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TensorFlow with Cudo Compute

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)

Quick start guide

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

Prerequisites

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

Running TensorFlow on Cudo Compute with Docker

SSH into your VM and run the following commands

docker run --gpus all -it --rm tensorflow/tensorflow:latest-gpu

Or for the NVIDIA optimised 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")

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

Learn more about TensorFlow on Cudo Compute

or get started with Cudo Compute.