PyPI 官网下载 | fastai_xla_extensions-0.0.5.tar.gz

# FastAI XLA Extensions Library > The FastAI XLA Extensions library package allows your fastai/Pytorch models to run on TPUs using the Pytorch-XLA library. ## Install `pip install git+https://github.com/butchland/fastai_xla_extensions` ## How to use ### Configure TPU Environment Access The Pytorch XLA package requires an environment supporting TPUs (Kaggle kernels, GCP or Colab environments required). Nominally, Pytorch XLA also supports GPUs so please see the [Pytorch XLA site for more instructions](https://pytorch.org/xla/release/1.7/index.html). If running on Colab, make sure the Runtime Type is set to TPU. ## Install fastai Use the latest fastai and fastcore versions ``` #hide_output #colab !pip install -Uqq fastcore --upgrade !pip install -Uqq fastai --upgrade ``` ## Install Pytorch XLA This is the official way to install Pytorch-XLA 1.7 as per the [instructions here](https://colab.research.google.com/github/pytorch/xla/blob/master/contrib/colab/getting-started.ipynb#scrollTo=CHzziBW5AoZH) ``` #hide_output #colab !pip install -Uqq cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.7-cp36-cp36m-linux_x86_64.whl ``` ## Check if XLA is available ### Import the libraries Import the fastai and fastai_xla_extensions libraries ``` #colab #hide_output import fastai_xla_extensions.core ``` ``` from fastai.vision.all import * ``` ### Example Build a MNIST classifier -- adapted from fastai course [Lesson 4 notebook](https://github.com/fastai/course-v4/blob/master/nbs/04_mnist_basics.ipynb) Load MNIST dataset ``` path = untar_data(URLs.MNIST_TINY) ``` Create Fastai DataBlock ``` datablock = DataBlock( blocks=(ImageBlock,CategoryBlock), get_items=get_image_files, splitter=GrandparentSplitter(), get_y=parent_label, item_tfms=Resize(28), batch_tfms=aug_transforms(do_flip=False,min_scale=0.8) ) ``` ``` #colab datablock.summary(path) ``` Setting-up type transforms pipelines Collecting items from /root/.fastai/data/mnist_tiny Found 1428 items 2 datasets of sizes 709,699 Setting up Pipeline: PILBase.create Setting up Pipeline: parent_label -> Categorize -- {'vocab': None, 'sort': True, 'add_na': False} Building one sample Pipeline: PILBase.create starting from /root/.fastai/data/mnist_tiny/train/7/7770.png applying PILBase.create gives PILImage mode=RGB size=28x28 Pipeline: parent_label -> Categorize -- {'vocab': None, 'sort': True, 'add_na': False} starting from /root/.fastai/data/mnist_tiny/train/7/7770.png applying parent_label gives 7 applying Categorize -- {'vocab': None, 'sort': True, 'add_na': False} gives TensorCategory(1) Final sample: (PILImage mode=RGB size=28x28, TensorCategory(1)) Collecting items from /root/.fastai/data/mnist_tiny Found 1428 items 2 datasets of sizes 709,699 Setting up Pipeline: PILBase.create Setting up Pipeline: parent_label -> Categorize -- {'vocab': None, 'sort': True, 'add_na': False} Setting up after_item: Pipeline: Resize -- {'size': (28, 28), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (2, 0), 'p': 1.0} -> ToTensor Setting up before_batch: Pipeline: Setting up after_batch: Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} -> Warp -- {'magnitude': 0.2, 'p': 1.0, 'draw_x': None, 'draw_y': None, 'size': None, 'mode': 'bilinear', 'pad_mode': 'reflection', 'batch': False, 'align_corners': True, 'mode_mask': 'nearest'} -> RandomResizedCropGPU -- {'size': None, 'min_scale': 0.8, 'ratio': (1, 1), 'mode': 'bilinear', 'valid_scale': 1.0, 'p': 1.0} -> Brightness -- {'max_lighting': 0.2, 'p': 1.0, 'draw': None, 'batch': False} Building one batch Applying item_tfms to the first sample: Pipeline: Resize -- {'size': (28, 28), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (2, 0), 'p': 1.0} -> ToTensor starting from (PILImage mode=RGB size=28x28, TensorCategory(1)) applying Resize -- {'size': (28, 28), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (2, 0), 'p': 1.0} gives (PILImage mode=RGB size=28x28, TensorCategory(1)) applying ToTensor gives (TensorImage of size 3x28x28, TensorCategory(1)) Adding the next 3 samples No before_batch transform to apply Collating items in a batch Applying batch_tfms to the batch built Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} -> Warp -- {'magnitude': 0.2, 'p': 1.0, 'draw_x': None, 'draw_y': None, 'size': None, 'mode': 'bilinear', 'pad_mode': 'reflection', 'batch': False, 'align_corners': True, 'mode_mask': 'nearest'} -> RandomResizedCropGPU -- {'size': None, 'min_scale': 0.8, 'ratio': (1, 1), 'mode': 'bilinear', 'valid_scale': 1.0, 'p': 1.0} -> Brightness -- {'max_lighting': 0.2, 'p': 1.0, 'draw': None, 'batch': False} starting from (TensorImage of size 4x3x28x28, TensorCategory([1, 1, 1, 1])) applying IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} gives (TensorImage of size 4x3x28x28, TensorCategory([1, 1, 1, 1])) applying Warp -- {'magnitude': 0.2, 'p': 1.0, 'draw_x': None, 'draw_y': None, 'size': None, 'mode': 'bilinear', 'pad_mode': 'reflection', 'batch': False, 'align_corners': True, 'mode_mask': 'nearest'} gives (TensorImage of size 4x3x28x28, TensorCategory([1, 1, 1, 1])) applying RandomResizedCropGPU -- {'size': None, 'min_scale': 0.8, 'ratio': (1, 1), 'mode': 'bilinear', 'valid_scale': 1.0, 'p': 1.0} gives (TensorImage of size 4x3x27x27, TensorCategory([1, 1, 1, 1])) applying Brightness -- {'max_lighting': 0.2, 'p': 1.0, 'draw': None, 'batch': False} gives (TensorImage of size 4x3x27x27, TensorCategory([1, 1, 1, 1])) Create the dataloader ``` dls = datablock.dataloaders(path) ``` ``` #colab dls.show_batch() ```  Create a Fastai CNN Learner ``` learner = cnn_learner(dls, resnet18, metrics=accuracy) ``` ``` #colab learner.summary() ``` <table border="1" class="dataframe"> <thead> <tr style="text-align: left;"> <th>epoch</th> <th>train_loss</th> <th>valid_loss</th> <th>accuracy</th> <th>time</th> </tr> </thead> <tbody> <tr> <td>0</td> <td>None</td> <td>None</td> <td>00:00</td> </tr> </tbody> </table> Sequential (Input shape: ['64 x 3 x 28 x 28']) ================================================================ Layer (type) Output Shape Param # Trainable ================================================================ Conv2d 64 x 64 x 14 x 14 9,408 True ________________________________________________________________ BatchNorm2d 64 x 64 x 14 x 14 128 True ________________________________________________________________ ReLU 64 x 64 x 14 x 14 0 False ________________________________________________________________ MaxPool2d 64 x 64 x 7 x 7 0 False ________________________________________________________________ Conv2d 64 x 64 x 7 x 7 36,864 True ________________________________________________________________ BatchNorm2d 64 x 64 x 7 x 7 128 True ________________________________________________________________ ReLU 64 x 64 x 7 x 7 0 False ________________________________________________________________ Conv2d 64 x 64 x 7 x 7 36,864 True ________________________________________________________________ BatchNorm2d 64 x 64 x 7 x 7 128 True __________________