Public Release v1.0

travis test

links

fix readme

Public Release v1.0

.firebaserc

@@ -0,0 +1,5 @@
+{
+ "projects": {
+ "default": "alzheimersai-2121"
+ }
+}

.gitignore

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+venv/
+
+*.pyc
+__pycache__/
+
+instance/
+
+.pytest_cache/
+.coverage
+htmlcov/
+
+dist/
+build/
+*.egg-info/
+
+.vscode/
+
+page_design/
+
+.firebase/
+
+firebase-debug.log
+node_modules/
+functions/node_modules/
+
+package-lock.json
+
+*.pth
+
+static/assets/usr/*
+!.keep
+
+server/src/templates/usr_data/*
+!.keep
+
+export.pkl

.travis.yml

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+matrix:
+ include:
+ - language: generic
+ python: "3.7"
+ node_js: "12.12.0"
+ services: docker
+ env:
+ - GCP_PROJECT_ID=alzheimersai-2121
+ - IMAGE=gcr.io/alzheimersai-2121/alzheimersai
+ - CLOUD_RUN_SERVICE=alzheimersai
+ - CLOUD_RUN_REGION=us-central1
+ - CLOUDSDK_CORE_DISABLE_PROMPTS=1 # prevent gcloud from prompting
+ before_install:
+ - nvm install node
+ - npm i -g firebase-tools
+ - pip install gdown --user
+ - python travis_builder.py
+ - openssl aes-256-cbc -K $encrypted_98d237b7dbf4_key -iv $encrypted_98d237b7dbf4_iv -in google-key.json.enc -out google-key.json -d
+ - curl https://sdk.cloud.google.com | bash > /dev/null
+ - source "$HOME/google-cloud-sdk/path.bash.inc"
+ - gcloud auth activate-service-account --key-file=google-key.json
+ - gcloud auth configure-docker # enable "docker push" to gcr
+ - gcloud config set project "${GCP_PROJECT_ID}"
+ - gcloud config set builds/use_kaniko True
+ - gcloud components install beta # until Cloud Run is generally available (GA)
+ install: true
+ script:
+ - |
+ set -ex;
+ docker build -t "${IMAGE}:${TRAVIS_COMMIT}" ./server && \
+ docker push "${IMAGE}:${TRAVIS_COMMIT}" && \
+ gcloud beta run deploy "${CLOUD_RUN_SERVICE}" --memory=1Gi \
+ --image="${IMAGE}:${TRAVIS_COMMIT}" \
+ --platform=managed \
+ --region="${CLOUD_RUN_REGION}" \
+ --allow-unauthenticated;
+ set +x
+ deploy:
+ provider: firebase
+ token:
+ secure: "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"

AI/NeuralZ-training/.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

AI/NeuralZ-training/.ipynb_checkpoints/Untitled1-checkpoint.ipynb

@@ -0,0 +1,109 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from fastai.vision import *\n",
+ "import numpy as np\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path = Path('../data/train')\n",
+ "path.ls()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.random.seed(42)\n",
+ "data = ImageDataBunch.from_folder(path, train=\".\", valid_pct=0.2,\n",
+ " ds_tfms=get_transforms(), size=128, num_workers=4).normalize(imagenet_stats)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data.classes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data.classes, data.c, len(data.train_ds), len(data.valid_ds)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = cnn_learner(data, models.resnet101, metrics=error_rate)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn.fit_one_cycle(4)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn.save('stage-1')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

AI/NeuralZ-training/.ipynb_checkpoints/Untitled2-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

AI/NeuralZ-training/.ipynb_checkpoints/Untitled3-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

AI/NeuralZ-training/.ipynb_checkpoints/Untitled4-checkpoint.ipynb

@@ -0,0 +1,140 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from fastai.vision import *\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import torch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path = Path('../data/train')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[WindowsPath('../data/train/MildDemented'),\n",
+ " WindowsPath('../data/train/models'),\n",
+ " WindowsPath('../data/train/ModerateDemented'),\n",
+ " WindowsPath('../data/train/NonDemented'),\n",
+ " WindowsPath('../data/train/VeryMildDemented')]"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "path.ls()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.random.seed(42)\n",
+ "data = ImageDataBunch.from_folder(path, train=\".\", valid_pct=0.2,\n",
+ " size=128, num_workers=4).normalize(imagenet_stats)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data.classes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data.classes, data.c, len(data.train_ds), len(data.valid_ds)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn = cnn_learner(data, models.resnet101, metrics=error_rate)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn.fit_one_cycle(4)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learn.save('stage-1') # Save the first stage of the model\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.7.7 64-bit ('python36': conda)",
+ "language": "python",
+ "name": "python37764bitpython36condadac793e125124000a42e65e438db1e77"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}