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http://www.djangocrew.com/blog/how-manage-concurrency-django-models/ In this Post we are going to present two approaches for managing #concurrency in #Django models.

We’re excited that you’ve chosen to develop on the #LinkedIn#platform. Our Developer Program enables you to create innovative professional applications that make the best use of LinkedIn. When you develop on the LinkedIn platform you are agreeing to be bound by the following terms, so please take a few minutes to read over the LinkedIn #API Terms of Use below. Last revised on February 12, 2015. https://developer.linkedin.com/legal/api-terms-of-use

https://docs.python.org/3/library/asyncio-dev.html#asyncio-multithreading 18.5.9.3. #Concurrency and #multithreading An event loop runs in a thread and executes all callbacks and tasks in the same thread. While a task is running in the event loop, no other task is running in the same thread. But when the task uses yield from, the task is suspended and the event loop executes the next task. To schedule a callback from a different thread, the BaseEventLoop.call_soon_threadsafe() method should be used. Example: loop.call_soon_threadsafe(callback, *args) Most asyncio objects are not thread safe. You should only worry if you access objects outside the event loop. For example, to cancel a future, don’t call directly its Future.cancel() method, but: loop.call_soon_threadsafe(fut.cancel) To handle signals and to execute subprocesses, the event loop must be run in the main thread. To schedule a coroutine object from a different thread, the run_coroutine_threadsafe() function should be used. It returns a concurrent.futures.Future to access the result: future = asyncio.run_coroutine_threadsafe(coro_func(), loop) result = future.result(timeout) # Wait for the result with a timeout The BaseEventLoop.run_in_executor() method can be used with a thread pool executor to execute a callback in different thread to not block the thread of the event loop. See also The Synchronization primitives section describes ways to synchronize tasks. The Subprocess and threads section lists asyncio limitations to run subprocesses from different threads.

https://www.infoworld.com/article/3209651/python/how-to-convert-python-to-javascript-and-back-again.html How to convert #Python to #JavaScript (and back again) Love Python? JavaScript, not so much? Here are four tools that turn Python to JavaScript for use in web applications

http://stackoverflow.com/questions/29269370/how-to-properly-create-and-run-concurrent-tasks-using-pythons-asyncio-module In the case of trying to concurrently run two looping Tasks, I've noticed that unless the Task has an internal await expression, it will get stuck in the while loop, effectively blocking other tasks from running (much like a normal while loop). However, as soon the Tasks have to wait--even for just a fraction of a second--they seem to run concurrently without an issue. Thus, the await statements seem to provide the event loop with a foothold for switching back and forth between the tasks, giving the effect of #concurrency. Example output with internal await: running async test ...#boo 0 ...#baa 0 ...boo 1 ...baa 1 ...boo 2 ...baa 2

https://docs.python.org/2/library/multiprocessing.html #multiprocessing is a package that supports spawning processes using an #API similar to the #threading module. The multiprocessing package offers both local and remote #concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of #threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows.

gping, ping, but with a graph. #tools#cli @thedevs https://kutt.it/QpMxM4

croc, easily and securely send things from one computer to another. #tools#cli @thedevs https://kutt.it/VBv2Ka

top prev next #ZeroMQ (also known as ØMQ, 0MQ, or #zmq) looks like an embeddable networking #library but acts like a #concurrency framework. It gives you sockets that carry atomic messages across various transports like in-process, inter-process, TCP, and multicast. You can connect sockets N-to-N with patterns like fan-out, pub-sub, task distribution, and request-reply. It's fast enough to be the fabric for clustered products. Its asynchronous I/O model gives you scalable multicore applications, built as asynchronous message-processing tasks. It has a score of language APIs and runs on most operating systems. ZeroMQ is from iMatix and is LGPLv3 open source. http://zguide.zeromq.org/page:all

https://github.com/safarijv/kubelib If you're adopting Kubernetes as an orchestration system for #machine_learning jobs, the last thing you want is for the mere act of using Kubernetes to create more problems than it solves. Kubelib provides a set of Pythonic interfaces to #Kubernetes, originally to aid with Jenkins scripting. But it can be used without Jenkins as well, and it can do everything exposed through the kubectl #CLI or the Kubernetes #API.