# Build a custom model with Amazon SageMaker

In this tutorial, you'll learn how to build a custom deep learning image classification model with Amazon Sagemaker.

## 1. Create the notebook instance

The main interface for Amazon Sagemaker work is through Jupyter notebooks. Jupyter is an interactive Python environment designed for rapid iteration. Amazon Sagemaker makes deploying and managing Jupyter notebooks easy.

From your Amazon Sagemaker console, select **Notebook instances** then **Create notebook instance**.

Enter a name for your notebook instance, leave everything else the default except for the volume size. Enter volume size of *50 GB* or more because we'll first download the data to our notebook instance before uploading the data to Amazon S3.

![lab4-sagemaker-create-notebook-1](images/lab4-sagemaker-create-notebook-1.png)

![lab4-sagemaker-create-notebook-2](images/lab4-sagemaker-create-notebook-2.png)



If you use Amazon SageMaker for the first time, please create an IAM role by choosing "Create a new role" from the selection list.

![l400-lab0-4](images/lab4-sagemaker-create-notebook-6.png)

On the pop-up menu, select **Any S3 bucket** to allow the notebook instance to any S3 buckets in your account. Then, click on "Create role" button on the bottom.

![l400-lab0-4](images/lab4-sagemaker-create-notebook-5.png)

Under the **Git repositories** section, choose **Clone a public Git repo** and enter the following for the repo URL:

https://github.com/aws-samples/aws-deeplens-reinvent-2019-workshops

![lab4-sagemaker-create-notebook-4](images/lab4-sagemaker-create-notebook-4.png)



## 2. Open the Jupyter notebook

Click on **Open Jupyter**.

![lab4-sagemaker-create-notebook-3](images/lab4-sagemaker-create-notebook-3.png)



You should see the page below

![lab4-sagemaker-notebook-1](images/lab4-sagemaker-notebook-1.png)



## 3. Run training notebook

Navigate to the [Lab2 notebook](src/Image-classification-bear.ipynb) (AIM229 > Lab2 > src > Image-classification-bear.ipynb)

The only part of the code that you should modify is the bucket name. Change it to an Amazon S3 bucket that you've created with that starts with "deeplens" in its name.

![lab4-sagemaker-notebook-5](images/lab4-sagemaker-notebook-5.png)

**Note**: Your AWS account might not have the Sagemaker limits available to kick off the training job on `ml.p2.xlarge` or `ml.p3.2xlarge` instances. If this is the case, you can use `ml.m4.xlarge` but know it will take a long time without a GPU instance. If you encounter this limitation, change the code below:

```python
s3_output_location = 's3://{}/{}/output'.format(bucket, prefix)
ic = sagemaker.estimator.Estimator(training_image,
                                         role, 
                                         train_instance_count=1, 
                                         train_instance_type='ml.p2.xlarge',
                                         train_volume_size = 50,
                                         train_max_run = 360000,
                                         input_mode= 'File',
                                         output_path=s3_output_location,
                                         sagemaker_session=sess)
```



From `ml.p2.xlarge` to ``ml.m4.xlarge`. We'll provide a pretrained model for you to deploy for the follow up labs.

Click on the **Run** button in the top toolbar to execute the code/text in that section. Continue clicking the run button for subsequent cells until you get to the bottom of the notebook. Alternatively, you can also use the keyboard shortcuts **Shift + Enter**.

As each section runs, it will spit out log output of what it's doing. Sometimes you'll see a **[ * ]** on the left hand side. This means that the code is still running. Once the code is done running, you'll see a number. This number represents the order in which the code was executed.

Run the notebook example through the end.