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154 lines
4.4 KiB
Python
154 lines
4.4 KiB
Python
# Copyright 2020 The Ray Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This source file is adapted here because ray does not fully support Windows.
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# Copyright (c) Microsoft Corporation.
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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 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) Default (anonymous) metric when using tune.report(x)
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DEFAULT_METRIC = "_metric"
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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 loss for training iteration
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NEG_MEAN_LOSS = "neg_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 overridden 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 = (
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TRAINING_ITERATION,
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TIME_TOTAL_S,
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TIMESTEPS_TOTAL,
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MEAN_ACCURACY,
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MEAN_LOSS,
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)
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# Make sure this doesn't regress
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AUTO_RESULT_KEYS = (
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TRAINING_ITERATION,
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TIME_TOTAL_S,
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EPISODES_TOTAL,
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TIMESTEPS_TOTAL,
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NODE_IP,
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HOSTNAME,
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PID,
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TIME_TOTAL_S,
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TIME_THIS_ITER_S,
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"timestamp",
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"experiment_id",
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"date",
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"time_since_restore",
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"iterations_since_restore",
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"timesteps_since_restore",
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"config",
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)
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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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# __stdout_file__/__stderr_file__ are magic keywords used internally
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# to pass log file locations to the Trainable via the constructor.
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STDOUT_FILE = "__stdout_file__"
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STDERR_FILE = "__stderr_file__"
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# Where Tune writes result files by default
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DEFAULT_RESULTS_DIR = (
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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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)
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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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