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SAM template changes, parameter store in DAG, simplified setup #8

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e25e604
SAM template changes, parameter store in DAG, simplified setup
mrichman Aug 23, 2021
89890a3
Added put-public-access-block; fixed custom resource
mrichman Aug 27, 2021
701385c
Made collection_id dynamic based on CFn param
mrichman Sep 8, 2021
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16 changes: 16 additions & 0 deletions usecases/image-processing/Pipfile
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[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"

[packages]

[dev-packages]
pylama = "*"
black = "*"

[requires]
python_version = "3.7"

[pipenv]
allow_prereleases = true
150 changes: 150 additions & 0 deletions usecases/image-processing/Pipfile.lock
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61 changes: 17 additions & 44 deletions usecases/image-processing/README.md
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@@ -1,4 +1,4 @@
## Airflow sample image processing pipeline with Airflow 1.10.12
# Airflow sample image processing pipeline with Airflow 2.0.2

This is an Ariflow verion of image processing workflow in [workshop](https://image-processing.serverlessworkshops.io/)

Expand All @@ -22,41 +22,19 @@ This is an Ariflow verion of image processing workflow in [workshop](https://ima

## How to Deploy

### 1. Create Rekognition collections
```
aws rekognition create-collection --collection-id image_processing
```
### 2. Create S3 Bucket to store Dags, Requirement.txt and plugins.
```
aws s3api create-bucket --bucket {bucket_name} --region {region}
aws s3api put-bucket-versioning --bucket {bucket_name} --versioning-configuration Status=Enabled

aws s3api put-public-access-block --bucket {bucket_name} --public-access-block-configuration "BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true"
### 1. Create S3 Bucket and upload DAG, requirements.txt, and images

```
### 3. Copy requirements to S3
```
aws s3api put-object --bucket {bucket_name} --key requirements.txt --body dags/2.0/requirements.txt --output text
```sh
./setup.sh <bucket name>
```

Note down the version number from the last command. This will be used during next step.

### 4. Deploy the SAM template
```
### 2. Deploy the SAM template

```sh
sam build
sam deploy --stack-name MWAA-image-processing -g

```

### 5. Copy dag and images

Replace TABLE_NAME with Stack Output.DynamoDBTableName and LAMBDA_FN_NAME with Stack Output.LambdaFunctionName in dags/image-processing.py. Copy the dag and images(to be tested) to the S3 Bucket created in Step 2

```
aws s3 cp dags/2.0/image_processing.py s3://{bucket_name}/dags/image-processing.py

aws s3 cp images s3://{bucket_name}/images --recursive

```

## Access Airflow UI
Expand All @@ -65,24 +43,19 @@ aws s3 cp images s3://{bucket_name}/images --recursive

- Trigger the Dag using the JSON given below

```
```json
{
"s3Bucket":"{bucket_name}",
"s3Key":"images/1_happy_face.jpg",
"RekognitionCollectionId":"image_processing",
"userId": "userId"
"s3Bucket":"{bucket_name}",
"s3Key":"images/1_happy_face.jpg",
"RekognitionCollectionId":"image_processing",
"userId": "userId"
}
```

## Useful cli commands while testing
```
aws rekognition list-faces --collection-id image_processing

aws rekognition delete-faces \
--collection-id image_processing \
--face-ids REPLACE_WITH_FACE_ID \
```

<!-- aws s3 cp requirement.txt s3://{bucket_name}/requirement.txt -->
## Useful CLI commands while testing

```sh
aws rekognition list-faces --collection-id image_processing

aws rekognition delete-faces --collection-id image_processing --face-ids '["faceid"]'
```
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