case.py

from uuid import uuid1

import pytest

from aidisdk import AIDIClient
from aidisdk.algo_house.algorithm_module import AlgoConfig, AlgoFieldEnum
from aidisdk.compute.job_abstract import (
    JobType,
    RunningResourceConfig,
    StartUpConfig,
)
from aidisdk.compute.package_abstract import (
    CodePackageConfig,
    LocalPackageItem,
)
from aidisdk.model import ModelFramework


@pytest.mark.skip("unused")
def test_create_algo_for_eval_detection3d(unittest_client):
    client: AIDIClient = unittest_client
    # create a algorithm with a raw config file

    algorithm = client.algo_house.create(
        algo_name="eval_for_detection3d_" + str(uuid1()).replace("-", "_"),
        field=AlgoFieldEnum.AUTO,
        scene="高速",
        module="感知",
        task_types=["2D检测"],
        framework=ModelFramework.pytorch,
        startup="cd ${WORKING_PATH} && python3 local_example.py ",  # noqa
        code_package="test/test_data/eval_experiment",
        docker_image="docker.hobot.cc/auto/eval-traincli:v1.0.36test",
        desc="算法仓库发起评测使用，请勿删除.",
        tags=["test", "unittest"],
        config_files=[
            AlgoConfig(
                name="eval_setting",
                local_path="test/test_data/"
                + "eval_experiment/setting_example.yaml",
            ),
        ],
    )
    client.algo_house.__delete__(algorithm.algo_id)


@pytest.mark.skip("unused")
def test_update_algo(unittest_client):
    client: AIDIClient = unittest_client
    algo_name = "eval_for_Semantic_Segmentation"
    algorithm = client.algo_house.update(
        algo_name=algo_name,
        field=AlgoFieldEnum.AUTO,
        scene="高速",
        module="感知",
        task_types=["2D检测"],
        framework=ModelFramework.pytorch,
        startup="python3 local_example.py --task_type ${TASK_TYPE} "
        + "--endpoint"
        + " 'http://aidi-test.hobot.cc' "
        + "--group_name ${GROUP_NAME} "
        + "--experiment_name ${EXPERIMENT_NAME} --run_name '${RUN_NAME}' "
        + "--gt_dataset_id "
        + "'${GT_DATASET_ID}' "
        + "--images_dataset_id "  # detection 3d & 分割
        + "'${IMAGES_DATASET_ID}' "
        + "--prediction_name '${PREDICTION_NAME}' "
        + "--predictions_dataset_id '${PREDICTIONS_DATASET_ID}' "  # 分割
        + "--labels_dataset_id '${LABELS_DATASET_ID}' "  # 分割
        + "--setting_file_name ${EVAL_SETTING}",


        code_package="test/test_data/eval_experiment",
        docker_image="docker.hobot.cc/auto/eval-traincli:v1.0.36test",
        desc="算法仓库发起评测使用，请勿删除.",
        tags=["test", "unittest"],
        config_files=[
            AlgoConfig(
                name="eval_setting",
                local_path="test/test_data/eval_experiment/wk_setting.yaml",  # 分割
                # local_path="test/test_data/eval_experiment/setting_example.yaml",
                placeholder="${EVAL_SETTING}",
            ),
        ],
    )
    print(algorithm)


# @pytest.mark.skip("unused")
def test_create_eval_task_env_test(unittest_client):
    client: AIDIClient = unittest_client
    # algo_name = "eval_for_detection3d"
    algo_name = "eval_for_Semantic_Segmentation"
    algo = client.algo_house.get(
        algo_name=algo_name, download_config=True, download_package=True
    )
    # experiment group name + experiment name + prediction in experiment
    # 参数会替换cmd命令中的占位符，默认cmd_args_dict的key大写为占位符，如 task_type -> ${TASK_TYPE}
    cmd_args_dict = {
        "task_type": "Semantic_Segmentation",
        "predictions_dataset_id": "dataset://25616",
        "labels_dataset_id": "dataset://25615",

        # "gt_dataset": "dataset://25575",
        "gt_dataset": "",
        "images_dataset_id": "dataset://25613",
        "group_name": "train-withBN",
        "experiment_name": "wjx_test_095",
        "run_name": "test_run_name_wjx_003",
        # "prediction_name": "wjx_test_023/prediction.json",
        "prediction_name": "",
    }

    config_files = [
        AlgoConfig(
            name="eval_setting",
            local_path="test/test_data/eval_experiment/wk_setting.yaml",  # 分割
            # local_path="test/test_data/eval_experiment/setting_example.yaml",
            placeholder="${EVAL_SETTING}",
        ),
    ]

    algo.update_config(config_files)
    algo.update_cmd(cmd_args_dict)

    # TODO gen job obj
    # job = algo.gen_job()
    cpu_count = 6
    cpu_mem_ratio = 6
    queue = "svc-aip-cpu"
    project = "PD20210425"
    job = client.single_job.create(
        job_name="eval_from_algo_%s_%s"
        % (algo.name, str(uuid1()).replace("-", "_")),
        job_type=JobType.APP_EVAL,
        ipd_number=project,
        queue_name=queue,
        running_resource=RunningResourceConfig(
            docker_image=algo.docker_image,
            instance=1,
            cpu=cpu_count,
            gpu=0,
            cpu_mem_ratio=cpu_mem_ratio,
        ),
        mount=[],
        startup=StartUpConfig(
            command=algo.startup_command,  # noqa
        ),
        code_package=CodePackageConfig(
            raw_package=LocalPackageItem(
                lpath=algo.package_path,
                encrypt_passwd="12345",
                follow_softlink=True,
            ).set_as_startup_dir(),
        ),
        # subscribers=["dan.song", "shulan.shen"],
    )
    print(job)