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
This is code of book "Learn Deep Learning with PyTorch"

深度学习入门之PyTorch Learn Deep Learning with PyTorch 非常感谢您能够购买此书，这个github repository包含有深度学习入门之PyTorch的实例代码。由于本人水平有限，在写此书的时候参考了一些网上的资料，在这里对他们表示敬意。由于深度学习的技术在

Xingyu Liao 2.5k Jan 04, 2023
PyTorch implementation of the TTC algorithm

Trust-the-Critics This repository is a PyTorch implementation of the TTC algorithm and the WGAN misalignment experiments presented in Trust the Critic

0 Nov 29, 2021
True Few-Shot Learning with Language Models

This codebase supports using language models (LMs) for true few-shot learning: learning to perform a task using a limited number of examples from a single task distribution.

Ethan Perez 124 Jan 04, 2023
automated systems to assist guarding corona Virus precautions for Closed Rooms (e.g. Halls, offices, etc..)

Automatic-precautionary-guard automated systems to assist guarding corona Virus precautions for Closed Rooms (e.g. Halls, offices, etc..) what is this

badra 0 Jan 06, 2022
CS50's Introduction to Artificial Intelligence Test Scripts

CS50's Introduction to Artificial Intelligence Test Scripts 🤷‍♂️ What's this? 🤷‍♀️ This repository contains Python scripts to automate tests for mos

Jet Kan 2 Dec 28, 2022
[NeurIPS'20] Self-supervised Co-Training for Video Representation Learning. Tengda Han, Weidi Xie, Andrew Zisserman.

CoCLR: Self-supervised Co-Training for Video Representation Learning This repository contains the implementation of: InfoNCE (MoCo on videos) UberNCE

Tengda Han 271 Jan 02, 2023
Tensors and neural networks in Haskell

Hasktorch Hasktorch is a library for tensors and neural networks in Haskell. It is an independent open source community project which leverages the co

hasktorch 920 Jan 04, 2023
Constraint-based geometry sketcher for blender

Constraint-based sketcher addon for Blender that allows to create precise 2d shapes by defining a set of geometric constraints like tangent, distance,

1.7k Dec 31, 2022
ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning. In ICCV, 2021.

ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning This repository contains the code for our ICCV 202

sangho.lee 28 Nov 08, 2022
MLOps will help you to understand how to build a Continuous Integration and Continuous Delivery pipeline for an ML/AI project.

page_type languages products description sample python azure azure-machine-learning-service azure-devops Code which demonstrates how to set up and ope

1 Nov 01, 2021
TensorFlow-based implementation of "ICNet for Real-Time Semantic Segmentation on High-Resolution Images".

ICNet_tensorflow This repo provides a TensorFlow-based implementation of paper "ICNet for Real-Time Semantic Segmentation on High-Resolution Images,"

HsuanKung Yang 406 Nov 27, 2022
PyTorch implementation for paper StARformer: Transformer with State-Action-Reward Representations.

StARformer This repository contains the PyTorch implementation for our paper titled StARformer: Transformer with State-Action-Reward Representations.

Jinghuan Shang 14 Dec 09, 2022
The Noise Contrastive Estimation for softmax output written in Pytorch

An NCE implementation in pytorch About NCE Noise Contrastive Estimation (NCE) is an approximation method that is used to work around the huge computat

Kaiyu Shi 287 Nov 25, 2022
Revisting Open World Object Detection

Revisting Open World Object Detection Installation See INSTALL.md. Dataset Our n

58 Dec 23, 2022
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dea

MIC-DKFZ 1.2k Jan 04, 2023
implement of SwiftNet:Real-time Video Object Segmentation

SwiftNet The official PyTorch implementation of SwiftNet:Real-time Video Object Segmentation, which has been accepted by CVPR2021. Requirements Python

haochen wang 64 Dec 14, 2022
Multi-View Radar Semantic Segmentation

Multi-View Radar Semantic Segmentation Paper Multi-View Radar Semantic Segmentation, ICCV 2021. Arthur Ouaknine, Alasdair Newson, Patrick Pérez, Flore

valeo.ai 37 Oct 25, 2022
Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens

TensorLayer Community 7.1k Dec 29, 2022
Exploring the Dual-task Correlation for Pose Guided Person Image Generation

Dual-task Pose Transformer Network The source code for our paper "Exploring Dual-task Correlation for Pose Guided Person Image Generation“ (CVPR2022)

63 Dec 15, 2022
A CV toolkit for my papers.

PyTorch-Encoding created by Hang Zhang Documentation Please visit the Docs for detail instructions of installation and usage. Please visit the link to

Hang Zhang 2k Jan 04, 2023