aws · dr-natetorious · Mar 9, 2022 · Mar 9, 2022 · Mar 9, 2022 · Mar 9, 2022
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
@@ -1,4 +1,3 @@
-## Code of Conduct
-This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
-For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
-opensource-codeofconduct@amazon.com with any additional questions or comments.
+# Code of Conduct
+
+This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct). For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact [opensource-codeofconduct@amazon.com](mailto:opensource-codeofconduct@amazon.com) with any additional questions or comments.
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -6,7 +6,6 @@ documentation, we greatly value feedback and contributions from our community.
 Please read through this document before submitting any issues or pull requests to ensure we have all the necessary
 information to effectively respond to your bug report or contribution.
 
-
 ## Reporting Bugs/Feature Requests
 
 We welcome you to use the GitHub issue tracker to report bugs or suggest features.
@@ -19,8 +18,8 @@ reported the issue. Please try to include as much information as you can. Detail
 * Any modifications you've made relevant to the bug
 * Anything unusual about your environment or deployment
 
-
 ## Contributing via Pull Requests
+
 Contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that:
 
 1. You are working against the latest source on the *main* branch.
@@ -39,20 +38,19 @@ To send us a pull request, please:
 GitHub provides additional document on [forking a repository](https://help.github.com/articles/fork-a-repo/) and
 [creating a pull request](https://help.github.com/articles/creating-a-pull-request/).
 
-
 ## Finding contributions to work on
-Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' issues is a great place to start.
 
+Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' issues is a great place to start.
 
 ## Code of Conduct
+
 This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
 For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
 opensource-codeofconduct@amazon.com with any additional questions or comments.
 
-
 ## Security issue notifications
-If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue.
 
+If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public github issue.
 
 ## Licensing
 

diff --git a/README.md b/README.md
@@ -1,13 +1,12 @@
-## SageMaker Studio Lab Sample Notebooks
+# SageMaker Studio Lab Sample Notebooks
 
 [![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws/studio-lab-examples/blob/main/natural-language-processing/NLP_Disaster_Recovery_Translation.ipynb)
 
 ***Available today in public preview.***
 
-If you are looking for a no-cost compute environment to run Jupyter notebooks, then look no further than SageMaker Studio Lab! This repository contains example notebooks that demonstrate both core concepts of machine learning and some advanced functionality of SageMaker Studio Lab. 
+If you are looking for a no-cost compute environment to run Jupyter notebooks, then look no further than SageMaker Studio Lab! This repository contains example notebooks that demonstrate both core concepts of machine learning and some advanced functionality of SageMaker Studio Lab.
 
-Once you've gotten started, you can sign up to participate in our hackathon with DevPost on Disaster Response! The winning team takes home $15,000 USD and $5,000 in AWS credits.
-- https://awsdisasterresponse.devpost.com/
+Once you've gotten started, you can sign up to participate in our hackathon with [DevPost on Disaster Response](https://awsdisasterresponse.devpost.com/)! The winning team takes home $15,000 USD and $5,000 in AWS credits.
 
 ## Security
 
@@ -16,4 +15,3 @@ See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more inform
 ## License
 
 This project is licensed under the Apache-2.0 License.
-
diff --git a/computer-vision/kmnist/cv-kminst.ipynb b/computer-vision/kmnist/cv-kminst.ipynb
diff --git a/computer-vision/weather-data/weather-image-classification-pytorch.ipynb b/computer-vision/weather-data/weather-image-classification-pytorch.ipynb
diff --git a/connect-to-aws/Access_AWS_from_Studio_Lab.ipynb b/connect-to-aws/Access_AWS_from_Studio_Lab.ipynb
@@ -9,16 +9,16 @@
     "\n",
     "[![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws/studio-lab-examples/blob/main/connect-to-aws/Access_AWS_from_Studio_Lab.ipynb)\n",
     "\n",
-    "Following guidance here\n",
-    "https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html"
+    "This lab uses the Python package boto3 to connect to AWS resources. For additional information, see the boto3 [Quickstart Guide](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html).\n"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "75c10a5f-5cf1-4217-aff4-a520e7f10b5c",
    "metadata": {},
    "source": [
-    "### Step 0. Install AWS CLI, boto3, and configure with your AWS credentials. \n Also create and paste in your SageMaker execution role. "
+    "### Step 0. Install AWS CLI, boto3, and configure with your AWS credentials. \n",
+    " Also create and paste in your SageMaker execution role. "
    ]
   },
   {
@@ -62,12 +62,13 @@
    "source": [
     "---\n",
     "# Exercise Caution on Using AWS Credentials\n",
-    "The next step should only be undertaken by professionals who are already comfortable using AWS access and secret keys. These credentials are similar to the keys to a car - if someone takes them inadvertenly, they can steal your vehicle. While there are additional AWS permissions you can apply, the basic concept still stands. Under no circumstances should you share these resources publicly. \n",
+    "\n",
+    "Be mindful not to share your AWS credentials with other persons or commit them to your source repository. These credentials are similar to the keys to a car - if someone takes them inadvertently, they can steal your vehicle. While you can apply for additional AWS permissions, the basic concept still stands. **Under no circumstances should you share these resources publicly.** \n",
     "\n",
     "Please refer here for getting started with your AWS credentials.\n",
     "https://docs.aws.amazon.com/general/latest/gr/aws-sec-cred-types.html \n",
     "\n",
-    "That being said, if you are handling your keys carefully, you can in fact access your AWS account from Studio Lab. We'll walk through that here."
+    "That being said, if you are handling your keys carefully, you can access your AWS account from Studio Lab. We'll walk through that here."
    ]
   },
   {
@@ -118,11 +119,10 @@
    "id": "4370662b-6253-4aab-90b7-8ec66f3b88a8",
    "metadata": {},
    "source": [
-    "If you are already used to using SageMaker within your own AWS account, please copy and paste the arn for your execution role below. If you are new to thise, follow the steps to create one here.\n",
+    "If you are already used to using SageMaker within your AWS account, please copy and paste the execution role’s ARN (Amazon Resource Name). Otherwise, follow these steps to [create it](https://docs.aws.amazon.com/glue/latest/dg/create-an-iam-role-sagemaker-notebook.html).\n",
     "\n",
-    "https://docs.aws.amazon.com/glue/latest/dg/create-an-iam-role-sagemaker-notebook.html\n",
     "\n",
-    "Please note, in order to complete this you will need to have already created this SageMaker IAM Execution role."
+    "Please note that to complete this lab, you will need the SageMaker IAM Execution role created."
    ]
   },
   {
@@ -143,11 +143,12 @@
    "id": "1843a796-5bcf-4f49-be8f-42a28f614599",
    "metadata": {},
    "source": [
-    "### Step 1. Copy your local data to your preferred S3 bucket, or vice versa \n",
-    "This notebook will assume you already have access to a training dataset relevant for language translation. If you don't, please step through this notebook to create the relevant train files locally.\n",
-    "- https://github.com/aws/studio-lab-examples/blob/main/natural-language-processing/NLP_Disaster_Recovery_Translation.ipynb \n",
+    "## Step 1. Copy your local data to your preferred S3 bucket, or vice versa \n",
+    "\n",
+    "This notebook assumes you already have access to a training dataset relevant for language translation. Please step through [this notebook](../natural-language-processing/NLP_Disaster_Recovery_Translation.ipynb) to create the relevant train files locally if you don't.\n",
+    "\n",
     "\n",
-    "We'll demonstrate copying that data up to your AWS account via the cli here, but you can also upload through the UI, or use boto3. Many good options here."
+    "We'll demonstrate copying that data to your AWS account via the [AWS CLI](https://aws.amazon.com/cli/). Alternatively, you can also use the [AWS Management Console](https://console.aws.amazon.com/) or an [AWS SDK](https://aws.amazon.com/getting-started/tools-sdks/) (such as [Boto3](https://aws.amazon.com/sdk-for-python/))."
    ]
   },
   {
@@ -185,11 +186,12 @@
    "id": "3f9a523e-e4c8-45fe-b4f3-7d24e267a138",
    "metadata": {},
    "source": [
-    "### Step 2. Point to the Hugging Face containers and train a model\n",
+    "## Step 2. Point to the Hugging Face container and train a model\n",
+    "\n",
     "We strongly recommend using the Hugging Face models webpage to generate the configuration code for your desired model or resource. You can do so here:\n",
     "- https://huggingface.co/models\n",
     "\n",
-    "AWS has prebuilt deep learning containers for 5 software frameworks, including TensorFlow, PyTorch, MXNet, Hugging Face, and AutoGloun. You can extend these base images, or simply use the script mode construct as below.\n",
+    "AWS has prebuilt deep learning containers for five software frameworks, including TensorFlow, PyTorch, MXNet, Hugging Face, and AutoGloun. You can extend these base images or use the script mode construct below.\n",
     "- https://github.com/aws/deep-learning-containers \n",
     "\n",
     "To learn more about script mode on SageMaker, check out our documentation here: \n",
@@ -247,7 +249,8 @@
    "source": [
     "---\n",
     "# Evaluate your job in the cloud and clean up\n",
-    "That's a wrap! Please make sure that before you share this notebook with anyone you remove your access and secret keys from the cell above. You can also delete the core files themselves, but that will disable you from accessing your AWS account locally going forward."
+    "\n",
+    "That's a wrap! Before you share this notebook with anyone, please make sure that you remove your access and secret keys from the cell above. You can also delete the files under `~/.aws` files to disable access from this notebook."
    ]
   },
   {

diff --git a/custom-environments/AutoGluon/env_validation.ipynb b/custom-environments/AutoGluon/env_validation.ipynb
@@ -11,9 +11,9 @@
     "\n",
     "[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws/studio-lab-examples/blob/main/custom-environments/AutoGluon/env_validation.ipynb>)\n",
     "\n",
-    "AutoML is a technology that aims to automate the process of applying machine learning to real-world problems, or to automate the process of building machine learning models from data. [AutoGluon](https://github.com/awslabs/autogluon) enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning text, image, and tabular data.\n",
+    "AutoML is a technology that aims to automate applying machine learning to real-world problems. Additionally, it automates the process of building machine learning models from data. [AutoGluon](https://github.com/awslabs/autogluon) enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning text, image, and tabular data.\n",
     "\n",
-    "In this notebook, we will set up AutoGluon in [Amazon SageMaker Studio Lab](https://studiolab.sagemaker.aws/), a free machine learning development environment, and train machine learning models using AutoGluon on a sample dataset.\n",
+    "This notebook will set up AutoGluon in [Amazon SageMaker Studio Lab](https://studiolab.sagemaker.aws/), a free machine learning development environment, and train machine learning models using AutoGluon on a sample dataset.\n",
     "\n",
     "Run the following cell to generate a YAML file for creating the Anaconda virtual environment."
    ]
@@ -49,7 +49,7 @@
    "id": "d27d5c92-6601-4d8d-bd7a-7e69a85e8000",
    "metadata": {},
    "source": [
-    "After `autogluon_cpu.yml` is generated, run `conda env create -f autogluon_cpu.yml` in your system terminal to create a virtual environment for Anaconda with AutoGluon set up.\n",
+    "After writing `autogluon_cpu.yml`, run `conda env create -f autogluon_cpu.yml` in your system terminal to create a virtual environment for Anaconda with AutoGluon enabled.\n",
     "\n",
     "If the installation is successful, you should be able to select `autogluon_cpu:Python` in the popup that appears when you click the kernel name in the upper right corner of the screen. Switch to the `autogluon_cpu:Python` kernel. You should now be able to use AutoGluon."
    ]

diff --git a/custom-environments/fastai/env_validation.ipynb b/custom-environments/fastai/env_validation.ipynb
@@ -13,15 +13,15 @@
    "id": "93f9190c-31af-4b0e-945c-23ada2d37cdd",
    "metadata": {},
    "source": [
-    "This notebook validates your Fastai environment. This assumes you have a default installation of Fastai with all dependencies and datasets and the environment was built using the example `.yml` file. Open this notebook with the `fastai` kernel to verify your environment."
+    "This notebook validates your Fastai environment. It assumes you have a default installation of Fastai with all dependencies and datasets, and the environment contains the [fastai.yml](fastai.yml) file. Open this notebook and select the `fastai` kernel to verify your configuration."
    ]
   },
   {
    "cell_type": "markdown",
    "id": "3c76f580-82af-4caa-ac38-6394910fa92d",
    "metadata": {},
    "source": [
-    "### Import Packages\n",
+    "## Import Packages\n",
     "Make sure you can import packages without any errors"
    ]
   },

diff --git a/custom-environments/julia/1-install-julia.ipynb b/custom-environments/julia/1-install-julia.ipynb
@@ -10,7 +10,7 @@
     "\n",
     "[![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws/studio-lab-examples/blob/main/custom-environments/julia/1-install-julia.ipynb)\n",
     "\n",
-    "This notebook will demonstrate installing a recent version of Julia (currently 1.7.1) and an associated Julia kernel."
+    "This notebook demonstrates installing a recent version of Julia (>=1.7.1) and an associated Julia kernel."
    ]
   },
   {
@@ -20,7 +20,7 @@
    "source": [
     "## 1. Downloading and installing Julia\n",
     "\n",
-    "Julia is available either via direct download or via the Conda package manager. Here we demonstrate installing the official Julia release, as it is self-contained and is as simple as downloading, decompressing, and then linking the julia executable into a path recognized by our conda environment."
+    "Julia is available either via direct download or via the Conda package manager. Here we demonstrate installing the official Julia release. It is self-contained and as simple as downloading, decompressing, and then linking the Julia executable into a path recognized by our Anaconda environment."
    ]
   },
   {
@@ -59,7 +59,7 @@
    "source": [
     "## 2. Install the IJulia kernel\n",
     "\n",
-    "Installing the IJulia kernel is similiarly simple. Here we invoke the Julia executable we installed in the previous section, and then use the Julia package manager to install the IJulia kernel"
+    "Installing the IJulia kernel is straightforward. You need to invoke the Julia executable from the previous step and then use the Julia package manager to install the IJulia kernel."
    ]
   },
   {
@@ -137,7 +137,7 @@
    "id": "a3e58026-678e-415e-abd1-44c71af184f8",
    "metadata": {},
    "source": [
-    "Lastly, let's register a couple of kernel profiles with the default conda environment so they show up in our SageMaker Studio Lab launcher. Here we add two profiles: one single-threaded, and one with multiple threads enabled at startup."
+    "Lastly, let's register a couple of kernel profiles with the default Anaconda environment so it shows up in our SageMaker Studio Lab launcher. You will need to add two profiles for single-threaded, and multi-threaded startup enabled."
    ]
   },
   {
@@ -191,7 +191,7 @@
    "source": [
     "## 3. Using Julia\n",
     "\n",
-    "With Julia installed, and the IJulia kernel registered, you can create a new Julia notebook from the SageMaker Studio Lab launcher. You can also follow along with the example Julia notebook in this folder."
+    "With Julia installed and the IJulia kernel registered, you can create a new Julia notebook from the SageMaker Studio Lab launcher. You can also follow along with the example Julia notebook in this folder."
    ]
   }
  ],