Merge pull request #86 from qiuosier/job_monitor

Update README and remove unused code in job monitor

Author: darenr

jobs/flask_job_monitor/README.md

@@ -2,6 +2,13 @@
 
 Incubator project to augment the OCI console with useful functionality to support development of Data Science Jobs.
 
+This job monitor is a Python Flask app build on top of [Oracle ADS](https://docs.oracle.com/en-us/iaas/tools/ads-sdk/latest/index.html). It allows users to monitor the status and outputs of OCI data science job runs.
+
+![Job Monitor UI](assets/images/job_monitor.png)
+
+User can also see the job definition in YAML:
+![Job YAML](assets/images/job_yaml.png)
+
 ## How to run
 ### Requirements
 This tool requires `oci>=2.45.1` and `oracle-ads>=2.4.2`.
@@ -41,3 +48,20 @@ The following config can be used in the VS Code `launch.json` to launch the Flas
     "jinja": true
 },
 ```
+
+### UI Development
+If you would like to develop the UI (HTML and JavaScript) without OCI authentication, you can start the app in "Recording Mode" to cache the data to your local computer.
+
+
+ "mocked" version of the Flask app:
+```
+OCI_PYTHON_SDK_NO_SERVICE_IMPORTS=1 FLASK_APP=job_monitor_mocked flask run
+```
+
+The mocked version will use the cached data from the "mock" directory to render the web page.
+
+The content of the "mocked" version can be generated by running the app in "Recording Mode":
+```
+OCI_PYTHON_SDK_NO_SERVICE_IMPORTS=1 RECORDING=1 FLASK_APP=job_monitor flask run
+```
+In this mode, the context of each API response is saved as pickle files in the "mock" directory. The files are named in the format of `{endpoint}/{identifier}.pickle`. This allow us to easily save real data from OCI for mocking purpose. Once the data is saved, we can simply switch to the mocked version work offline.

jobs/flask_job_monitor/assets/images/job_monitor.png

@@ -1,26 +1,12 @@
-import inspect
 import os
 import oci
-import sys
 
 from flask import Flask, render_template, jsonify
 from ads.common.oci_resource import OCIResource
 from ads.jobs import Job
 from ads.jobs.builders.infrastructure.dsc_job import DataScienceJobRun
 
-import logging
-import json
-import time
 
-
-cwd = os.path.dirname(__file__)
-if cwd not in sys.path:
-    sys.path.append(cwd)
-from job_monitor_mocked import save_mocked_context
-
-
-# When RECORDING is set to true, the endpoint context will be saved as pickle for future mocking
-RECORDING = os.environ.get("RECORDING")
 app = Flask(__name__, template_folder=os.path.dirname(__file__))
 config = oci.config.from_file()
 
@@ -57,8 +43,6 @@ def job_monitor(compartment_id=None, project_id=None):
         project_id=project_id,
         compartments=compartments,
     )
-    if RECORDING:
-        save_mocked_context(context, inspect.currentframe().f_code.co_name, compartment_id, project_id)
     return render_template(
         'job_monitor.html',
         **context
@@ -73,8 +57,6 @@ def list_projects(compartment_id):
         "compartment_id": compartment_id,
         "projects": [{"display_name": project.display_name, "ocid": project.id} for project in projects]
     }
-    if RECORDING:
-        save_mocked_context(context, inspect.currentframe().f_code.co_name, compartment_id)
     return jsonify(context)
 
 
@@ -107,8 +89,6 @@ def get_logs(job_run_ocid):
         "statusDetails": run.lifecycle_details,
         "stopped": True if run.lifecycle_state in DataScienceJobRun.TERMINAL_STATES else False
     }
-    if RECORDING:
-        save_mocked_context(context, inspect.currentframe().f_code.co_name, job_run_ocid)
     return jsonify(context)