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Updated: Dec 20, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Progress Indication for Deep Learning Model Training: A Feasibility Demonstration.

Qifei Dong1, Gang Luo1

  • 1Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA.

IEEE Access : Practical Innovations, Open Solutions
|June 3, 2020
PubMed
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Training deep learning models can be lengthy. This study introduces progress indicators to estimate remaining training time and completion, improving user-friendliness and workload management for deep learning.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning models are state-of-the-art for many machine learning tasks.
  • Training deep learning models on large datasets is computationally intensive and time-consuming, often taking days or months.
  • Accurate progress indicators are needed for user-friendliness and workload management during lengthy training processes.

Purpose of the Study:

  • To present novel techniques for non-trivial progress indicators in deep learning model training.
  • To enable accurate estimation of remaining training time and work completion, especially when early stopping is employed.
  • To enhance the user experience and assist in managing computational resources for deep learning tasks.

Main Methods:

  • Development and implementation of techniques for progress indicators in TensorFlow.
Keywords:
Deep learningTensorFlowmodel trainingprogress indicator

Related Experiment Videos

Last Updated: Dec 20, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

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  • Evaluation of the techniques using both convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
  • Testing the robustness of the progress indicators under varying run-time system load conditions.

    • Main Results:

    • The implemented progress indicators provide useful information even with dynamic system load.
    • The progress indicator system demonstrates the ability to self-correct initial estimation errors over time.
    • Successful application to both CNN and RNN model training scenarios.

    Conclusions:

    • The proposed techniques offer a valuable solution for monitoring deep learning model training progress.
    • These progress indicators improve usability and facilitate workload management for computationally intensive AI tasks.
    • The system is robust to environmental variations and capable of accurate, self-correcting estimations.