Learn to train a deep learning model for melanoma classification using Docker, NVIDIA docker runtimes, and PyTorch on a single GPU. Explore the process of creating training code, installing Docker runtime, utilizing Nvidia CUDA, building Docker images, and handling data paths and volumes. Gain insights into troubleshooting common errors and saving trained models. This 21-minute video tutorial is part of a series on melanoma detection, with links provided to related videos on model training, web application development, and Dockerizing a Flask web app.
How to Train a Deep Learning Model Using Docker
Overview
Syllabus
Introduction
The library
Creating the training code
Installing the docker runtime
Using Nvidia CUDA
Build Docker Image
Train Model
Change GPU
Train model again
docker file
data path
volumes
error
model saved
Taught by
Abhishek Thakur
Related Courses
-
Deep Learning with PyTorch : Image Segmentation
-
Train compute-intensive models with Azure Machine Learning
-
Create a Large Language Model from Scratch with Python – Tutorial
-
PyTorch for Deep Learning & Machine Learning – Full Course
-
Inference with Torch-TensorRT Deep Learning Prediction for Beginners - CPU vs CUDA vs TensorRT
-
Build a Neural Network with Python Tutorial - Deep Learning with PyTorch
