--- title: "AI Notebooks - Tutorial - Weights & Biases integration" description: "How to use wandb in notebooks" url: https://docs.ovhcloud.com/es/guides/public-cloud/ai-machine-learning/ai-notebooks-weights-biases lang: es lastUpdated: 2022-09-01 --- # AI Notebooks - Tutorial - Weights & Biases integration ## Objective The purpose of this tutorial is to show how it is possible to use **Weights & Biases**, one of the most famous Developer tools for machine learning, with OVHcloud AI Notebooks. Weight and Biases allow you to track your machine learning experiments, version your datasets and manage your models easily, like shown below : ![image](/images/public-cloud/ai-machine-learning/notebook-tuto-03-weight-biases/overview_wandb.png) This tutorial presents two examples of using Weights & Biases. In the first notebook we will use TensorFlow and in the second a PyTorch docker image. ## Requirements - access to the OVHcloud Control Panel; - a Public Cloud project created; - a Public Cloud user with the ability to start AI Notebooks; - a Weights & Biases account, you can create it on their [website](https://wandb.ai/site). It's Free for individuals. ## Instructions ### Launch and access a Jupyter notebook The first step consists of creating a Jupyter Notebook with OVHcloud AI Notebooks. First, you have to [install the OVHAI CLI](/es/guides/public-cloud/ai-machine-learning/ai-cli-install-client.md) then just choose the name of the notebook (``) and the number of GPUs (``) to use on your job and use the following command: - TensorFlow image docker: ```bash ovhai notebook run tensorflow jupyterlab \ --name \ --gpu ``` - PyTorch image docker: ```bash ovhai notebook run pytorch jupyterlab \ --name \ --gpu ``` Whatever the selected method, you should now be able to reach your notebook's URL (see in the output of the command, the field `Url:`). ### Experiment with OVHcloud examples notebooks Once the repository has been cloned, find the notebook of your choice. Instructions are directly shown inside the notebooks. You can run them with the standard "Play" button inside the notebook interface. #### Notebook using TensorFlow and Weights & Biases is based on the MNIST dataset The notebook using TensorFlow and Weights & Biases is based on the MNIST dataset. To access it, follow this path: `ai-training-examples` > `notebooks` > `computer-vision` > `image-classification` > `tensorflow` > `weights-and-biases` > `notebook_Weights_and_Biases_MNIST.ipynb` The aim of this tutorial is to show how it is possible, thanks to Weights & Biases, to compare the results of trainings according to the chosen hyperparameters. For example, you can display the accuracy and loss curves for your valid and train data. These metrics will be displayed for each epoch of each training. ![image](/images/public-cloud/ai-machine-learning/notebook-tuto-03-weight-biases/valid_train_metrics_mnist_wandb.png) You can then compare your trainings using the **Parallel coordinates** graph type: ![image](/images/public-cloud/ai-machine-learning/notebook-tuto-03-weight-biases/parallel_coordinates_mnist_wandb.png) You can also compare the **Test error rates**: ![image](/images/public-cloud/ai-machine-learning/notebook-tuto-03-weight-biases/test_error_rate_mnist_wandb.png) A preview of this notebook can be found on [GitHub](https://github.com/ovh/ai-training-examples/tree/main/notebooks/computer-vision/image-classification/tensorflow/weights-and-biases). #### Notebook using PyTorch and Weights & Biases is based on YOLOv5 and the COCO dataset The notebook using PyTorch and Weights & Biases is based on YOLOv5 and the COCO dataset. To access it, follow this path: `ai-training-examples` > `notebooks` > `computer-vision` > `object-detection` > `miniconda` > `weights-and-biases` > `notebook_Weights_and_Biases_yolov5.ipynb` The aim of this tutorial is to show how Weights & Biases can be used with the YOLOv5 real-time object detection framework. In order to achieve this, the YOLOv5 s, m, l and x models performance will be compared on the COCO dataset for the same number of epochs. ![image](/images/public-cloud/ai-machine-learning/notebook-tuto-03-weight-biases/loss_train_valid_yolov5_wandb.png) Another possibility with Weights & Biases is to display the use of your computing resources: ![image](/images/public-cloud/ai-machine-learning/notebook-tuto-03-weight-biases/system_utilization_yolov5_wandb.png) You can also create your report with your curves and images and share it with your team! ![image](/images/public-cloud/ai-machine-learning/notebook-tuto-03-weight-biases/report_yolov5_wandb.png) A preview of this notebook can be found on [GitHub](https://github.com/ovh/ai-training-examples/tree/main/notebooks/computer-vision/object-detection/miniconda/weights-and-biases). ## Conclusion To sum up, **Weights & Biases** allows you to quickly track your experiments, version and iterate data sets, evaluate model performance, reproduce models, visualise results and spot regressions, and share results with your colleagues. You can use it directly on OVHcloud AI Notebooks in few minutes. ## Go further - You can also use the **Weights & Biases** tool in an AI Training job by following this [tutorial](/es/guides/public-cloud/ai-machine-learning/ai-training-models-comparaison-wandb.md). - It is possible to integrate **Weights and Biases** to compare the performance of pre-trained models like ResNet50 for image classification. Take a look at this [notebook](/es/guides/public-cloud/ai-machine-learning/ai-notebooks-transfer-learning-resnet.md). If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/en-gb/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project. ## Feedback Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud)