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Key Summary
This article from InApps Technology, authored by Phu Nguyen, provides a step-by-step tutorial on using Amazon SageMaker Studio Lab, a free, JupyterHub-based machine learning (ML) development environment introduced at AWS re:Invent 2021. The tutorial guides users through training an image classification model to distinguish between cats and dogs using TensorFlow/Keras, covering environment setup, model training, and inference. It emphasizes the platform’s accessibility and integration with AWS services for scalable ML workflows.
- Context:
- SageMaker Studio Lab: A free, standalone ML environment based on JupyterHub, distinct from Amazon SageMaker, designed for experimentation and learning.
- Tutorial Focus: Demonstrates building an end-to-end deep learning model for image classification (cats vs. dogs) using SageMaker Studio Lab.
- Objective: Guide users through account setup, environment configuration, model training, and inference, with plans for future serverless deployment tutorials.
- Tutorial Steps:
- Request Access and Sign In:
- Visit https://studiolab.sagemaker.aws/ to request a free account (approval may take hours to days).
- Sign in, select GPU compute type, and start the runtime.
- Open the JupyterHub project environment for experimentation.
- Prepare the Environment:
- Open a terminal in JupyterHub and clone the Git repository:
text
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Copy - git clone https://github.com/janakiramm/dogs-vs-cats
- Navigate to the dogs-vs-cats folder and create a Conda environment using env_tf2.yaml (contains TensorFlow/Keras dependencies).
- Refresh the browser to access the new tf2:Python kernel.
- Download the Dogs vs. Cats dataset (train.zip) from Kaggle, upload it to the dataset folder, and unzip it to create /dogs-vs-cats/dataset/train/.
- Open a terminal in JupyterHub and clone the Git repository:
- Train the Model:
- Open the dogs-vs-cats.ipynb notebook in the train folder, select the tf2:Python kernel, and run all cells.
- The notebook loads the dataset and trains the image classification model, taking ~15 minutes with an accuracy of 87.5% (improvable with more epochs).
- The trained model is exported to model/export/Servo/1 in TensorFlow Serving format.
- Perform Inference:
- Open the inference notebook in the infer folder, load the saved model:
python
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Copy - model = tensorflow.keras.models.load_model(“../model/export/Servo/1/”)
- Preprocess and resize images for inference, achieving accurate predictions for cats and dogs.
- Optionally, upload the model to Amazon S3 using Boto3 for deployment in Amazon SageMaker.
- Open the inference notebook in the infer folder, load the saved model:
- Request Access and Sign In:
- Key Features of SageMaker Studio Lab:
- Free Access: No cost for experimentation, ideal for developers and learners.
- JupyterHub-Based: Familiar IDE for ML workflows, supporting TensorFlow, Keras, and other libraries.
- GPU Support: Enables faster training for deep learning models.
- Scalability: Integrates with AWS services (e.g., S3, SageMaker) for production deployment.
- Future Tutorials: Upcoming guides will cover creating serverless inference endpoints with SageMaker.
- InApps Insight:
- InApps Technology, ranked 1st in Vietnam and 5th in Southeast Asia for app and software development, excels in ML development and AWS integrations.
- Leverages React Native, ReactJS, Node.js, Vue.js, Microsoft’s Power Platform, Azure, Power Fx (low-code), Azure Durable Functions, and GraphQL APIs (e.g., Apollo) to build ML-driven solutions.
- Offers outsourcing services for startups and enterprises, delivering AI/ML applications at 30% of local vendor costs, supported by Vietnam’s 430,000 software developers and 1.03 million ICT professionals.
- Call to Action:
- Contact InApps Technology at www.inapps.net or sales@inapps.net to develop ML models using SageMaker Studio Lab or explore AWS-based AI solutions.
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Introduced as a preview at the Amazon Web Services‘ re:Invent 2021 conference, SageMaker Studio Lab is a free stand-alone machine learning development environment based on the popular JupyterHub IDE. Except for the branding, the service has almost nothing to do with SageMaker. For a detailed overview of the service, read my previous article.
In this tutorial, I will walk you through the steps of training an end-to-end deep learning model to perform image classification based on Amazon SageMaker Studio Lab. We will build a model that distinguishes between cats and dogs (Be sure to check back all this week for additional SageMaker Studio Lab tutorials).
Step 1: Request Access and Sign In
Visit https://studiolab.sagemaker.aws/ to request a free Amazon SageMaker Studio Lab account.
It may take a few hours to a couple of days for you to get access to the environment. Wait for the email confirmation.
Once approved, sign in to your account with the credentials.
Select GPU compute type, and click on the Start runtime button.
When the runtime is ready, click on Open project.
The JupyterHub environment is ready for experimentation.
Step 2: Preparing the Environment
From the launcher, click on the terminal icon to start a new terminal session. Clone the Git repository that has the Conda environment configuration and the notebooks.
git clone https://github.com/janakiramm/dogs-vs-cats
Navigate to the the dogs-vs-cats folder, and right click on env_tf2.yaml file to create a new Conda environment. This file has all the modules needed to train a TensorFlow/Keras model.
Refresh the browser to see a new kernel named tf2:Python
Before we can start training the model, we need to download the dataset. For this, login to Kaggle and download the file train.zip from the Dogs vs. Cats competition.
Upload the file, train.zip into the dataset folder of the repo that we cloned in the previous step. Launch a terminal session and unzip the file in the same folder. You should now have a new folder — /dogs-vs-cats/dataset/train/.
We now have the environment fully configured to kickoff the training job within Amazon SageMaker Studio Lab.
Step 3: Train the Computer Vision Model to Classify Images
Navigate to the train folder of the repository and launch dogs-vs-cats.ipynb notebook.
If prompted for the kernel, choose tf2:Python.
This notebook loads the dataset we downloaded and trains the image classification model. Run the cells to complete the training. It may take up to 15 minutes for the training to complete.
In my experiment, the model was trained with an accuracy of 87.5%. This may be improved by increasing the number of epochs.
When the model is ready, it is exported to the model/export/Servo/1 directory in the TensorFlow Serving format.
Step 3: Perform Inference on the Trained Model
Navigate to the infer folder to open the inference notebook. We load the saved model from /model/export/Servo/1/ and use it for inference.
model = tensorflow.keras.models.load_model("../model/export/Servo/1/")
When an image is appropriately resized and preprocessed, it can be sent to the model. Below are the screenshots predicting the correct classes.
You can easily upload the model to Amazon S3 using the Python Boto3 module to deploy it in Amazon SageMaker.
In the next part of this series — which will run all this week — we will utilize the image classification model to create a serverless inference endpoint in Amazon SageMaker. Stay tuned.
Source: InApps.net
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