chore: import upstream snapshot with attribution

python/ray/air/constants.py

@@ -0,0 +1,94 @@
+# Key to denote the preprocessor in the checkpoint dict.
+PREPROCESSOR_KEY = "_preprocessor"
+
+# Key to denote the model in the checkpoint dict.
+MODEL_KEY = "model"
+
+# Key to denote which dataset is the evaluation dataset.
+# Only used in trainers which do not support multiple
+# evaluation datasets.
+EVALUATION_DATASET_KEY = "evaluation"
+
+# Key to denote which dataset is the training dataset.
+# This is the dataset that the preprocessor is fit on.
+TRAIN_DATASET_KEY = "train"
+
+# Name to use for the column when representing tensors in table format.
+TENSOR_COLUMN_NAME = "__value__"
+
+# The maximum length of strings returned by `__repr__` for AIR objects constructed with
+# default values.
+MAX_REPR_LENGTH = int(80 * 1.5)
+
+# Timeout used when putting exceptions raised by runner thread into the queue.
+_ERROR_REPORT_TIMEOUT = 10
+
+# Timeout when fetching new results after signaling the training function to continue.
+_RESULT_FETCH_TIMEOUT = 0.2
+
+# Timeout for fetching exceptions raised by the training function.
+_ERROR_FETCH_TIMEOUT = 1
+
+# The key used to identify whether we have already warned about ray.air.session
+# functions being used outside of the session
+SESSION_MISUSE_LOG_ONCE_KEY = "air_warn_session_misuse"
+
+# Name of attribute in Checkpoint storing current Tune ID for restoring
+# training with Ray Train
+CHECKPOINT_ID_ATTR = "_current_checkpoint_id"
+
+# Name of the marker dropped by the Trainable. If a worker detects
+# the presence of the marker in the trial dir, it will use lazy
+# checkpointing.
+LAZY_CHECKPOINT_MARKER_FILE = ".lazy_checkpoint_marker"
+
+
+# The timestamp of when the result is generated.
+# Default to when the result is processed by tune.
+TIMESTAMP = "timestamp"
+
+# (Auto-filled) Time in seconds this iteration took to run.
+# This may be overridden to override the system-computed time difference.
+TIME_THIS_ITER_S = "time_this_iter_s"
+
+# (Auto-filled) The index of this training iteration.
+TRAINING_ITERATION = "training_iteration"
+
+# File that stores parameters of the trial.
+EXPR_PARAM_FILE = "params.json"
+
+# Pickle File that stores parameters of the trial.
+EXPR_PARAM_PICKLE_FILE = "params.pkl"
+
+# File that stores the progress of the trial.
+EXPR_PROGRESS_FILE = "progress.csv"
+
+# File that stores results of the trial.
+EXPR_RESULT_FILE = "result.json"
+
+# File that stores the pickled error file
+EXPR_ERROR_PICKLE_FILE = "error.pkl"
+
+# File that stores the error file
+EXPR_ERROR_FILE = "error.txt"
+
+# File that stores the checkpoint metadata
+CHECKPOINT_TUNE_METADATA_FILE = ".tune_metadata"
+
+# ==================================================
+#               Environment Variables
+# ==================================================
+
+# Integer value which if set will copy files in reported AIR directory
+# checkpoints instead of moving them (if worker is on the same node as Trainable)
+COPY_DIRECTORY_CHECKPOINTS_INSTEAD_OF_MOVING_ENV = (
+    "TRAIN_COPY_DIRECTORY_CHECKPOINTS_INSTEAD_OF_MOVING"
+)
+
+# NOTE: When adding a new environment variable, please track it in this list.
+# TODO(ml-team): Most env var constants should get moved here.
+AIR_ENV_VARS = {
+    COPY_DIRECTORY_CHECKPOINTS_INSTEAD_OF_MOVING_ENV,
+    "RAY_AIR_FULL_TRACEBACKS",
+    "RAY_AIR_NEW_OUTPUT",
+}