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Plot GPU compute and memory utilization curve
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| #!/usr/bin/env bash | |
| fname=$1 | |
| # if the log file does not exist yet, record it (stop by pressing ctrl+c) | |
| if ! test -f $fname; then | |
| nvidia-smi -f $fname -l 1 --format=csv --query-gpu=timestamp,memory.total,memory.used,utilization.gpu,utilization.memory & | |
| bg=$! | |
| read -p "press return to stop recording $fname and start plotting to ${fname:0:${#fname}-4}.png " | |
| kill -INT $bg | |
| fi | |
| # run actual plot script | |
| python - <<"EOF" $fname | |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| import sys | |
| fname=sys.argv[1] | |
| def intdropunit(s): | |
| # get rid of units (" MiB", " %") | |
| return int(s.split()[0]) | |
| # parse the CSV | |
| with open(fname) as file: | |
| log = pd.read_csv(file, | |
| parse_dates=['timestamp'], | |
| converters={1: intdropunit, 2: intdropunit, 3: intdropunit, 4: intdropunit} | |
| ) | |
| # filter non-zero (i.e. non-min) rows | |
| log = log[~(log.iloc[:,1:] == log.min(axis="index")[1:]).all(axis="columns")] | |
| # subplot for MiB and subplot for % | |
| fig, axs = plt.subplots(2, sharex=True, dpi=600) | |
| log.plot('timestamp', [1, 2], ax=axs[0]) | |
| log.plot('timestamp', [3, 4], ax=axs[1]) | |
| fig.savefig(fname[:-4] + '.png') | |
| EOF | |
| ls ${fname:0:${#fname}-4}.png |
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example output:
(very low average utilisation: too small batch size and GPU waiting on CPU)