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