fix: auto review (#798)

* feat: tune template

* feat: tune template

* fix: auto review

* fix: auto review

* fix: auto review

* fix: auto review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auto review

* fix: auto review

notebooks/community/ml_ops/stage1/get_started_bq_datasets.ipynb

@@ -90,17 +90,6 @@
         "- Extract data from `BigQuery` table into a `DMatrix` -- compatible for custom training `XGBoost` models."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:gsod,lrg"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). In this version of the dataset you consider the fields year, month and day to predict the value of mean daily temperature (mean_temp)."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -140,8 +129,26 @@
         "- Alternatively:\n",
         "    - Extract the BigQuery table to CSV files.\n",
         "    - Preprocess the CSV files.\n",
-        "    - Create a tf.data.Dataset generator from the CSV files.\n",
-        "    \n",
+        "    - Create a tf.data.Dataset generator from the CSV files."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:gsod,lrg"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). In this version of the dataset you consider the fields year, month and day to predict the value of mean daily temperature (mean_temp)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9e483012a752"
+      },
+      "source": [
         "### Costs\n",
         "This tutorial uses billable components of Google Cloud:\n",
         "\n",

notebooks/community/ml_ops/stage1/get_started_dataflow.ipynb

@@ -90,17 +90,6 @@
         "    - image data"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:gsod,lrg"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). The version of the dataset you use only the fields year, month and day to predict the value of mean daily temperature (mean_temp)."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -137,6 +126,33 @@
         "Alternately for AutoML tabular model training, you can reconfigure the otherwise default preprocessing."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:gsod,lrg"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). The version of the dataset you use only the fields year, month and day to predict the value of mean daily temperature (mean_temp)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9e483012a752"
+      },
+      "source": [
+        "### Costs\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "- Vertex AI\n",
+        "- Cloud Storage\n",
+        "- BigQuery\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing) and [BigQuery pricing](https://cloud.google.com/bigquery/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {

notebooks/community/ml_ops/stage1/mlops_data_management.ipynb

@@ -93,17 +93,6 @@
         "- Preprocess a portion of the BigQuery data using `Dataflow` -- for custom training."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:bq,chicago,lbn"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the [Chicago Taxi](https://www.kaggle.com/chicago/chicago-taxi-trips-bq). The version of the dataset used in this tutorial is stored in a public BigQuery table. The trained model predicts whether someone leaves a tip for a taxi fare."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -120,6 +109,33 @@
         " - Preprocess the data with `Dataflow`"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:bq,chicago,lbn"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the [Chicago Taxi](https://www.kaggle.com/chicago/chicago-taxi-trips-bq). The version of the dataset used in this tutorial is stored in a public BigQuery table. The trained model predicts whether someone leaves a tip for a taxi fare."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9e483012a752"
+      },
+      "source": [
+        "### Costs\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "- Vertex AI\n",
+        "- Cloud Storage\n",
+        "- BigQuery\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing) and [BigQuery pricing](https://cloud.google.com/bigquery/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {

notebooks/notebook_template_review.py

@@ -11,6 +11,8 @@ parser.add_argument('--errors', dest='errors',
                     default=False, type=bool, help='Report errors')
 parser.add_argument('--errors-csv', dest='errors_csv',
                     default=False, type=bool, help='Report errors as CSV')
+parser.add_argument('--errors-codes', dest='errors_codes',
+                    default=None, type=str, help='Report only specified errors')
 parser.add_argument('--desc', dest='desc',
                     default=False, type=bool, help='Output description')
 parser.add_argument('--uses', dest='uses',
@@ -19,6 +21,9 @@ parser.add_argument('--steps', dest='steps',
                     default=False, type=bool, help='Ouput steps')
 args = parser.parse_args()
 
+if args.errors_codes:
+    args.errors_codes = args.errors_codes.split(',')
+
 if not os.path.isdir(args.notebook_dir):
     print("Error: not a directory:", args.notebook_dir)
     exit(1)
@@ -111,6 +116,11 @@ def parse_notebook(path):
         # Dataset
         if not cell['source'][0].startswith("### Dataset") and not cell['source'][0].startswith("### Model") and not cell['source'][0].startswith("### Embedding"):
             report_error(path, 13, "Dataset/Model section not found")
+            
+        # Costs
+        cell, nth = get_cell(path, cells, nth)
+        if not cell['source'][0].startswith("### Costs"):
+            report_error(path, 14, "Costs section not found")
 
 def get_cell(path, cells, nth):
     while empty_cell(path, cells, nth):
@@ -155,6 +165,10 @@ def check_sentence_case(path, heading):
             
 def report_error(notebook, code, msg):
     if args.errors:
+        if args.errors_codes:
+            if str(code) not in args.errors_codes:
+                return
+            
         if args.errors_csv:
             print(notebook, ',', code)
         else:

notebooks/notebook_template_review.sav.py

@@ -0,0 +1,141 @@
+
+import argparse
+import json
+import os
+import urllib.request
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--notebook-dir', dest='notebook_dir',
+                    required=True, type=str, help='Notebook directory')
+parser.add_argument('--errors', dest='errors',
+                    default=False, type=bool, help='Report errors')
+parser.add_argument('--errors-csv', dest='errors_csv',
+                    default=False, type=bool, help='Report errors as CSV')
+parser.add_argument('--desc', dest='desc',
+                    default=False, type=bool, help='Output description')
+parser.add_argument('--uses', dest='uses',
+                    default=False, type=bool, help='Output uses (resources)')
+parser.add_argument('--steps', dest='steps',
+                    default=False, type=bool, help='Ouput steps')
+args = parser.parse_args()
+
+if not os.path.isdir(args.notebook_dir):
+    print("Error: not a directory:", args.notebook_dir)
+    exit(1)
+
+def parse_dir(directory):
+    entries = os.scandir(directory)
+    for entry in entries:
+        if entry.is_dir():
+            if entry.name[0] == '.':
+                continue
+            if entry.name == 'src' or entry.name == 'images':
+                continue
+            parse_dir(entry.path)
+        elif entry.name.endswith('.ipynb'):
+            parse_notebook(entry.path)
+
+def parse_notebook(path):
+    with open(path, 'r') as f:
+        try:
+            content = json.load(f)
+        except:
+            print("Corrupted notebook:", path)
+            return
+        
+        cells = content['cells']
+        
+        # cell 1 is copyright
+        nth = 0
+        cell, nth = get_cell(cells, nth)
+        if not cell['source'][0].startswith('# Copyright'):
+            report_error(path, 0, "missing copyright cell")
+            
+        # check for notices
+        cell, nth = get_cell(cells, nth)
+        if cell['source'][0].startswith('This notebook'):
+            cell, nth = get_cell(cells, nth)
+            
+        # cell 2 is title and links
+        if not cell['source'][0].startswith('# '):
+            report_error(path, 1, "title cell must start with H1 heading")
+        else:
+            title = cell['source'][0][2:].strip()
+            check_sentence_case(path, title)
+           
+        # check links.
+        source = ''
+        for line in cell['source']:
+            source += line
+            if '<a href="https://github.com' in line:
+                link = line.strip()[9:-2]
+                try:
+                    code = urllib.request.urlopen(link).getcode()
+                except Exception as e:
+                    report_error(path, 7, f"bad GitHub link: {link}")
+            if '<a href="https://colab.research.google.com/' in line:
+                link = 'https://github.com/' + line.strip()[50:-2]
+                try:
+                    code = urllib.request.urlopen(link).getcode()
+                except Exception as e:
+                    report_error(path, 8, f"bad Colab link: {link}")
+            if '<a href="https://console.cloud.google.com/vertex-ai/workbench/' in line:
+                link = line.strip()[91:-2]
+                try:
+                    code = urllib.request.urlopen(link).getcode()
+                except Exception as e:
+                    report_error(path, 9, f"bad Workbench link: {link}")
+
+        if 'View on GitHub' not in source:
+            report_error(path, 4, 'Missing link for GitHub')
+        if 'Open in Vertex AI Workbench' not in source:
+            report_error(path, 5, 'Missing link for Workbench')
+        if 'master' in source:
+            report_error(path, 6, 'Outdated branch (master) used in link')
+            
+        # Overview
+        cell, nth = get_cell(cells, nth)
+        if not cell['source'][0].startswith("## Overview"):
+            report_error(path, 11, "Overview section not found")
+            
+        # Datasetcell, nth = get_cell(cells, nth)
+        cell, nth = get_cell(cells, nth)
+        if not cell['source'][0].startswith("### Dataset") and not cell['source'][0].startswith("### Model"):
+            report_error(path, 12, "Dataset/Model section not found")
+
+def get_cell(cells, nth):
+    while empty_cell(cells, nth):
+        nth += 1
+    return cells[nth], nth + 1
+
+
+def empty_cell(cells, nth):
+    if len(cells[nth]['source']) == 0:
+        report_error(path, 10, f'empty cell: cell #{nth}')
+        return True
+    else:
+        return False
+
+def check_sentence_case(path, heading):
+    words = heading.split(' ')
+    if not words[0][0].isupper():
+        report_error(path, 2, f"heading must start with capitalized word: {words[0]}")
+        
+    for word in words[1:]:
+        word = word.replace(':', '').replace('(', '').replace(')', '')
+        if word in ['E2E', 'Vertex', 'AutoML', 'ML', 'AI', 'GCP', 'API', 'R', 'CMEK', 'TFX', 'TFDV', 'SDK',
+                    'VM', 'CPR', 'NVIDIA']:
+            continue
+        if word.isupper():
+            report_error(path, 3, f"heading is not sentence case: {word}")
+
+
+def report_error(notebook, code, msg):
+    if args.errors:
+        if args.errors_csv:
+            print(notebook, ',', code)
+        else:
+            print(f"{notebook}: ERROR ({code}): {msg}")
+
+
+parse_dir(args.notebook_dir)