Cloud Integration

You can run NVTabular on the cloud using:

Amazon Web Services

Amazon Web Services (AWS) offers EC2 instances with NVIDIA GPU support. NVTabular can be used with 1x, 4x, or 8x GPU instances (or multi-node setup). We’re using an EC2 instance with 8x NVIDIA A100 GPUs to demonstrate the steps below. Please check out the $/h for this instance type and adjust the type.

To run NVTabular on the cloud using AWS, do the following:

  1. Start the AWS EC2 instance with the NVIDIA Deep Learning AMI image using the aws-cli.

    # Starts the P4D instance with 8x NVIDIA A100 GPUs (take a look at the $/h for this instance type before using them)
aws ec2 run-instances --image-id ami-04c0416d6bd8e4b1f --count 1 --instance-type p4d.24xlarge --key-name <MyKeyPair> --security-groups <my-sg>

  2. SSH into the machine.

  3. Create a RAID volume by running the following command:

    Depending on the EC2 instance, the machine may include local disk storage. We can optimize the performance by creating a RAID volume. Based on our experience, two NVMe volumes yield the best performance.

    sudo mdadm --create --verbose /dev/md0 --level=0 --name=MY_RAID --raid-devices=2 /dev/nvme1n1 /dev/nvme2n1

sudo mkfs.ext4 -L MY_RAID /dev/md0
sudo mkdir -p /mnt/raid
sudo mount LABEL=MY_RAID /mnt/raid

sudo chmod -R 777 /mnt/raid

# Copy dataset inside raid directory:
cp -r data/ /mnt/raid/data/

  4. Launch the NVTabular docker container by running the following command:

    docker run --gpus all --rm -it -p 8888:8888 -p 8797:8787 -p 8796:8786 --ipc=host --cap-add SYS_PTRACE -v /mnt/raid:/raid nvcr.io/nvidia/nvtabular:0.3 /bin/bash

  5. Start the jupyter-lab server by running the following command:

    jupyter-lab --allow-root --ip='0.0.0.0' --NotebookApp.token='<password>'

Google Cloud Platform

The Google Cloud Platform offers Compute Engine instances with NVIDIA GPU support. We’re using a VM with 8x NVIDIA A100 GPUs and eight local SSD-NVMe devices configured as RAID 0 to demonstrate the steps below.

To run NVTabular on the cloud using GCP, do the following:

  1. Configure and create the VM.

    • GPU: 8xA100 (a2-highgpu-8g)

    • Boot Disk: Ubuntu version 18.04

    • Storage: Local 8xSSD-NVMe

  2. Install the appropriate NVIDIA drivers and CUDA.

    curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"
sudo apt -y update
sudo apt -y install cuda
nvidia-smi # Check installation

  3. Install Docker.

    distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
   && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
   && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get -y update
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi # Check Installation

  4. Configure the storage as RAID 0 by running the following commands:

    sudo mdadm --create --verbose /dev/md0 --level=0 --name=MY_RAID --raid-devices=2 /dev/nvme0n1 /dev/nvme0n2
sudo mkfs.ext4 -L MY_RAID /dev/md0
sudo mkdir -p /mnt/raid
sudo mount LABEL=MY_RAID /mnt/raid
sudo chmod -R 777 /mnt/raid

# Copy data to RAID
cp -r data/ /mnt/raid/data/

  5. Run the container using the following command:

    docker run --gpus all --rm -it -p 8888:8888 -p 8797:8787 -p 8796:8786 --ipc=host --cap-add SYS_PTRACE -v /mnt/raid:/raid nvcr.io/nvidia/nvtabular:0.3 /bin/bash