Elastic Transcoder for preprocessing AI training data.

Question

Pulumi · Accepted Answer

Elastic Transcoder is an AWS service that facilitates the conversion or "transcoding" of media files into formats suitable for playback on various devices. Moreover, it's a scalable and easy-to-use tool that can adjust the quality and display properties of video files, for example.

For AI training, it's common to require preprocessing of video and audio data to ensure it's in the right format, resolution, or compression, making it suitable for input into machine learning models. By using AWS Elastic Transcoder, you can automate this process and prepare large datasets without manual effort.

To automate this process with Pulumi, you'll need two main resources:

1. `Preset`: This defines the settings for the transcoding job, such as video size, codecs, and bitrates.
2. `Pipeline`: This sets up the input and output locations (S3 buckets) and connects to the `Preset` created earlier. It also defines the role and notifications for job statuses.

Now, let's write a Pulumi program to create a preset for video preprocessing and a pipeline to use this preset.

```python
import pulumi
import pulumi_aws as aws

# Define the Preset for the Elastic Transcoder.
# Provides a set of configurations like codec, bitrate, etc.
preset = aws.elastictranscoder.Preset("aiPreprocessingPreset",
    container="mp4",  # Specify the container type for the output file.
    description="Preset for AI training video data preprocessing",
    video={
        "codec": "H.264",  # Video codec to use.
        "bitRate": "1500",  # Target video bitrate in kbps.
        "frameRate": "auto",  # Frame rate to use for the video.
        "resolution": "1080p",  # Target video resolution.
        "aspectRatio": "auto",  # Aspect ratio to use, 'auto' to maintain the same aspect ratio as the input.
    },
    audio={  # Optionally, specify the audio settings if your AI training requires audio processing.
        "codec": "AAC",  # Audio codec to use.
        "bitRate": "160",  # Target audio bitrate in kbps.
        "channels": "2",  # Number of audio channels.
        "sampleRate": "44100",  # Sample rate in Hz.
    },
    thumbnails={  # If you require thumbnails for your AI training, configure them here.
        "format": "png",  # Thumbnails format.
        "interval": "120",  # Interval in seconds for thumbnails.
        "maxHeight": "1080",  # Maximum height of thumbnails.
        "maxWidth": "1920",  # Maximum width of thumbnails.
        "sizingPolicy": "ShrinkToFit",  # Sizing policy for thumbnails.
        "paddingPolicy": "NoPad",  # Padding policy for thumbnails.
    })

# The IAM Role that AWS Elastic Transcoder will use to create jobs. Make sure the Role has the necessary permissions.
transcoder_role = aws.iam.Role("transcoderRole",
    assume_role_policy={
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Principal": {"Service": "elastictranscoder.amazonaws.com"},
            "Effect": "Allow",
        }],
    })

# AWS Elastic Transcoder Pipeline that sets up the infrastructure for your media transcoding workflow.
pipeline = aws.elastictranscoder.Pipeline("aiPreprocessingPipeline",
    input_bucket="your-input-bucket-name",
    output_bucket="your-output-bucket-name",
    role=transcoder_role.arn,
    content_config={
        "bucket": "your-content-config-bucket-name",  # S3 bucket for transcoded content.
        "storageClass": "Standard",
    },
    thumbnail_config={
        "bucket": "your-thumbnail-config-bucket-name",  # S3 bucket for thumbnails.
        "storageClass": "Standard",
    })

# Expose the preset and pipeline ID for later use.
pulumi.export("preset_id", preset.id)
pulumi.export("pipeline_id", pipeline.id)
```

This Pulumi program defines a preset with typical settings that may be needed for AI training data preprocessing. It also sets up a pipeline which links to S3 buckets for input and output data. Adjust the `bitRate`, `resolution`, and other settings based on your specific dataset requirements. Make sure you replace `your-input-bucket-name`, `your-output-bucket-name`, `your-content-config-bucket-name`, and `your-thumbnail-config-bucket-name` with your actual AWS S3 bucket names.

Any submitted transcoding jobs to this pipeline will use the settings from the preset and output the processed media to the specified S3 buckets. This allows for an automated way to prepare your media files for AI training.

Remember to [configure your AWS provider](https://www.pulumi.com/docs/intro/cloud-providers/aws/setup/) for Pulumi by setting up your AWS access key, secret key, and region before deploying this program.