wehub-resource-sync
134 lines
3.4 KiB
Python
134 lines
3.4 KiB
Python
# 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"
|