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Description

Dask is a freely available open-source library that is developed in collaboration with various community initiatives such as NumPy, pandas, and scikit-learn. It leverages the existing Python APIs and data structures, allowing users to seamlessly transition between NumPy, pandas, and scikit-learn and their Dask-enhanced versions. The schedulers in Dask are capable of scaling across extensive clusters with thousands of nodes, and its algorithms have been validated on some of the most powerful supercomputers globally. However, getting started doesn't require access to a large cluster; Dask includes schedulers tailored for personal computing environments. Many individuals currently utilize Dask to enhance computations on their laptops, taking advantage of multiple processing cores and utilizing disk space for additional storage. Furthermore, Dask provides lower-level APIs that enable the creation of customized systems for internal applications. This functionality is particularly beneficial for open-source innovators looking to parallelize their own software packages, as well as business executives aiming to scale their unique business strategies efficiently. In essence, Dask serves as a versatile tool that bridges the gap between simple local computations and complex distributed processing.

Description

Fast and adaptable, the concepts of vectorization, indexing, and broadcasting in NumPy have become the benchmark for array computation in the present day. This powerful library provides an extensive array of mathematical functions, random number generators, linear algebra capabilities, Fourier transforms, and beyond. NumPy is compatible with a diverse array of hardware and computing environments, seamlessly integrating with distributed systems, GPU libraries, and sparse array frameworks. At its core, NumPy is built upon highly optimized C code, which allows users to experience the speed associated with compiled languages while enjoying the flexibility inherent to Python. The high-level syntax of NumPy makes it user-friendly and efficient for programmers across various backgrounds and skill levels. By combining the computational efficiency of languages like C and Fortran with the accessibility of Python, NumPy simplifies complex tasks, resulting in clear and elegant solutions. Ultimately, this library empowers users to tackle a wide range of numerical problems with confidence and ease.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Coiled Yes 
Anaconda Yes 
Avanzai No 
Codédex No 
Cython No 
Dash No 
Domino Enterprise AI Platform Yes 
Gensim No 
Kedro Yes 
MPI for Python (mpi4py) No 
NVIDIA DIGITS Yes 
Prefect Yes 
Ray Yes 
Spell Yes 
Spyder No 
Train in Data No 
Yandex Data Proc No 
h5py No 
imageio No 
scikit-learn No 

Integrations

Coiled Yes 
Anaconda No 
Avanzai Yes 
Codédex Yes 
Cython Yes 
Dash Yes 
Domino Enterprise AI Platform No 
Gensim Yes 
Kedro No 
MPI for Python (mpi4py) Yes 
NVIDIA DIGITS No 
Prefect No 
Ray No 
Spell No 
Spyder Yes 
Train in Data Yes 
Yandex Data Proc Yes 
h5py Yes 
imageio Yes 
scikit-learn Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Dask

Founded

2019

Website

dask.org

Vendor Details

Company Name

NumPy

Website

numpy.org

Product Features

Data Science

Access Control No 
Advanced Modeling No 
Audit Logs No 
Data Discovery No 
Data Ingestion No 
Data Preparation No 
Data Visualization No 
Model Deployment No 
Reports No 

Product Features

Alternatives

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Ray

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