![]() ![]() Conda Installīashconda install -c pytorch -c fastai fastai More advanced installation issues, such as installing only partial dependencies are covered in a dedicated installation doc. ![]() If you are planning on using fastai in the jupyter notebook environment, make sure to also install the corresponding packages. If you experience installation problems, please read about installation issues. Instead use the normal pytorch and it works with and without GPU. Starting with pytorch-1.x you no longer need to install a special pytorch-cpu version. It's not that you must, but if you experience problems with any dependency packages, please consider using a fresh virtual environment just for fastai. It's highly recommended you install fastai and its dependencies in a virtual environment ( conda or others), so that you don't interfere with system-wide python packages. Note that PyTorch v1 and Python 3.6 are the minimal version requirements. At the moment you can't just run install, since you first need to get the correct pytorch version installed - thus to get fastai-1.x installed choose one of the installation recipes below using your favorite python package manager. Since Macs don't currently have good Nvidia GPU support, we do not currently prioritize Mac development.įastai-1.x can be installed with either conda or pip package managers and also from source. Windows support is at an experimental stage: it should work fine but it's much slower and less well tested. NB: fastai v1 currently supports Linux only, and requires PyTorch v1 and Python 3.6 or later. the machine learning course, which isn't updated for v1) you need to use fastai 0.7 please follow the installation instructions here. If you're following along with a course at (i.e. This document is written for fastai v1, which we use for the current version the deep learning courses. Pythonfrom fastai.vision import *path = untar_data(MNIST_PATH)data = image_data_from_folder(path)learn = cnn_learner(data, models.resnet18, metrics=accuracy)learn.fit(1) Note for students ![]() For instance, here's how to train an MNIST model using resnet18 (from the vision example): For brief examples, see the examples folder detailed examples are provided in the full documentation. The library is based on research into deep learning best practices undertaken at fast.ai, and includes \"out of the box\" support for vision, text, tabular, and collab (collaborative filtering) models. The fastai library simplifies training fast and accurate neural nets using modern best practices. The fastai deep learning library, plus lessons and tutorials ![]()
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