-
Notifications
You must be signed in to change notification settings - Fork 848
SageMaker
Ravi Panchumarthy edited this page Nov 3, 2022
·
2 revisions
The steps below assume that you have an AWS account and access to Amazon SageMaker Studio. The entire one-time setup process may take up to 15 minutes.
- Log into your Amazon SageMaker Studio Environment and
Add user
![](https://user-images.githubusercontent.com/4837253/199801883-7bb64ad2-bb7f-4477-ace1-25111d4fd43c.png)
- Choose desired user profile name
![](https://user-images.githubusercontent.com/4837253/199802173-8d65c851-604b-4b92-bafa-cae86b17d1ec.png)
- Choose Jupyter Lab version 3.0
![](https://user-images.githubusercontent.com/4837253/199802353-14c17233-3dae-4649-bbfe-59b8a598450c.png)
- Choose the remaining default setting and click Submit to Add user.
- Click "Open Studio" to Launch the Amazon SageMaker Studio environment.
![](https://user-images.githubusercontent.com/4837253/199802726-97c85732-ff25-4cdd-ad6e-d491b4ed122b.png)
Note: The Amazon SageMaker free tier usage per month for the first 2 months is 250 hours of ml.t3.medium instance on Studio notebook. In this example, we are using an ml.t3.medium instance.
- Allow a couple of minutes for your environment to spin up. You should see the following loading screen:
![](https://user-images.githubusercontent.com/15709723/199784252-c8581c73-342a-4c70-9207-5543d7b87346.png)
- Then, Choose
Data Science 3.0
in "select a SageMaker image" drop-down under Notebooks and compute resources - Then, Click on
**+**
onImage Terminal
to open a terminal session:
![](https://user-images.githubusercontent.com/4837253/199805717-5d102d27-e92e-4426-8d14-0484fd5ba24c.png)
- Inside the terminal, follow the steps below.
![](https://user-images.githubusercontent.com/4837253/199807022-3cc5dd9e-f9f0-445d-be5e-d429dc1b752c.png)
apt update
apt install build-essential -y
apt install libpython3.9-dev -y
apt install libgl1-mesa-glx -y
conda create --name openvino_env python=3.9
conda activate openvino_env
conda install ipykernel
set PATH="/anaconda/envs/openvino_env/bin;%PATH%"
git clone https://github.com/openvinotoolkit/openvino_notebooks.git
cd openvino_notebooks
# Install OpenVINO and OpenVINO notebook Requirements
python -m pip install --upgrade pip
pip install -r requirements.txt
- To run the notebooks, click on the top level ‘openvino_notebooks’ folder and navigate to your example:
![](https://user-images.githubusercontent.com/4837253/199810405-0f6748e1-d5f5-469e-8305-a96724dfffba.png)
- Choose Image -
Data Science 3.0
, Kernel -Python [conda env:openvino_env]
, Instance type - your desired compute instance.
![Choose Image](https://user-images.githubusercontent.com/4837253/199812540-c52ea429-9d53-4bdb-aec1-a0b8616c6fcc.png)
![Choose Kernel](https://user-images.githubusercontent.com/4837253/199812587-20c3e360-3a31-4032-b17a-8b242d6ccc26.png)
![Choose Compute](https://user-images.githubusercontent.com/4837253/199812713-32074aa7-8190-43c8-815c-231542c7b286.png)
Note: Please also ensure you use the Python [conda env:openvino_env]
environment (not Python 3).
- Next, run the cells of the notebook. Try other notebooks to explore OpenVINO features and examples !!
© Copyright 2018-2023, OpenVINO team