![]() Shut down the connected scheduler and workersīasic information about the workers in the clusterĬlient.submit(func, *args) Run a function on all workers outside of task scheduling systemĬn_on_scheduler(function, *args, .)Ĭlient.scatter(data) Set replication of futures within networkĬlient.restart()Ĭn(function, *args) The number of threads/cores available on each worker nodeĬlient.profile()Ĭollect statistical profiling information about recent workĬlient.replicate(futures) Map a function on a sequence of arguments List named datasets available on the schedulerĬlient.map(func, *iterables) Return a concurrent.futures Executor for submitting tasks on this Client Get named dataset from the scheduler if present. If desired, this exampleĬould be adapted to machine learning with a more complex function to minimize.Ĭonnect to and submit computation to a Dask clusterĬlient.cancel(futures)Ĭlient.gather(futures) ![]() The (simple) equationĪbove is minimize, so each \(p_i\) converges to 1. This example works, and the loss function is minimized. put ( 'parameters', new_params ) print ( new_params. result () new_params = train ( params ) ps. random ( 1000 )) for k in range ( 20 ): params = ps. submit ( ParameterServer, actor = True ) ps = ps_future. data def train ( params, lr = 0.1 ): grad = 2 * ( params - 1 ) # gradient of (params - 1)**2 new_params = params - lr * grad return new_params ps_future = client. data = value def get ( self, key ): return self. data = dict () def put ( self, key, value ): self. Import numpy as np from dask.distributed import Client client = Client ( processes = False ) class ParameterServer : def _init_ ( self ): self. We recommend thatīeginning users stick with using the simpler futures found above (likeĬlient.submit and Client.gather) rather than embracing needlessly These features are rarely necessary for common use of Dask. ![]() Side-channels between many workers, clients, and tasks sensibly. Resources, track progress of ongoing computations, or share data in These can be used to control access to external Queues, global variables, and pub-sub systems that, where appropriate, match In theseĬases Dask provides additional primitives to help in complex situations.ĭask provides distributed versions of coordination primitives like locks, events, With each other in ways beyond normal task scheduling with futures. Sometimes situations arise where tasks, workers, or clients need to coordinate Publish data with Publish-Subscribe pattern This semaphore will track leases on the scheduler which can be acquired and released by an instance of this class. Rm -rf /usr/local/cpanel/whostmgr/cgi/addon_spamscan_monitor.cgi > /dev/null 2>
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