Related video: Using the multiprocessing module to speed up Python Daskįrom the outside, Dask looks a lot like Ray. For instance, Ray Tune lets you perform hyperparameter turning at scale for most common machine learning systems ( PyTorch and TensorFlow, among others). Other Ray libraries let you scale common machine learning and data science workloads, so you don't have to manually scaffold them. Ray even includes its own built-in cluster manager, which can automatically spin up nodes as needed on local hardware or popular cloud computing platforms. This last feature comes in handy when dealing with NumPy arrays, for instance. The results of each distributed function are returned as Python objects, so they’re easy to manage and store, and the amount of copying across or within nodes is minimal. The decorator distributes that function across any available nodes in a Ray cluster, with optionally specified parameters for how many CPUs or GPUs to use. Ray’s syntax is minimal, so you don’t need to rework existing applications extensively to parallelize them. You can break up and distribute any type of Python task across multiple systems with Ray. But Ray isn’t limited to machine learning tasks alone, even if that was its original use case. Rayĭeveloped by a team of researchers at the University of California, Berkeley, Ray underpins a number of distributed machine learning libraries. That’s where the Python libraries and frameworks introduced in this article come in. Here are seven frameworks you can use to spread an existing Python application and its workload across multiple cores, multiple machines, or both. In some cases, the job calls for distributing work not only across multiple cores, but also across multiple machines.
0 kommentar(er)
