chore: import upstream snapshot with attribution

python/ray/tune/result.py

@@ -0,0 +1,133 @@
+# Importing for Backward Compatibility
+from ray.air.constants import (  # noqa: F401
+    EXPR_ERROR_FILE,
+    EXPR_ERROR_PICKLE_FILE,
+    EXPR_PARAM_FILE,
+    EXPR_PARAM_PICKLE_FILE,
+    EXPR_PROGRESS_FILE,
+    EXPR_RESULT_FILE,
+    TIME_THIS_ITER_S,
+    TIMESTAMP,
+    TRAINING_ITERATION,
+)
+
+# fmt: off
+# __sphinx_doc_begin__
+# (Optional/Auto-filled) training is terminated. Filled only if not provided.
+DONE = "done"
+
+# (Optional) Enum for user controlled checkpoint
+SHOULD_CHECKPOINT = "should_checkpoint"
+
+# (Auto-filled) The hostname of the machine hosting the training process.
+HOSTNAME = "hostname"
+
+# (Auto-filled) The auto-assigned id of the trial.
+TRIAL_ID = "trial_id"
+
+# (Auto-filled) The auto-assigned id of the trial.
+EXPERIMENT_TAG = "experiment_tag"
+
+# (Auto-filled) The node ip of the machine hosting the training process.
+NODE_IP = "node_ip"
+
+# (Auto-filled) The pid of the training process.
+PID = "pid"
+
+# (Optional) Default (anonymous) metric when using tune.report(x)
+DEFAULT_METRIC = "_metric"
+
+# (Optional) Mean reward for current training iteration
+EPISODE_REWARD_MEAN = "episode_reward_mean"
+
+# (Optional) Mean loss for training iteration
+MEAN_LOSS = "mean_loss"
+
+# (Optional) Mean accuracy for training iteration
+MEAN_ACCURACY = "mean_accuracy"
+
+# Number of episodes in this iteration.
+EPISODES_THIS_ITER = "episodes_this_iter"
+
+# (Optional/Auto-filled) Accumulated number of episodes for this trial.
+EPISODES_TOTAL = "episodes_total"
+
+# Number of timesteps in this iteration.
+TIMESTEPS_THIS_ITER = "timesteps_this_iter"
+
+# (Auto-filled) Accumulated number of timesteps for this entire trial.
+TIMESTEPS_TOTAL = "timesteps_total"
+
+# (Auto-filled) Accumulated time in seconds for this entire trial.
+TIME_TOTAL_S = "time_total_s"
+
+# __sphinx_doc_end__
+# fmt: on
+
+DEFAULT_EXPERIMENT_INFO_KEYS = ("trainable_name", EXPERIMENT_TAG, TRIAL_ID)
+
+DEFAULT_RESULT_KEYS = (
+    TRAINING_ITERATION,
+    TIME_TOTAL_S,
+    MEAN_ACCURACY,
+    MEAN_LOSS,
+)
+
+# Metrics that don't require at least one iteration to complete
+DEBUG_METRICS = (
+    TRIAL_ID,
+    "experiment_id",
+    "date",
+    TIMESTAMP,
+    PID,
+    HOSTNAME,
+    NODE_IP,
+    "config",
+)
+
+# Make sure this doesn't regress
+AUTO_RESULT_KEYS = (
+    TRAINING_ITERATION,
+    TIME_TOTAL_S,
+    EPISODES_TOTAL,
+    TIMESTEPS_TOTAL,
+    NODE_IP,
+    HOSTNAME,
+    PID,
+    TIME_TOTAL_S,
+    TIME_THIS_ITER_S,
+    TIMESTAMP,
+    "date",
+    "time_since_restore",
+    "timesteps_since_restore",
+    "iterations_since_restore",
+    "config",
+    # TODO(justinvyu): Move this stuff to train to avoid cyclical dependency.
+    "checkpoint_dir_name",
+)
+
+# __duplicate__ is a magic keyword used internally to
+# avoid double-logging results when using the Function API.
+RESULT_DUPLICATE = "__duplicate__"
+
+# __trial_info__ is a magic keyword used internally to pass trial_info
+# to the Trainable via the constructor.
+TRIAL_INFO = "__trial_info__"
+
+# __stdout_file__/__stderr_file__ are magic keywords used internally
+# to pass log file locations to the Trainable via the constructor.
+STDOUT_FILE = "__stdout_file__"
+STDERR_FILE = "__stderr_file__"
+
+DEFAULT_EXPERIMENT_NAME = "default"
+
+# Meta file about status under each experiment directory, can be
+# parsed by automlboard if exists.
+JOB_META_FILE = "job_status.json"
+
+# Meta file about status under each trial directory, can be parsed
+# by automlboard if exists.
+EXPR_META_FILE = "trial_status.json"
+
+# Config prefix when using ExperimentAnalysis.
+CONFIG_PREFIX = "config"