A Lightweight Experiment & Resource Monitoring Tool 📺

Related tags

Deep Learningmle-monitor
Overview

Lightweight Experiment & Resource Monitoring 📺

Pyversions PyPI version Code style: black Colab codecov

"Did I already run this experiment before? How many resources are currently available on my cluster?" If these are common questions you encounter during your daily life as a researcher, then mle-monitor is made for you. It provides a lightweight API for tracking your experiments using a pickle protocol database (e.g. for hyperparameter searches and/or multi-configuration/multi-seed runs). Furthermore, it comes with built-in resource monitoring on Slurm/Grid Engine clusters and local machines/servers.

mle-monitor provides three core functionalities:

  • MLEProtocol: A composable protocol database API for ML experiments.
  • MLEResource: A tool for obtaining server/cluster usage statistics.
  • MLEDashboard: A dashboard visualizing resource usage & experiment protocol.

To get started I recommend checking out the colab notebook and an example workflow.

drawing

MLEProtocol: Keeping Track of Your Experiments 📝 

from mle_monitor import MLEProtocol

# Load protocol database or create new one -> print summary
protocol_db = MLEProtocol("mle_protocol.db", verbose=False)
protocol_db.summary(tail=10, verbose=True)

# Draft data to store in protocol & add it to the protocol
meta_data = {
    "purpose": "Grid search",  # Purpose of experiment
    "project_name": "MNIST",  # Project name of experiment
    "experiment_type": "hyperparameter-search",  # Type of experiment
    "experiment_dir": "experiments/logs",  # Experiment directory
    "num_total_jobs": 10,  # Number of total jobs to run
    ...
}
new_experiment_id = protocol_db.add(meta_data)

# ... train your 10 (pseudo) networks/complete respective jobs
for i in range(10):
    protocol_db.update_progress_bar(new_experiment_id)

# Wrap up an experiment (store completion time, etc.)
protocol_db.complete(new_experiment_id)

The meta data can contain the following keys:

Search Type Description Default
purpose Purpose of experiment 'None provided'
project_name Project name of experiment 'default'
exec_resource Resource jobs are run on 'local'
experiment_dir Experiment log storage directory 'experiments'
experiment_type Type of experiment to run 'single'
base_fname Main code script to execute 'main.py'
config_fname Config file path of experiment 'base_config.yaml'
num_seeds Number of evaluations seeds 1
num_total_jobs Number of total jobs to run 1
num_job_batches Number of jobs in single batch 1
num_jobs_per_batch Number of sequential job batches 1
time_per_job Expected duration: days-hours-minutes '00:01:00'
num_cpus Number of CPUs used in job 1
num_gpus Number of GPUs used in job 0

Additionally you can synchronize the protocol with a Google Cloud Storage (GCS) bucket by providing cloud_settings. In this case also the results stored in experiment_dir will be uploaded to the GCS bucket, when you call protocol.complete().

# Define GCS settings - requires 'GOOGLE_APPLICATION_CREDENTIALS' env var.
cloud_settings = {
    "project_name": "mle-toolbox",  # GCP project name
    "bucket_name": "mle-protocol",  # GCS bucket name
    "use_protocol_sync": True,  # Whether to sync the protocol to GCS
    "use_results_storage": True,  # Whether to sync experiment_dir to GCS
}
protocol_db = MLEProtocol("mle_protocol.db", cloud_settings, verbose=True)

The MLEResource: Keeping Track of Your Resources 📉

On Your Local Machine

from mle_monitor import MLEResource

# Instantiate local resource and get usage data
resource = MLEResource(resource_name="local")
resource_data = resource.monitor()

On a Slurm Cluster

resource = MLEResource(
    resource_name="slurm-cluster",
    monitor_config={"partitions": ["<partition-1>", "<partition-2>"]},
)

On a Grid Engine Cluster

resource = MLEResource(
    resource_name="sge-cluster",
    monitor_config={"queues": ["<queue-1>", "<queue-2>"]}
)

The MLEDashboard: Dashboard Visualization 🎞️ 

from mle_monitor import MLEDashboard

# Instantiate dashboard with protocol and resource
dashboard = MLEDashboard(protocol, resource)

# Get a static snapshot of the protocol & resource utilisation printed in console
dashboard.snapshot()

# Run monitoring in while loop - dashboard
dashboard.live()

Installation ⏳

A PyPI installation is available via:

pip install mle-monitor

Alternatively, you can clone this repository and afterwards 'manually' install it:

git clone https://github.com/mle-infrastructure/mle-monitor.git
cd mle-monitor
pip install -e .

Development & Milestones for Next Release

You can run the test suite via python -m pytest -vv tests/. If you find a bug or are missing your favourite feature, feel free to contact me @RobertTLange or create an issue 🤗 .

You might also like...
Meta Representation Transformation for Low-resource Cross-lingual Learning

MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning This repo hosts the code for MetaXL, published at NAACL 2021. [Meta

Microsoft 36 Aug 17, 2022
OpenDILab RL Kubernetes Custom Resource and Operator Lib

DI Orchestrator DI Orchestrator is designed to manage DI (Decision Intelligence) jobs using Kubernetes Custom Resource and Operator. Prerequisites A w

OpenDILab 205 Dec 29, 2022
Punctuation Restoration using Transformer Models for High-and Low-Resource Languages
Punctuation Restoration using Transformer Models for High-and Low-Resource Languages

Punctuation Restoration using Transformer Models This repository contins official implementation of the paper Punctuation Restoration using Transforme

Tanvirul Alam 142 Jan 1, 2023
Byte-based multilingual transformer TTS for low-resource/few-shot language adaptation.

One model to speak them all 🌎 Audio Language Text ▷ Chinese 人人生而自由，在尊严和权利上一律平等。 ▷ English All human beings are born free and equal in dignity and rig

Mutian He 60 Nov 14, 2022
PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference

PyTorch implementation of [1611.06440 Pruning Convolutional Neural Networks for Resource Efficient Inference] This demonstrates pruning a VGG16 based

Jacob Gildenblat 836 Dec 26, 2022
Real-Time Social Distance Monitoring tool using Computer Vision
Real-Time Social Distance Monitoring tool using Computer Vision

Social Distance Detector A Real-Time Social Distance Monitoring Tool Table of Contents Motivation YOLO Theory Detection Output Tech Stack Functionalit

Pranav B 13 Oct 14, 2022
An air quality monitoring service with a Raspberry Pi and a SDS011 sensor.

Raspberry Pi Air Quality Monitor A simple air quality monitoring service for the Raspberry Pi. Installation Clone the repository and run the following

rydercalmdown 24 Dec 9, 2022
Attendance Monitoring with Face Recognition using Python
Attendance Monitoring with Face Recognition using Python

Attendance Monitoring with Face Recognition using Python A python GUI integrated attendance system using face recognition to take attendance. In this

Vaibhav Rajput 2 Jun 21, 2022
Complete system for facial identity system. Include one-shot model, database operation, features visualization, monitoring

Complete system for facial identity system. Include one-shot model, database operation, features visualization, monitoring

null 2 Dec 28, 2021
Comments
  • Is the dashboard pooling squeue?

    Is the dashboard pooling squeue?

    Hey, Thanks for publishing the library, the dashboard looks great!

    However, I was a bit concerned to see you are using squeue since the official documentation says

    "Executing squeue sends a remote procedure call to slurmctld. If enough calls from squeue or other Slurm client commands that send remote procedure calls to the slurmctld daemon come in at once, it can result in a degradation of performance of the slurmctld daemon, possibly resulting in a denial of service.

    Do not run squeue or other Slurm client commands that send remote procedure calls to slurmctld from loops in shell scripts or other programs. Ensure that programs limit calls to squeue to the minimum necessary for the information you are trying to gather."

    Do you poll squeue or is there some other, smarter management of it that I missed?

    Thanks, Eliahu

    opened by eliahuhorwitz 0
Releases(v0.0.1)

  • v0.0.1(Dec 9, 2021)

    Basic API for MLEProtocol, MLEResource & MLEDashboard:

    from mle_monitor import MLEProtocol

# Load protocol database or create new one -> print summary
protocol_db = MLEProtocol("mle_protocol.db", verbose=False)
protocol_db.summary(tail=10, verbose=True)

# Draft data to store in protocol & add it to the protocol
meta_data = {
    "purpose": "Grid search",  # Purpose of experiment
    "project_name": "MNIST",  # Project name of experiment
    "experiment_type": "hyperparameter-search",  # Type of experiment
    "experiment_dir": "experiments/logs",  # Experiment directory
    "num_total_jobs": 10,  # Number of total jobs to run
    ...
}
new_experiment_id = protocol_db.add(meta_data)

# ... train your 10 (pseudo) networks/complete respective jobs
for i in range(10):
    protocol_db.update_progress_bar(new_experiment_id)

# Wrap up an experiment (store completion time, etc.)
protocol_db.complete(new_experiment_id)
    Source code(tar.gz)
    Source code(zip)
Owner
GitHub Repository
Categorizing comments on YouTube into different categories.

Youtube Comments Categorization This repo is for categorizing comments on a youtube video into different categories. negative (grievances, complaints,

Rhitik 5 Nov 26, 2022
A tool to visualise the results of AlphaFold2 and inspect the quality of structural predictions

AlphaFold Analyser This program produces high quality visualisations of predicted structures produced by AlphaFold. These visualisations allow the use

Oliver Powell 3 Nov 13, 2022
QuakeLabeler is a Python package to create and manage your seismic training data, processes, and visualization in a single place — so you can focus on building the next big thing.

QuakeLabeler Quake Labeler was born from the need for seismologists and developers who are not AI specialists to easily, quickly, and independently bu

Hao Mai 15 Nov 04, 2022
Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Auto-ViML Automatically Build Variant Interpretable ML models fast! Auto_ViML is pronounced "auto vimal" (autovimal logo created by Sanket Ghanmare) N

AutoViz and Auto_ViML 397 Dec 30, 2022
This repository holds the code for the paper "Deep Conditional Gaussian Mixture Model forConstrained Clustering".

Deep Conditional Gaussian Mixture Model for Constrained Clustering. This repository holds the code for the paper Deep Conditional Gaussian Mixture Mod

17 Oct 30, 2022
Rethinking Transformer-based Set Prediction for Object Detection

Rethinking Transformer-based Set Prediction for Object Detection Here are the code for the ICCV paper. The code is adapted from Detectron2 and AdelaiD

Zhiqing Sun 62 Dec 03, 2022
PaddleBoBo是基于PaddlePaddle和PaddleSpeech、PaddleGAN等开发套件的虚拟主播快速生成项目

PaddleBoBo - 元宇宙时代，你也可以动手做一个虚拟主播。 PaddleBoBo是基于飞桨PaddlePaddle深度学习框架和PaddleSpeech、PaddleGAN等开发套件的虚拟主播快速生成项目。PaddleBoBo致力于简单高效、可复用性强，只需要一张带人像的图片和一段文字，就能

502 Jan 08, 2023
CIFAR-10_train-test - training and testing codes for dataset CIFAR-10

CIFAR-10_train-test - training and testing codes for dataset CIFAR-10

Frederick Wang 3 Apr 26, 2022
Official PyTorch implementation of Segmenter: Transformer for Semantic Segmentation

Segmenter: Transformer for Semantic Segmentation Segmenter: Transformer for Semantic Segmentation by Robin Strudel*, Ricardo Garcia*, Ivan Laptev and

594 Jan 06, 2023
A rule-based log analyzer & filter

Flog 一个根据规则集来处理文本日志的工具。 前言 在日常开发过程中，由于缺乏必要的日志规范，导致很多人乱打一通，一个日志文件夹解压缩后往往有几十万行。 日志泛滥会导致信息密度骤减，给排查问题带来了不小的麻烦。 以前都是用grep之类的工具先挑选出有用的，再逐条进行排查，费时费力。在忍无可忍之后决

上山打老虎 9 Jun 23, 2022
Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations Requirements The code is implemented in Python and requires

1 Nov 03, 2021
An Implementation of Transformer in Transformer in TensorFlow for image classification, attention inside local patches

Transformer-in-Transformer An Implementation of the Transformer in Transformer paper by Han et al. for image classification, attention inside local pa

Rishit Dagli 40 Jul 25, 2022
MPI-IS Mesh Processing Library

Perceiving Systems Mesh Package This package contains core functions for manipulating meshes and visualizing them. It requires Python 3.5+ and is supp

Max Planck Institute for Intelligent Systems 494 Jan 06, 2023
U-Net: Convolutional Networks for Biomedical Image Segmentation

Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras This tutorial shows how to use Keras library to build deep ne

Yihui He 401 Nov 21, 2022
Python wrapper to access the amazon selling partner API

PYTHON-AMAZON-SP-API Amazon Selling-Partner API If you have questions, please join on slack Contributions very welcome! Installation pip install pytho

Michael Primke 330 Jan 06, 2023
Official PyTorch implementation of the paper "Recycling Discriminator: Towards Opinion-Unaware Image Quality Assessment Using Wasserstein GAN", accepted to ACM MM 2021 BNI Track.

RecycleD Official PyTorch implementation of the paper "Recycling Discriminator: Towards Opinion-Unaware Image Quality Assessment Using Wasserstein GAN

Yunan Zhu 23 Nov 05, 2022
Official implementation of Long-Short Transformer in PyTorch.

Long-Short Transformer (Transformer-LS) This repository hosts the code and models for the paper: Long-Short Transformer: Efficient Transformers for La

NVIDIA Corporation 198 Dec 29, 2022
The CLRS Algorithmic Reasoning Benchmark

Learning representations of algorithms is an emerging area of machine learning, seeking to bridge concepts from neural networks with classical algorithms.

DeepMind 251 Jan 05, 2023
BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation

BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation This is a demo implementation of BYOL for Audio (BYOL-A), a self-sup

NTT Communication Science Laboratories 160 Jan 04, 2023
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition

Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition How Fast Compare to Other Zero-Shot NAS Proxies on CIFAR-10/100 Pre-trained Model

190 Dec 29, 2022