Table of Contents
About The Project
It is a simple example demonstrates how to use the Wandb API to monitor and analyze your Machine Learning projects.
Wandb page of this project: https://wandb.ai/anon234523452346/CIFAR10
With free Wandb account you will have a 100 GB of cloud storage for your logs, models, artifacts, and other data.
It also uses Docker and Docker Compose. And runs on GPU and CPU as well.
Getting Started
- First thing first, you should have a Wandb account to monitor your experiments.
- You need a Wandb API key. You can get one from Wandb account settings.
Dependencies
1. Python
tensorflow==2.9.1
tensorflow_datasets==4.6.0
keras-cv==0.2.6
PyYAML>=6.0
wandb>=0.12.17
matplotlib>=3.5.2
2. wandb account for tracking your experiments.
- Create here: https://wandb.ai/signup
Installation
- Clone this repo
git clone https://github.com/Alex-Kopylov/wandb-tensorflow-example.git
- Setup your environment. You have several options:
Docker is preferable for further integration in complex CI/CD pipelines.
docker compose build
docker compose up
You can use Conda or default Python virtual environment.
conda create -n wandb-tensorflow python=3.8
conda activate wandb-tensorflow
pip install -r requirements.txt
Hints
- For dry run:
$ wandb disabled
$ export WANDB_MODE=disabled
wandb.init(mode="disabled")