In Dask, we can use the dask.array module to chunk large arrays into smaller pieces that can be processed in parallel. Here's an example:
import dask.array as daimport numpy as np# Create a large random arrayx = np.random.rand(100000000)# Convert the array to a Dask array and chunk it into 10,000 piecesd = da.from_array(x, chunks=(len(x)//10000,))# Compute the mean of the arrayresult = d.mean().compute()print(result) |
In this example, we first create a large NumPy array x with 100 million elements. We then use the from_array function in the dask.array module to convert the NumPy array to a Dask array d. We specify the chunks parameter to indicate that we want to split the array into 10,000 chunks, each with approximately 10,000 elements.
We can then use the mean method to compute the mean of the Dask array d. Finally, we use the compute method to actually execute the computation and obtain the result.
When we call d.mean(), Dask will create a task graph that represents the computation required to compute the mean of the array. Because the array is split into chunks, each chunk can be processed independently on a separate worker in parallel. Dask will automatically parallelize the computation and manage the communication between workers to efficiently compute the result.
By chunking arrays in this way, we can perform computations on large arrays that would not fit in memory, and we can also take advantage of distributed computing to perform computations in parallel across multiple CPUs or nodes in a cluster.