Metadata-Version: 2.1
Name: fastai
Version: 1.0.38
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
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        # fastai
        
        The fastai library simplifies training fast and accurate neural nets using modern best practices. See the [fastai website](https://docs.fast.ai) to get started. The library is based on research into deep learning best practices undertaken at [fast.ai](http://www.fast.ai), and includes \"out of the box\" support for [`vision`](https://docs.fast.ai/vision.html#vision), [`text`](https://docs.fast.ai/text.html#text), [`tabular`](https://docs.fast.ai/tabular.html#tabular), and [`collab`](https://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](https://docs.fast.ai/). 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 = create_cnn(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`. Please ignore the rest of this document, which is written for `fastai v1`, and instead follow the installation instructions [here](https://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
        
        **NB:** *fastai v1 currently supports Linux only, and requires **PyTorch v1** and **Python 3.6** or later. Windows support is at an experimental stage: it should work fine but we haven't thoroughly tested it. Since Macs don't currently have good Nvidia GPU support, we do not currently prioritize Mac development.*
        
        `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. Note that **PyTorch v1** and **Python 3.6** are the minimal version requirements.
        
        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).
        
        More advanced installation issues, such as installing only partial dependencies are covered in a dedicated [installation doc](https://docs.fast.ai/install.html).
        
        ### Conda Install
        
        ```bash
        conda install -c pytorch -c fastai fastai
        ```
        
        Note that JPEG decoding can be a bottleneck, particularly if you have a fast GPU. You can optionally install an optimized JPEG decoder as follows (Linux):
        
        ```bash
        conda uninstall --force jpeg libtiff -y
        conda install -c conda-forge libjpeg-turbo
        CC="cc -mavx2" pip install --no-cache-dir -U --force-reinstall --no-binary :all: --compile pillow-simd
        ```
        If you only care about faster JPEG decompression, it can be `pillow` or `pillow-simd` in the last command above, the latter speeds up other image processing operations. For the full story see [Pillow-SIMD](https://docs.fast.ai/performance.html#faster-image-processing).
        
        ### PyPI Install
        
        ```bash
        pip install fastai
        ```
        
        ### Developer Install
        
        The following instructions will result in a [pip editable install](https://pip.pypa.io/en/stable/reference/pip_install/#editable-installs), so that you can `git pull` at any time and your environment will automatically get the updates:
        
        ```bash
        git clone https://github.com/fastai/fastai
        cd fastai
        tools/run-after-git-clone
        pip install -e ".[dev]"
        ```
        
        Note that this will install the `cuda9.0` `pytorch` build via default dependencies. If you need a higher or lower `cudaXX` build, following the instructions [here]( https://pytorch.org/get-started/locally/), to install the desired `pytorch` build.
        
        Next, you can test that the build works by starting the jupyter notebook:
        
        ```bash
        jupyter notebook
        ```
        and executing an example notebook. For example load `examples/tabular.ipynb` and run it.
        
        Alternatively, you can do a quick CLI test:
        
        ```bash
        jupyter nbconvert --execute --ExecutePreprocessor.timeout=600 --to notebook examples/tabular.ipynb
        ```
        
        Please refer to [CONTRIBUTING.md](https://github.com/fastai/fastai/blob/master/CONTRIBUTING.md) and [Notes For Developers](https://docs.fast.ai/dev/develop.html) 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:
        
           ```bash
           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:
        
           ```bash
           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 refer to the [troubleshooting document](https://docs.fast.ai/troubleshoot.html).
        
        If you encounter installation problems with conda, make sure you have the latest `conda` client (`conda install` will do an update too):
        ```bash
        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 1.0 version supports:
        
            | Platform | GPU    | CPU    |
            |----------|--------|--------|
            | linux    | binary | binary |
            | mac      | source | binary |
            | windows  | binary | binary |
        
           Legend: `binary` = can be installed directly, `source` = needs to be built from source.
        
           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/).
        
        4. How do you know which pytorch cuda version build to choose?
        
           It depends on the version of the installed NVIDIA driver. Here are the requirements for CUDA versions supported by pre-built `pytorch` releases:
        
            | CUDA Toolkit | NVIDIA (Linux x86_64) |
            |--------------|-----------------------|
            | CUDA 10.0    | >= 410.00             |
            | CUDA 9.0     | >= 384.81             |
            | CUDA 8.0     | >= 367.48             |
        
           So if your NVIDIA driver is less than 384, then you can only use `cuda80`. Of course, you can upgrade your drivers to more recent ones if your card supports it.
        
           You can find a complete table with all variations [here](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html).
        
           If you use NVIDIA driver 410+, you most likely want to install the `cuda100` pytorch variant, via:
           ```bash
           conda install -c pytorch pytorch cuda100
           ```
           or if you need a lower version (`cuda90` is installed by default), use:
           ```bash
           conda install -c pytorch pytorch cuda80
           ```
           For other options refer to the complete list of [the available pytorch variants](https://pytorch.org/get-started/locally/).
        
        ## Updates
        
        In order to update your environment, simply install `fastai` in exactly the same way you did the initial installation.
        
        Top level files `environment.yml` and `environment-cpu.yml` belong to the old fastai (0.7). `conda env update` is no longer the way to update your `fastai-1.x` environment. These files remain because the fastai course-v2 video instructions rely on this setup. Eventually, once fastai course-v3 p1 and p2 will be completed, they will probably be moved to where they belong - under `old/`.
        
        
        
        ## History
        
        A detailed history of changes can be found [here](https://github.com/fastai/fastai/blob/master/CHANGES.md).
        
        
        
        ## Copyright
        
        Copyright 2017 onwards, fast.ai, Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this project's files except in compliance with the License. A copy of the License is provided in the LICENSE file in this repository.
        
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
