import pandas as pd
import seaborn as sns

df = pd.read_csv('gpu-performance.csv')

Raw Data

df

	GPU	fit	Tensor_Cores	transistors_M	CUDA_Cores	TFLOPS_(FP16)	gpu_mem	CPUs	MEM_(GB)
0	A100	280	432	54,000	6912	78.0000	40.5	12	85
1	V100	529	640	21,100	5120	31.4000	16.0	8	30
2	P100	846	0	15,300	3840	19.0000	16.0	4	15
3	T4	1508	320	14	2560	65.0000	16.0	8	30
4	T4-Colab	1608	320	14	2560	65.0000	16.0	2	12
5	K80	3184	0	7	4992	0.0000	12.0	4	15
6	Macbook-AMD-Metal	3769	0	6	0	6.4000	4.0	6	16
7	Macbook-CPU	57236	0	3,000	0	0.0025	0.0	6	16

Peformance~GPU (less is faster)

p = df[['GPU','fit']].iloc[:-1].plot.bar(x='GPU', y='fit', rot=45)

df.corr()[['fit']].sort_values('fit')

	fit
CUDA_Cores	-0.567090
gpu_mem	-0.545376
TFLOPS_(FP16)	-0.454271
Tensor_Cores	-0.387415
MEM_(GB)	-0.223066
CPUs	-0.060719
fit	1.000000

dataplot = sb.heatmap(df.corr(), cmap="YlGnBu", annot=True)