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TensorFlow is an open source framework for machine learning. With CUDO Compute you can deploy TensorFlow docker containers to the latest NVIDIA Ampere Architecture GPUs. Prebuilt images with NVIDIA drivers and docker and ready to deploy in the marketplace. Common uses for TensorFlow:
  • Deep Neural Networks (DNN)
  • Convolutional Neural Networks (CNN)
  • Conversational AI
  • Recurrent Neural Networks (RNN)
  • Reinforcement Learning
  • Natural Language Processing (NLP)

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Quick start guide

  1. Prerequisites
  2. TensorFlow with Docker
  3. TensorFlow Serving with Docker

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Prerequisites

  • Create a project and add an SSH key
  • Optionally download CLI tool
  • Choose a virtual machine with an NVIDIA GPU and Configure
  • Use the Ubuntu 22.04 + NVIDIA drivers + Docker image (in CLI tool type -image ubuntu-nvidia-docker)

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Running TensorFlow on CUDO Compute with Docker

SSH into your virtual machine and run the following commands 
docker run --gpus all -it --rm tensorflow/tensorflow:latest-gpu
Or for the NVIDIA optimized TensorFlow container 
docker run --gpus all -it --rm nvcr.io/nvidia/tensorflow:22.08-tf2-py3
NGC tags can be found here At the prompt 
python
>>> import tensorflow as tf
>>> tf.config.list_physical_devices("GPU")

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TensorFlow serving on CUDO Compute

docker pull tensorflow/serving:latest-gpu
Try an example model: 
mkdir -p /tmp/tfserving
cd /tmp/tfserving
git clone https://github.com/tensorflow/serving

docker run --gpus all -p 8501:8501 \
--mount type=bind,\
source=/tmp/tfserving/serving/tensorflow_serving/servables/tensorflow/testdata/saved_model_half_plus_two_gpu,\
target=/models/half_plus_two \
  -e MODEL_NAME=half_plus_two -t tensorflow/serving:latest-gpu &

curl -d '{"instances": [1.0, 2.0, 5.0]}' \
  -X POST http://PUBLIC_IP_ADDRESS:8501/v1/models/half_plus_two:predict
Get more information in the official docs