o `۷iU@sddlZddlZddlmZddlmZddlmZddlm Z ddl m Z ddl m Z ddd Zd d Ziad ed ejfddZdS)N)CheckpointConfig) TuneError)Trial)json_to_resources)TuneFunctionEncoder)SafeFallbackEncodercKs&|r |d(i|}ntjd(i|}|jddtdd|jddtjdd|jddtjd d|jd dtd d|jd d tdd|jddtdd|jdddd|jddtdd|jddtdd|jdddd|jddtd d|jd!d"td#d|jd$dtjd%d|jd&dtd'd|S))zReturns a base argument parser for the ray.tune tool. Args: parser_creator: A constructor for the parser class. kwargs: Non-positional args to be passed into the parser class constructor. z--runNzThe algorithm or model to train. This may refer to the name of a built-on algorithm (e.g. RLlib's DQN or PPO), or a user-defined trainable function or class registered in the tune registry.)defaulttypehelpz--stopz{}zThe stopping criteria, specified in JSON. The keys may be any field returned by 'train()' e.g. '{"time_total_s": 600, "training_iteration": 100000}' to stop after 600 seconds or 100k iterations, whichever is reached first.z--configzLAlgorithm-specific configuration (e.g. env, hyperparams), specified in JSON.z--resources-per-trialzOverride the machine resources to allocate per trial, e.g. '{"cpu": 64, "gpu": 8}'. Note that GPUs will not be assigned unless you specify them here. For RLlib, you probably want to leave this alone and use RLlib configs to control parallelism.z --num-samplesz%Number of times to repeat each trial.z--checkpoint-freqrz`How many training iterations between checkpoints. A value of 0 (default) disables checkpointing.z--checkpoint-at-end store_truezEWhether to checkpoint at the end of the experiment. Default is False.)actionr z--keep-checkpoints-numz]Number of best checkpoints to keep. Others get deleted. Default (None) keeps all checkpoints.z--checkpoint-score-attrtraining_iterationzSpecifies by which attribute to rank the best checkpoint. Default is increasing order. If attribute starts with min- it will rank attribute in decreasing order. Example: min-validation_lossz--export-formatszList of formats that exported at the end of the experiment. Default is None. For RLlib, 'checkpoint' and 'model' are supported for TensorFlow policy graphs.)rr z--max-failureszsTry to recover a trial from its last checkpoint at least this many times. Only applies if checkpointing is enabled.z --schedulerFIFOzGFIFO (default), MedianStopping, AsyncHyperBand, HyperBand, or HyperOpt.z--scheduler-configz(Config options to pass to the scheduler.z --restorez+If specified, restore from this checkpoint.)argparseArgumentParser add_argumentstrjsonloadsrint)parser_creatorkwargsparserrrW/home/ubuntu/vllm_env/lib/python3.10/site-packages/ray/tune/experiment/config_parser.py _make_parsers     rc Csg}|D]Q\}}d|vrtd||durqt|tr!|r-|d|ddt|tr8||qt|tr>qt|rM|t j |t dq|t j |t dq|S)z9Converts configuration to a command line argument format.-zUse '_' instead of '-' in `{}`Nz--{}_)cls) items ValueErrorformat isinstanceboolappendreplacercallablerdumpsrr)configargvkvrrr_to_argvs    r.specrcKs|}|dd}z |t|\}}Wn ty!td|w|r(||d<|dt}td|d|di|di||d g|d |d |d |d |j |dd |S)aCreates a Trial object from parsing the spec. Args: spec: A resolved experiment specification. Arguments should The args here should correspond to the command line flags in ray.tune.experiment.config_parser. parser: An argument parser object from make_parser. trial_kwargs: Extra keyword arguments used in instantiating the Trial. Returns: A trial object with corresponding parameters to the specification. resources_per_trialNz%Error parsing args, see above messageplacement_group_factorycheckpoint_configrunr*stopexport_formatsrestoretrial_name_creatortrial_dirname_creator log_to_filestorage) trainable_namer*stopping_criterionr2r5 restore_pathr7r8r9 max_failuresr:r) copypopparse_known_argsr. SystemExitrgetrrr>)r/r trial_kwargs resourcesargsrr2rrr_create_trial_from_specs2      rG)N)rrray.tunerray.tune.errorrray.tune.experimentrray.tune.resourcesrray.tune.utils.serializationrray.tune.utils.utilrrr. _cached_pgfdictrrGrrrrs        z