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https://github.com/wassname/ray.git
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Richard Liaw
ea10cd212c
* add accessible trial_info * trial name and info * doc * fix gp * Update doc/source/tune-package-ref.rst * Apply suggestions from code review * fix * trial * fixtest * testfix
99 lines
3.0 KiB
Python
99 lines
3.0 KiB
Python
import os
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# yapf: disable
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# __sphinx_doc_begin__
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# (Optional/Auto-filled) training is terminated. Filled only if not provided.
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DONE = "done"
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# (Optional) Enum for user controlled checkpoint
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SHOULD_CHECKPOINT = "should_checkpoint"
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# (Auto-filled) The hostname of the machine hosting the training process.
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HOSTNAME = "hostname"
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# (Auto-filled) The auto-assigned id of the trial.
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TRIAL_ID = "trial_id"
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# (Auto-filled) The auto-assigned id of the trial.
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EXPERIMENT_TAG = "experiment_tag"
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# (Auto-filled) The node ip of the machine hosting the training process.
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NODE_IP = "node_ip"
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# (Auto-filled) The pid of the training process.
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PID = "pid"
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# (Optional) Mean reward for current training iteration
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EPISODE_REWARD_MEAN = "episode_reward_mean"
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# (Optional) Mean loss for training iteration
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MEAN_LOSS = "mean_loss"
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# (Optional) Mean accuracy for training iteration
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MEAN_ACCURACY = "mean_accuracy"
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# Number of episodes in this iteration.
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EPISODES_THIS_ITER = "episodes_this_iter"
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# (Optional/Auto-filled) Accumulated number of episodes for this trial.
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EPISODES_TOTAL = "episodes_total"
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# Number of timesteps in this iteration.
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TIMESTEPS_THIS_ITER = "timesteps_this_iter"
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# (Auto-filled) Accumulated number of timesteps for this entire trial.
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TIMESTEPS_TOTAL = "timesteps_total"
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# (Auto-filled) Time in seconds this iteration took to run.
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# This may be overriden to override the system-computed time difference.
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TIME_THIS_ITER_S = "time_this_iter_s"
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# (Auto-filled) Accumulated time in seconds for this entire trial.
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TIME_TOTAL_S = "time_total_s"
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# (Auto-filled) The index of this training iteration.
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TRAINING_ITERATION = "training_iteration"
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# __sphinx_doc_end__
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# yapf: enable
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DEFAULT_EXPERIMENT_INFO_KEYS = ("trainable_name", EXPERIMENT_TAG, TRIAL_ID)
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DEFAULT_RESULT_KEYS = (TRAINING_ITERATION, TIME_TOTAL_S, TIMESTEPS_TOTAL,
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MEAN_ACCURACY, MEAN_LOSS)
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# __duplicate__ is a magic keyword used internally to
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# avoid double-logging results when using the Function API.
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RESULT_DUPLICATE = "__duplicate__"
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# __trial_info__ is a magic keyword used internally to pass trial_info
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# to the Trainable via the constructor.
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TRIAL_INFO = "__trial_info__"
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# Where Tune writes result files by default
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DEFAULT_RESULTS_DIR = (os.environ.get("TEST_TMPDIR")
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or os.environ.get("TUNE_RESULT_DIR")
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or os.path.expanduser("~/ray_results"))
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# Meta file about status under each experiment directory, can be
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# parsed by automlboard if exists.
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JOB_META_FILE = "job_status.json"
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# Meta file about status under each trial directory, can be parsed
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# by automlboard if exists.
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EXPR_META_FILE = "trial_status.json"
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# File that stores parameters of the trial.
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EXPR_PARAM_FILE = "params.json"
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# Pickle File that stores parameters of the trial.
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EXPR_PARAM_PICKLE_FILE = "params.pkl"
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# File that stores the progress of the trial.
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EXPR_PROGRESS_FILE = "progress.csv"
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# File that stores results of the trial.
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EXPR_RESULT_FILE = "result.json"
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# Config prefix when using Analysis.
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CONFIG_PREFIX = "config/"
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