mirror of
https://github.com/wassname/ray.git
synced 2026-06-28 19:33:06 +08:00
Richard Liaw
81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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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 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 experiment.
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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 experiment.
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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 experiment.
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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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# __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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# Where Tune writes result files by default
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DEFAULT_RESULTS_DIR = (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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# 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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