(Py)TOD: Tensor-based Outlier Detection, A General GPU-Accelerated Framework

Related tags

Deep Learning machine-learning gpu-acceleration outlier-detection anomaly-detection gpu-systems
Overview

(Py)TOD: Tensor-based Outlier Detection, A General GPU-Accelerated Framework

Background: Outlier detection (OD) is a key data mining task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.

To scale outlier detection (OD) to large-scale, high-dimensional datasets, we propose TOD, a novel system that abstracts OD algorithms into basic tensor operations for efficient GPU acceleration.

The corresponding paper. The code is being cleaned up and released. Please watch and star!

One reason to use it:

On average, TOD is 11 times faster than PyOD!

If you need another reason: it can handle much larger datasets:more than a million sample OD within an hour!

TOD is featured for:

  • Unified APIs, detailed documentation, and examples for the easy use (under construction)
  • Supports more than 10 different OD algorithms and more are being added
  • TOD supports multi-GPU acceleration
  • Advanced techniques like provable quantization

Programming Model Interface

Complex OD algorithms can be abstracted into common tensor operators.

https://raw.githubusercontent.com/yzhao062/pytod/master/figs/abstraction.png

For instance, ABOD and COPOD can be assembled by the basic tensor operators.

https://raw.githubusercontent.com/yzhao062/pytod/master/figs/abstraction_example.png

End-to-end Performance Comparison with PyOD

Overall, it is much (on avg. 11 times) faster than PyOD takes way less run time.

https://raw.githubusercontent.com/yzhao062/pytod/master/figs/run_time.png

Code is being released. Watch and star for the latest news!

Comments
  • Error while installing package

    Error while installing package

    I installed Pytorch 1.10 from their site. It seen in virtual environment. I try pip install pytod but when searching for pytorch, it cannot find it because it searches with the "pytorch" package, not the "torch" package.

    ERROR: Could not find a version that satisfies the requirement pytorch>=1.7 (from pytod) (from versions: 0.1.2, 1.0.2)
ERROR: No matching distribution found for pytorch>=1.7
    opened by nuriakiin 1
  • decision_function() returns None

    decision_function() returns None

    Thanks for the package. When I try to implement LOF (or KNN) decision_function() on test data returns empty object. Is there a fix to this? Following is the code that replicates the issue (on GPU):

    from pytod.models.lof import LOF import torch import numpy as np

    x = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [75,80]], dtype=np.float32) x = torch.from_numpy(x)

    y = np.array([[6, 5], [1, 2], [3, 4], [5, 1], [11,12]], dtype=np.float32) y = torch.from_numpy(y)

    lof = LOF(n_neighbors=2, device = 'cuda:0')

    lof.fit(x)

    print(lof.decision_function(y))

    opened by sugatc 0
  • Support for novelty detection and changing distance metric with local outlier factor

    Support for novelty detection and changing distance metric with local outlier factor

    The current implementation of LOF doesn't allow changing the distance metric to 'cosine', for example or setting novelty = True which prevents it from being used for novelty detection task. It will be great if support can be added for these.

    opened by sugatc 2
  • can't fit model in colab

    can't fit model in colab

    when i try fit on any model in colab gpu instance i get the following error. my dataset has 2 columns and 1 million rows:

    AttributeError Traceback (most recent call last) in () 4 clf_name = 'KNN' 5 clf = LOF() ----> 6 clf.fit(X)

    3 frames /usr/local/lib/python3.7/dist-packages/pandas/core/generic.py in getattr(self, name) 5485 ): 5486 return self[name] -> 5487 return object.getattribute(self, name) 5488 5489 def setattr(self, name: str, value) -> None:

    AttributeError: 'DataFrame' object has no attribute 'to'

    opened by yairVanti 0
  • clean up reproducibility scripts

    clean up reproducibility scripts

    We are cleaning up these scripts for an easy run, while the primary results are reproducible with the compare_real_data.py (https://github.com/yzhao062/pytod/tree/main/reproducibility)

    enhancement 
    opened by yzhao062 0
Releases(v0.0.2)

  • v0.0.2(Jun 19, 2022)

    v<0.0.1>, <04/12/2021> -- Add LOF. v<0.0.1>, <04/23/2021> -- Add ABOD. v<0.0.2>, <06/19/2021> -- Add PCA and HBOS. v<0.0.2>, <06/19/2021> -- Turn on test suites.

    Now we have updated both the paper the repo to cover more algorithms.

    Source code(tar.gz)
    Source code(zip)
Owner
Yue Zhao
Ph.D. Student @ CMU. Outlier Detection Systems | ML Systems (MLSys) | Anomaly/Outlier Detection | AutoML. Twitter@ yzhao062
Yue Zhao
GitHub https://www.andrew.cmu.edu/user/yuezhao2/papers/21-preprint-tod.pdf
GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks

GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks This repository implements a capsule model Inten

Joel Huang 15 Dec 24, 2022
(JMLR'19) A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

Python Outlier Detection (PyOD) Deployment & Documentation & Stats Build Status & Coverage & Maintainability & License PyOD is a comprehensive and sca

Yue Zhao 6.6k Jan 3, 2023
A Python Library for Graph Outlier Detection (Anomaly Detection)

PyGOD is a Python library for graph outlier detection (anomaly detection). This exciting yet challenging field has many key applications, e.g., detect

PyGOD Team 757 Jan 4, 2023
SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021]

SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021] Pdf: https://openreview.net/forum?id=v5gjXpmR8J Code for our ICLR 2021 pape

Princeton INSPIRE Research Group 113 Nov 27, 2022
PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

Don’t be Contradicted with Anything!CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System This repository contains the PyTorch im

Libo Qin 25 Sep 6, 2022
PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

Libo Qin 12 Sep 26, 2021
GPU-Accelerated Deep Learning Library in Python

Hebel GPU-Accelerated Deep Learning Library in Python Hebel is a library for deep learning with neural networks in Python using GPU acceleration with

Hannes Bretschneider 1.2k Dec 21, 2022
NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.

NVIDIA Merlin NVIDIA Merlin is an open source library designed to accelerate recommender systems on NVIDIA’s GPUs. It enables data scientists, machine

null 419 Jan 3, 2023
GPU Accelerated Non-rigid ICP for surface registration

GPU Accelerated Non-rigid ICP for surface registration Introduction Preivous Non-rigid ICP algorithm is usually implemented on CPU, and needs to solve

Haozhe Wu 144 Jan 4, 2023
General purpose GPU compute framework for cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends)

General purpose GPU compute framework for cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.

The Kompute Project 1k Jan 6, 2023
Outlier Exposure with Confidence Control for Out-of-Distribution Detection

OOD-detection-using-OECC This repository contains the essential code for the paper Outlier Exposure with Confidence Control for Out-of-Distribution De

Nazim Shaikh 64 Nov 2, 2022
Deep Anomaly Detection with Outlier Exposure (ICLR 2019)

Outlier Exposure This repository contains the essential code for the paper Deep Anomaly Detection with Outlier Exposure (ICLR 2019). Requires Python 3

Dan Hendrycks 464 Dec 27, 2022
Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks"

LUNAR Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks" Adam Goodge, Bryan Hooi, Ng See Kiong and

Adam Goodge 25 Dec 28, 2022
Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation. Intel iHD GPU (iGPU) support. NVIDIA GPU (dGPU) support.

mtomo Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation.

Katsuya Hyodo 24 Mar 2, 2022
High performance Cross-platform Inference-engine, you could run Anakin on x86-cpu,arm, nv-gpu, amd-gpu,bitmain and cambricon devices.

Anakin2.0 Welcome to the Anakin GitHub. Anakin is a cross-platform, high-performance inference engine, which is originally developed by Baidu engineer

null 514 Dec 28, 2022
GrabGpu_py: a scripts for grab gpu when gpu is free

GrabGpu_py a scripts for grab gpu when gpu is free. WaitCondition: gpu_memory >

tianyuluan 3 Jun 18, 2022
PyTorch framework, for reproducing experiments from the paper Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks

Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks. Code, based on the PyTorch framework, for reprodu

Asaf 3 Dec 27, 2022
Lunar is a neural network aimbot that uses real-time object detection accelerated with CUDA on Nvidia GPUs.

Lunar Lunar is a neural network aimbot that uses real-time object detection accelerated with CUDA on Nvidia GPUs. About Lunar can be modified to work

Zeyad Mansour 276 Jan 7, 2023
A gesture recognition system powered by OpenPose, k-nearest neighbours, and local outlier factor.

OpenHands OpenHands is a gesture recognition system powered by OpenPose, k-nearest neighbours, and local outlier factor. Currently the system can iden

Paul Treanor 12 Jan 10, 2022