AI Training - Tutorial - Run your first Tensorflow code with GPUs
Example on how to use Tensorflow library with GPUs
Last updated 1st September, 2022.
Objective
This tutorial covers the process of starting a new Jupyter notebook and experiment examples on using GPUs with it.
Requirements
- access to the OVHcloud Control Panel
- an AI Training project created inside a Public Cloud project
- a user for AI Training
- some knowledge about Tensorflow library
Launch and access Jupyter notebook with Tensorflow library
If you want to launch it from the OVHcloud Control Panel, just follow this guide and select the Tensorflow 2 docker image.
If you want to launch it with the CLI, just choose the number of GPUs (<nb-gpus>) to use on jour job and use the following command:
ovhai job run ovhcom/ai-training-tensorflow:2.3.0 --gpu <nb-gpus>
You can then reach your notebook's URL once the job is Running.
Clone the GitHub example repository
The GitHub repository containing all examples for OVHcloud AI TRAINING is available here.
Inside your notebook, open a new Terminal tab by clicking File > New > Terminal.
Run the following command in the notebook's terminal to clone the repository:
git clone https://github.com/ovh/ai-training-examples.git
Experiment with examples notebooks
We currently provide the following tutorials for Tensorflow as ipython notebooks:
- Basic computation using single CPU or GPU: accessible on
notebooks/tensorflow/tuto/basic_gpu_cpu_benchmark.ipynb - Basic computation using multiple GPUs: accessible on
notebooks/tensorflow/tuto/multiple_gpus_computation.ipynb.ipynb
Basic computation using a single CPU or GPU
The aim of this tutorial is to do a very simple tensor computation with the Tensorflow library and to compare performances of running it over CPU versus GPU.
A preview of this notebook can be found on GitHub here.
Basic computation using multiple GPUs
The aim of this tutorial is to do a very simple tensor computation with the Tensorflow library and to compare performances of running it over CPU versus GPU.
A preview of this notebook can be found on GitHub here.
Go further
- You can compare AI models based on resource consumption, accuracy and training time. Refer to this tutorial.
- It is possible to do the same thing with PyTorch based on this notebook.
Feedback
Please send us your questions, feedback and suggestions to improve the service:
- On the OVHcloud Discord server
Did you find this guide useful?
Please feel free to give any suggestions in order to improve this documentation.
Whether your feedback is about images, content, or structure, please share it, so that we can improve it together.
Your support requests will not be processed via this form. To do this, please use the "Create a ticket" form.
Thank you. Your feedback has been received.