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Marlon
61 lines
1.9 KiB
Python
61 lines
1.9 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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# (Optional/Auto-filled) training is terminated. Filled only if not provided.
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DONE = "done"
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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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# Number of timesteps in this iteration.
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EPISODES_THIS_ITER = "episodes_this_iter"
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# (Optional/Auto-filled) Accumulated time in seconds 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 thistraining iteration.
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TRAINING_ITERATION = "training_iteration"
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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_PARARM_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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