Calling Julia from Python - an experiment on data loading

Related tags

Deep Learningcall-julia-from-python-experiments
Overview

Calling Julia from Python - an experiment on data loading

DOI

See the slides.

TLDR

After reading Patrick's blog post, we decided to try to replace C++ with Julia to check:

  • How easy/hard it is
  • How much improvement can be gained with a basic version
  • How much improvement can be gained with an optimized version

A basic version is already an improvement over the pure Python version, and an optimized version was faster than the C++ version.

Reproduction

  • Follow Patrick's blog post to install the C++ part.
  • Install Julia (We've used Julia 1.6.3)
    • I recommend using Jill
    • We'll refer to this Julia as path/to/julia.
  • Install Python
    • Ideally, one dynamically linked to libpython.
    • To test it, use ldd path/to/python and look for libpython3.9. It should exist for the shared version.
    • If you don't have, look into workarounds here
    • Tip: Archlinux's system Python is dynamically linked.
    • We've used Python 3.9.7 from Archlinux.
  • Open Julia and enter the following commands:
    • ENV["PYTHON"] = "path/to/python"
    • using Pkg
    • Pkg.add("PyCall")
    • This will make sure that the packages we are installing use the correct Python version
  • Install juliapy with path/to/python -m pip install julia
  • Run path/to/python and enter
    • import julia
    • julia.install("julia=path/to/julia")
  • Download dataset and store in gen-data folder: Zenodo badge
  • Run scalability_test.py - it should take several hours (over 10) and consume a moderate amount of memory.
  • Run scalability_analysis.py.
You might also like...
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Master Docs License Apache MXNet (incubating) is a deep learning framework designed for both efficiency an

ROCm Software Platform 29 Nov 16, 2022
Numba-accelerated Pythonic implementation of MPDATA with examples in Python, Julia and Matlab
Numba-accelerated Pythonic implementation of MPDATA with examples in Python, Julia and Matlab

PyMPDATA PyMPDATA is a high-performance Numba-accelerated Pythonic implementation of the MPDATA algorithm of Smolarkiewicz et al. used in geophysical

Atmospheric Cloud Simulation Group @ Jagiellonian University 15 Nov 23, 2022
Python and Julia in harmony.
Python and Julia in harmony.

PythonCall & JuliaCall Bringing Python® and Julia together in seamless harmony: Call Python code from Julia and Julia code from Python via a symmetric

Christopher Rowley 414 Jan 7, 2023
Pythonic particle-based (super-droplet) warm-rain/aqueous-chemistry cloud microphysics package with box, parcel & 1D/2D prescribed-flow examples in Python, Julia and Matlab
Pythonic particle-based (super-droplet) warm-rain/aqueous-chemistry cloud microphysics package with box, parcel & 1D/2D prescribed-flow examples in Python, Julia and Matlab

PySDM PySDM is a package for simulating the dynamics of population of particles. It is intended to serve as a building block for simulation systems mo

Atmospheric Cloud Simulation Group @ Jagiellonian University 32 Oct 18, 2022
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

null 152 Jan 2, 2023
Small-bets - Ergodic Experiment With Python

Ergodic Experiment Based on this video. Run this experiment with this command: p

Michael Brant 3 Jan 11, 2022
Perspective: Julia for Biologists

Perspective: Julia for Biologists 1. Examples Speed: Example 1 - Single cell data and network inference Domain: Single cell data Methodology: Network

Elisabeth Roesch 55 Dec 2, 2022
MacroTools provides a library of tools for working with Julia code and expressions.

MacroTools.jl MacroTools provides a library of tools for working with Julia code and expressions. This includes a powerful template-matching system an

FluxML 278 Dec 11, 2022
✔️ Visual, reactive testing library for Julia. Time machine included.
✔️ Visual, reactive testing library for Julia. Time machine included.

PlutoTest.jl (alpha release) Visual, reactive testing library for Julia A macro @test that you can use to verify your code's correctness. But instead

Pluto 68 Dec 20, 2022
Comments
  • Fix python versions ~~using poetry~~

    Fix python versions ~~using poetry~~

    To prevent this pull request from becoming too large, I'll merge this and create a new issue to set the python versions.

    Originally posted by @abelsiqueira in https://github.com/abelsiqueira/call-julia-from-python-experiments/issues/1#issuecomment-987970132

    opened by abelsiqueira 1
  • Improve docker-10

    Improve docker-10

    Fixes: #10

    • Changes Ubuntu version to 21.10
    • Adds extra environment variables
    • Removes the Python virtual environment
    • Add make flags to compile the tools faster
    • Remove the downloaded tar files
    • Uninstall dev dependencies
    opened by fdiblen 0
Releases(v0.3.0)
Owner
Abel Siqueira
Abel Siqueira
GitHub Repository
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching

RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching This repository contains the source code for our paper: RAFT-Stereo: Multilevel

Princeton Vision & Learning Lab 328 Jan 09, 2023
Largest list of models for Core ML (for iOS 11+)

Since iOS 11, Apple released Core ML framework to help developers integrate machine learning models into applications. The official documentation We'v

Kedan Li 5.6k Jan 08, 2023
Cross-modal Retrieval using Transformer Encoder Reasoning Networks (TERN). With use of Metric Learning and FAISS for fast similarity search on GPU

Cross-modal Retrieval using Transformer Encoder Reasoning Networks This project reimplements the idea from "Transformer Reasoning Network for Image-Te

Minh-Khoi Pham 5 Nov 05, 2022
deep-prae

Deep Probabilistic Accelerated Evaluation (Deep-PrAE) Our work presents an efficient rare event simulation methodology for black box autonomy using Im

Safe AI Lab 4 Apr 17, 2021
Uncertain natural language inference

Uncertain Natural Language Inference This repository hosts the code for the following paper: Tongfei Chen*, Zhengping Jiang*, Adam Poliak, Keisuke Sak

Tongfei Chen 14 Sep 01, 2022
Benchmark for evaluating open-ended generation

OpenMEVA Contributed by Jian Guan, Zhexin Zhang. Thank Jiaxin Wen for DeBugging. OpenMEVA is a benchmark for evaluating open-ended story generation me

25 Nov 15, 2022
The code for MM2021 paper "Multi-Level Counterfactual Contrast for Visual Commonsense Reasoning"

The Code for MM2021 paper "Multi-Level Counterfactual Contrast for Visual Commonsense Reasoning" Setting up and using the repo Get the dataset. Follow

4 Apr 20, 2022
(SIGIR2020) “Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback’’

Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback About This repository accompanies the real-world experiments conducted i

yuta-saito 19 Dec 01, 2022
Json2Xml tool will help you convert from json COCO format to VOC xml format in Object Detection Problem.

JSON 2 XML All codes assume running from root directory. Please update the sys path at the beginning of the codes before running. Over View Json2Xml t

Nguyễn Trường Lâu 6 Aug 22, 2022
[CVPR'21 Oral] Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning

Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning [CVPR'21, Oral] By Zhicheng Huang*, Zhaoyang Zeng*, Yupan H

Multimedia Research 196 Dec 13, 2022
This is a simple plugin for Vim that allows you to use OpenAI Codex.

🤖 Vim Codex An AI plugin that does the work for you. This is a simple plugin for Vim that will allow you to use OpenAI Codex. To use this plugin you

Tom Dörr 195 Dec 28, 2022
Code for the ACL2021 paper "Lexicon Enhanced Chinese Sequence Labelling Using BERT Adapter"

Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter Code and checkpoints for the ACL2021 paper "Lexicon Enhanced Chinese Sequence Labelling

274 Dec 06, 2022
MRI reconstruction (e.g., QSM) using deep learning methods

deepMRI: Deep learning methods for MRI Authors: Yang Gao, Hongfu Sun This repo is devloped based on Pytorch (1.8 or later) and matlab (R2019a or later

Hongfu Sun 17 Dec 18, 2022
A Bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations

Overview Code and supplemental materials for Karduni et al., 2020 IEEE Vis. "A Bayesian cognition approach for belief updating of correlation judgemen

Ryan Wesslen 1 Feb 08, 2022
[CVPR 2022] Unsupervised Image-to-Image Translation with Generative Prior

GP-UNIT - Official PyTorch Implementation This repository provides the official PyTorch implementation for the following paper: Unsupervised Image-to-

Shuai Yang 125 Jan 03, 2023
Explore extreme compression for pre-trained language models

Code for paper "Exploring extreme parameter compression for pre-trained language models ICLR2022"

twinkle 16 Nov 14, 2022
Network Enhancement implementation in pytorch

network_enahncement_pytorch Network Enhancement implementation in pytorch Research paper Network Enhancement: a general method to denoise weighted bio

Yen 1 Nov 12, 2021
Lecture materials for Cornell CS5785 Applied Machine Learning (Fall 2021)

Applied Machine Learning (Cornell CS5785, Fall 2021) This repo contains executable course notes and slides for the Applied ML course at Cornell and Co

Volodymyr Kuleshov 103 Dec 31, 2022
Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.

Algo-ScriptML Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The goal of this project is not t

Algo Phantoms 81 Nov 26, 2022
Source code of NeurIPS 2021 Paper ''Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration''

CaGCN This repo is for source code of NeurIPS 2021 paper "Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration". Paper L

6 Dec 19, 2022