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@ -126,7 +126,7 @@ class ConfigSchema(BaseModel):
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verbose=("Display more information for debugging purposes", "flag", "VV", bool),
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verbose=("Display more information for debugging purposes", "flag", "VV", bool),
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use_gpu=("Use GPU", "option", "g", int),
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use_gpu=("Use GPU", "option", "g", int),
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num_workers=("Parallel Workers", "option", "j", int),
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num_workers=("Parallel Workers", "option", "j", int),
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strategy=("Distributed training strategy", "option", "strat", str),
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strategy=("Distributed training strategy (requires spacy_ray)", "option", "strat", str),
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tag_map_path=("Location of JSON-formatted tag map", "option", "tm", Path),
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tag_map_path=("Location of JSON-formatted tag map", "option", "tm", Path),
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omit_extra_lookups=("Don't include extra lookups in model", "flag", "OEL", bool),
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omit_extra_lookups=("Don't include extra lookups in model", "flag", "OEL", bool),
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# fmt: on
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# fmt: on
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@ -196,9 +196,9 @@ def train_cli(
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if num_workers and num_workers > 1:
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if num_workers and num_workers > 1:
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try:
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try:
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from ray_spacy import distributed_setup_and_train
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from spacy_ray import distributed_setup_and_train
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except ImportError:
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except ImportError:
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msg.fail("Need to install ray_spacy to use distributed training!", exits=1)
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msg.fail("Need to `pip install spacy_ray` to use distributed training!", exits=1)
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distributed_setup_and_train(use_gpu, num_workers, strategy, train_args)
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distributed_setup_and_train(use_gpu, num_workers, strategy, train_args)
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else:
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else:
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if use_gpu >= 0:
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if use_gpu >= 0:
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