fast.ai lesson 1 - training on Notebook Instance and export to torch.jit model¶

Overview¶

This notebook shows how to use the SageMaker Python SDK to train your fast.ai model on a SageMaker notebook instance then export it as a torch.jit model to be used for inference on AWS Lambda.

Set up the environment¶

You will need a Jupyter notebook with the boto3 and fastai libraries installed. You can do this with the command pip install boto3 fastai

This notebook was created and tested on a single ml.p3.2xlarge notebook instance.

Train your model¶

We are going to train a fast.ai model as per Lesson 1 of the fast.ai MOOC course locally on the SageMaker Notebook instance. We will then save the model weights and upload them to S3.

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%reload_ext autoreload
%autoreload 2
%matplotlib inline

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import os
import io
import tarfile

import PIL

import boto3

from fastai.vision import *

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path = untar_data(URLs.PETS); path

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path_anno = path/'annotations'
path_img = path/'images'
fnames = get_image_files(path_img)
np.random.seed(2)
pat = re.compile(r'/([^/]+)_\d+.jpg$')

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bs=64
img_size=299

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data = ImageDataBunch.from_name_re(path_img, fnames, pat, ds_tfms=get_transforms(),
                                   size=img_size, bs=bs//2).normalize(imagenet_stats)

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learn = cnn_learner(data, models.resnet50, metrics=error_rate)

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learn.lr_find()
learn.recorder.plot()

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learn.fit_one_cycle(8)

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learn.unfreeze()
learn.fit_one_cycle(3, max_lr=slice(1e-6,1e-4))

Export model and upload to S3¶

Now that we have trained our model we need to export it, create a tarball of the artefacts and upload to S3.

First we need to export the class names from the data object into a text file.

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save_texts(path_img/'models/classes.txt', data.classes)

Now we need to export the model in the PyTorch TorchScript format so we can load into an AWS Lambda function.

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trace_input = torch.ones(1,3,img_size,img_size).cuda()
jit_model = torch.jit.trace(learn.model.float(), trace_input)
model_file='resnet50_jit.pth'
output_path = str(path_img/f'models/{model_file}')
torch.jit.save(jit_model, output_path)

Next step is to create a tarfile of the exported classes file and model weights.

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tar_file=path_img/'models/model.tar.gz'
classes_file='classes.txt'

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with tarfile.open(tar_file, 'w:gz') as f:
    f.add(path_img/f'models/{model_file}', arcname=model_file)
    f.add(path_img/f'models/{classes_file}', arcname=classes_file)

Now we need to upload the model tarball to S3.

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s3 = boto3.resource('s3')
s3.meta.client.upload_file(str(tar_file), 'REPLACE_WITH_YOUR_BUCKET_NAME', 'fastai-models/lesson1/model.tar.gz')