From: rajdeep11 <rajdeep.sin@samsung.com>
Date: Fri, 13 Dec 2024 11:40:53 +0000 (+0530)
Subject: changes to fix the retrain pipeline
X-Git-Tag: 4.0.0~17
X-Git-Url: https://gerrit.o-ran-sc.org/r/gitweb?a=commitdiff_plain;h=refs%2Fchanges%2F63%2F13863%2F1;p=portal%2Faiml-dashboard.git

changes to fix the retrain pipeline

Change-Id: Ibff607268f35d89b845aa3b5d81aa36feea032da
Signed-off-by: rajdeep11 <rajdeep.sin@samsung.com>
---

diff --git a/kf-pipelines/qoe-pipeline-retrain-2.ipynb b/kf-pipelines/qoe-pipeline-retrain-2.ipynb
index 958bd32..471eedc 100644
--- a/kf-pipelines/qoe-pipeline-retrain-2.ipynb
+++ b/kf-pipelines/qoe-pipeline-retrain-2.ipynb
@@ -24,13 +24,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "@component(base_image=BASE_IMAGE,packages_to_install=['requests'])\n",
-    "def train_export_model(trainingjobName: str, epochs: str, version: str):\n",
+    "def train_export_model(featurepath: str, epochs: str, modelname: str, modelversion:str, artifactversion:str):\n",
     "    \n",
+    "    import re\n",
     "    import tensorflow as tf\n",
     "    from numpy import array\n",
     "    from tensorflow.keras.models import Sequential\n",
@@ -39,18 +40,18 @@
     "    from tensorflow.keras.layers import LSTM\n",
     "    import numpy as np\n",
     "    import requests\n",
+    "    import zipfile\n",
     "    print(\"numpy version\")\n",
     "    print(np.__version__)\n",
     "    import pandas as pd\n",
     "    import os\n",
-    "    import zipfile\n",
     "    from featurestoresdk.feature_store_sdk import FeatureStoreSdk\n",
     "    from modelmetricsdk.model_metrics_sdk import ModelMetricsSdk\n",
     "    \n",
     "    fs_sdk = FeatureStoreSdk()\n",
     "    mm_sdk = ModelMetricsSdk()\n",
-    "    print(\"job name is: \", trainingjobName)\n",
-    "    features = fs_sdk.get_features(trainingjobName, ['pdcpBytesDl','pdcpBytesUl'])\n",
+    "    print(\"featurepath is: \", featurepath)\n",
+    "    features = fs_sdk.get_features(featurepath, ['pdcpBytesDl','pdcpBytesUl'])\n",
     "    print(\"Dataframe:\")\n",
     "    print(features)\n",
     "\n",
@@ -80,12 +81,23 @@
     "    y = y.reshape((y.shape[0], y.shape[2]))\n",
     "    print(X.shape)\n",
     "    print(y.shape)\n",
-    "    \n",
+    " \n",
     "    print(\"Loading the saved model\")\n",
     "    print(os.listdir(os.getcwd()))\n",
+    "    \n",
+    "    pattern = r'(.*?)_(\\d+)$'\n",
+    "\n",
+    "    # Search for the pattern in the input string\n",
+    "    match = re.search(pattern, featurepath)\n",
+    "    trainingjob_id = None\n",
+    "    if match:\n",
+    "        trainingjob_id = int(match.group(2))\n",
+    "        print(\"Training Job ID:\", trainingjob_id)\n",
+    "    else:\n",
+    "        print(\"Pattern not found\")\n",
     "\n",
     "    # Download the model zip file\n",
-    "    model_url= f\"http://tm.traininghost:32002/model/{trainingjobName}/{version}/Model.zip\"\n",
+    "    model_url= f\"http://tm.traininghost:32002/model/{trainingjob_id}/Model.zip\"\n",
     "    print(f\"Downloading model from :{model_url}\")\n",
     "    response = requests.get(model_url)\n",
     "\n",
@@ -169,36 +181,36 @@
     "    data['metrics'] = []\n",
     "    data['metrics'].append({'Accuracy': str(np.mean(np.absolute(np.asarray(xx)-np.asarray(yy))<5))})\n",
     "    \n",
-    "    mm_sdk.upload_metrics(data, trainingjobName, version)\n",
-    "    mm_sdk.upload_model(\"./retrain\", trainingjobName, version)\n"
+    "    mm_sdk.upload_metrics(data, modelname, modelversion,artifactversion)\n",
+    "    mm_sdk.upload_model(\"./\", modelname, modelversion, artifactversion)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "@dsl.pipeline(\n",
-    "    name=\"qoe Pipeline retrain\",\n",
-    "    description=\"qoe retrain\",\n",
+    "    name=\"qoe Pipeline\",\n",
+    "    description=\"qoe\",\n",
     ")\n",
     "def super_model_pipeline( \n",
-    "    trainingjob_name: str, epochs: str, version: str):\n",
+    "    featurepath: str, epochs: str, modelname: str, modelversion:str, artifactversion:str):\n",
     "    \n",
-    "    trainop=train_export_model(trainingjobName=trainingjob_name, epochs=epochs, version=version)\n",
+    "    trainop=train_export_model(featurepath=featurepath, epochs=epochs, modelname=modelname, modelversion=modelversion, artifactversion=artifactversion)\n",
     "    trainop.set_caching_options(False)\n",
     "    kubernetes.set_image_pull_policy(trainop, \"IfNotPresent\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "pipeline_func = super_model_pipeline\n",
-    "file_name = \"qoe_model_pipeline_retrain_2\"\n",
+    "file_name = \"qoe_model_pipeline_retrain\"\n",
     "\n",
     "kfp.compiler.Compiler().compile(pipeline_func,  \n",
     "  '{}.yaml'.format(file_name))"
@@ -211,7 +223,7 @@
    "outputs": [],
    "source": [
     "import requests\n",
-    "pipeline_name=\"qoe_Pipeline_retrain_2\"\n",
+    "pipeline_name=\"qoe_Pipeline_retrain\"\n",
     "pipeline_file = file_name+'.yaml'\n",
     "requests.post(\"http://tm.traininghost:32002/pipelines/{}/upload\".format(pipeline_name), files={'file':open(pipeline_file,'rb')})"
    ]