English | 2022 | ISBN: 9781098117139 | 105 pages | PDF,EPUB | 7.85 MB
The exponentially-increasing volume and complexity of data make scalability and reliability increasingly challenging issues. But while modern systems contain multi-core CPUs and GPUs that have the potential for parallel computing, many Python tools weren't designed to leverage this parallelism. Using Dask to parallelize Python workflows delivers a competitive advantage by reducing turn-around time, freeing you to work on more interesting or complex data problems.
With this essential guide at your side, you'll be able to
Deploy Dask on the cloud or on-prem
Scale your Python code to bigger datasets and CPU-intensive workflows
Speed up data pipelines that often take weeks or months to execute
Overcome the limits of serial computing on your local machine (or system of machines)
Use the examples provided to scale your workflows, whether you're working with NumPy, pandas, scikit-learn, PyTorch, XGBoost, or other tools
Develop a specialized data science library that leverages parallel and distributed computing
Scale computations to a cluster of machines and to the cloud securely and efficientlyand much more
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