Metadata-Version: 2.1
Name: fastai
Version: 1.0.7
Summary: fastai makes deep learning with PyTorch faster, more accurate, and easier
Home-page: https://github.com/fastai/fastai
Author: Jeremy Howard
Author-email: info@fast.ai
License: Apache Software License 2.0
Description: [![Build Status](https://dev.azure.com/fastdotai/fastai/_apis/build/status/fastai.fastai)](https://dev.azure.com/fastdotai/fastai/_build/latest?definitionId=1)
        [![fastai version](https://img.shields.io/pypi/v/fastai.svg)](https://pypi.python.org/pypi/fastai)
        [![Anaconda-Server Badge](https://anaconda.org/fastai/fastai/badges/version.svg)](https://anaconda.org/fastai/fastai)
        
        [![Anaconda-Server Badge](https://anaconda.org/fastai/fastai/badges/platforms.svg)](https://anaconda.org/fastai/fastai)
        [![fastai python compatibility](https://img.shields.io/pypi/pyversions/fastai.svg)](https://pypi.python.org/pypi/fastai)
        [![fastai license](https://img.shields.io/pypi/l/fastai.svg)](https://pypi.python.org/pypi/fastai)
        
        # fastai
        
        The fastai library simplifies training fast and accurate neural nets using modern best practices. See the [fastai website](http://docs.fast.ai) to get started. The library is based on research in to deep learning best practices undertaken at [fast.ai](http://www.fast.ai), and includes \"out of the box\" support for [`vision`](http://docs.fast.ai/vision.html#vision), [`text`](http://docs.fast.ai/text.html#text), [`tabular`](http://docs.fast.ai/tabular.html#tabular), and [`collab`](http://docs.fast.ai/collab.html#collab) (collaborative filtering) models. For brief examples, see the [examples](https://github.com/fastai/fastai/tree/master/examples) folder; detailed examples are provided in the full documentation. For instance, here's how to train an MNIST model using [resnet18](https://arxiv.org/abs/1512.03385) (from the [vision example](https://github.com/fastai/fastai/blob/master/examples/vision.ipynb)):
        
        ```python
        untar_data(MNIST_PATH)
        data = image_data_from_folder(MNIST_PATH)
        learn = ConvLearner(data, tvm.resnet18, metrics=accuracy)
        learn.fit(1)
        ```
        
        ## Note for [course.fast.ai](http://course.fast.ai) students
        
        If you are using `fastai` for any [course.fast.ai](http://course.fast.ai) course, you need to use `fastai 0.7.x`. Please ignore the rest of this document, which is written for `fastai 1.0.x`, and instead follow the installation instructions [here](http://forums.fast.ai/t/fastai-v0-install-issues-thread/24652).
        
        *Note: If you want to learn how to use fastai v1 from its lead developer, Jeremy Howard, he will be teaching it in the [Deep Learning Part I](https://www.usfca.edu/data-institute/certificates/deep-learning-part-one) course at the University of San Francisco from Oct 22nd, 2018.*
        
        ## Installation
        
        `fastai-1.x` can be installed with either `conda` or `pip` package managers and also from source. 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 favourite python package manager.
        
        If your system has a [recent NVIDIA card](https://www.geforce.com/hardware/technology/cuda/supported-gpus) with the correctly configured NVIDIA driver please follow the GPU installation instructions. Otherwise, the CPU-ones.
        
        It's highly recommended you install `fastai` and its dependencies in a virtual environment (`[conda](https://conda.io/docs/user-guide/tasks/manage-environments.html)` or others), so that you don't interfere with system-wide python packages. 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`.
        
        If you experience installation problems, please read about [installation issues](https://github.com/fastai/fastai/blob/master/README.md#installation-issues).
        
        
        
        ### Conda Install
        
        * GPU
        
           ```
           conda install -c pytorch pytorch-nightly cuda92
           conda install -c fastai torchvision-nightly
           conda install -c fastai fastai
           ```
        
        * CPU
        
           ```
           conda install -c pytorch pytorch-nightly-cpu
           conda install -c fastai torchvision-nightly-cpu
           conda install -c fastai fastai
           ```
        
        
        ### PyPI Install
        
        * GPU
        
           ```
           pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cu92/torch_nightly.html
           pip install fastai
           ```
        
        * CPU
        
           ```
           pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
           pip install fastai
           ```
        
        NB: this set will also fetch `torchvision-nightly`, which supports `torch-1.x`.
        
        
        
        ### Developer Install
        
        First, follow the instructions above for either `PyPi` or `Conda`. Then uninstall the `fastai` package using the **same package manager you used to install it**, i.e. `pip uninstall fastai` or `conda uninstall fastai`, and then, replace it with a [pip editable install](https://pip.pypa.io/en/stable/reference/pip_install/#editable-installs).
        
        
        ```
        git clone https://github.com/fastai/fastai
        cd fastai
        tools/run-after-git-clone
        pip install -e .[dev]
        ```
        
        You can test that the build works by starting the jupyter notebook:
        
        ```
        jupyter notebook
        ```
        and executing an example notebook. For example load `examples/tabular.ipynb` and run it.
        
        Alternatively, you can do a quick CLI test:
        
        ```
        jupyter nbconvert --execute --ExecutePreprocessor.timeout=600 --to notebook examples/tabular.ipynb
        ```
        
        If anything goes wrong please [read and report installation
        issues](http://forums.fast.ai/t/fastai-v1-install-issues-thread/24111).
        
        Please refer to [CONTRIBUTING.md](https://github.com/fastai/fastai/blob/master/CONTRIBUTING.md) and  [develop.md](https://github.com/fastai/fastai/blob/master/docs/develop.md) for more details on how to contribute to the `fastai` project.
        
        
        
        ### Building From Source
        
        If for any reason you can't use the prepackaged packages and have to build from source, this section is for you.
        
        1. To build `pytorch` from source follow the [complete instructions](https://github.com/pytorch/pytorch#from-source). Remember to first install CUDA, CuDNN, and other required libraries as suggested - everything will be very slow without those libraries built into `pytorch`.
        
        2. Next, you will also need to build `torchvision` from source:
        
           ```
           git clone https://github.com/pytorch/vision
           cd vision
           python setup.py install
           ```
        
        3. When both `pytorch` and `torchvision` are installed, first test that you can load each of these libraries:
        
           ```
           import torch
           import torchvision
           ```
        
           to validate that they were installed correctly
        
           Finally, proceed with `fastai` installation as normal, either through prepackaged pip or conda builds or installing from source ("the developer install") as explained in the sections above.
        
        
        
        ## Installation Issues
        
        If the installation process fails, first make sure [your system is supported](https://github.com/fastai/fastai/blob/master/README.md#is-my-system-supported). And if the problem is still not addressed, please see  [this installation issues thread](http://forums.fast.ai/t/fastai-v1-install-issues-thread/24111).
        
        If you encounter installation problems with conda, make sure you have the latest `conda` client:
        ```
        conda update conda
        ```
        
        Sometimes you have to run the following instead:
        
        ```
        conda install conda
        ```
        
        
        ### Is My System Supported?
        
        1. Python: You need to have python 3.6 or higher
        
        2. CPU or GPU
        
           The `pytorch` binary package comes with its own CUDA, CuDNN, NCCL, MKL, and other libraries so you don't have to install system-wide NVIDIA's CUDA and related libraries if you don't need them for something else. If you have them installed already it doesn't matter which NVIDIA's CUDA version library you have installed system-wide. Your system could have CUDA 9.0 libraries, and you can still use `pytorch` build with `cuda9.2` libraries without any problem, since the `pytorch` binary package is self-contained.
        
           The only requirement is that you have installed and configured the NVIDIA driver correctly. Usually you can test that by running `nvidia-smi`. While it's possible that this application is not available on your system, it's very likely that if it doesn't work, than your don't have your NVIDIA drivers configured properly. And remember that a reboot is always required after installing NVIDIA drivers.
        
        3. Operating System:
        
           Since fastai-1.0 relies on pytorch-1.0, you need to be able to install pytorch-1.0 first.
        
           As of this moment pytorch.org's pre-1.0.0 version (`torch-nightly`) supports:
        
            | Platform | GPU    | CPU    |
            | ---      | ---    | ---    |
            | linux    | binary | binary |
            | mac      | source | binary |
            | windows  | source | source |
        
           Legend: `binary` = can be installed directly, `source` = needs to be built from source.
        
           This will change once `pytorch` 1.0.0 is released and installable packages made available for your system, which could take some time after the official release is made. Please watch for updates [here](https://pytorch.org/get-started/locally/).
        
           If there is no `pytorch` preview conda or pip package available for your system, you may still be able to [build it from source](https://pytorch.org/get-started/locally/).
        
           Alternatively, please consider installing and using the very solid "0.7.x" version of `fastai`. Please see the [instructions](https://github.com/fastai/fastai/tree/master/old).
        
        
        
        ## Copyright
        
        Copyright 2017 onwards, fast.ai, Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. A copy of the License is provided in the LICENSE file in this repository.
        
        
        # Changes
        
        Most recent releases are shown at the top. Each release shows:
        
        - **New**: New classes, methods, functions, etc
        - **Changed**: Additional paramaters, changes to inputs or outputs, etc
        - **Fixed**: Bug fixes that don't change documented behaviour
        
        Note that the top-most release is changes in the unreleased master branch on Github.
        
        <!-- template
        ## 1.0.7dev (Work In Progress)
        
        ### New:
        
        ### Changed:
        
        ### Fixed:
        -->
        
        ## 1.0.7 (2018-10-19)
        
        ### New:
        
        - New class `ImagePoints` for targets that are a set of point coordinates
        - New function `Image.predict(learn:Learner)` to get the activations of the model in `Learner` for an image
        - New function `Learner.validate` to validate on a given dl (default `valid_dl`), with maybe new metrics or callbacks
        - New function `error_rate` which is just `1-accuracy()`
        
        ### Changed:
        
        - `data` is now called `basic_data` to avoid weird conflicts when naming our data objects data.
        - All vision models are now in the `models` module, including torchvision models (where tested and supported). So use `models` instead of `tvm` now. If your preferred torchvision model isn't imported, feel free to test it out and tell us on the forum if it works. And if it doesn't, a PR with a test and a fix would be appreciated!
        - `ImageBBox` is now a subclass of `ImagePoints`
        - All metrics are now `Callback`. You can pass a regular function like `accuracy` that will get averaged over batch or a full `Callback` that can do more complex things
        - All datasets convenience functions and paths are inside the `URLs` class
        - `URLs` that are a sample have name now suffixed with `_SAMPLE`
        
        ### Fixed:
        
        - Fix `WeightDropout` in RNNs when `p=0`
        - `pad_collate` gets its `kwargs` from `TextClasDataBunch`
        - Add small `eps` to `std` in `TabularDataset` to avoid division by zero
        - `fit_one_cycle` doesn't take other callbacks
        - Many broken docs links fixed
        
        ## 1.0.6 (2018-10-01)
        
        - Last release without CHANGES updates
        
        ## 1.0.0 (2018-10-01)
        
        - First release
        
        
Keywords: fastai,deep learning,machine learning
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
