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Paperspace Blog

Machine Learning • Artificial Intelligence • 3D Graphics • Cloud GPUs

Latest

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Latest

Generalist models on Paperspace: Images, text, audio and video combined

A new class of deep learning called a generalist model is capable of running on images, text, audio, video and more all at the same time. Here we explore the capabilities of 3 of these models: Perceiver IO, Data2vec, and Gato. We show how to run Perceiver IO on Paperspace.

By Nick Ball

• 16 hours ago

public

Tutorial

Versatile Diffusion: the first unified multi-flow multimodal diffusion framework

In this blogpost, we examined the architecture and capabilities of the Versatile Diffusion framework. We then demonstrated this model within a Gradient Notebook to perform txt2img, img2txt, image variation, text variation, dual-guided, and Latent Image to Text to Image synthesis.

By James Skelton

• 7 days ago

public

Tutorial

Fast Track to Deployments

Follow this guide to learn how to deploy your model with FastAPI using a Gradient Deployment. Readers should expect to learn how to upload their trained model as a Gradient model artifact, create a Docker image that will serve their model, and deploy their image on Gradient using a deployment.

By Joshua Robison

• 9 days ago

public

Tutorial

Training, Validation and Accuracy in PyTorch

In this article, we examine the processes of implementing training, undergoing validation, and obtaining accuracy metrics - theoretically explained at a high level. We then demonstrate them by combining all three processes in a class, and using them to train a convolutional neural network.

By Oreolorun Olu-Ipinlaye

• 13 days ago

public

Tutorial

End-to-end Data Science on Gradient: Nvidia Merlin

In this article, we discuss the process of conducting end-to-end data science on Gradient with Nvidia Merlin. This includes walkthroughs on 3 examples: Multi-stage recommenders, training and serving a MovieLens model, and scaling for the massive Criteo dataset.

By Nick Ball

• 14 days ago

public

Tutorial

Convolutional Neural Network Dimensions & Model Performance

In this article, we explore what dimensions imply in a convolutional neural network context. We show how to create a custom convnet as a baseline model, and then proceed to create new versions of it, with increased width, increased depth and the last one with both increased depth and width.

By Oreolorun Olu-Ipinlaye

• 15 days ago

public

Tutorial

Class Imbalance in Image Datasets & It's Effect on Convolutional Neural Networks

This tutorial provides step-by-step instructions on how to handle class imbalances in image datasets for training computer vision models.

By Oreolorun Olu-Ipinlaye

• 20 days ago

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Tutorial

Stable Diffusion Tutorial Part 2: Using Textual Inversion Embeddings to gain substantial control over your generated images

This tutorial shows in detail how to train Textual Inversion for Stable Diffusion in a Gradient Notebook, and use it to generate samples that accurately represent the features of the training images using control over the prompt.

By James Skelton

• 21 days ago

public

Tutorial

Data Augmentation: A Class Imbalance Mitigative Measure

In this article, we took a look at data augmentation as an upsampling technique for handing class imbalance by looking at 5 sample methods. Thereafter, we augment a dataset and train it on a convnet using said dataset show how it improved accuracy and recall scores.

By Oreolorun Olu-Ipinlaye

• 22 days ago

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