This is an example of how to analyze the results logged by a number of runs of fastgpu on Imagenette.

path = Path('~/git/fastai/nbs/examples/exps').expanduser()
path_out = path/'out'
path_scr = path/'complete'

def fn_name(fn): return fn.name.split('.')[0]

hdrs = 'name epoch trn_loss val_loss top1 top5 time'.split()

def scr_results(fn):
    return L([dict(zip(hdrs, [fn_name(fn)] + o.strip().split()))
           for o in fn.readlines() if re.match(r'\d+   ', o)])

all_results = L([scr_results(o) for o in path_out.glob('*.stdout')]).concat()
df_results = DataFrame.from_records(all_results).query('time==time').astype(
    dict(epoch=int, trn_loss=float, val_loss=float, top5=float, top1=float))

def scr_params(fn):
    return merge(dict([o.strip().split() for o in fn.read_text().split('--')[1:]]),
                 {'name':fn_name(fn)})

df_params = DataFrame([scr_params(o) for o in path_scr.ls()]).astype({'epochs':int})
df_params.head()

	woof	lr	size	sqrmom	mom	eps	epochs	bs	opt	sa	fp16	arch	name	pool	meta	runs	mixup	sh	sym	beta
0	0	8e-3	192	0.99	0.95	1e-6	5	64	ranger	1	0	xse_resnext18	02a	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	0	8e-3	128	0.99	0.95	1e-6	20	64	ranger	1	1	xse_resnext50	xf104a	MaxPool	NaN	NaN	NaN	NaN	NaN	NaN
2	0	8e-3	256	0.99	0.95	1e-6	5	64	ranger	1	0	xse_resnext18	03a	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	1	8e-3	128	0.99	0.95	1e-6	5	64	ranger	1	1	xse_resnext50	x01b	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	0	8e-3	192	0.99	0.95	1e-6	20	64	ranger	1	1	xse_resnext50	x05a	NaN	NaN	NaN	NaN	NaN	NaN	NaN

df_merge = pd.merge(df_params, df_results, 'left', on='name')
assert df_merge[pd.isna(df_merge.arch)].empty

df_merge.fillna(0.0, inplace=True)
df_merge['repoch'] = df_merge.epochs - df_merge.epoch

df_merge.to_csv('imagenette.csv', index=False)
FileLink('imagenette.csv')