api

towhee.pipe

alias of Pipeline

class towhee.AutoConfig

Bases: object

Auto configuration.

__init__()

static register(config)

static load_config(name: str, *args, **kwargs)

static LocalCPUConfig()

Auto configuration to run with local CPU.

Examples

>>> from towhee import pipe, AutoConfig
>>> p = (pipe.input('a')
...          .flat_map('a', 'b', lambda x: [y for y in x], config=AutoConfig.LocalCPUConfig())
...          .output('b'))

static LocalGPUConfig(device: int = 0)

Auto configuration to run with local GPU.

Parameters:

device (int) – the number of GPU device, defaults to 0.

Examples

>>> from towhee import pipe, AutoConfig
>>> p = (pipe.input('url')
...          .map('url', 'image', ops.image_decode.cv2())
...          .map('image', 'vec', ops.image_embedding.timm(model_name='resnet50'), config=AutoConfig.LocalGPUConfig())
...          .output('vec')
... )

static TritonCPUConfig(num_instances_per_device: int = 1, max_batch_size: Optional[int] = None, batch_latency_micros: Optional[int] = None, preferred_batch_size: Optional[list] = None)

Auto configuration to run with triton server(CPU).

Parameters:

• max_batch_size (int) – maximum batch size, defaults to None, and it will be auto-generated by triton.

• batch_latency_micros (int) – time to the request, in microseconds, defaults to None, and it will auto-generated by triton.

• num_instances_per_device (int) – the number of instances per device, defaults to 1.

• preferred_batch_size (list) – preferred batch sizes for dynamic batching, defaults to None, and it will be auto-generated by triton.

Examples

>>> from towhee import pipe, AutoConfig
>>> p = (pipe.input('url')
...          .map('url', 'image', ops.image_decode.cv2())
...          .map('image', 'vec', ops.image_embedding.timm(model_name='resnet50'), config=AutoConfig.TritonCPUConfig())
...          .output('vec')
... )

You can also to set the configuration: >>> from towhee import pipe, AutoConfig >>> config = AutoConfig.TritonCPUConfig(num_instances_per_device=3, … max_batch_size=128, … batch_latency_micros=100000, … preferred_batch_size=[8, 16]) >>> p = (pipe.input(‘url’) … .map(‘url’, ‘image’, ops.image_decode.cv2()) … .map(‘image’, ‘vec’, ops.image_embedding.timm(model_name=’resnet50’), config=config) … .output(‘vec’) … )

static TritonGPUConfig(device_ids: Optional[list] = None, num_instances_per_device: int = 1, max_batch_size: Optional[int] = None, batch_latency_micros: Optional[int] = None, preferred_batch_size: Optional[list] = None)

Auto configuration to run with triton server(GPUs).

Parameters:

• device_ids (list) – list of GPUs, defaults to [0].

• max_batch_size (int) – maximum batch size, defaults to None, and it will be auto-generated by triton.

• batch_latency_micros (int) – time to the request, in microseconds, defaults to None, and it will auto-generated by triton.

• num_instances_per_device (int) – the number of instances per device, defaults to 1.

• preferred_batch_size (list) – preferred batch sizes for dynamic batching, defaults to None, and it will be auto-generated by triton.

Examples

>>> from towhee import pipe, AutoConfig
>>> p = (pipe.input('url')
...          .map('url', 'image', ops.image_decode.cv2())
...          .map('image', 'vec', ops.image_embedding.timm(model_name='resnet50'), config=AutoConfig.TritonGPUConfig())
...          .output('vec')
... )

You can also to set the configuration: >>> from towhee import pipe, AutoConfig >>> config = AutoConfig.TritonGPUConfig(device_ids=[0, 1], … num_instances_per_device=3, … max_batch_size=128, … batch_latency_micros=100000, … preferred_batch_size=[8, 16]) >>> p = (pipe.input(‘url’) … .map(‘url’, ‘image’, ops.image_decode.cv2()) … .map(‘image’, ‘vec’, ops.image_embedding.timm(model_name=’resnet50’), config=config) … .output(‘vec’) … )

__dict__ = mappingproxy({'__module__': 'towhee.runtime.auto_config', '__doc__': '\n    Auto configuration.\n    ', '_REGISTERED_CONFIG': {'LocalCPUConfig': <function AutoConfig.LocalCPUConfig>, 'LocalGPUConfig': <function AutoConfig.LocalGPUConfig>, 'TritonCPUConfig': <function AutoConfig.TritonCPUConfig>, 'TritonGPUConfig': <function AutoConfig.TritonGPUConfig>}, '_lock': <unlocked _thread.lock object>, '__init__': <function AutoConfig.__init__>, 'register': <staticmethod object>, 'load_config': <staticmethod object>, 'LocalCPUConfig': <staticmethod object>, 'LocalGPUConfig': <staticmethod object>, 'TritonCPUConfig': <staticmethod object>, 'TritonGPUConfig': <staticmethod object>, '__dict__': <attribute '__dict__' of 'AutoConfig' objects>, '__weakref__': <attribute '__weakref__' of 'AutoConfig' objects>, '__annotations__': {}})

__module__ = 'towhee.runtime.auto_config'

__weakref__

list of weak references to the object (if defined)

class towhee.AutoPipes

Bases: object

Load Predefined pipeines.

__init__()

static register(pipe_def)

static pipeline(name, *args, **kwargs) → Optional[RuntimePipeline]

__dict__ = mappingproxy({'__module__': 'towhee.runtime.auto_pipes', '__doc__': '\n    Load Predefined pipeines.\n    ', '_PIPES_DEF': {}, '_lock': <unlocked _thread.lock object>, '__init__': <function AutoPipes.__init__>, 'register': <staticmethod object>, 'pipeline': <staticmethod object>, '__dict__': <attribute '__dict__' of 'AutoPipes' objects>, '__weakref__': <attribute '__weakref__' of 'AutoPipes' objects>, '__annotations__': {}})

__module__ = 'towhee.runtime.auto_pipes'

__weakref__

list of weak references to the object (if defined)

towhee.accelerate(model)