Add fastai example

examples/README.md

@@ -14,10 +14,12 @@
 
 - [Iris classification](pytorch/iris-classifier): deploy a model to classify iris flowers.
 
-- [Search completion](pytorch/search-completer): deploy a Facebook's RoBERTa model to complete search terms.
-
 - [Text generation](pytorch/text-generator): deploy Hugging Face's DistilGPT2 model to generate text.
 
+- [Sentiment analysis](pytorch/sentiment-analyzer): deploy a fastai model for sentiment analysis.
+
+- [Search completion](pytorch/search-completer): deploy a Facebook's RoBERTa model to complete search terms.
+
 - [Answer generation](pytorch/answer-generator): deploy Microsoft's DialoGPT model to answer questions.
 
 - [Text summarization](pytorch/text-summarizer): deploy a BERT model to summarize text.

examples/pytorch/sentiment-analyzer/README.md

@@ -0,0 +1 @@
+Please refer to the [tutorial](https://cortex.dev/iris-classifier) to see how to deploy an example with Cortex.

examples/pytorch/sentiment-analyzer/cortex.yaml

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+# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+
+- kind: deployment
+  name: sentiment
+
+- kind: api
+  name: analyzer
+  predictor:
+    type: python
+    path: predictor.py
+  compute:
+    cpu: 1

examples/pytorch/sentiment-analyzer/fastai_sentiment_ulmfit.ipynb

@@ -0,0 +1,127 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "fastai-sentiment-ulmfit.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U7wT7Te0Jxzr",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Exporting ULMFiT with fastai\n",
+        "WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`\n",
+        "\n",
+        "This file includes code which was modified from https://docs.fast.ai/text.html\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "pPj45JaXsmfT",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "from fastai.text import *\n",
+        "import pandas as pd"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_BB4WshHJuZ3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "path = untar_data(URLs.IMDB_SAMPLE)\n",
+        "path"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CJ4B9JVgJvUC",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "df = pd.read_csv(path/'texts.csv')\n",
+        "df.head()\n",
+        "\n",
+        "# Language model data\n",
+        "data_lm = TextLMDataBunch.from_csv(path, 'texts.csv')\n",
+        "\n",
+        "# Classifier model data\n",
+        "data_clas = TextClasDataBunch.from_csv(path, 'texts.csv', vocab=data_lm.train_ds.vocab, bs=32)\n",
+        "data_lm.save('data_lm_export.pkl')\n",
+        "data_clas.save('data_clas_export.pkl')\n",
+        "data_lm = load_data(path, 'data_lm_export.pkl')\n",
+        "data_clas = load_data(path, 'data_clas_export.pkl', bs=16)\n",
+        "\n",
+        "# Fine-tune model\n",
+        "learn = language_model_learner(data_lm, AWD_LSTM, drop_mult=0.5)\n",
+        "learn.fit_one_cycle(1, 1e-2)\n",
+        "learn.unfreeze()\n",
+        "learn.fit_one_cycle(1, 1e-3)\n",
+        "learn.predict(\"This is a review about\", n_words=10)\n",
+        "learn.save_encoder('ft_enc')\n",
+        "learn = text_classifier_learner(data_clas, AWD_LSTM, drop_mult=0.5)\n",
+        "learn.load_encoder('ft_enc')\n",
+        "data_clas.show_batch()\n",
+        "learn.fit_one_cycle(1, 1e-2)\n",
+        "learn.freeze_to(-2)\n",
+        "learn.fit_one_cycle(1, slice(5e-3/2., 5e-3))\n",
+        "learn.unfreeze()\n",
+        "learn.fit_one_cycle(1, slice(2e-3/100, 2e-3))\n",
+        "learn.predict(\"This was a great movie!\")\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PhaDZglceCro",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Connect to Drive\n",
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SftzK0UadjZB",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Export model\n",
+        "learn.export('/content/drive/My Drive/export.pkl')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}

examples/pytorch/sentiment-analyzer/predictor.py

@@ -0,0 +1,19 @@
+# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+
+from fastai.text import *
+import requests
+
+
+class PythonPredictor:
+    def __init__(self, config):
+        req = requests.get(
+            "https://cortex-examples.s3-us-west-2.amazonaws.com/pytorch/sentiment-analyzer/export.pkl"
+        )
+        with open("export.pkl", "wb") as model:
+            model.write(req.content)
+
+        self.predictor = load_learner(".")
+
+    def predict(self, payload):
+        prediction = self.predictor.predict(payload["text"])
+        return prediction[0].obj

examples/pytorch/sentiment-analyzer/requirements.txt

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+fastai==1.*

examples/pytorch/sentiment-analyzer/sample.json

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+{
+  "text": "best day ever"
+}