This is a project for re-implementing and re-training some famous deep vision models on tiny-Imagenet [Introduction] [Download Dataset] with AWS EC2 instances.

Models are implemented by Pytorch. The general training scripts are modified from pytorch/examples, supporting distributed training in single-machine multi-GPU and multi-machine.

I only have a GTX 1650 laptop which is hard to train deep models on big dataset within an accepted time. As a result, I focus on AWS EC2 P3 instances for a complete training with a pre-configured Deep Learning AMI (Ubuntu 18.04) Version 56.0.

That's saying, you can train models with the same/similar GPU-supported EC2 instances and AMI.

Create the basic environment

Use an AWS EC2 Instance

1. Create a p3.2xlarge instance with Deep Learning AMI (Ubuntu 18.04) Version 56.0 ami-0e14490e647237ed6

2. Attach an enough EBS disk >= 200GB

3. SSH to the machine

Or use your local machine

TODO

Setup

Run this script, the required libraries would be installed. Currently, this script and even the whole project are tightly dependent on a specific AMI. We will introduce docker supports in the future.

source script/setup.sh

Train models

Prepare tinyImagenet

Download dataset

bash script/tinyImagenetDataset.sh

Train Alexnet on the tiny-Imagenet

python3 train_image_classification.py  -a alexnet --lr 0.01 -b 256 --num-classes 200 --multiprocessing-distributed --world-size 1 --dist-url tcp://127.0.0.1:10086 --rank 0  ./tiny-imagenet-200

Train VGG16 on the tiny-Imagenet

Supports: vgg11, vgg13, vgg16, and vgg19

python3 train_image_classification.py  -a vgg16 --lr 0.01 -b 64 --num-classes 200 --multiprocessing-distributed --world-size 1 --dist-url tcp://127.0.0.1:10086 --rank 0  ./tiny-imagenet-200

Train GoogLeNet on the tiny-Imagenet

python3 train_image_classification.py  -a googlenet --lr 0.01 -b 128 --num-classes 200 --multiprocessing-distributed --world-size 1 --dist-url tcp://127.0.0.1:10086 --rank 0  ./tiny-imagenet-200

Train Resnet on the tiny-Imagenet

Supports: resnet18, resnet34, resnet50, resnet101, resnet152

python3 train_image_classification.py  -a resnet18 --lr 0.1 -b 256 --num-classes 200 --multiprocessing-distributed --world-size 1 --dist-url tcp://127.0.0.1:10086 --rank 0  ./tiny-imagenet-200

Train Mobilenet on the tiny-Imagenet

Supports: mobilenet_v2

python3 train_image_classification.py  -a mobilenet_v2 --lr 0.045 --wd 4e-5 -b 256 --num-classes 200 --multiprocessing-distributed --world-size 1 --dist-url tcp://127.0.0.1:10086 --rank 0  ./tiny-imagenet-200