How can I use Dagster to evaluate multiple ML models on each image, where a partition refers to an image. The number of ML models that is applied and evaluated on each image is variable per image.

In Dagster, you can model this scenario using partitioned assets, where each partition corresponds to an image. Since the number of ML models applied to each image is variable, you can use dynamic partitioning to handle this variability. Here's a step-by-step approach based on the documentation: 1. Define Dynamic Partitions: Use

DynamicPartitionsDefinition

to define dynamic partitions for your images. This allows you to add new partitions dynamically, which is useful when the set of partitions (images in your case) is not known ahead of time. 2. Create ML Model Assets: Define assets for each of your ML models. These assets will represent the application of the ML models to the images. 3. Apply ML Models to Images: Create an asset that takes an image (partition) as input and applies all relevant ML models to that image. The asset's computation can dynamically determine which ML models to apply based on the image or other criteria. 4. Evaluate ML Models: After applying the ML models, you can have another asset or set of assets that evaluate the performance of the models on each image. This can involve calculating metrics like accuracy, precision, recall, etc., and storing these as metadata for analysis. 5. Materialize Results: Use

define_asset_job

to define a job that materializes the assets. If you want to automatically materialize new partitions as they are added, you can use

AutoMaterializePolicy.eager()

. 6. Enhance the UI with Metadata: Utilize metadata to track the evaluation metrics of your ML models. This metadata can be visualized in the Dagster UI, allowing you to monitor the performance of each model on each image over time. Here's a simplified example of how you might define the assets: ```python from dagster import DynamicPartitionsDefinition, asset, define_asset_job, AutoMaterializePolicy # Define dynamic partitions for images image_partition = DynamicPartitionsDefinition(name="image_partition") # Define an asset that applies ML models to an image @asset(partitions_def=image_partition, auto_materialize_policy=AutoMaterializePolicy.eager()) def apply_ml_models(context): image = load_image(context.partition_key) model_results = {} for model in get_relevant_models(image): model_results[model.name] = model.apply(image) return model_results # Define an asset that evaluates the ML models @asset(partitions_def=image_partition) def evaluate_ml_models(apply_ml_models): evaluation_results = {} for model_name, model_output in apply_ml_models.items(): evaluation_results[model_name] = evaluate_model(model_output) return evaluation_results # Define a job to materialize the assets ml_evaluation_job = define_asset_job("ml_evaluation_job", selection=[apply_ml_models, eva

@Scout, what if the number of ML models is variable for each image? This means image 1 might need to be evaluated by 3 models, and image 2 might need to be evaluated by 2 models. The number of ML models to apply to each image is not known beforehand. Please show me how I can best capture this with Dagster.

In Dagster, to handle a variable number of ML models for each image, you can use dynamic orchestration to create assets on-the-fly based on the specific requirements of each image. This approach allows you to dynamically determine which ML models to apply to each image during runtime. Here's how you can approach this scenario: 1. Dynamic Asset Generation: Use a

multi_asset

to dynamically generate assets based on the number of ML models that need to be applied to each image. The

multi_asset

decorator allows you to yield multiple assets from a single computation. 2. Image Partitioning: Define a partition for each image, as each image will be processed independently. 3. ML Model Application: Inside the

multi_asset

, load the image corresponding to the current partition, determine the relevant ML models to apply, and then apply each model to the image. 4. Evaluation and Metadata: After applying each model, evaluate its performance and capture the evaluation metrics as metadata. This metadata can be used to enhance the Dagster UI with relevant information about the model's performance on the image. 5. Materialization: Define a job that materializes the dynamically generated assets. You can use

AutoMaterializePolicy

to automatically materialize new assets as they are created. Here's a conceptual example of how you might define the dynamic asset:

python
from dagster import DynamicPartitionsDefinition, multi_asset, AssetOut, AssetMaterialization, MetadataValue

# Define dynamic partitions for images
image_partition = DynamicPartitionsDefinition(name="image_partition")

@multi_asset(
    outs=lambda _: {"model_output": AssetOut(metadata={"model_name": "dynamic"})},
    partitions_def=image_partition
)
def apply_and_evaluate_models(context):
    image = load_image(context.partition_key)
    relevant_models = determine_models_for_image(image)

    for model in relevant_models:
        model_output = model.apply(image)
        evaluation_metrics = evaluate_model(model_output)
        
        # Yield an asset materialization with metadata for each model
        yield AssetMaterialization(
            asset_key=f"model_output_{model.name}",
            metadata={
                "evaluation_metrics": MetadataValue.json(evaluation_metrics),
                "model_name": model.name
            }
        )
        # Yield the model output as an asset
        yield "model_output", model_output

# Define a job to materialize the assets
ml_evaluation_job = define_asset_job("ml_evaluation_job", selection=[apply_and_evaluate_models])

In this example,

load_image

is a function that loads the image for the given partition key,

determine_models_for_image

dynamically determines which models to apply to the image, and `evaluate_mo