AWS SageMaker is the platform that helps data scientists and developers to prepare, build, train and deploy machine learning models. It is gradually becoming the de facto compute environment for both private and public sector organisations because of its popularity and easy-to-use Jupyter notebook environment.
There are different prices depending on the level of computation required however, accelerated computing which provisions Graphical Processing Units (GPUs) are not preconfigured to work with Python 3.7 and Tensorflow 2.
My goal is to show the step-by-step installation process of getting Tensorflow to recognise and use SageMaker GPU configured for Python 3.7.
Tensorflow is an open source software library developed by Google for data flow programming. It is perhaps the most popular deep learning library today used for tasks such as image recognition. This will be a quick walk-through using CIFAR-10 dataset. The CIFAR-10 data consists of 60,000 32x32 color images in 10 classes, with 6000 images per class. There are 50,000 training images and 10,000 test images in the official data. This demonstration is also Tensorflow’s Convolutional Neural Network (CNN)example on Github.
from __future__ import print_function import keras from keras.datasets import cifar10 from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from…
Data Scientist and AI Engineer