celery

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I defined some tasks with a time limit of 1200: @celery.task(time_limit=1200) def create_ne_list(text): c = Client() return c.create_ne_list(text) I'm also using the worker_process_init signal to do some initialization, each time a new process starts: @worker_process_init.connect def init(sender=None, conf=None, **kwargs): init_system(celery.conf) init_pdf(celery.conf) This initialization function takes several seconds to execute. Besides that, I'm using the following configuration: CELERY_RESULT_SERIALIZER = 'json' CELERY_TASK_SERIALIZER =

I have some keras code which works synchronously to predict a given input, I have even made amendments so it can work with standard multi-threading (using locks in a seperate class from this) however when running via asynchronous celery (even with one worker and one task) I get an error on calling predict on the keras model. @app.task def predict_task(param): """Run task.""" json_file = open('keras_model.json', 'r') loaded_model_json = json_file.read() json_file.close() model = model_from_json(loaded_model_json) model.load_weights('keras_weights.h5') tokenizer_file = open('tokenizer.pickle',

I want to upload images to S3 server, but before uploading I want to generate thumbnails of 3 different sizes, and I want it to be done out of request/response cycle hence I am using celery. I have read the docs, here is what I have understood. Please correct me if I am wrong. Celery helps you manage your task queues outside the request response cycle. Then there is something called carrot/kombu - its a django middleware that packages tasks that get created via celery. Then the third layer PyAMQP that facilitates the communication of carrot to a broker. eg. RabbitMQ, AmazonSQS, ironMQ etc.

In my models.py: from django.db import models from core import tasks class Image(models.Model): image = models.ImageField(upload_to='images/orig') thumbnail = models.ImageField(upload_to='images/thumbnails', editable=False) def save(self, *args, **kwargs): super(Image, self).save(*args, **kwargs) tasks.create_thumbnail.delay(self.id) In my tasks.py: from celery.decorators import task from core.models import Image @task() def create_thumbnail(image_id): ImageObj = Image.objects.get(id=image_id) # other stuff here This is returning the following: Exception Type: ImportError Exception Value:

一、什么是celery Celery是一个简单、灵活且可靠的，处理大量消息的分布式系统 专注于实时处理的异步任务队列 同时也支持任务调度 二、Celery架构 Celery的架构由三部分组成，消息中间件（message broker）、任务执行单元（worker）和 任务执行结果存储（task result store）组成。 2.1 消息中间件 Celery本身不提供消息服务，但是可以方便的和第三方提供的消息中间件集成。包括，RabbitMQ, Redis等等 2.2 任务执行单元 Worker是Celery提供的任务执行的单元，worker并发的运行在分布式的系统节点中。 2.3 任务结果存储 Task result store用来存储Worker执行的任务的结果，Celery支持以不同方式存储任务的结果，包括AMQP，Redis等 2.4 版本支持情况 Celery version 4.0 runs on 　　　　Python ❨2.7, 3.4, 3.5❩ 　　　　PyPy ❨5.4, 5.5❩ This is the last version to support Python 2.7, and from the next version (Celery 5.x) Python 3.5 or newer is required. If you’re running an

Celery框架 1、celery其实就是一个简单灵活的分布式系统，它的主要作用是被用来处理异步任务。 2、celery框架自带socket，所以自身是一个独立运行的服务 3、启动celery服务，是来执行服务中的任务的，服务中带一个执行任务的对象，会执行准备就绪的任务，将执行任务的结果保存起来 4、celery框架由三部分组成：存放要执行的任务broker，执行任务的对象worker，存放任务结果的backend 5、安装的celery主体模块，默认只提供worker，要结合其他技术提供broker和backend（两个存储的单位） Celery架构 Celery的架构由三部分组成，消息中间件（message broker）、任务执行单元（worker）和 任务执行结果存储（backend - task result store）组成。 消息中间件 Celery本身不提供消息服务，但是可以方便的和第三方提供的消息中间件集成。包括，RabbitMQ, Redis等等 任务执行单元 Worker是Celery提供的任务执行的单元，worker并发的运行在分布式的系统节点中。 任务结果存储 Task result store用来存储Worker执行的任务的结果，Celery支持以不同方式存储任务的结果，包括AMQP, redis等 使用场景 异步任务