examples/facial-keypoints-detection-.../facial-keypoints-detection-...

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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import kfp\n",
"from kfp import dsl\n",
"\n",
"\n",
"def SendMsg(trial, epoch, patience):\n",
" vop = dsl.VolumeOp(name=\"pvc\",\n",
" resource_name=\"pvc\", size='5Gi', \n",
" modes=dsl.VOLUME_MODE_RWO)\n",
"\n",
" return dsl.ContainerOp(\n",
" name = 'Train', \n",
" image = 'hubdocker76/demotrain:v8', # use this prebuilt image or replace image with your own custom image\n",
" command = ['python3', 'train.py'],\n",
" arguments=[\n",
" '--trial', trial,\n",
" '--epoch', epoch,\n",
" '--patience', patience\n",
" ],\n",
" pvolumes={\n",
" '/data': vop.volume\n",
" }\n",
" )\n",
"\n",
"def GetMsg(comp1):\n",
" return dsl.ContainerOp(\n",
" name = 'Evaluate',\n",
" image = 'hubdocker76/demoeval:v3', # use this prebuilt image or replace image with your own custom image\n",
" pvolumes={\n",
" '/data': comp1.pvolumes['/data']\n",
" },\n",
" command = ['python3', 'eval.py']\n",
" )\n",
"\n",
"@dsl.pipeline(\n",
" name = 'face pipeline',\n",
" description = 'pipeline to detect facial landmarks')\n",
"def passing_parameter(trial, epoch, patience):\n",
" comp1 = SendMsg(trial, epoch, patience).add_pod_label(\"kaggle-secret\", \"true\")\n",
" comp2 = GetMsg(comp1)\n",
"\n",
"if __name__ == '__main__':\n",
" import kfp.compiler as compiler\n",
" compiler.Compiler().compile(passing_parameter, 'facial-keypoints-detection-kfp.py.yaml')\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"kubeflow_notebook": {
"autosnapshot": true,
"experiment": {
"id": "",
"name": ""
},
"experiment_name": "",
"katib_metadata": {
"algorithm": {
"algorithmName": "grid"
},
"maxFailedTrialCount": 3,
"maxTrialCount": 12,
"objective": {
"objectiveMetricName": "",
"type": "minimize"
},
"parallelTrialCount": 3,
"parameters": []
},
"katib_run": false,
"pipeline_description": "",
"pipeline_name": "",
"snapshot_volumes": true,
"steps_defaults": [
"label:access-ml-pipeline:true",
"label:access-rok:true"
],
"volume_access_mode": "rwm",
"volumes": [
{
"annotations": [],
"mount_point": "/home/jovyan",
"name": "test-face-keypoint-workspace-54wqj",
"size": 5,
"size_type": "Gi",
"snapshot": false,
"type": "clone"
}
]
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}