verbosity settings: 0 = silent, 1 = only status updates, 2 = status and brief results, 3 = status and detailed results. progress_reporter: Progress reporter for reporting intermediate experiment progress. Defaults to CLIReporter if running in command-line, or JupyterNotebookReporter if running in a Jupyter notebook. log_to_file: Log stdout and stderr to files in Tune's trial directories. If this is `False` (default), no files are written. If `true`, outputs are written to `trialdir/stdout` and `trialdir/stderr`, respectively. If this is a single string, this is interpreted as a file relative to the trialdir, to which both streams are written. If this is a Sequence (e.g. a Tuple), it has to have length 2 and the elements indicate the files to which stdout and stderr are written, respectively. trial_name_creator: Optional function that takes in a Trial and returns its name (i.e. its string representation). Be sure to include some unique identifier (such as `Trial.trial_id`) in each trial's name. trial_dirname_creator: Optional function that takes in a trial and generates its trial directory name as a string. Be sure to include some unique identifier (such as `Trial.trial_id`) is used in each trial's directory name. Otherwise, trials could overwrite artifacts and checkpoints of other trials. The return value cannot be a path. chdir_to_trial_dir: Deprecated. Set the `RAY_CHDIR_TO_TRIAL_DIR` env var instead sync_config: Configuration object for syncing. See tune.SyncConfig. export_formats: List of formats that exported at the end of the experiment. Default is None. max_failures: Try to recover a trial at least this many times. Ray will recover from the latest checkpoint if present. Setting to -1 will lead to infinite recovery retries. Setting to 0 will disable retries. Defaults to 0. fail_fast: Whether to fail upon the first error. If fail_fast='raise' provided, Tune will automatically raise the exception received by the Trainable. fail_fast='raise' can easily leak resources and should be used with caution (it is best used with `ray.init(local_mode=True)`). restore: Path to checkpoint. Only makes sense to set if running 1 trial. Defaults to None. resume: One of [True, False, "AUTO"]. Can be suffixed with one or more of ["+ERRORED", "+ERRORED_ONLY", "+RESTART_ERRORED", "+RESTART_ERRORED_ONLY"] (e.g. ``AUTO+ERRORED``). `resume=True` and `resume="AUTO"` will attempt to resume from a checkpoint and otherwise start a new experiment. The suffix "+ERRORED" resets and reruns errored trials upon resume - previous trial artifacts will be left untouched. It will try to continue from the last observed checkpoint. The suffix "+RESTART_ERRORED" will instead start the errored trials from scratch. "+ERRORED_ONLY" and "+RESTART_ERRORED_ONLY" will disable resuming non-errored trials - they will be added as finished instead. New trials can still be generated by the search algorithm. resume_config: [Experimental] Config object that controls how to resume trials of different statuses. Can be used as a substitute to the `resume` suffixes described above. reuse_actors: Whether to reuse actors between different trials when possible. This can drastically speed up experiments that start and stop actors often (e.g., PBT in time-multiplexing mode). This requires trials to have the same resource requirements. Defaults to ``False``. raise_on_failed_trial: Raise TuneError if there exists failed trial (of ERROR state) when the experiments complete. callbacks: List of callbacks that will be called at different times in the training loop. Must be instances of the ``ray.tune.callback.Callback`` class. If not passed, `LoggerCallback` (json/csv/tensorboard) callbacks are automatically added. max_concurrent_trials: Maximum number of trials to run concurrently. Must be non-negative. If None or 0, no limit will be applied. This is achieved by wrapping the ``search_alg`` in a :class:`ConcurrencyLimiter`, and thus setting this argument will raise an exception if the ``search_alg`` is already a :class:`ConcurrencyLimiter`. Defaults to None. _remote: Whether to run the Tune driver in a remote function. This is disabled automatically if a custom trial executor is passed in. This is enabled by default in Ray client mode. local_dir: Deprecated. Use `storage_path` instead. keep_checkpoints_num: Deprecated. use checkpoint_config instead. checkpoint_score_attr: Deprecated. use checkpoint_config instead. checkpoint_freq: Deprecated. use checkpoint_config instead. checkpoint_at_end: Deprecated. use checkpoint_config instead. checkpoint_keep_all_ranks: Deprecated. use checkpoint_config instead. checkpoint_upload_from_workers: Deprecated. use checkpoint_config instead. Returns: ExperimentAnalysis: Object for experiment analysis. Raises: TuneError: Any trials failed and `raise_on_failed_trial` is True. rz(...) param_spacez..restore(path="{path}", ...))rsearch_space_argrestore_entrypointz Tuner(...)z+Tuner.restore(path="{path}", trainable=...)tune.run_experiments(...)zexperiment=Experiment(config)z&tune.run_experiments(..., resume=True)z tune.run(...)rztune.run(..., resume=True)rrN[output] This uses the legacy output and progress reporter, as Ray client is not supported by the new engine. For more information, see https://github.com/ray-project/ray/issues/36949rnum_cpusrFaXThe environment variable `{}` is deprecated. It is no longer used and will not have any effect. You should set the `storage_path` instead. Files will no longer be written to `~/ray_results` as long as `storage_path` is set.See the docs: https://docs.ray.io/en/latest/train/user-guides/persistent-storage.html#setting-the-local-staging-directoryTUNE_RESULT_DIRRAY_AIR_LOCAL_CACHE_DIRzThe `local_dir` argument is deprecated. You should set the `storage_path` instead. See the docs: https://docs.ray.io/en/latest/train/user-guides/persistent-storage.html#setting-the-local-staging-directorytune)minmaxz The `mode` parameter passed to `z` must be one of ['min', 'max']z[output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949zThe `rz ` passed to `z` must be a dict. Got 'z ' instead.zbkeep_checkpoints_num is deprecated and will be removed. use checkpoint_config.num_to_keep instead.zrcheckpoint_score_attr is deprecated and will be removed. use checkpoint_config.checkpoint_score_attribute instead.zmin-zusing min- and max- prefixes to specify checkpoint score order is deprecated. Use CheckpointConfig.checkpoint_score_order insteadrrzfcheckpoint_freq is deprecated and will be removed. use checkpoint_config.checkpoint_frequency instead.zecheckpoint_at_end is deprecated and will be removed. use checkpoint_config.checkpoint_at_end instead.z,`chdir_to_trial_dir` is deprecated. Use the zm environment variable instead. Set it to 0 to disable the default behavior of changing the working directory.)create_schedulerTUNE_RESULT_BUFFER_LENGTHzYou are using a z2 scheduler, but TUNE_RESULT_BUFFER_LENGTH is set (zf). This can lead to undesired and faulty behavior, so the buffer length was forcibly set to 1 instead.rzrConsider boosting PBT performance by enabling `reuse_actors` as well as implementing `reset_config` for Trainable.rrunrrrrrrrrrrrrrrz)max_failures must be 0 if fail_fast=True.rS)max_concurrentz*You have specified `max_concurrent_trials=zO`, but the `search_alg` is already a `ConcurrencyLimiter` with `max_concurrent=z3. FIX THIS by setting `max_concurrent_trials=None`.zc`, but the `search_alg` is already a `ConcurrencyLimiter`. `max_concurrent_trials` will be ignored.z=`max_concurrent_trials` must be greater or equal than 1, got .zYou have passed a `SearchGenerator` instance as the `search_alg`, but `max_concurrent_trials` requires a `Searcher` instance`. `max_concurrent_trials` will be ignored.zYou passed a `z` parameter to `z` with unresolved parameters, but the search algorithm was already instantiated with a search space. Make sure that `config` does not contain any more parameter definitions - include them in the search algorithm's search space if necessary.z-You passed a `metric` or `mode` argument to `z`, but the scheduler you are using was already instantiated with their own `metric` and `mode` parameters. Either remove the arguments from your scheduler or from `z` args.) air_verbosityrrrrprogress_metricsrqrwaTune detects GPUs, but no trials are using GPUs. To enable trials to use GPUs, wrap `train_func` with `tune.with_resources(train_func, resources_per_trial={'gpu': 1})` which allows Tune to expose 1 GPU to each trial. For Ray Train Trainers, you can specify GPU resources through `ScalingConfig(use_gpu=True)`. You can also override `Trainable.default_resource_request` if using the Trainable API.zBAIR_VERBOSITY is set, ignoring passed-in ProgressReporter for now.) _trainer_api) rplaceholder_resolversrstopperrbrrrtrial_checkpoint_configrstoragerzYou have resumed the Tune run, which means that any newly specified `Experiment`s will be ignored. Tune will just continue what was previously running.) experimentstotal_num_samples) start_time total_samplesrrr<)rrc3@K|]}t|tVdSr@)rAr ).0cbs rH zrun..s- A A:b+ , , A A A A A ArJ)experiment_nameexperiment_path searcher_str scheduler_strrtensorboard_pathT)rwaitzFWrote the latest version of all result files and experiment state to 'z' in z.4fzs.z%Experiment state snapshotting failed:)exc_info stack_infor})rzTrials did not completezTrials did not complete: %szTotal run time: z.2fz seconds (z seconds for the tuning loop).r)pathzZTraining has been interrupted, but the most recent state was saved. Resume training with: z^Experiment has been interrupted, but the most recent state was saved. Resume experiment with: )experiment_checkpoint_pathdefault_metric default_moderrr)localscopypopr TRAINERTUNERTUNE_RUN_EXPERIMENTSrrutilclient is_connectedrrVERBOSEr7V3_TRIAL_DETAILSrrremoterrr$r%rurrrZformat_commonr usage_librecord_library_usage air_usagetag_ray_air_env_varstag_air_entrypointtimer_rr9rArrrvrBrrwarningswarn num_to_keep startswithcheckpoint_score_attributecheckpoint_score_order score_attrcheckpoint_frequencyrrrsysmaxsizerEray.tune.schedulersrr'supports_buffered_resultsrr(r)rrdeepcopylist enumeraterr1_privateworker_mode LOCAL_MODEr,r-rrr/r0searcher_set_search_propsset_search_properties public_specr2scheduler_set_search_propsr"rItag_storage_typer tag_callbacksr6cluster_resourcesrxrir=rr#rdrrrresumedadd_configurationsupdate_max_pending_trialsdebugupdate_pending_trial_resourcessetup_experimentsrsetupray.tune.experimental.outputr!_storageexperiment_driver_staging_pathexperiment_dir_nameanyexperiment_startedr __class__r is_finishedis_setstepr8 V1_EXPERIMENTrr Exceptioncleanup checkpointerrorr~statusr TERMINATEDrrr)IrrrrrrrrrrrrrrrrrrrrrrrrrKrbrrrrrrrrrrrrrremote_run_kwargserror_message_map remote_run string_queue remote_futureENV_VAR_DEPRECATION_MESSAGE all_startrrenv_result_buffer_lengthrriexp is_local_moderr is_trainer runner_kwargsry tune_startair_progress_reporterrcallbackexperiment_local_pathrKr  tune_takenfinal_sync_start all_trialsincomplete_trialstrial all_takenrsI rHrr sR )$$$m+++3 -"R    + + +& -"O    : : :5 ?"J  * (">   / =>>>>(/%2244$L$899WY=W& W % % 1 KKB   ,SZ+++C00 ,J77 *S w(<+ + '< 2C-.) )MM%M;LMM       w}%%% F  z~~'((X !     a g \rF&'"""!! fd # #  -%&89 - -!,/ - -LL - - -   -K)?-=-?-?'  9    )=%(  H    ! + +F 3 3 = M#     l,K  U U U   0?P0Q0QRR {1~5666I&&&*# )I ""5!,, #.A B B B   .{1~/L M M   NN!   $:#;#; !]6 P   ! M$99 - 1A#2 2 2 %X)PQW)X)X 3A&# +A @!A& M$99   M , ,m , ,F > G 1 1C  ) )3% 0 0 0 0 ((****  C    G  1 12E F F F :3KJ !$2      YXXXXX!  H($788 (0%%++)9Q,  #OJ /= A Ay A A AAA 0 00/"2#-6#-6(6- 1    $$&& D/K/R/R/T/T D KKMMMY455 < ):;;;($V-BCCC $$&& D/K/R/R/T/T D  z)Jy{{  4000  T& T T-1Y[[;K-K S T T T         3dt       Y,--?!2>>>> V%:$GGGG""$$J NN)) <5+ + + % ( K  K)E)L)L)N)N K57HII I LL68I J J J i'IY,--   ?y 7 ? ? > ? ? ?   $**,, ./CDKK'L   -/ / / NN>);>>     NNH3EHH    #)#9&q>1D    s B!n==!o Aq(r r r concurrentc | (tjjj} t d|%t t jp tj }| rt |t dtj dt} t| } tj| |||||||||| d St!|}t#|||||||| t$j } |rt)|fi| jSg} |D]}| t)|fi| jz } | S) afRuns and blocks until all trials finish. Example: >>> from ray.tune.experiment import Experiment >>> from ray.tune.tune import run_experiments >>> def my_func(config): return {"score": 0} >>> experiment_spec = Experiment("experiment", my_func) # doctest: +SKIP >>> run_experiments(experiments=experiment_spec) # doctest: +SKIP >>> experiment_spec = {"experiment": {"run": my_func}} # doctest: +SKIP >>> run_experiments(experiments=experiment_spec) # doctest: +SKIP Returns: List of Trial objects, holding data for each executed trial. 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