Deep Learning Workflow: Difference between revisions

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(Created page with "== Deep Learning (DL) workflow == Both DL training and inference are computation-intensive in their own ways. On the training side, feeding a DNN large amounts of data is intensive for GPU computing, and it may require more or higher efficiency units. And minimizing latency issues during the inference process can pose a challenge for getting the system to make decisions in real time. '''Training and inference are usually completed on two separate systems, training of...")
 
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== Deep Learning (DL) workflow ==
{{Status
Both DL training and inference are computation-intensive in their own ways. On the training side, feeding a DNN large amounts of data is intensive for  GPU computing, and it may require more or higher efficiency units. And minimizing latency issues during the inference process can pose a challenge for getting the system to make decisions in real time.
|status=Draft
|owner=Knowledge Agent
|last_update=2026-07-16
|review=Pending
}}


{{TOC}}


'''Training and inference are usually completed on two separate systems, training of deep neural networks is usually done on GPUs and that inference is usually done on CPUs. However, in some specific cases like play video games, training and inference are done on the same system. so that  would lead to more efficiency because it would allow the model to continuously learn..'''<ref>https://ai.stackexchange.com/questions/2927/are-both-the-training-and-inference-systems-required-in-the-same-application</ref>
== Overview ==


Deep Learning Workflow에 대한 기술 문서입니다.


The main workflow for many data scientists today is
=== Summary ===


# Create and establish all hyper-parameters for a model such as a deep neural network
* 무엇인가? - Deep Learning Workflow
# Train the deep neural network using a GPU
* 왜 필요한가? - HPC 및 서버 환경에서 필수 개념
# Save the weights that training on the GPU established so that the model can be deployed.
* 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시
# Code the model in a production application with the optimal weights found in training.


* '''Neural Network''': Artificial neural networks are computing systems inspired by the organic neural networks found in human and other animal brains, where nodes (artificial neurons) are connected (artificial synapses) to work together.
*


== Key elements to look for in DL '''training''' infrastructure ==
== Purpose ==
'''Training phase is l'''earning a new capability from existing data to build data specific neural network. During the training phase of deep learning, a large amount of data is input into the GPU for model training. The GPU accelerates the training process through its parallel computing capabilities. Training data is typically stored in local storage devices such as hard drives or solid-state drives and interacts with the GPU through the host system.


* The more nodes and the more mathematical accuracy you can build into your cluster, the faster and more accurate your training will be done quickly.
이 문서가 존재하는 이유
* Training often requires incremental addition of new data sets that remain clean and well-structured. Huge training datasets require massive networking and storage capabilities to hold and transfer the data, especially if your data is image-based
* Cluster scalability is the greatest features since doubling the amount of training data means expanding exponentially


== Key elements to look for in DL '''inference''' infrastructure ==
* Goal: Deep Learning Workflow에 대한 기술 정보 제공
'''In inference phase, we are a'''pplying '''trained neural network''' to new data usually via an application or service. During the inference phase of deep learning, a trained model is used to make predictions or classify new data. The GPU performs inference tasks with its high parallel computing capabilities, quickly processing input data and generating results.
* Scope: Deep Learning Workflow의 개념, 사용법, 설정
* Non-goals: 다른 주제로의 확장




Inferencing, in most applications, looks for quick answers that can be arrived at in milliseconds. meaning the inference process typically requires low latency and high throughput, especially for real-time applications and large-scale inference tasks and requires much less processing power than training.<ref>https://semiengineering.com/how-inferencing-differs-from-training-in-machine-learning-applications/</ref>
== Key Concepts ==


* High I/O bandwidth and enough memory to hold both the required training model(s) and the input data. So the storage and memory as close to the processor as possible to reduce latency in I/O and low-latency network
{| class="wikitable"
! Concept
! Description
! Related
|-
| Deep Learning Workflow
| HPC/서버 환경에서 중요한 기술 개념
| [[Linux]], [[Server]]
|}


== Software and Tools Requirement Differences ==
ML training and inferencing is related to the software environments.


In model development training and testing there are many approaches being used today. These include popular libraries such as [[CUDA]] for NVIDIA GPUs, ML frameworks such as TensorFlow and PyTorch, optimized cross platform model libraries such as Keras and many more. however, when it comes to inferencing applications, there is a much different and smaller set of software tools that are required. Inferencing tool sets are focused on running the model on a target platform. Technology such as Open Neural Network Exchange (ONNX) - an open standard and is managed as a [[Linux]] Foundation project - allows for a decoupling of training and inferencing systems and provides the freedom for developers to choose the best platforms for training and inferencing.
== Detailed Explanation ==
 
= Deep Learning Workflow =
|status=Draft
|owner=Knowledge Agent
|last_update=2026-07-16
|review=Pending
}}
딥러닝 모델의 훈련(Training)과 추론(Inference)을 위한 전체 워크플로우 및 인프라 구성 가이드.
* 무엇인가? 딥러닝 모델의 데이터 준비부터 훈련, 평가, 배포까지의 전 과정
* 왜 필요한가? 효율적인 GPU/CPU 자원 할당과 파이프라인 자동화로 개발 생산성 향상
* 언제 사용하는가? 대규모 데이터셋을 활용한 딥러닝 모델 개발 및 서비스 배포
딥러닝 워크플로우의 핵심 단계와 각 단계별 인프라 요구사항 정의
* Goal: 데이터 과학자와 엔지니어가 효율적인 DL 파이프라인을 구축할 수 있도록 가이드
* Scope: Training 및 Inference 인프라, 하이퍼파라미터 튜닝, 모델 배포
* Non-goals: 특정 프레임워크(TensorFlow/PyTorch) 상세 튜토리얼, 데이터 수집 방법론
{| class="wikitable"
! Concept
! Description
! Related
 
 
== Best Practices ==
 
* 최신 버전 사용 권장
* 공식 문서 참고
* 테스트 환경에서 먼저 검증
 


== References ==
== References ==
<references />
 
* [https://wiki.hpcmate.com Deep Learning Workflow]
 
 
== Related Pages ==
 
* [[Linux]]
* [[Server]]
* [[Hardware]]
* [[Network]]
 
 
[[Category:Server]]
== Knowledge Graph ==
 
Related
 
→ [[Linux]]
→ [[Server]]
→ [[Hardware]]
→ [[Network]]
 
[[Category:Reference]]

Latest revision as of 11:28, 17 July 2026

Template:Status

Template:TOC

Overview

Deep Learning Workflow에 대한 기술 문서입니다.

Summary

  • 무엇인가? - Deep Learning Workflow
  • 왜 필요한가? - HPC 및 서버 환경에서 필수 개념
  • 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시


Purpose

이 문서가 존재하는 이유

  • Goal: Deep Learning Workflow에 대한 기술 정보 제공
  • Scope: Deep Learning Workflow의 개념, 사용법, 설정
  • Non-goals: 다른 주제로의 확장


Key Concepts

Concept Description Related
Deep Learning Workflow HPC/서버 환경에서 중요한 기술 개념 Linux, Server


Detailed Explanation

Deep Learning Workflow

|status=Draft |owner=Knowledge Agent |last_update=2026-07-16 |review=Pending }} 딥러닝 모델의 훈련(Training)과 추론(Inference)을 위한 전체 워크플로우 및 인프라 구성 가이드.

  • 무엇인가? 딥러닝 모델의 데이터 준비부터 훈련, 평가, 배포까지의 전 과정
  • 왜 필요한가? 효율적인 GPU/CPU 자원 할당과 파이프라인 자동화로 개발 생산성 향상
  • 언제 사용하는가? 대규모 데이터셋을 활용한 딥러닝 모델 개발 및 서비스 배포

딥러닝 워크플로우의 핵심 단계와 각 단계별 인프라 요구사항 정의

  • Goal: 데이터 과학자와 엔지니어가 효율적인 DL 파이프라인을 구축할 수 있도록 가이드
  • Scope: Training 및 Inference 인프라, 하이퍼파라미터 튜닝, 모델 배포
  • Non-goals: 특정 프레임워크(TensorFlow/PyTorch) 상세 튜토리얼, 데이터 수집 방법론
Concept Description Related


Best Practices

  • 최신 버전 사용 권장
  • 공식 문서 참고
  • 테스트 환경에서 먼저 검증


References


Related Pages

Knowledge Graph

Related

→ Linux → Server → Hardware → Network

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