#Fastai multilabel evaluation free#The main advantage though is that you are free to create any kind of Virtual Machines for your project. #Fastai multilabel evaluation install#You basically have to create a Virtual Machine and install everything you need for your project. Working with GCP definitely requires more time and more work. #Fastai multilabel evaluation for free#You can even get 300$ of credits for free for one year when registering. It provides many services for a reasonable price. Google Cloud Platform seems to be the perfect place for that matter. Since we want to be able to export our model and re-train our AI every month in an automatic way, we need to find a better host for our task where we can deeply custom our environment according to our needs. But for serious deep learning projects, you will have to switch to a real provider that gives you more flexibility, more ressources. I see Kaggle as a nice website where you can play and try different kind of things. While Kaggle seems to be the perfect place for our project, it has some limitations. #Fastai multilabel evaluation how to#You can find a nice tutorial on how to start using FastAI on Kaggle on their official website. He got excellent results with only a few lines of code. Actually, this project was heavily inspired by this kernel that you can fork where Jeremy Howard - the creator of FastAI -, use his library to participate to the famous Planet: Understanding the Amazon from Space competition. Within a few clicks, you can start training an AI!įastAI, the library we are about to use for this project, is available on Kaggle as well. They give you everything you need: a Jupyter-like interface, some disk space, a good CPU and a GPU. You can then join competitions and fight with other data scientists directly on their website. In fact, Kaggle also provides to data scientists hundreds of datasets for free to work on, courses to learn the most used tools in the field and more importantly, a ready-to-use environment with great ressources for free. You are free to use any deep learning library you wish to.īut say that Kaggle is only about competitions would be incorrect. After joining a competition, you are given a dataset and you have to create an AI that gives the best results on it. Kaggle is a well-known community website for data scientists to compete in deep learning challenges. # Kaggle, a place for data science projects That’s fortunate since there are two main platforms online which can provide in a very easy way the environment we are looking for. Therefore, we need to find a platform that can give us these ressources for a cheap price or even for free, on demand. But Computer Vision needs a lot of ressources, especially a good GPU - which our servers don’t have. Our website runs on two dedicated servers with great hardware to serve a Ruby on Rails app. To train our AI with FastAI, we need to find the best environment available that can handle working over 250 000 images in a short time. Then, we will pick FastAI, one of the most promising deep learning librairies available nowadays, to make our AI and hopefully have, at the end of the article, a good model that we could export in order to use it on our website. In this second part of the series, we are first going to find and create an environment suitable for this heavy task. But how to do that? What library can we pick to make this AI? Where are we going to find the CPU and GPU ressources needed to train our AI quickly? We are now going to use this dataset to create and train an AI that will be able to detect and suggest elements of the images uploaded by users. Our final dataset: an image ID and its tags.
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