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
The official PyTorch implementation of the paper: *Xili Dai, Xiaojun Yuan, Haigang Gong, Yi Ma. "Fully Convolutional Line Parsing." *.

F-Clip — Fully Convolutional Line Parsing This repository contains the official PyTorch implementation of the paper: *Xili Dai, Xiaojun Yuan, Haigang

Xili Dai 115 Dec 28, 2022
Split your patch similarly to `git add -p` but supporting multiple buckets

split-patch.py This is git add -p on steroids for patches. Given a my.patch you can run ./split-patch.py my.patch You can choose in which bucket to p

102 Oct 06, 2022
Benchmarking Pipeline for Prediction of Protein-Protein Interactions

B4PPI Benchmarking Pipeline for the Prediction of Protein-Protein Interactions How this benchmarking pipeline has been built, and how to use it, is de

Loïc Lannelongue 4 Jun 27, 2022
library for nonlinear optimization, wrapping many algorithms for global and local, constrained or unconstrained, optimization

NLopt is a library for nonlinear local and global optimization, for functions with and without gradient information. It is designed as a simple, unifi

Steven G. Johnson 1.4k Dec 25, 2022
Multi-layer convolutional LSTM with Pytorch

Convolution_LSTM_pytorch Thanks for your attention. I haven't got time to maintain this repo for a long time. I recommend this repo which provides an

Zijie Zhuang 733 Dec 30, 2022
Implementation of MA-Trace - a general-purpose multi-agent RL algorithm for cooperative environments.

Off-Policy Correction For Multi-Agent Reinforcement Learning This repository is the official implementation of Off-Policy Correction For Multi-Agent R

4 Aug 18, 2022
Opinionated code formatter, just like Python's black code formatter but for Beancount

beancount-black Opinionated code formatter, just like Python's black code formatter but for Beancount Try it out online here Features MIT licensed - b

Launch Platform 16 Oct 11, 2022
Metrics to evaluate quality and efficacy of synthetic datasets.

An Open Source Project from the Data to AI Lab, at MIT Metrics for Synthetic Data Generation Projects Website: https://sdv.dev Documentation: https://

The Synthetic Data Vault Project 129 Jan 03, 2023
TensorFlow implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"

TokenLearner: What Can 8 Learned Tokens Do for Images and Videos? Source: Improving Vision Transformer Efficiency and Accuracy by Learning to Tokenize

Aritra Roy Gosthipaty 23 Dec 24, 2022
Generative Adversarial Networks(GANs)

Generative Adversarial Networks(GANs) Vanilla GAN ClusterGAN Vanilla GAN Model Structure Final Generator Structure A MLP with 2 hidden layers of hidde

Zhenbang Feng 2 Nov 05, 2021
Joint detection and tracking model named DEFT, or ``Detection Embeddings for Tracking.

DEFT: Detection Embeddings for Tracking DEFT: Detection Embeddings for Tracking, Mohamed Chaabane, Peter Zhang, J. Ross Beveridge, Stephen O'Hara

Mohamed Chaabane 253 Dec 18, 2022
Credo AI Lens is a comprehensive assessment framework for AI systems. Lens standardizes model and data assessment, and acts as a central gateway to assessments created in the open source community.

Lens by Credo AI - Responsible AI Assessment Framework Lens is a comprehensive assessment framework for AI systems. Lens standardizes model and data a

Credo AI 27 Dec 14, 2022
Implementation of temporal pooling methods studied in [ICIP'20] A Comparative Evaluation Of Temporal Pooling Methods For Blind Video Quality Assessment

Implementation of temporal pooling methods studied in [ICIP'20] A Comparative Evaluation Of Temporal Pooling Methods For Blind Video Quality Assessment

Zhengzhong Tu 5 Sep 16, 2022
Pytorch implementation of "Get To The Point: Summarization with Pointer-Generator Networks"

About this repository This repo contains an Pytorch implementation for the ACL 2017 paper Get To The Point: Summarization with Pointer-Generator Netwo

wxDai 7 Oct 14, 2022
SAAVN - Sound Adversarial Audio-Visual Navigation,ICLR2022 (In PyTorch)

SAAVN SAAVN Code release for paper "Sound Adversarial Audio-Visual Navigation,IC

YinfengYu 10 Aug 30, 2022
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Intro Real-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In

Trieu 6.1k Jan 04, 2023
A PoC Corporation Relationship Knowledge Graph System on top of Nebula Graph.

Corp-Rel is a PoC of Corpartion Relationship Knowledge Graph System. It's built on top of the Open Source Graph Database: Nebula Graph with a dataset

Wey Gu 20 Dec 11, 2022
Code for Two-stage Identifier: "Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition"

Code for Two-stage Identifier: "Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition", accepted at ACL 2021. For details of the model and experiments, please see our paper.

tricktreat 87 Dec 16, 2022
CLIP (Contrastive Language–Image Pre-training) for Italian

Italian CLIP CLIP (Radford et al., 2021) is a multimodal model that can learn to represent images and text jointly in the same space. In this project,

Italian CLIP 114 Dec 29, 2022
Source code for the NeurIPS 2021 paper "On the Second-order Convergence Properties of Random Search Methods"

Second-order Convergence Properties of Random Search Methods This repository the paper "On the Second-order Convergence Properties of Random Search Me

Adamos Solomou 0 Nov 13, 2021