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Oracle Cloud Infrastructure Virtual Machines for Data Science
With the explosion of business data—ranging from customer data to the Internet of Things—data scientists need the flexibility to explore and build models quickly. But purchasing new hardware to meet temporary or peak demand can involve significant capital expense as well as a considerable amount of time.
Oracle Cloud Infrastructure Virtual Machines (VMs) for Data Science are preconfigured environments that enable you to build models and deliver business value faster. Built on Oracle Cloud Infrastructure, these VMs offer exceptional performance, security, and control. You can expand your compute resources as needed using compute autoscaling and keep costs under control by stopping compute instances when they are not needed.
Compute options suitable for this VM image include a virtual machine with an NVIDIA GPU that can be up and running in under 15 minutes with preinstalled common IDEs, notebooks, and frameworks. Oracle Cloud Infrastructure VMs for Data Science include basic sample data and code for you to test and explore.
Major wireless carrier achieves faster performance with AI solution built on Oracle Cloud Infrastructure
A large mobile network operator delivers an AI-powered virtual voice assistant in multiple languages to millions of users. The environment uses a cluster with 2 nodes of 8 GPUs each, connected as a cluster with 16 GPUs and 768GB of memory in each node, significantly reducing the training time of the model.
The solution uses 100 million trainable parameters optimized in each iteration. Results include a speech-to-text performance increase of 2.4x and text-to-speech handled 30 to 50 percent faster, along with faster training of models.
Virtual Machines for Data Science
Benefits
Built on Oracle Cloud Infrastructure, our solution for data science provides exceptional performance, security, and control and enables you to build models and deliver business value faster.
Fast
Get up and running quickly. Just deploy the preconfigured image and start working. When you’re finished, teardown is just as easy.
Easy to Use
Launch these images yourself in the cloud, quickly and easily—without the assistance or intervention of your IT organization.
Everything You Need
The all-in-one image includes a complete set of preinstalled tools. You can easily add and customize, either before deployment with the Terraform script or manually after the system is running.
Flexible
Add additional compute resources in the cloud quickly and easily, by autoscaling or using Oracle Cloud Infrastructure Resource Manager.
Customizable
Use a GPU shape for deep-learning model training and inference or CPU-based compute for machine learning, according to your needs.
Low Cost
Reduce your IT costs. For about US$30, you can run one model for a day on a Tesla P100 GPU in the cloud.
Use Cases
Oracle’s preconfigured environment for deep learning is useful in many industries across a wide range of applications.
Natural language processing
Image recognition and classification
Fraud detection for financial services
Recommendation engines for online retailers
Risk management
Virtual Machine for Data Science Image Content
Operating System
- Image Family: Oracle Linux 7.x
- Operating System: Oracle Linux
- Kernel Version: kernel-uek-4.14.35-1902.8.4.el7uek.x86_64
- CUDA Version: 10-1-10.1.168-1
- cuDNN Version: 7.3.1
- Release Date: Dec. 19, 2019
Machine Learning Frameworks (Python-based)
- TensorFlow
- Keras
- Theano
- scikit-learn
- PyTorch
- NumPy
- Pandas
- Seaborn
Integrated Development Environments (IDEs) and Notebooks
- Anaconda Open Source Distribution
- Spyder
- PyCharm
- Atom
- Jupyter Notebook
- Sublime Text
Included Labs
If you are looking to test the environment or learn more about deep learning and data science, Jupyter Notebooks that provide self-guided instruction are included. Just open the readme.md file in the Jupyter Notebook in the virtual machine.
- Lab 1: Introduction to Machine Learning Packages: scikit-learn
Walks through the scikit-learn tutorial, and covers how to build and tune models on scikit-learn. Includes exercises. - Lab 2: Introduction to ML Packages: PyTorch
Covers the PyTorch tutorial and how to build and tune models on neural networks in PyTorch for vision tasks, natural language processing, and related uses. - Lab 3: Advanced Neural Networks and Transfer Learning for Natural Language Processing
Provides a tutorial on convolutional and recurrent neural networks. - Lab 4: Advanced Neural Networks and Transfer Learning for Vision
Explains how to implement custom CNNs and use pretrained, state-of-the-art CNNs.