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program/setup.qmd

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+---
+title: "Configuration Setup"
+listing:
+  contents: posts
+  sort: "date desc"
+  type: default
+  categories: true
+---
+
+Here is a summary of the steps you need to follow:
+
+1. Fork the program's [GitHub Repository](https://github.com/svpino/ml.school) and clone your fork on your local computer.
+
+2. Go inside the folder of the program, create a virtual environment, and install the libraries from the `requirements.txt` file:
+
+```bash
+python3 -m venv .venv
+source .venv/bin/activate
+python -m pip install --upgrade pip
+pip install -r requirements.txt
+```
+
+3. Install a Jupyter kernel on your virtual environment:
+
+   ```bash
+   $ pip install ipykernel
+   $ python -m ipykernel install --user --name=.venv
+   ```
+
+4. If you don't have it, install [Docker](https://docs.docker.com/) on your computer. You can check if Docker is already installed and running using the following command:
+
+   ```bash
+   $ docker ps
+   ```
+
+5. [Install the AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html). Open the IAM service, find your user, select Security Credentials, then assign a Multi-Factor Authentication (MFA) device and follow the prompts. After setup and verified, you can click to create an access key. This is the key you will use to [configure the AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-files.html) on your computer.
+
+6. Create an `.env` file in the root folder of the repository with the following content. Make sure you replace the value of each variable with the correct value:
+
+   ```bash
+    # Update this variable to your bucket name. This name must be unique 
+    # across all AWS accounts.
+    BUCKET=mlschool
+
+    # These are your SageMaker Domain Id and User Profile. You can find
+    # these values in your Amazon SageMaker dashboard under "Domains"
+    DOMAIN_ID=d-givocgtibv1g
+    USER_PROFILE=default-1682182522641
+
+    # Update this variable to the ARN of the Execution Role associated to 
+    # the user profile attached to your SageMaker Studio Domain.
+    ROLE=arn:aws:iam::325223348818:role/service-role/AmazonSageMaker-ExecutionRole-20230312T160501
+   ```
+7. Open the Amazon IAM service, find the Execution Role you used in the previous step and edit the custom Execution Policy assigned to it. Edit the permissions of the Execution Policy and replace them with the contents of the `execution-policy.json` file. These permissions will give the Execution Role access to the resources we'll use during the program.
+
+8. Now find the "Trust relationships" section under the same Execution Role, edit the configuration, and replace it with the contents of the `trust-relationships.json` file.
+
+9. We'll need access to `ml.m5.xlarge` instances to build our system. By default, the quota for a new account is zero, so you need to request a quota increase. You can do that in your AWS account, under Service Quotas > AWS Services > Amazon SageMaker. Find `ml.m5.xlarge` and request a quota increase for processing jobs, training jobs, transform jobs, and endpoint usage. Ask for a minimum of 3 instances.
+
+10. If your local environment is running on a Mac with an M-series chip, you need to build a TensorFlow docker image to train and evaluate the model we'll build in the program. You can do so with the following command:
+
+   ```bash
+   $ docker build -t sagemaker-tensorflow-training-toolkit-local penguins/container/.
+   ```
+
+11. If you are planning to run the notebook from inside SageMaker Studio, you need to create a Lifecycle Configuration to update the kernel. Load and execute the [studio-setup.ipynb](penguins/studio-setup.ipynb) notebook once inside SageMaker Studio. After doing this, you can open the [cohort.ipynb](penguins/cohort.ipynb) notebook and use the "TensorFlow 2.11 Python 3.9 CPU Optimized" kernel with the start-up script named "ml-school."
+

program/sidebar.yml

@@ -1,5 +1,5 @@
 website:
   sidebar:
     contents:
-      - instructions.qmd
+      - setup.qmd
       - index.ipynb