VGG-19 is a deep convolutional network for object recognition developed and trained by Oxford's renowned Visual Geometry Group (VGG), which achieved very good performance on the ImageNet dataset. You can check Karen Simonyan and Andrew Zisserman publication: Very Deep Convolutional Networks for Large-Scale Image Recognition.
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.
In this lab, we will train the VGG-19 network using the CIFAR-10 dataset with Keras.
Run the file, wait the code to perform a few steps and record the remaining time info that Keras offers, then you can stop the execution. If you can not, kill the container.Please compelte the following table about your computer and the execution (Currently filled with an example):
|Processor (Ghz / Cores / Threads)||Step||Remaining time||Accuracy|
|Intel Core i7 5500U (2.6Ghz / 2 Cores / 4 Threads)||96/50000||42h 36m 21s||0.0312|
Hint: If the execution fails, check the Docker docs in order to increase the RAM of your container.
DATASET_DIR = "cifar-10"
module purge; module load K80 cuda/8.0 mkl/2017.1 CUDNN/5.1.10-cuda_8.0 intel-opencl/2016 python/3.6.0+_ML
#SBATCH --constraint=k80 #SBATCH --gres: gpu:NUMBER_OF_GPUS
|Nodes||GPUs asked / GPUs used||Cores||Job time wall||Step||Remaining time||Accuracy|
|1||2 / 1||16||15 min||?/50000|
|1||2 / 2||16||15 min||?/50000|
|1||4 / 4||16||15 min||?/50000|
Hint: Example job file
#!/bin/bash #SBATCH --job-name=keras_k80 #SBATCH -D . #SBATCH --output=k80_%j.out #SBATCH --error=k80_%j.err #SBATCH --ntasks=1 #SBATCH --gres gpu:2 #SBATCH --cpus-per-task=8 #SBATCH --constraint=k80 #SBATCH --time=00:15:00 module purge; module load K80 cuda/8.0 mkl/2017.1 CUDNN/5.1.10-cuda_8.0 intel-opencl/2016 python/3.6.0+_ML python vgg.py --num_gpu=1
Hint: Add this to run a job using the reservation queue
Hint: Add this to run a job using the debug queue
#SBATCH --partition=debug #SBATCH --qos=debug
If you don’t have time to finish all tasks during this lab session, please, follow the indications of your teacher about how to create your lab report and how to submit it.