From a203f1b3582c4eb909bbec1372162401ffea13bc Mon Sep 17 00:00:00 2001 From: Mohammad Ali Salahuddin Date: Wed, 13 Nov 2024 20:25:09 -0500 Subject: [PATCH] Update README.md --- labs/lab3/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/labs/lab3/README.md b/labs/lab3/README.md index a38036b..ace5867 100644 --- a/labs/lab3/README.md +++ b/labs/lab3/README.md @@ -59,15 +59,15 @@ print(response) # Compose Slice-Level KPIs (1/2) -We've already explored examples of slice-level metrics, like `slice_throughput`, as we saw in our Grafana dashboard. +We have already explored examples of slice-level metrics, like `slice_throughput`, as we saw in our Grafana dashboard. -Monarch's KPI computation module leverages the Python HTTP API to query raw metrics, calculate KPIs at the slice level, and store them back in a `data_store`, making them accessible for visualization in Grafana. +Monarch's KPI computation module leverages the Python HTTP API to query raw metrics, calculates KPIs at the slice level, and stores them back in a `data_store`, making them accessible for visualization in Grafana. **Task**: - Task 3 of [exercise.py](exercise.py) dives deeper into composing slice-level metrics, specifically focusing on calculating resource utilization for slice 1 (with `SNSSAI=1-000001`). -- You'll combine multiple queries to determine memory usage, providing a practical understanding of how to build KPIs from various metric sources. +- You will combine multiple queries to determine memory usage, providing a practical understanding of how to build KPIs from various metric sources. --- # Compose Slice-Level KPIs (2/2)