How to generate CPU and GPU utilization time series for cluster-trace-gpu-v2020 dataset

Please, what is the correct table and approach to generate a time series of `CPU` and `GPU` utilization (with respect to total cluster `CPU` and `GPU` availability) from the `cluster-trace-gpu-v2020` dataset? Currently, I am joining `pai_machine_metric.csv` and `pai_machine_spec.csv` tables then calculating the 1-minute utilization as:

```python
# generate timestamps with relevant resolution with respect to earliest start time and latest end time
date_range = pd.date_range(df['start_time'].min(), df['end_time'].max(), freq='60s')
records = []

for d in date_range:
# find all records in pai_machine_metric where current timestamp, d, is within start and end time
match_df = df[(df['start_time']d)].copy()

# calculate the total number of CPUs and GPUs being utilized at current timestamp
# then divide by the total number of available CPUs and GPUs to get the utilization between [0, 1]
cpu_utlization = (match_df['machine_cpu']*match_df['cap_cpu']/100).sum()/match_df['cap_cpu'].sum()
gpu_utilization = (match_df['machine_gpu']/100).sum()/match_df['cap_gpu'].sum()
records.append(dict(
timestamp=d,
cpu_utilization=cpu_utilization,
gpu_utilization=gpu_utilization,
))

utilization_df = pd.DataFrame(records)
```

Is this a correct way to about it or should I be making use of a different table and/or approach?

Also, please could you clarify what the `machine_load_1` variable in `pai_machine_metric` is reporting? Specifically, what is the `load` referring to?

Lastly, I am finding datapoints where the `cap_gpu` is less than the `machine_gpu/100` value, implying that more GPUs were utilized than available on the machine. How should I interpret such datapoints?

Start by reading the definitions and relationships in pai_machine_metric.csv and pai_machine_spec.csv, then review the proposed pandas calculation for CPU and GPU utilization. Document what machine_load_1 and cap_gpu represent, how the utilization fields should be interpreted, and what the reported datapoints mean; done means the tables, approach, and anomalies are unambiguous.

Written by the indexing model from the issue text.