With the explosion of business data, ranging from customer data to the Internet of Things (IoT), data scientists need the flexibility to explore and build models quickly, often more quickly and flexibly than traditional on-premises IT hardware can provide.
Oracle Cloud Infrastructure’s VM for Data Science and AI is a preconfigured environment that includes a virtual machine (VM) with an NVIDIA GPU and CUDA and cuDNN drivers, common Python and R integrated development environments (IDEs), Jupyter Notebooks, and open source machine learning (ML) and deep learning (DL) frameworks. You can expand your compute resources by using compute autoscaling, or you can stop the compute instance when it’s not needed, to control costs. The VM even includes basic sample data and code for you to test and explore.
This solution is built on Oracle Cloud Infrastructure, with its exceptional performance, security, and control, and enables you to build models and deliver business value faster.
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Following are some immediate benefits of using this solution:
The VM or bare metal instance is provisioned through Oracle Resource Manager, the Core Services API, or the Console. Immediately after the instance starts, the AI/ML/DL environment is ready for configuration. You can configuration the environment locally by accessing the instance across the internet or from an external node, such as a bastion host. Follow these steps.
To configure and activate the sandbox environment for the AI/ML/DL developer user, run the following commands:
conda create --name sandbox pip python=3.7 conda activate sandbox
Reset the password for the Jupyter Notebook (read and write access over the network) as follows:
jupyter notebook password
You can also use this command to reset the password.
Put a TLS certificate for the Jupyter Notebook Web Interface for security. For a quick start, we have included a self-signed certificate and configured the Notebook environment. You can put your own self-signed certificate by running the following command:
openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout <Your Key>.key -out <Your Certificate>.pem
Start the Jupyter server by running the following command:
jupyter notebook --certfile=<Your Certificate>.pem --keyfile=<Your Key>.key
You can access the Jupyter environment at https://<instance-ip-address>:8888
sudo firewall-cmd --zone=public --add-port=8888/tcp –permanent sudo firewall-cmd –reload
To list the AI Datascience packages included in the image, run the command:
The proposed architecture for running DL training workloads on Oracle Cloud is located in the Oracle Cloud Infrastructure Architecture Center.
This image will be updated in conjunction with the kernel updates and major framework updates. We hope that you can use this image to jump start your ML or DL workloads on Oracle Cloud Infrastructure. Please provide feedback in the Comments section of this post.