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Announcing the Availability of VM for Data Science and AI on Oracle Cloud Infrastructure

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.

Please checkout the Oracle Cloud Marketplace for more details -

https://cloudmarketplace.oracle.com/marketplace/en_US/listing/69064648

Benefits

Following are some immediate benefits of using this solution:

  • All-in-one image: The image includes a complete set of preinstalled tools that you can easily add to and customize, either before deployment with the Terraform script or manually.
  • Quick implementation: Just deploy the preconfigured image and start working. When you’re finished, deleting it is just as easy.
  • Compute shapes that meet your needs: For deep learning model training and inference, use a GPU-based shape. For machine learning, use a CPU-based shape.
  • Easy to launch: Launch these images yourself in the cloud quickly and easily, without the assistance or intervention of your IT organization.
  • Easy to add resources: Add more compute resources in the cloud quickly and easily, by autoscaling or using Resource Manager.
  • Keep costs low: You can run a model for a day on a Tesla P100 GPU in the cloud for about US$30.

Get Started

Sign up for an Oracle Cloud Free Tier account, and then go to the VM for Data Science and AI page in the Marketplace to launch an image in your tenancy and view the usage instructions.

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.

  1. 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
  2. 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.

  3. 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
  4. Start the Jupyter server by running the following command:

    jupyter notebook --certfile=<Your Certificate>.pem --keyfile=<Your Key>.key
  5. You can access the Jupyter environment at https://<instance-ip-address>:8888. The OS firewall is already configured to allow this access. If there is any problem in accessing, ensure that the TCP port 8888 in allowed for incoming traffic by reconfiguring the firewall:

    sudo firewall-cmd --zone=public --add-port=8888/tcp –permanent
    sudo firewall-cmd –reload
  6. To list the AI Datascience packages included in the image, run the command:

    conda list

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.

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