cube-studio/myapp/init-pipeline.json

{
    "darknet-yolov3":{
        "pipeline":{
            "name":"imageAI",
            "describe":"图像预测+物体检测+视频跟踪",
            "project":"public",
            "parameter":{
                "demo":"true",
                "img":"https://user-images.githubusercontent.com/20157705/170216784-91ac86f7-d272-4940-a285-0c27d6f6cd96.jpg"
            },
            "dag_json": {
                "download-data": { "upstream": [] },
                "yolov3-object-recognition": { "upstream": [ "download-data" ] },
                "deploy-darknet-web-service": { "upstream": ["yolov3-object-recognition"] }
            }
        },
        "tasks":[
            {
                "job_templete":"自定义镜像",
                "name":"download-data",
                "label":"下载处理标注数据",
                "volume_mount": "kubeflow-user-workspace(pvc):/mnt/",
                "args":{
                    "images":"ccr.ccs.tencentyun.com/cube-studio/ubuntu-gpu:cuda10.1-cudnn7-python3.6",
                    "workdir":"/mnt/admin/",
                    "command":"wget https://docker-76009.sz.gfp.tencent-cloud.com/github/cube-studio/pipeline/coco_data_sample.zip && unzip -o coco_data_sample.zip && cd  coco_data_sample && bash reset_file.sh"
                }
            },
            {
                "job_templete":"object-detection-on-darknet",
                "name":"yolov3-object-recognition",
                "label":"目标识别训练",
                "volume_mount": "kubeflow-user-workspace(pvc):/mnt/",
                "resource_memory": "5G",
                "resource_cpu": "5",
                "args": {
                    "--train_cfg": "[net]\n# Testing\n# batch=1\n# subdivisions=1\n# Training\nbatch=64\nsubdivisions=16\nwidth=608\nheight=608\nchannels=3\nmomentum=0.9\ndecay=0.0005\nangle=0\nsaturation = 1.5\nexposure = 1.5\nhue=.1\n\nlearning_rate=0.001\nburn_in=1000\nmax_batches = 50023\npolicy=steps\nsteps=400000,450000\nscales=.1,.1\n\n[convolutional]\nbatch_normalize=1\nfilters=32\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n# Downsample\n\n[convolutional]\nbatch_normalize=1\nfilters=64\nsize=3\nstride=2\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=32\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=64\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n# Downsample\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=3\nstride=2\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=64\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=64\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n# Downsample\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=2\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n[convolutional]\nbatch_normalize=1\nfilters=128\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n# Downsample\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=3\nstride=2\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear\n\n\n[convolutional]\nbatch_normalize=1\nfilters=256\nsize=1\nstride=1\npad=1\nactivation=leaky\n\n[convolutional]\nbatch_normalize=1\nfilters=512\nsize=3\nstride=1\npad=1\nactivation=leaky\n\n[shortcut]\nfrom=-3\nactivation=linear
                    "--data_cfg": "classes= 80\ntrain = /mnt/admin/coco_data_sample/train.txt\nvalid = /mnt/admin/coco_data_sample/valid.txt\nnames = /mnt/admin/coco_data_sample/coco.names\nbackup = /mnt/admin/coco_data_sample/yolo\neval=coco\n\n",
                    "--weights": "/mnt/admin/coco_data_sample/yolov3.weights"
                }
            },
            {
                "job_templete":"deploy-service",
                "name":"deploy-darknet-web-service",
                "volume_mount": "kubeflow-user-workspace(pvc):/mnt/",
                "label":"部署模型web服务",
                "args":{
                    "--label":"目标识别推理服务",
                    "--model_name":"yolov3",
                    "--model_version":"v2022.10.01.1",
                    "--model_path":"",
                    "--service_type":"serving",
                    "--images":"ccr.ccs.tencentyun.com/cube-studio/target-detection",
                    "--working_dir":"",
                    "--command":"",
                    "--args":"",
                    "--env":"YOLO_DATA_PATH=/mnt/admin/coco_data_sample/yolo/coco.data\nYOLO_CFG_PATH=/mnt/admin/coco_data_sample/yolo/yolov3.cfg\nYOLO_WEIGHTS_PATH=/mnt/admin/coco_data_sample/yolo/yolov3.weights",
                    "--ports":"8080",
                    "--replicas":"1",
                    "--resource_memory": "5G",
                    "--resource_cpu": "5",
                    "--resource_gpu": "0"

                }
            }
        ]
    }
}