A flexible open-source Python library for parallel computing, enabling scaling of familiar Python tools like NumPy, pandas, and scikit-learn to larger-than-memory datasets or distributed environments.
डस्क
- पायथन
- एआई और मशीन लर्निंग, डेटा एनालिटिक्स, गणित, सिमुलेशन
- कृत्रिम बुद्धिमत्ता (एआई), क्लाउड कंप्यूटिंग, मशीन लर्निंग, सॉफ्टवेयर, सेवा के रूप में सॉफ्टवेयर (सास), उपयोगकर्ता अनुभव (UX), यूज़र इंटरफ़ेस (UI)
विशेषताएँ:
- Parallel collections (arrays, dataframes, bags), dynamic task scheduling, scales NumPy, pandas, and scikit-learn, out-of-core computation, distributed computing across clusters, dashboard for diagnostics and monitoring computations.
मूल्य निर्धारण:
- मुफ़्त
- Scales familiar Python libraries for big data processing, easy to integrate into existing workflows, flexible for various types of parallelism (multi-core, distributed), provides useful diagnostics.
- Adds a layer of complexity compared to direct use of pandas/NumPy for smaller datasets, performance tuning for complex distributed computations can be challenging, memory management can require attention.
इसके लिए सबसे अच्छा:
- Data scientists and engineers working with datasets that are too large to fit in memory or who need to speed up computationally intensive tasks by leveraging multi-core processors or distributed clusters.