This repository contains a model for classifying whether a person has squamous cell carcinoma or adenocarcinoma based on the processing of chest scan data. In this repository, we will use AWS Sagemaker for training our model.
1. Install the AWS CLI
If you haven't already installed the AWS Command Line Interface (CLI) on your local machine, you can follow these steps:
Install AWS CLI:
- Download and install the AWS CLI from the official AWS CLI website.
- After installing, open your terminal or command prompt and run aws --version to confirm that it's installed correctly.
Configure AWS CLI:
- Run AWS configure in your terminal and provide your AWS access key, secret key, default region, and output format (json, yaml etc). You can find these in your AWS account.
2. Create a SageMaker Notebook Instance
- Go to the AWS Management Console.
- Navigate to Amazon SageMaker.
- Click "Create notebook instance."
- Configure your notebook instance with a name, and an IAM role with appropriate permissions, and choose the instance type.
- For "Permissions and encryption", you can choose the default settings or customize as needed.
- Create the notebook instance.
3. Access Your SageMaker Notebook
Once your notebook instance is created, click on "Open Jupyter" to access the Jupyter Notebook interface.
4. Create a Conda Environment
Inside your Jupyter Notebook, create a new Jupyter Notebook or Python script.
- To create a Conda environment, you can use the following commands in a Jupyter Notebook cell:
!conda create -n myenv python=3.8
- Activate your Conda environment:
!conda activate myenv
- Install the necessary packages:
!conda install -c conda-forge boto3 awscli sagemaker
- https://sagemaker.readthedocs.io/en/stable/frameworks/tensorflow/using_tf.html
- https://github.com/GuldenizBektas/aws-vision-transformer-project/tree/main
This project is under the Apache License 2.0 License. Feel free to use and modify the code as per the license terms.