与 Ray 并行服务 Tensorflow 模型

Question

我正在查看有关使用 ray.serve 并行预测保存的 TF 模型的 Whosebug 线程：

我尝试了类似的方法：

import ray
from ray import serve; serve.init()
import tensorflow as tf

class A:
    def __init__(self):
        self.model = tf.constant(1.0) # dummy example

   @serve.accept_batch
    def __call__(self, *, input_data=None):
        print(input_data) # test if method is entered
        # do stuff, serve model

if __name__ == '__main__':
    serve.create_backend("tf", A,
        # configure resources
        ray_actor_options={"num_cpus": 2},
        # configure replicas
        config={
            "num_replicas": 2, 
            "max_batch_size": 24,
            "batch_wait_timeout": 0.1
        }
    )
    serve.create_endpoint("tf", backend="tf")
    handle = serve.get_handle("tf")

    args = [1,2,3]

    futures = [handle.remote(input_data=i) for i in args]
    result = ray.get(futures)

但是，我收到以下错误： TypeError: __call__() takes 1 positional argument but 2 positional arguments (and 1 keyword-only argument) were given。传递给 __call__.

的参数有问题

这似乎是一个简单的错误，我应该如何更改 args 数组以便真正进入 __call__ 方法？

Answer 1

Ray 1.0 的 API 已更新。请参阅迁移指南 https://gist.github.com/simon-mo/6d23dfed729457313137aef6cfbc7b54

对于您发布的具体代码示例，您可以将其更新为：

import ray
from ray import serve
import tensorflow as tf

class A:
    def __init__(self):
        self.model = tf.Constant(1.0) # dummy example

   @serve.accept_batch
    def __call__(self, requests):
        for req in requests:
            print(req.data) # test if method is entered
        
        # do stuff, serve model

if __name__ == '__main__':
    client = serve.start()
    client.create_backend("tf", A,
        # configure resources
        ray_actor_options={"num_cpus": 2},
        # configure replicas
        config={
            "num_replicas": 2, 
            "max_batch_size": 24,
            "batch_wait_timeout": 0.1
        }
    )
    client.create_endpoint("tf", backend="tf")
    handle = client.get_handle("tf")

    args = [1,2,3]

    futures = [handle.remote(i) for i in args]
    result = ray.get(futures)

与 Ray 并行服务 Tensorflow 模型

Serve Tensorflow models in parallel with Ray

python

ray

tensorflow