For instance, you can fill it row by row using something like X_np = load_row(i). Note: you do not need to copy all the data to the newly created RawArray at once. X_np = np.frombuffer(X, dtype=np.float64).reshape(X_shape) # Wrap X as an numpy array so we can easily manipulates its data. The following code shows how a RawArray is wrapped as an numpy array and how pyto can be used to fill the RawArray with existing data. To make it easier to manipulate its data, we can wrap it as an numpy array by using the frombuffer function. This RawArray is an 1D array, or a chunk of memory that will be used to hold the data matrix. The following code will create a RawArray of doubles: # Create an 100-element shared array of double precision without a lock. Because we only need read only access and we want to share a matrix, we will use RawArray. Basically, RawValue and RawArray do not come with a lock, while Value and Array do. The multiprocessing package provides the following sharable objects: RawValue, RawArray, Value, Array. To mitigate this problem, we can share the data matrix among the child processes. If we send the data matrix to each child process normally, it will be pickled each time we schedule a new task, and lots of memory space and bandwidth will be wasted. We want each child process to perform independent tasks based on this data matrix, and they only need read only access to the data matrix. Now suppose that we have a big data matrix. However, unlike multithreading, when pass arguments to the the child processes, these data in the arguments must be pickled. The multiprocessing.Pool provides easy ways to parallel CPU bound tasks in Python. Therefore this tutorial may not work on earlier versions of Python. Hope it helps :) It should be noted that I am using Python 3.6. I will write about this small trick in this short article. While not explicitly documented, this is indeed possible. Recently, I was asked about sharing large numpy arrays when using Python's multiprocessing.Pool.
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