"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
" \n",
" xx = y\n",
" yy = yhat\n",
- " model.save(\"./\")\n",
+ " model_save_filepath = \"./\"\n",
+ " model.export(model_save_filepath)\n",
+ " \n",
" import json\n",
" data = {}\n",
" data['metrics'] = []\n",
" data['metrics'].append({'Accuracy': str(np.mean(np.absolute(np.asarray(xx)-np.asarray(yy))<5))})\n",
" \n",
- "# as new artifact after training will always be 1.0.0\n",
+ " #as new artifact after training will always be 1.0.0\n",
" artifactversion=\"1.0.0\"\n",
" url = f\"http://modelmgmtservice.traininghost:8082/ai-ml-model-registration/v1/model-registrations/updateArtifact/{modelname}/{modelversion}/{artifactversion}\"\n",
" updated_model_info= requests.post(url).json()\n",
" trainingjob_id = featurepath.split('_')[-1]\n",
" mm_sdk.upload_metrics(data, trainingjob_id)\n",
" print(\"Model-metric : \", mm_sdk.get_metrics(trainingjob_id))\n",
- " mm_sdk.upload_model(\"./\", modelname, modelversion, artifactversion)\n",
+ " mm_sdk.upload_model(model_save_filepath, modelname, modelversion, artifactversion)\n",
" "
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
"<Response [200]>"
]
},
- "execution_count": 6,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}