How to compute multiple outputs at once in Dask?
This recipe helps you compute multiple outputs at once in Dask
Recipe Objective
How to compute multiple outputs at once in Dask?
To include a lot of computation in a single call we have to create many dask.delayed calls and pass them through a for loop and then we make a dask.compute call to compute all the results.
Step 1- Importing Library
import dask
Step 2- Defining function.
We will define a incremental function.
def func(y):
y = y + 1
return y
Step 3- Computing results.
Now we will compute all the results by iterating all the numerical values through the for loop.
lists=[0, 1, 2, 3, 4, 5, 6]
results = []
for y in lists:
a = dask.delayed(func)(y)
results.append(a)
results = dask.compute(*results)
results
What Users are saying..
Ray han
Tech Leader | Stanford / Yale University
I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More
Relevant Projects
Machine Learning Projects
Data Science Projects
Python Projects for Data Science
Data Science Projects in R
Machine Learning Projects for Beginners
Deep Learning Projects
Neural Network Projects
Tensorflow Projects
NLP Projects
Kaggle Projects
IoT Projects
Big Data Projects
Hadoop Real-Time Projects Examples
Spark Projects
Data Analytics Projects for Students
You might also like
Data Science Tutorial
Data Scientist Salary
How to Become a Data Scientist
Data Analyst vs Data Scientist
Data Scientist Resume
Data Science Projects for Beginners
Machine Learning Engineer
Machine Learning Projects for Beginners
Datasets
Pandas Dataframe
Machine Learning Algorithms
Regression Analysis
MNIST Dataset
Data Science Interview Questions
Python Data Science Interview Questions
Spark Interview Questions
Hadoop Interview Questions
Data Analyst Interview Questions
Machine Learning Interview Questions
AWS vs Azure
Hadoop Architecture
Spark Architecture
Relevant Projects
Linear Regression Model Project in Python for Beginners Part 2
Machine Learning Linear Regression Project for Beginners in Python to Build a Multiple Linear Regression Model on Soccer Player Dataset.
Demand prediction of driver availability using multistep time series analysis
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.
Build a Credit Default Risk Prediction Model with LightGBM
In this Machine Learning Project, you will build a classification model for default prediction with LightGBM.
Build a Face Recognition System in Python using FaceNet
In this deep learning project, you will build your own face recognition system in Python using OpenCV and FaceNet by extracting features from an image of a person's face.
NLP Project for Beginners on Text Processing and Classification
This Project Explains the Basic Text Preprocessing and How to Build a Classification Model in Python
Build Time Series Models for Gaussian Processes in Python
Time Series Project - A hands-on approach to Gaussian Processes for Time Series Modelling in Python
Build a Customer Churn Prediction Model using Decision Trees
Develop a customer churn prediction model using decision tree machine learning algorithms and data science on streaming service data.
Hands-On Approach to Master PyTorch Tensors with Examples
In this deep learning project, you will learn how to perform various operations on the building block of PyTorch : Tensors.
Learn to Build a Siamese Neural Network for Image Similarity
In this Deep Learning Project, you will learn how to build a siamese neural network with Keras and Tensorflow for Image Similarity.
AWS MLOps Project to Deploy Multiple Linear Regression Model
Build and Deploy a Multiple Linear Regression Model in Python on AWS