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Jupyter notebooks that use the Fastai library
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Jupyter notebooks that use the Fastai library
In this notebook (nbviewer version), we show how to use Pytorch.
In this notebook (nbviewer version), we show how to use the fastai code from the first notebook and second notebook of the 2020 fastai course with another dataset of images. The idea is to prove the possibility of quickly reusing the fastai code with its own data.
In this notebook (nbviewer version), we show how to use the fastai code from the first notebook of the 2020 fastai course with another dataset of images. The idea is to prove the possibility of quickly reusing the fastai code with its own data.
In this notebook (pdf, nbviewer version), a Web App is created from the training of a Deep Learning model which uses all the fastai v2 techniques taught by Jeremy Howard in his 2020 course (Practical Deep Learning for Coders). A post in medium (Product based on a Deep Learning model (by fastai v2)) gives all explanation about the use of its app.
[ Update of the notebook - 04/19/2021 ] notebook (nbviewer version)
In this notebook (nbviewer version), instead of training from scratch, we will see how to fine-tune in just over a day, on one GPU and with a little more than 1GB of training data an English pre-trained transformer-based language model to any another language. As a practical case, we fine-tune to Portuguese the English pre-trained GPT-2 by wrapping the Transformers and Tokenizers libraries of Hugging Face into fastai v2. We thus create a new language model: GPorTuguese-2, a language model for Portuguese text generation (and more NLP tasks...).
Note: as the full notebook is very detailed, use this fast notebook (nbviewer version) if you just want to run the code without explanation.
In this study, we will see that, while it is true that a BBPE tokenizer (Byte-level Byte-Pair-Encoding) trained on a huge monolingual corpus can tokenize any word of any language (there is no unknown token), it requires on average almost 70% of additional tokens when it is applied to a text in a language different from that used for its training. This information is key when it comes to choosing a tokenizer to train a natural language model like a Transformer model.
The script 05_pet_breeds_DDP.py gives the code to run for training a Deep Learning model in Distributed Data Parallel (DDP) mode with fastai v2. It is inspired by the notebook 05_pet_breeds.ipynb from the fastbook (fastai v2), the Distributed and parallel training fastai v2 documentation and the notebook train_imagenette.py.
In order to get it run, you need to launch the following command within a fastai 2 virtual environment in a Terminal of a server with at least 2 GPUs:
python -m fastai2.launch 05_pet_breeds_DDP.py
The notebook 05_pet_breeds_DataParallel.ipynb (nbviewer version) gives the code to run for training a Deep Learning model in Data Parallel (DP) mode with PyTorch and fastai v2. It is inspired by the notebook 05_pet_breeds.ipynb from the fastbook (fastai v2), the Distributed and parallel training fastai v2 documentation and the notebook train_imagenette.py.
The objective of this notebook (nbviewer version) is to explain how to create parameters groups for a model with fastai v2 in order to train each one with a different learning rate, how to pass the list of Learning rates and how to check the Learning Rates effectively used by the Optimizer during the training.
The objective of this notebook is to explain how fastai v2 deals with batch sizes for the training and validation datasets.
The objective of this notebook is to show that the sizes of pkl files created by learn.export() of fastai v2 are different depending on the batch size used. This is odd, no?
Este repositório pode user usado como ponto de partida para fazer deploy de modelos do fastai no Heroku.
A aplicativo simples descrito aqui está em https://glasses-or-not.herokuapp.com/. Teste com imagens de você com e sem oculos!
Este é um tutorial rápido para fazer o deploy no Heroku dos seus modelos treinados com apenas alguns cliques. Ele vem com este repositório template que usa o modelo de Classificação de Ursos do Jeremy Howard da lição 2.
This notebook lesson1-pets_essential_with_xc_to_3c.ipynb (nbviewer) shows how to modify learner.py to a new file learner_xc_to_3c.py (learner x channels to 3 channels) to put a ConvNet in a fastai cnn_learner() before the pre-trained model like resnet (followed by a normalization by imagenet_stats).
This ConvNet as first layer allows to transform any images of the dataloader with n channels to an image with 3 channels. During the training, the filters of this ConvNet as first layer will be learnt. Thanks to that, it is possible to go on using fastai Transfer Learning functions even with images with more than 3 channels RGB like satellite images.
Warning As the Oxford IIIT Pet dataset already has 3 channels by image, there is no need here to change this number of channels. We only used this dataset to create our code. However, it would be more interesting to apply this code to images with more than 3 channels like images with 16 channels of the Dstl Satellite Imagery Feature Detection.
Following our publication of the WikiExtractor.py file which is platform-independent (ie running on all platforms, especially Windows), we publish our nlputils2.py file, which is the platform-independent version of the nlputils.py file of the fastai NLP course (more: we have split the original methods into many to use them separately and we have added one that cleans a text file).
[ EDIT 09/23/2019 ]
Selected from shared topics, language and repository description—not editorial ratings.
ageron /
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
90/100 healthExample 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
89/100 healthjupyterlab /
An open source extension that connects AI agents to computational notebooks in JupyterLab.
ageron /
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, PyTorch, and Hugging Face libraries.
85/100 healthtomasonjo /
Jupyter notebooks that support my graph data science blog posts at https://bratanic-tomaz.medium.com/
71/100 healthDeqianBai /
A series of Jupyter notebooks with Chinese comment that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
81/100 health