Jason Brownlee's Blog, page 22

Configure the Number of BLAS/LAPACK Threads for NumPy

You can configure the number of threads used by BLAS functions called by NumPy by setting the OMP_NUM_THREADS environment variable. In this tutorial, you will discover how to configure the number of threads used by BLAS and LAPACK via NumPy. Let’s get started. Need to Configure the Number of Threads Used By BLAS NumPy is […]

Check BLAS Library Installed For NumPy

You can check which BLAS library is used by NumPy by calling numpy.show_config(). In this tutorial, you will discover how to check which BLAS library numpy is used on your system. Let’s get started. Need to Know Which BLAS Library NumPy is Using NumPy is an array library in Python. It makes use of third-party […]

How to Install BLAS Libraries for NumPy

You can install a BLAS library when installing NumPy. In this tutorial, you will discover how to install NumPy with a BLAS library in Python Let’s get started. What Is BLAS? NumPy is an array library in Python. It makes use of third-party libraries to perform array functions efficiently. One example is the BLAS and […]

NumPy BLAS/LAPACK Libraries

You can use one of many open-source and proprietary BLAS/LAPACK libraries to accelerate vector and matrix operations in NumPy. In this tutorial, you will discover common BLAS and LAPACK libraries that can be used with NumPy. Let’s get started. What is BLAS/LAPACK NumPy is an array library in Python. It makes use of third-party libraries […]

What is BLAS and LAPACK in NumPy

NumPy makes use of BLAS and LAPACK libraries to execute linear algebra functions with vectors and matrices efficiently, allowing NumPy to make the best use of available system hardware. Modern BLAS library implementations like OpenBLAS allow NumPy to perform vector and matrix operations like SVD, matrix multiplication, and least squares efficiently and seamlessly using multiple […]

Asyncio gather() vs wait() in Python

You can use the gather() function to wait for multiple coroutines to complete and then process their results, whereas you can use the wait() function to wait on a collection of tasks, such as all or the first to complete or the first to fail. In this tutorial, you will know the similarities and differences […]

What is Multiprocessing Shared Memory

You can share memory directly between processes in process-based concurrency using classes in the multiprocessing.shared_memory module. In this tutorial, you will discover how to use shared memory between processes in Python. Let’s get started. Sharing Memory Between Processes Python processes do not have shared memory. This is unlike threads that are able to read and […]

How to Use the SharedMemoryManager in Python

You can use the SharedMemoryManager to create shared memory and automatically release and destroy all shared memory created by the manager. This can help to avoid memory leaks when using shared memory with process-based concurrency in Python. In this tutorial, you will discover how to use the SharedMemoryManager to manage shared memory for processes in […]

How to Use SharedMemory in Python

You can create and share a memory block between processes via the SharedMemory class. In this tutorial, you will discover how to use shared memory between processes in Python. Let’s get started. What is SharedMemory The multiprocessing.shared_memory.SharedMemory class allows a block of memory to be used by multiple Python processes. A SharedMemory object can be […]

How to use a ShareableList in Python

You can efficiently share the same list among multiple processes via the ShareableList class. In this tutorial, you will discover how to use a ShareableList with processes in Python. Let’s get started. What is a ShareableList A multiprocessing.shared_memory.ShareableList is a list that can be shared efficiently between multiple processes. Shared between processes means that changes […]