def create_jumpstart_model_table()

in doc/doc_utils/jumpstart_doc_utils.py [0:0]

• 87 lines of code
• 7 McCabe index (conditional complexity)

def create_jumpstart_model_table():
    sdk_manifest = get_jumpstart_sdk_manifest()
    sdk_manifest_top_versions_for_models = {}

    for model in sdk_manifest:
        if model["model_id"] not in sdk_manifest_top_versions_for_models:
            sdk_manifest_top_versions_for_models[model["model_id"]] = model
        else:
            if Version(
                sdk_manifest_top_versions_for_models[model["model_id"]]["version"]
            ) < Version(model["version"]):
                sdk_manifest_top_versions_for_models[model["model_id"]] = model

    file_content_intro = []

    file_content_intro.append(".. _all-pretrained-models:\n\n")
    file_content_intro.append(".. |external-link| raw:: html\n\n")
    file_content_intro.append('   <i class="fa fa-external-link"></i>\n\n')

    file_content_intro.append("================================================\n")
    file_content_intro.append("Built-in Algorithms with pre-trained Model Table\n")
    file_content_intro.append("================================================\n")
    file_content_intro.append(
        """
    The SageMaker Python SDK uses model IDs and model versions to access the necessary
    utilities for pre-trained models. This table serves to provide the core material plus
    some extra information that can be useful in selecting the correct model ID and
    corresponding parameters.\n"""
    )
    file_content_intro.append(
        """
    If you want to automatically use the latest version of the model, use "*" for the `model_version` attribute.
    We highly suggest pinning an exact model version however.\n"""
    )
    file_content_intro.append(
        """
    These models are also available through the
    `JumpStart UI in SageMaker Studio <https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html>`__\n"""
    )
    file_content_intro.append("\n")
    file_content_intro.append(".. list-table:: Available Models\n")
    file_content_intro.append("   :widths: 50 20 20 20 30 20\n")
    file_content_intro.append("   :header-rows: 1\n")
    file_content_intro.append("   :class: datatable\n")
    file_content_intro.append("\n")
    file_content_intro.append("   * - Model ID\n")
    file_content_intro.append("     - Fine Tunable?\n")
    file_content_intro.append("     - Latest Version\n")
    file_content_intro.append("     - Min SDK Version\n")
    file_content_intro.append("     - Problem Type\n")
    file_content_intro.append("     - Source\n")

    dynamic_table_files = []
    open_weight_content_entries = []

    for model in sdk_manifest_top_versions_for_models.values():
        model_spec = get_jumpstart_sdk_spec(model["spec_key"])
        model_task = get_model_task(model_spec["model_id"])
        string_model_task = get_string_model_task(model_spec["model_id"])
        model_source = get_model_source(model_spec["url"])
        open_weight_content_entries.append("   * - {}\n".format(model_spec["model_id"]))
        open_weight_content_entries.append("     - {}\n".format(model_spec["training_supported"]))
        open_weight_content_entries.append("     - {}\n".format(model["version"]))
        open_weight_content_entries.append("     - {}\n".format(model["min_version"]))
        open_weight_content_entries.append("     - {}\n".format(model_task))
        open_weight_content_entries.append(
            "     - `{} <{}>`__ |external-link|\n".format(model_source, model_spec["url"])
        )

        if (string_model_task, TO_FRAMEWORK[model_source]) in MODALITY_MAP:
            file_content_single_entry = []

            if (
                MODALITY_MAP[(string_model_task, TO_FRAMEWORK[model_source])]
                not in dynamic_table_files
            ):
                file_content_single_entry.append("\n")
                file_content_single_entry.append(".. list-table:: Available Models\n")
                file_content_single_entry.append("   :widths: 50 20 20 20 20\n")
                file_content_single_entry.append("   :header-rows: 1\n")
                file_content_single_entry.append("   :class: datatable\n")
                file_content_single_entry.append("\n")
                file_content_single_entry.append("   * - Model ID\n")
                file_content_single_entry.append("     - Fine Tunable?\n")
                file_content_single_entry.append("     - Latest Version\n")
                file_content_single_entry.append("     - Min SDK Version\n")
                file_content_single_entry.append("     - Source\n")

                dynamic_table_files.append(
                    MODALITY_MAP[(string_model_task, TO_FRAMEWORK[model_source])]
                )

            file_content_single_entry.append("   * - {}\n".format(model_spec["model_id"]))
            file_content_single_entry.append("     - {}\n".format(model_spec["training_supported"]))
            file_content_single_entry.append("     - {}\n".format(model["version"]))
            file_content_single_entry.append("     - {}\n".format(model["min_version"]))
            file_content_single_entry.append(
                "     - `{} <{}>`__\n".format(model_source, model_spec["url"])
            )
            f = open(MODALITY_MAP[(string_model_task, TO_FRAMEWORK[model_source])], "a")
            f.writelines(file_content_single_entry)
            f.close()

    proprietary_content_entries = create_proprietary_model_table()

    f = open("doc_utils/pretrainedmodels.rst", "a")
    f.writelines(file_content_intro)
    f.writelines(open_weight_content_entries)
    f.writelines(proprietary_content_entries)
    f.close()