def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli
:param parser: Root parser to register command-specific arguments
:return:
"""
serve_parser = parser.add_parser(
"serve", help="CLI tool to run inference requests through REST and GraphQL endpoints."
)
serve_parser.add_argument(
"--task", type=str, choices=SUPPORTED_TASKS.keys(), help="The task to run the pipeline on"
)
serve_parser.add_argument("--host", type=str, default="localhost", help="Interface the server will listen on.")
serve_parser.add_argument("--port", type=int, default=8888, help="Port the serving will listen to.")
serve_parser.add_argument("--workers", type=int, default=1, help="Number of http workers")
serve_parser.add_argument("--model", type=str, help="Model's name or path to stored model.")
serve_parser.add_argument("--config", type=str, help="Model's config name or path to stored model.")
serve_parser.add_argument("--tokenizer", type=str, help="Tokenizer name to use.")
serve_parser.add_argument(
"--device",
type=int,
default=-1,
help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)",
)
serve_parser.set_defaults(func=serve_command_factory)
def register_subcommand(parser: ArgumentParser):
run_parser = parser.add_parser("run", help="Run a pipeline through the CLI")
run_parser.add_argument("--task", choices=SUPPORTED_TASKS.keys(), help="Task to run")
run_parser.add_argument("--input", type=str, help="Path to the file to use for inference")
run_parser.add_argument("--output", type=str, help="Path to the file that will be used post to write results.")
run_parser.add_argument("--model", type=str, help="Name or path to the model to instantiate.")
run_parser.add_argument("--config", type=str, help="Name or path to the model's config to instantiate.")
run_parser.add_argument(
"--tokenizer", type=str, help="Name of the tokenizer to use. (default: same as the model name)"
)
run_parser.add_argument(
"--column",
type=str,
help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)",
)
run_parser.add_argument(
"--format",
type=str,
default="infer",
choices=PipelineDataFormat.SUPPORTED_FORMATS,
help="Input format to read from",
)
run_parser.add_argument(
"--device",
type=int,
default=-1,
help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)",
)
run_parser.add_argument("--overwrite", action="store_true", help="Allow overwriting the output file.")
run_parser.set_defaults(func=run_command_factory)
def register_subcommand(parser: ArgumentParser):
run_parser = parser.add_parser('run', help="Run a pipeline through the CLI")
run_parser.add_argument('--device', type=int, default=-1, help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)')
run_parser.add_argument('--task', choices=SUPPORTED_TASKS.keys(), help='Task to run')
run_parser.add_argument('--model', type=str, required=True, help='Name or path to the model to instantiate.')
run_parser.add_argument('--tokenizer', type=str, help='Name of the tokenizer to use. (default: same as the model name)')
run_parser.add_argument('--column', type=str, required=True, help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)')
run_parser.add_argument('--format', type=str, default='infer', choices=PipelineDataFormat.SUPPORTED_FORMATS, help='Input format to read from')
run_parser.add_argument('--input', type=str, required=True, help='Path to the file to use for inference')
run_parser.add_argument('--output', type=str, required=True, help='Path to the file that will be used post to write results.')
run_parser.add_argument('kwargs', nargs='*', help='Arguments to forward to the file format reader')
run_parser.set_defaults(func=run_command_factory)
def test_load_default_pipelines_tf(self):
import tensorflow as tf
from transformers.pipelines import SUPPORTED_TASKS
set_seed_fn = lambda: tf.random.set_seed(0) # noqa: E731
for task in SUPPORTED_TASKS.keys():
if task == "table-question-answering":
# test table in seperate test due to more dependencies
continue
self.check_default_pipeline(task, "tf", set_seed_fn,
self.check_models_equal_tf)
def test_load_default_pipelines_pt(self):
import torch
from transformers.pipelines import SUPPORTED_TASKS
set_seed_fn = lambda: torch.manual_seed(0) # noqa: E731
for task in SUPPORTED_TASKS.keys():
if task == "table-question-answering":
# test table in seperate test due to more dependencies
continue
self.check_default_pipeline(task, "pt", set_seed_fn,
self.check_models_equal_pt)
class PipelineCommonTests(unittest.TestCase):
pipelines = SUPPORTED_TASKS.keys()
@slow
@require_tf
def test_tf_defaults(self):
# Test that pipelines can be correctly loaded without any argument
for task in self.pipelines:
with self.subTest(msg="Testing TF defaults with TF and {}".format(task)):
pipeline(task, framework="tf")
@slow
@require_torch
def test_pt_defaults(self):
# Test that pipelines can be correctly loaded without any argument
for task in self.pipelines:
with self.subTest(msg="Testing Torch defaults with PyTorch and {}".format(task)):
pipeline(task, framework="pt")
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli
:param parser: Root parser to register command-specific arguments
:return:
"""
serve_parser = parser.add_parser(
'serve',
help=
'CLI tool to run inference requests through REST and GraphQL endpoints.'
)
serve_parser.add_argument('--task',
type=str,
choices=SUPPORTED_TASKS.keys(),
help='The task to run the pipeline on')
serve_parser.add_argument('--host',
type=str,
default='localhost',
help='Interface the server will listen on.')
serve_parser.add_argument('--port',
type=int,
default=8888,
help='Port the serving will listen to.')
serve_parser.add_argument(
'--model', type=str, help='Model\'s name or path to stored model.')
serve_parser.add_argument(
'--config',
type=str,
help='Model\'s config name or path to stored model.')
serve_parser.add_argument('--tokenizer',
type=str,
help='Tokenizer name to use.')
serve_parser.add_argument(
'--device',
type=int,
default=-1,
help=
'Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'
)
serve_parser.set_defaults(func=serve_command_factory)