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https://github.com/clearml/clearml-serving
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Add support for serving engine container control
Fix storage packages install by default (issue #5)
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@ -178,18 +178,24 @@ class ServingService(object):
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self._task.reset()
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self._task.enqueue(task=self._task, queue_name=queue_name, queue_id=queue_id)
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def launch_engine(self, queue_name, queue_id=None, verbose=True):
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# type: (Optional[str], Optional[str], bool) -> None
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def launch_engine(self, queue_name, queue_id=None, container=None, container_args=None, verbose=True):
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# type: (Optional[str], Optional[str], Optional[str], Optional[str], bool) -> None
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"""
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Launch serving engine on a specific queue
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:param queue_name: Queue name to launch the engine service running the inference on.
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:param queue_id: specify queue id (unique stand stable) instead of queue_name
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:param container: Optional: specify serving engine container.
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:param container_args: Optional: specify serving engine container arguments.
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Notice these arguments will override any default container arguments!
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:param verbose: If True print progress to console
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"""
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# todo: add more engines
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if self._engine_type == 'triton':
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engine_type_container = "nvcr.io/nvidia/tritonserver:21.03-py3"
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engine_type_args = "--ipc=host -p 8000:8000 -p 8001:8001 -p 8002:8002"
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# create the serving engine Task
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engine_task = Task.create(
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project_name=self._task.get_project_name(),
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@ -200,9 +206,16 @@ class ServingService(object):
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commit="ad049c51c146e9b7852f87e2f040e97d88848a1f",
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script="clearml_serving/triton_helper.py",
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working_directory=".",
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docker="nvcr.io/nvidia/tritonserver:21.03-py3 --ipc=host -p 8000:8000 -p 8001:8001 -p 8002:8002",
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docker=container or engine_type_container,
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docker_args=container_args or engine_type_args,
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argparse_args=[('serving_id', self._task.id), ],
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add_task_init_call=False,
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packages=[
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'clearml',
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'azure-storage-blob>=2.0.1,<=2.1',
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'google-cloud-storage>=1.13.2',
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'boto3>=1.9',
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],
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)
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if verbose:
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print('Launching engine {} on queue {}'.format(self._engine_type, queue_id or queue_name))
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