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b389c3d302
Pushservice is the main recommendation service we use to surface recommendations to our users via notifications. It fetches candidates from various sources, ranks them in order of relevance, and applies filters to determine the best one to send.
60 lines
1.3 KiB
Python
60 lines
1.3 KiB
Python
from twml.trainers import DataRecordTrainer
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from .features import FEATURE_LIST_DEFAULT_PATH
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def get_training_arg_parser():
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parser = DataRecordTrainer.add_parser_arguments()
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parser.add_argument(
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"--feature_list",
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default=FEATURE_LIST_DEFAULT_PATH,
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type=str,
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help="Which features to use for training",
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)
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parser.add_argument(
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"--param_file",
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default=None,
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type=str,
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help="Path to JSON file containing the graph parameters. If None, model will load default parameters.",
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)
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parser.add_argument(
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"--directly_export_best",
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default=False,
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action="store_true",
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help="whether to directly_export best_checkpoint",
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)
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parser.add_argument(
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"--warm_start_from", default=None, type=str, help="model dir to warm start from"
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)
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parser.add_argument(
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"--warm_start_base_dir",
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default=None,
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type=str,
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help="latest ckpt in this folder will be used to ",
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)
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parser.add_argument(
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"--model_type",
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default=None,
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type=str,
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help="Which type of model to train.",
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)
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return parser
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def get_eval_arg_parser():
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parser = get_training_arg_parser()
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parser.add_argument(
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"--eval_checkpoint",
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default=None,
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type=str,
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help="Which checkpoint to use for evaluation",
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)
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return parser
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