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fmigneault-crim committed Mar 30, 2024
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Expand Up @@ -60,16 +60,15 @@ extension to synthesize common use cases into a single reference for Machine Lea
| mlm:tasks | [[Task Enum](#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](#model-output-object). |
| mlm:framework | string | **REQUIRED** Framework used to train the model (ex: PyTorch, TensorFlow). |
| mlm:framework_version | string | **REQUIRED** The `framework` library version. Some models require a specific version of the machine learning `framework` to run. |
| mlm:file_size | integer | **REQUIRED** The size on disk of the model artifact (bytes). |
| mlm:memory_size | integer | **REQUIRED** The in-memory size of the model on the accelerator during inference (bytes). |
| mlm:input | [[Model Input Object](#model-input-object)] | **REQUIRED** Describes the transformation between the EO data and the model input. |
| mlm:output | [[Model Output Object](#model-output-object)] | **REQUIRED** Describes each model output and how to interpret it. |
| mlm:accelerator | [Accelerator Enum](#accelerator-enum) | **REQUIRED** The intended computational hardware that runs inference. |
| mlm:accelerator_constrained | boolean | **REQUIRED** True if the intended `accelerator` is the only `accelerator` that can run inference. False if other accelerators, such as amd64 (CPU), can run inference. |
| mlm:hardware_summary | string | **REQUIRED** A high level description of the number of accelerators, specific generation of the `accelerator`, or other relevant inference details. |
| mlm:accelerator | [Accelerator Enum](#accelerator-enum) | The intended computational hardware that runs inference. If undefined, it should be assumed `amd64` (i.e.: CPU). |
| 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:total_parameters | integer | Total number of model parameters, including trainable and non-trainable parameters. |
| mlm:pretrained_source | string | The source of the pretraining. Can refer to popular pretraining datasets by name (i.e. Imagenet) or less known datasets by URL and description. |
| mlm:summary | string | Text summary of the model and it's purpose. |
| 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, the `null` value should be set explicitly. |
| mlm:batch_size_suggestion | number | A suggested batch size for the accelerator and summarized hardware. |

In addition, fields from the following extensions must be imported in the item:
Expand Down Expand Up @@ -106,21 +105,18 @@ Fields that accept the `null` value can be considered `null` when omitted entire
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.

### Accelerator Enum
### 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.
- `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

[stac-asset]: https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md#asset-object


## Assets Objects

| Field Name | Type | Description |
Expand All @@ -136,6 +132,8 @@ names for nesting the Assets in order to improve their quick identification, alt
left up to user preference. However, the MLM Asset definitions **MUST** include the
appropriate [MLM Asset Roles](#mlm-asset-roles) to ensure their discovery.

[stac-asset]: https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md#asset-object

### MLM Asset Roles

Asset `roles` should include relevant names that describe them. This does not only include
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