Seonglae ChoLibrary for parallel computing in Python with interface mimicking the Pandas DataFrame, Numpy Array and PySpark
PyTorch and Dask can be combined for effective handling of large-scale data processing and model training. Dask is a flexible parallel computing library for analytics that scales from a single CPU to thousands of nodes. Dask allows PyTorch to handle much larger datasets that can be loaded and processed in parallel, accelerating data preparation.
- Scalability: Handle datasets larger than your available memory.
- Parallel Computing: Leverage multiple cores for faster computation.
- Familiar Syntax: Use a syntax similar to Pandas, minimizing the learning curve.
- Memory Efficiency: Dask operates on out-of-core arrays, DataFrames, and lists.
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Architecture
Understanding Dask Architecture: Client, Scheduler, Workers
In A short introduction to Dask for Pandas developers, we looked at how the fundamental components of Dask work. We examined the Dask dataframe and some other data structures that Dask uses internally. Now we'll zoom out and see how the higher-level components of Dask work, and how its client, scheduler, and workers share data and instructions.
https://www.datarevenue.com/en-blog/understanding-dask-architecture-client-scheduler-workers
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Homepage
Dask: Scalable analytics in Python
Dask uses existing Python APIs and data structures to make it easy to switch between NumPy, pandas, scikit-learn to their Dask-powered equivalents. You don't have to completely rewrite your code or retrain to scale up.
https://dask.org/
