Example Jupyter notebooks that demonstrate how to build AI/ML learning environment using Amazon SageMaker Studio Lab.
SageMaker Studio Lab is a service for individual data scientist who wants to develop the career toward AI/ML practitioner. You can start your ML journey for free.
This repository introduces you to the way to set up Studio Lab according to your interest area, such as computer vision, natural language processing, etc. And also, we show how to deploy your project to the Amazon SageMaker to become the AI/ML practitioner.
Please follow the Onboard to Amazon SageMaker Studio Lab.
- Request a Studio Lab account
- Create a Studio Lab account
- Sign in to Studio Lab
If you would like to localize the user interface, please follow the instruction for user interface localization.
- Read: You can read the notebook in Studio Lab without Studio Lab account. Please feel free to click Open in Studio Lab button in Examples section.
- Run: You can run the notebook by copying the notebook or
git clonethe repository to your Studio Lab project. - Share: You can share the notebooks through the Git repository such as GitHub. If you add Open in Studio Lab button, the readers can copy the notebook or clone the repository by clicking button.
| No | Title | Open in Studio Lab |
|---|---|---|
| 1 | Train an image classification model with PyTorch |  |
| 2 | Weather Classification for Disaster Risk Reduction with DenseNet-161 | |
| No | Title | Open in Studio Lab |
|---|---|---|
| 1 | Finetune T5 locally for machine translation on COVID-19 Health Service Announcements with Hugging Face | |
| No | Title | Open in Studio Lab |
|---|---|---|
| 1 | Getting Started With Geospatial Data Analysis | |
| 2 | Exploratory Analysis for NOAA Weather and Climate Dataset | |
| No | Title | Open in Studio Lab |
|---|---|---|
| 1 | Introduction to JumpStart - Text to Image | |
| 2 | Prompting Mistral 7B Instruct | |
| No | Title | Open in Studio Lab |
|---|---|---|
| 1 | Using SageMaker Studio Lab with AWS Resources | |
| 2 | Deploy A Hugging Face Pretrained Model to Amazon SageMaker Serverless Endpoint - Boto3 | |
We provide .yml files to set up various programming language / framework environments. To use the .yml file, please proceed with the following instruction.
- Click this button right here -->
- Click the
Copy to Projectbutton- Sign-in and
Start runtimeis needed before it.
- Sign-in and
- When prompted, select
Clone Entire Repo - Click
Cloneafter confirmingOpen README files.is checked.- When
No Conda environment file foundshown, pleaseDismiss.
- When
- Once opening
README.mdpreview, please move toCustom Environmentssection and click the programming language / specific framework environment link as you need to open.ymlfile. - Right click the opened
.ymlfile tab and selectShow in File Browser. - Right click the
.ymlfile in the file browser and selectBuild Conda Environment. - Once command completed, please run notebook in the same folder to check the environment. When prompted
Select Kearnel, please select the created environment.
- AutoGluon (CPU) environment
- AutoGluon is AutoML library for quick prototype by state-of-the-art method without expert knowledge.
- fast.ai environment
- fast.ai is deep learning library which provides state-of-the-art results with high-level API for practitioners and low-level API for expert.
- SciPy environment
- SciPy is an open-source software for mathematics, science, and engineering.
- Diffusers environment
- diffusers provides pretrained diffusion models across multiple modalities, such as vision and audio, and serves as a modular toolbox for inference and training of diffusion models.
- RAPIDS environment (external link)
- RAPIDS provides GPU accelerated data science libraries.
- Geospatial environment
- Medical image AI environment
- Medial image AI environment is built from basic libraries for medical image analysis such as itkwidgets, monai.
- Gradio environment
- Gradio is an application that is suitable for demonstrating your model through an interactive interface.
Here are some more examples from the community.
Studio Lab Examples in GitHub.
Please add amazon-sagemaker-lab tag to your repositories that use Studio Lab! We will pick up the popular repositories in here or our blog.
This project is licensed under the Apache-2.0 License.
Although we're extremely excited to receive contributions from the community, we're still working on the best mechanism to take in examples from external sources. Please bear with us in the short-term if pull requests take longer than expected or are closed.
Please read our contributing guidelines if you'd like to open an issue or submit a pull request.