Machine Learning Model Extension Specification

• Title: Machine Learning Model Extension
• Identifier: https://crim-ca.github.io/mlm-extension/v1.1.0/schema.json
• Field Name Prefix: mlm
• Scope: Collection, Item, Asset, Links
• Extension Maturity Classification: Pilot
• Owner:
  • @fmigneault
  • @rbavery
  • @ymoisan
  • @sfoucher

The STAC Machine Learning Model (MLM) Extension provides a standard set of fields to describe machine learning models trained on overhead imagery and enable running model inference.

The main objectives of the extension are:

1. to enable building model collections that can be searched alongside associated STAC datasets
2. record all necessary bands, parameters, modeling artifact locations, and high-level processing steps to deploy an inference service.

Specifically, this extension records the following information to make ML models searchable and reusable:

1. Sensor band specifications
2. Model input transforms including resize and normalization
3. Model output shape, data type, and its semantic interpretation
4. An optional, flexible description of the runtime environment to be able to run the model
5. Scientific references

The MLM specification is biased towards providing metadata fields for supervised machine learning models. However, fields that relate to supervised ML are optional and users can use the fields they need for different tasks.

See Best Practices for guidance on what other STAC extensions you should use in conjunction with this extension. The Machine Learning Model Extension purposely omits and delegates some definitions to other STAC extensions to favor reusability and avoid metadata duplication whenever possible. A properly defined MLM STAC Item/Collection should almost never have the Machine Learning Model Extension exclusively in stac_extensions.

For details about the earlier (legacy) version of the MLM Extension, formerly known as the Deep Learning Model Extension (DLM), please refer to the DLM LEGACY document. DLM was renamed to the current MLM Extension and refactored to form a cohesive definition across all machine learning approaches, regardless of whether the approach constitutes a deep neural network or other statistical approach. It also combines multiple definitions from the predecessor ML-Model extension to synthesize common use cases into a single reference for Machine Learning Models.

For more details about the stac-model Python package, which provides definitions of the MLM extension using both Pydantic and PySTAC connectors, please refer to the STAC Model document.

:warning:
FIXME: update examples

• Examples:
  • Example with a ??? trained with torchgeo :warning: TODO update example
  • Collection example: Shows the basic usage of the extension in a STAC Collection
• JSON Schema TODO update
• Changelog

Item Properties and Collection Fields

The fields in the table below can be used in these parts of STAC documents:

• Catalogs
• Collections
• Item Properties (incl. Summaries in Collections)
• Assets (for both Collections and Items, incl. Item-Assets definitions in Collections)
• Links

Field Name	Type	Description
mlm:name	string	REQUIRED A name for the model. This can include, but must be distinct, from simply naming the model architecture. If there is a publication or other published work related to the model, use the official name of the model.
mlm:architecture	Model Architecture string	REQUIRED A generic and well established architecture name of the model.
mlm:tasks	[Task Enum]	REQUIRED Specifies the Machine Learning tasks for which the model can be used for. If multi-tasks outputs are provided by distinct model heads, specify all available tasks under the main properties and specify respective tasks in each Model Output Object.
mlm:framework	string	Framework used to train the model (ex: PyTorch, TensorFlow).
mlm:framework_version	string	The framework library version. Some models require a specific version of the machine learning framework to run.
mlm:memory_size	integer	The in-memory size of the model on the accelerator during inference (bytes).
mlm:total_parameters	integer	Total number of model parameters, including trainable and non-trainable parameters.
mlm:pretrained	boolean	Indicates if the model was pretrained. If the model was pretrained, consider providing pretrained_source if it is known.
mlm:pretrained_source	string | null	The source of the pretraining. Can refer to popular pretraining datasets by name (i.e. Imagenet) or less known datasets by URL and description. If trained from scratch (i.e.: pretrained = false), the null value should be set explicitly.
mlm:batch_size_suggestion	integer	A suggested batch size for the accelerator and summarized hardware.
mlm:accelerator	Accelerator Type Enum | null	The intended computational hardware that runs inference. If undefined or set to null explicitly, the model does not require any specific accelerator.
mlm:accelerator_constrained	boolean	Indicates if the intended accelerator is the only accelerator that can run inference. If undefined, it should be assumed false.
mlm:accelerator_summary	string	A high level description of the accelerator, such as its specific generation, or other relevant inference details.
mlm:accelerator_count	integer	A minimum amount of accelerator instances required to run the model.
mlm:input	[Model Input Object]	REQUIRED Describes the transformation between the EO data and the model input.
mlm:output	[Model Output Object]	REQUIRED Describes each model output and how to interpret it.
mlm:hyperparameters	Model Hyperparameters Object	Additional hyperparameters relevant for the model.

To decide whether above fields should be applied under Item properties or under respective Assets, the context of each field must be considered. For example, the mlm:name should always be provided in the Item properties, since it relates to the model as a whole. In contrast, some models could support multiple mlm:accelerator, which could be handled by distinct source code represented by different Assets. In such case, mlm:accelerator definitions should be nested under their relevant Asset. If a field is defined both at the Item and Asset level, the value at the Asset level would be considered for that specific Asset, and the value at the Item level would be used for other Assets that did not override it for their respective reference. For some of the fields, further details are provided in following sections to provide more precisions regarding some potentially ambiguous use cases.

In addition, fields from the multiple relevant extensions should be defined as applicable. See Best Practices - Recommended Extensions to Compose with the ML Model Extension for more details.

For the Extent Object in STAC Collections and the corresponding spatial and temporal fields in Items, please refer to section Best Practices - Using STAC Common Metadata Fields for the ML Model Extension.

Model Architecture

In most cases, this should correspond to common architecture names defined in the literature, such as ResNet, VGG, GAN or Vision Transformer. For more examples of proper names (including casing), the Papers With Code - Computer Vision Methods can be used. Note that this field is not an explicit "Enum", and is used only as an indicator of common architecture occurrences. If no specific or predefined architecture can be associated with the described model, simply employ unknown or another custom name as deemed appropriate.

Task Enum

It is recommended to define mlm:tasks of the entire model at the STAC Item level, and tasks of respective Model Output Object with the following values. Although other values are permitted to support more use cases, they should be used sparingly to allow better interoperability of models and their representation.

As a general rule of thumb, if a task is not represented below, an appropriate name can be formulated by taking definitions listed in Papers With Code. The names should be normalized to lowercase and use hyphens instead of spaces.

Task Name	Corresponding label:tasks	Description
regression	regression	Generic regression that estimates a numeric and continuous value.
classification	classification	Generic classification task that assigns class labels to an output.
scene-classification	n/a	Specific classification task where the model assigns a single class label to an entire scene/area.
detection	detection	Generic detection of the "presence" of objects or entities, with or without positions.
object-detection	n/a	Task corresponding to the identification of positions as bounding boxes of object detected in the scene.
segmentation	segmentation	Generic tasks that regroups all types of segmentations tasks consisting of applying labels to pixels.
semantic-segmentation	n/a	Specific segmentation task where all pixels are attributed labels, without consideration for segments as unique objects.
instance-segmentation	n/a	Specific segmentation task that assigns distinct labels for groups of pixels corresponding to object instances.
panoptic-segmentation	n/a	Specific segmentation task that combines instance segmentation of objects and semantic labels for non-objects.
similarity-search	n/a	Generic task to identify whether a query input corresponds to another reference within a corpus.
generative	n/a	Generic task that encompasses all synthetic data generation techniques.
image-captioning	n/a	Specific task of describing the content of an image in words.
super-resolution	n/a	Specific task that increases the quality and resolution of an image by increasing its high-frequency details.

If the task falls within the category of supervised machine learning and uses labels during training, this should align with the label:tasks values defined in STAC Label Extension for relevant STAC Collections and Items published with the model described by this extension.

It is to be noted that multiple "generic" tasks names (classification, detection, etc.) are defined to allow correspondance with label:tasks, but these can lead to some ambiguity depending on context. For example, a model that supports classification could mean that the model can predict patch-based classes over an entire scene (i.e.: scene-classification for a single prediction over an entire area of interest as a whole), or that it can predict pixel-wise "classifications", such as land-cover labels for every single pixel coordinate over the area of interest. Maybe counter-intuitively to some users, such a model that produces pixel-wise "classifications" should be attributed the segmentation task (and more specifically semantic-segmentation) rather than classification. To avoid this kind of ambiguity, it is strongly recommended that tasks always aim to provide the most specific definitions possible to explicitly describe what the model accomplishes.

Framework

This should correspond to the common library name of a well-established ML framework. No "Enum" are enforced to allow easy addition of newer frameworks, but it is STRONGLY recommended to use common names when applicable. Below are a few notable entries.

• PyTorch
• TensorFlow
• scikit-learn
• Hugging Face
• Keras
• ONNX
• rgee
• spatialRF
• JAX
• MXNet
• Caffe
• PyMC
• Weka

Accelerator Type Enum

It is recommended to define accelerator with one of the following values:

• amd64 models compatible with AMD or Intel CPUs (no hardware specific optimizations)
• cuda models compatible with NVIDIA GPUs
• xla models compiled with XLA. Models trained on TPUs are typically compiled with XLA.
• amd-rocm models trained on AMD GPUs
• intel-ipex-cpu for models optimized with IPEX for Intel CPUs
• intel-ipex-gpu for models optimized with IPEX for Intel GPUs
• macos-arm for models trained on Apple Silicon

:warning:
If mlm:accelerator = amd64, this explicitly indicates that the model does not (and will not try to) use any accelerator, even if some are available from the runtime environment. This is to be distinguished from the value mlm:accelerator = null, which means that the model could make use of some accelerators if provided, but is not constrained by any specific one. To improve comprehension by users, it is recommended that any model using mlm:accelerator = amd64 also set explicitly mlm:accelerator_constrained = true to illustrate that the model WILL NOT use accelerators, although the hardware resolution should be identical nonetheless.

When mlm:accelerator = null is employed, the value of mlm:accelerator_constrained can be ignored, since even if set to true, there would be no accelerator to contain against. To avoid confusion, it is suggested to set the mlm:accelerator_constrained = false or omit the field entirely in this case.

Model Input Object

Field Name	Type	Description
name	string	REQUIRED Name of the input variable defined by the model. If no explicit name is defined by the model, an informative name (e.g.: "RGB Time Series") can be used instead.
bands	[string]	REQUIRED The names of the raster bands used to train or fine-tune the model, which may be all or a subset of bands available in a STAC Item's Band Object. If no band applies for one input, use an empty array.
input	Input Structure Object	REQUIRED The N-dimensional array definition that describes the shape, dimension ordering, and data type.
description	string	Additional details about the input such as describing its purpose or expected source that cannot be represented by other properties.
norm_by_channel	boolean	Whether to normalize each channel by channel-wise statistics or to normalize by dataset statistics. If True, use an array of statistics of same dimensionality and order as the bands field in this object.
norm_type	Normalize Enum | null	Normalization method. Select an appropriate option or null when none applies. Consider using pre_processing_function for custom implementations or more complex combinations.
norm_clip	[number]	When norm_type = "clip", this array supplies the value for each bands item, which is used to divide each band before clipping values between 0 and 1.
resize_type	Resize Enum | null	High-level descriptor of the rescaling method to change image shape. Select an appropriate option or null when none applies. Consider using pre_processing_function for custom implementations or more complex combinations.
statistics	[Statistics Object]	Dataset statistics for the training dataset used to normalize the inputs.
pre_processing_function	Processing Expression | null	Custom preprocessing function where normalization and rescaling, and any other significant operations takes place.

Fields that accept the null value can be considered null when omitted entirely for parsing purposes. However, setting null explicitly when this information is known by the model provider can help users understand what is the expected behavior of the model. It is therefore recommended to provide null explicitly when applicable.

Bands and Statistics

Depending on the supported stac_version and other stac_extensions employed by the STAC Item using MLM, the STAC 1.1 - Band Object, the STAC Raster - Band Object or the STAC EO - Band Object can be used for representing bands information, including notably the nodata value, the data_type (see also Data Type Enum), and Common Band Names.

:information_source:
Due to how the schema for eo:bands is defined, it is not sufficient to only provide the eo:bands property at the STAC Item level. The schema validation of the EO extension explicitly looks for a corresponding set of bands under an Asset, and if none is found, it disallows eo:bands in the Item properties. Therefore, eo:bands should either be specified only under the Asset containing the mlm:model role (see Model Asset), or define them both under the Asset and Item properties. If the second approach is selected, it is recommended that the eo:bands under the Asset contains only the name or the common_name property, such that all other details about the bands are defined at the Item level.

For more details, refer to stac-extensions/eo#12.
For an example, please refer to examples/item_eo_bands.json. Notably in this example, the assets.weights.eo:bands property provides the name to fulfill the Asset requirement, while all additional band details are provided in properties.eo:bands.

Only bands used as input to the model should be included in the MLM bands field. To avoid duplicating the information, MLM only uses the name of whichever "Band Object" is defined in the STAC Item.

One distinction from the STAC 1.1 - Band Object in MLM is that Statistics object (or the corresponding STAC Raster - Statistics for STAC 1.0) are not defined at the "Band Object" level, but at the Model Input level. This is because, in machine learning, it is common to need overall statistics for the dataset used to train the model to normalize all bands, rather than normalizing the values over a single product. Furthermore, statistics could be applied differently for distinct Model Input definitions, in order to adjust for intrinsic properties of the model.

Data Type Enum

When describing the data_type provided by a Band, whether for defining the Input Structure or the Result Structure, the Data Types from the STAC Raster extension should be used if using STAC 1.0 or earlier, and can use Data Types from STAC 1.1 Core for later versions. Both definitions should define equivalent values.

Input Structure Object

Field Name	Type	Description
shape	[integer]	REQUIRED Shape of the input n-dimensional array (e.g.: $B \times C \times H \times W$), including the batch size dimension. Each dimension must either be greater than 0 or -1 to indicate a variable dimension size.
dim_order	[Dimension Order]	REQUIRED Order of the shape dimensions by name.
data_type	Data Type Enum	REQUIRED The data type of values in the n-dimensional array. For model inputs, this should be the data type of the processed input supplied to the model inference function, not the data type of the source bands.

A common use of -1 for one dimension of shape is to indicate a variable batch-size. However, this value is not strictly reserved for the b dimension. For example, if the model is capable of automatically adjusting its input layer to adapt to the provided input data, then the corresponding dimensions that can be adapted can employ -1 as well.

Dimension Order

Recommended values should use common names as much as possible to allow better interpretation by users and scripts that could need to resolve the dimension ordering for reshaping requirements according to the ML framework employed.

Below are some notable common names recommended for use, but others can be employed as needed.

• batch
• channel
• time
• height
• width
• depth
• token
• class
• score
• confidence

For example, a tensor of multiple RBG images represented as $B \times C \times H \times W$ should indicate dim_order = ["batch", "channel", "height", "width"].

Normalize Enum

Select one option from:

• min-max
• z-score
• l1
• l2
• l2sqr
• hamming
• hamming2
• type-mask
• relative
• inf
• clip

See OpenCV - Normalization Flags for details about the relevant methods. Equivalent methods from other packages are applicable as well.

When a normalization technique is specified, it is expected that the corresponding Statistics parameters necessary to perform it would be provided for the corresponding input. For example, the min-max normalization would require that at least the minimum and maximum statistic properties are provided, while the z-score would require mean and stddev.

If none of the above values applies, null (literal, not string) can be used instead. If a custom normalization operation, or a combination of operations (with or without Resize), must be defined instead, consider using a Processing Expression reference.

Resize Enum

Select one option from:

• crop
• pad
• interpolation-nearest
• interpolation-linear
• interpolation-cubic
• interpolation-area
• interpolation-lanczos4
• interpolation-max
• wrap-fill-outliers
• wrap-inverse-map

See OpenCV - Interpolation Flags for details about the relevant methods. Equivalent methods from other packages are applicable as well.

If none of the above values applies, null (literal, not string) can be used instead. If a custom rescaling operation, or a combination of operations (with or without Normalization), must be defined instead, consider using a Processing Expression reference.

Processing Expression

Taking inspiration from Processing Extension - Expression Object, the processing expression defines at the very least a format and the applicable expression for it to perform pre/post-processing operations on MLM inputs/outputs.

Field Name	Type	Description
format	string	REQUIRED The type of the expression that is specified in the expression property.
expression	*	REQUIRED An expression compliant with the format specified. The expression can be any data type and depends on the format given, e.g. string or object.

On top of the examples already provided by Processing Extension - Expression Object, the following formats are recommended as alternative scripts and function references.

Format	Type	Description	Expression Example
python	string	A Python entry point reference.	my_package.my_module:my_processing_function or my_package.my_module:MyClass.my_method
docker	string	An URI with image and tag to a Docker.	ghcr.io/NAMESPACE/IMAGE_NAME:latest
uri	string	An URI to some binary or script.	{"href": "https://raw.githubusercontent.com/ORG/REPO/TAG/package/cli.py", "type": "text/x-python"}

:information_source:
Above definitions are only indicative, and more can be added as desired with even more custom definitions. It is left as an implementation detail for users to resolve how these expressions should be handled at runtime.

:warning:
See also discussion regarding additional processing expressions: stac-extensions/processing#31

Model Output Object

Field Name	Type	Description
name	string	REQUIRED Name of the output variable defined by the model. If no explicit name is defined by the model, an informative name (e.g.: "CLASSIFICATION") can be used instead.
tasks	[Task Enum]	REQUIRED Specifies the Machine Learning tasks for which the output can be used for. This can be a subset of mlm:tasks defined under the Item properties as applicable.
result	Result Structure Object	REQUIRED The structure that describes the resulting output arrays/tensors from one model head.
description	string	Additional details about the output such as describing its purpose or expected result that cannot be represented by other properties.
classification:classes	[Class Object]	A list of class objects adhering to the Classification Extension.
post_processing_function	Processing Expression | null	Custom postprocessing function where normalization and rescaling, and any other significant operations takes place.

While only tasks is a required field, all fields are recommended for tasks that produce a fixed shape tensor and have output classes. Outputs that have variable dimensions, can define the result with the appropriate dimension value -1 in the shape field. When the model does not produce specific classes, such as for regression, image-captioning, super-resolution and some generative tasks, to name a few, the classification:classes can be omitted.

Result Structure Object

Field Name	Type	Description
shape	[integer]	REQUIRED Shape of the n-dimensional result array (e.g.: $B \times H \times W$ or $B \times C$), possibly including a batch size dimension. The dimensions must either be greater than 0 or -1 to indicate a variable size.
dim_order	[Dimension Order]	REQUIRED Order of the shape dimensions by name for the result array.
data_type	Data Type Enum	REQUIRED The data type of values in the n-dimensional array. For model outputs, this should be the data type of the result of the model inference without extra post processing.

Class Object

See the documentation for the Class Object.

Model Hyperparameters Object

The hyperparameters are an open JSON object definition that can be used to provide relevant configurations for the model. Those can combine training details, inference runtime parameters, or both. For example, training hyperparameters could indicate the number of epochs that were used, the optimizer employed, the number of estimators contained in an ensemble of models, or the random state value. For inference, parameters such as the model temperature, a confidence cut-off threshold, or a non-maximum suppression threshold to limit proposal could be specified. The specific parameter names, and how they should be employed by the model, are specific to each implementation.

Following is an example of what the hyperparameters definition could look like:

{
  "mlm:hyperparameters": {
    "nms_max_detections": 500,
    "nms_threshold": 0.25,
    "iou_threshold": 0.5,
    "random_state": 12345
  }
}

Assets Objects

Field Name	Type	Description
mlm:model	Asset Object	REQUIRED Asset object containing the model definition.
mlm:source_code	Asset Object	RECOMMENDED Source code description. Can describe a Git repository, ZIP archive, etc.
mlm:container	Asset Object	RECOMMENDED Information to run the model in a container with URI to the container.
mlm:training	Asset Object	RECOMMENDED Information to run the training pipeline of the model being described.
mlm:inference	Asset Object	RECOMMENDED Information to run the inference pipeline of the model being described.

It is recommended that the Assets defined in a STAC Item using MLM extension use the above field property names for nesting the Assets in order to improve their quick identification, although the specific names employed are left up to user preference. However, the MLM Asset definitions MUST include the appropriate MLM Asset Roles to ensure their discovery.

MLM Asset Roles

Asset roles should include relevant names that describe them. This does not only include the Recommended Asset Roles from the core specification, such as data or metadata, but also descriptors such as mlm:model, mlm:weights and so on, as applicable for the relevant MLM Assets being described. Please refer to the following sections for roles requirements by specific MLM Assets.

Note that mlm: prefixed roles are used for identification purpose of the Assets, but non-prefixed roles can be provided as well to offer generic descriptors. For example, ["mlm:model", "model", "data"] could be considered for the Model Asset.

In order to provide more context, the following roles are also recommended were applicable:

Asset Role	Additional Roles	Description
mlm:inference-runtime (*)	runtime	Describes an Asset that provides runtime reference to perform model inference.
mlm:training-runtime (*)	runtime	Describes an Asset that provides runtime reference to perform model training.
mlm:checkpoint (*)	weights, checkpoint	Describes an Asset that provides a model checkpoint with embedded model configurations.
mlm:weights	weights, checkpoint	Describes an Asset that provides a model weights (typically some Tensor representation).
mlm:model	model	Required role for Model Asset.
mlm:source_code	code	Required role for Model Asset.

:information_source:
(*) These roles are offered as direct conversions from the previous extension that provided ML-Model Asset Roles to provide easier upgrade to the MLM extension.

Model Asset

Field Name	Type	Description
title	string	Description of the model asset.
href	string	URI to the model artifact.
type	string	The media type of the artifact (see Model Artifact Media-Type.
roles	[string]	REQUIRED Specify mlm:model. Can include ["mlm:weights", "mlm:checkpoint"] as applicable.
mlm:artifact_type	Artifact Type Enum	Specifies the kind of model artifact. Typically related to a particular ML framework.

Recommended Asset roles include mlm:weights or mlm:checkpoint for model weights that need to be loaded by a model definition and mlm:compiled for models that can be loaded directly without an intermediate model definition. In each case, the mlm:model should be applied as well to indicate that this asset represents the model.

It is also recommended to make use of the file extension for this Asset, as it can provide useful information to validate the contents of the model definition, by comparison with fields file:checksum and file:size for example.

Model Artifact Media-Type

Very few ML framework, libraries or model artifacts provide explicit IANA registered media-type to represent the contents they handle. When those are not provided, custom media-types can be considered. However, "unofficial but well-established" parameters should be reused over custom media-types when possible.

For example, the unofficial application/octet-stream; framework=pytorch definition is appropriate to represent a PyTorch .pt file, since its underlying format is a serialized pickle structure, and its framework parameter provides a clearer indication about the targeted ML framework and its contents. Since artifacts will typically be downloaded using a request stream into a runtime environment in order to employ the model, the application/octet-stream media-type is relevant for representing this type of arbitrary binary data. Being an official media-type, it also has the benefit to increase chances that HTTP clients will handle download of the contents appropriately when performing requests. In contrast, custom media-types such as application/x-pytorch have higher chances to be considered unacceptable (HTTP 406 Not Acceptable) by servers, which is why they should preferably be avoided.

Users can consider adding more parameters to provide additional context, such as profile=compiled to provide an additional hint that the specific PyTorch Ahead-of-Time Compilation profile is used for the artifact described by the media-type. However, users need to remember that those parameters are not official. In order to validate the specific framework and artifact type employed by the model, the MLM properties mlm:framework (see MLM Fields) and mlm:artifact_type (see Model Asset) should be employed instead to perform this validation if needed.

Artifact Type Enum

This value can be used to provide additional details about the specific model artifact being described. For example, PyTorch offers various strategies for providing model definitions, such as Pickle (.pt), TorchScript, or PyTorch Ahead-of-Time Compilation (.pt2) approach. Since they all refer to the same ML framework, the Model Artifact Media-Type can be insufficient in this case to detect which strategy should be used with.

Following are some proposed Artifact Type values for corresponding approaches, but other names are permitted as well. Note that the names are selected using the framework-specific definitions to help the users understand the source explicitly, although this is not strictly required either.

Artifact Type	Description
torch.save	A model artifact obtained by Serialized Pickle Object (i.e.: .pt).
torch.jit.script	A model artifact obtained by TorchScript.
torch.export	A model artifact obtained by torch.export (i.e.: .pt2).
torch.compile	A model artifact obtained by torch.compile.

Source Code Asset

Field Name	Type	Description
title	string	Title of the source code.
href	string	URI to the code repository, a ZIP archive, or an individual code/script file.
type	string	Media-type of the URI.
roles	[string]	RECOMMENDED Specify one or more of ["model", "code", "metadata"]
description	string	Description of the source code.
mlm:entrypoint	string	Specific entrypoint reference in the code to use for running model inference.

If the referenced code does not directly offer a callable script to run the model, the mlm:entrypoint field should be added to the Asset Object in order to provide a pointer to the inference function to execute the model. For example, my_package.my_module:predict would refer to the predict function located in the my_module inside the my_package library provided by the repository.

It is strongly recommended to use a specific media-type such as text/x-python if the source code refers directly to a script of a known programming language. Using the HTML rendering of that source file, such as though GitHub for example, should be avoided. Using the "Raw Contents" endpoint for such cases is preferable. The text/html media-type should be reserved for cases where the URI generally points at a Git repository. Note that the URI including the specific commit hash, release number or target branch should be preferred over other means of referring to checkout procedures, although this specification does not prohibit the use of additional properties to better describe the Asset.

Since the source code of a model provides useful example on how to use it, it is also recommended to define relevant references to documentation using the example extension. See the Best Practices - Example Extension section for more details.

Recommended asset roles include code and metadata, since the source code asset might also refer to more detailed metadata than this specification captures.

Container Asset

Field Name	Type	Description
title	string	Description of the container.
href	string	URI of the published container, including the container registry, image and tag.
type	string	Media-type of the container, typically application/vnd.oci.image.index.v1+json.
roles	[string]	Specify ["runtime"] and any other custom roles.

If you're unsure how to containerize your model, we suggest starting from the latest official container image for your framework that works with your model and pinning the container version.

Examples:

• Pytorch Dockerhub
• Pytorch Docker Run Example
• Tensorflow Dockerhub
• Tensorflow Docker Run Example

Using a base image for a framework looks like:

# In your Dockerfile, pull the latest base image with all framework dependencies including accelerator drivers
FROM pytorch/pytorch:2.1.2-cuda11.8-cudnn8-runtime

### Your specific environment setup to run your model
RUN pip install my_package

You can also use other base images. Pytorch and Tensorflow offer docker images for serving models for inference.

• Torchserve
• TFServing

Relation Types

The following types should be used as applicable rel types in the Link Object of STAC Items describing Band Assets that result from the inference of a model described by the MLM extension.

Type	Description
derived_from	This link points to a STAC Collection or Item using MLM.

It is recommended that the link using derived_from referring to another STAC definition using the MLM extension specifies the mlm:name value to make the derived reference more explicit.

Note that a derived product from model inference described by STAC should also consider using additional indications that it came of a model, such as described by the Best Practices - Processing Extension.

Contributing

All contributions are subject to the STAC Specification Code of Conduct. For contributions, please follow the STAC specification contributing guide Instructions for running tests are copied here for convenience.

Running tests

The same checks that run as checks on PRs are part of the repository and can be run locally to verify that changes are valid. To run tests locally, you'll need npm, which is a standard part of any node.js installation.

First, install everything with npm once. Navigate to the root of this repository and on your command line run:

npm install

Then to check Markdown formatting and test the examples against the JSON schema, you can run:

npm test

This will spit out the same texts that you see online, and you can then go and fix your markdown or examples.

If the tests reveal formatting problems with the examples, you can fix them with:

npm run format-examples