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‎pipelines/samples/employee-attrition/employee-attrition-pipeline-ads.ipynb

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@@ -6,7 +6,7 @@
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"id": "c8a4f0d9",
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"metadata": {},
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"source": [
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"# Employee attrition sample using ML Pipelines"
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"# Employee attrition sample using ML Pipelines with ADS"
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]
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},
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{

‎pipelines/samples/employee-attrition/employee-attrition-pipeline.ipynb

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@@ -85,27 +85,49 @@
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"import oci\n",
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"import ads\n",
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"import os\n",
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"from os import environ\n",
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"from ads.catalog.project import ProjectCatalog\n",
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"\n",
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"ads.set_auth(auth='resource_principal')"
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"from os import environ"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7d72637e",
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"id": "f841d08d",
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"metadata": {
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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"# make sure there is access to the project and compartment\n",
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"compartment_id = os.environ['NB_SESSION_COMPARTMENT_OCID']\n",
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"pc = ProjectCatalog(compartment_id=compartment_id)\n",
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"pc.list_projects()"
102+
"if 'NB_SESSION_COMPARTMENT_OCID' in os.environ:\n",
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" # using the notebook's compartment if running on OCI Data Science Notebook Session\n",
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" compartment_id = os.environ['NB_SESSION_COMPARTMENT_OCID']\n",
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"else:\n",
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" # set the compartment OCID if you are working locally\n",
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" compartment_id = '<YOUR_COMPARTMENT_OCID>'\n",
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"print('compartment OCID = ', compartment_id)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ec3398d8",
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"metadata": {
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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"# this will work whether working on a local machine (using API key) or in OCI Notebook Session (using Resource Principal)\n",
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"if \"OCI_RESOURCE_PRINCIPAL_VERSION\" in os.environ:\n",
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" # Use resource principal\n",
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" print(\"using Resource Principal for auth\")\n",
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" ads.set_auth(auth=\"resource_principal\")\n",
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"else:\n",
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" # Use api_key with config file\n",
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" print(\"using API key for auth\")\n",
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" ads.set_auth(auth=\"api_key\") "
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]
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},
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{
@@ -145,20 +167,6 @@
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"log_group_id = \"<YOUR_LOG_GROUP_ID>\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "19a7855a",
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"metadata": {
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
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"data_location = \"<YOUR_OBJECT_STORAGE_LOCATION_TO_PASS_DATA>\" # use: 'oci://<bucket>@<workspace>/'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
@@ -254,8 +262,7 @@
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" \"environmentVariables\": {\n",
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" \"PIPELINE_STEP_RUN_ENTRYPOINT\": \"employee-attr-dataproc.py\",\n",
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" \"CONDA_ENV_TYPE\": \"service\",\n",
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" \"CONDA_ENV_SLUG\": \"generalml_p37_cpu_v1\",\n",
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" \"DATA_LOCATION\": data_location\n",
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" \"CONDA_ENV_SLUG\": \"generalml_p37_cpu_v1\"\n",
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" }\n",
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" }\n",
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" },\n",
@@ -273,8 +280,7 @@
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" \"environmentVariables\": {\n",
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" \"PIPELINE_STEP_RUN_ENTRYPOINT\": \"employee-attr-train-lr.py\",\n",
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" \"CONDA_ENV_TYPE\": \"service\",\n",
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" \"CONDA_ENV_SLUG\": \"generalml_p37_cpu_v1\",\n",
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" \"DATA_LOCATION\": data_location\n",
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" \"CONDA_ENV_SLUG\": \"generalml_p37_cpu_v1\"\n",
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" }\n",
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" },\n",
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" \"dependsOn\": [\"data_processing\"]\n",
@@ -293,8 +299,7 @@
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" \"environmentVariables\": {\n",
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" \"PIPELINE_STEP_RUN_ENTRYPOINT\": \"employee-attr-train-rf.py\",\n",
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" \"CONDA_ENV_TYPE\": \"service\",\n",
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" \"CONDA_ENV_SLUG\": \"generalml_p37_cpu_v1\",\n",
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" \"DATA_LOCATION\": data_location\n",
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" \"CONDA_ENV_SLUG\": \"generalml_p37_cpu_v1\"\n",
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" }\n",
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" },\n",
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" \"dependsOn\": [\"data_processing\"]\n",
@@ -313,8 +318,7 @@
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" \"environmentVariables\": {\n",
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" \"PIPELINE_STEP_RUN_ENTRYPOINT\": \"employee-attr-train-xgb.py\",\n",
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" \"CONDA_ENV_TYPE\": \"service\",\n",
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" \"CONDA_ENV_SLUG\": \"generalml_p37_cpu_v1\",\n",
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" \"DATA_LOCATION\": data_location\n",
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" \"CONDA_ENV_SLUG\": \"generalml_p37_cpu_v1\"\n",
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" }\n",
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" },\n",
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" \"dependsOn\": [\"data_processing\"]\n",
@@ -333,8 +337,7 @@
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" \"environmentVariables\": {\n",
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" \"PIPELINE_STEP_RUN_ENTRYPOINT\": \"employee-attr-eval-deploy.py\",\n",
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" \"CONDA_ENV_TYPE\": \"service\",\n",
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" \"CONDA_ENV_SLUG\": \"generalml_p37_cpu_v1\",\n",
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" \"DATA_LOCATION\": data_location\n",
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" \"CONDA_ENV_SLUG\": \"generalml_p37_cpu_v1\"\n",
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" }\n",
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" },\n",
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" \"dependsOn\": [\"train_logistic_regression\", \"train_random_forest\", \"train_xgboost\"]\n",
@@ -410,7 +413,7 @@
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"for name in steps_names_and_artifacts:\n",
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" fl = open(steps_names_and_artifacts[name], \"rb\")\n",
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" ret = data_science_client.create_step_artifact(pipeline_id, name, fl, content_disposition=f\"attachment; filename={steps_names_and_artifacts[name]}\")\n",
413-
" print(\"OK\" if ret.status==204 else ret.status) # 204 is ok"
416+
" print(f\"{steps_names_and_artifacts[name]}: OK\" if ret.status==204 else f\"{steps_names_and_artifacts[name]}: {ret.status}\") # 204 is ok"
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]
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},
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{
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"pipeline_run_name = f\"pipeline-run-{randrange(1000,9999)}\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ac586f6b",
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"metadata": {
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"vscode": {
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"languageId": "python"
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}
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},
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"outputs": [],
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"source": [
469+
"# uncomment the next line and set to the OCI Object Storage location to use for passing data. Make sure you have proper permissions.\n",
470+
"#data_location = \"<YOUR_OBJECT_STORAGE_BUCKET>\" # use: 'oci://<bucket>@<workspace>/'"
471+
]
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},
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{
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"cell_type": "code",
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"execution_count": null,

‎pipelines/samples/simple/mlpipeline_sample-ads.ipynb

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Original file line numberDiff line numberDiff line change
@@ -1,11 +1,12 @@
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "22859d56",
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"metadata": {},
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"source": [
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"### OCI Data Science - ML Pipelines sample notebook\n",
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"### OCI Data Science - ML Pipelines sample notebook with ADS\n",
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"\n",
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"This sample notebook demonstrates how to use ADS to create a pipeline with 3 steps:\n",
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" * the first step sets a paramter for the next step\n",

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