wehub-resource-sync
44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
from fastai.basics import *
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from fastai.tabular.all import *
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from fastai.callback.all import *
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from fastai.distributed import *
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from fastprogress import fastprogress
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from fastai.callback.mixup import *
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from fastcore.script import *
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torch.backends.cudnn.benchmark = True
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fastprogress.MAX_COLS = 80
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def pr(s):
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if rank_distrib()==0: print(s)
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def get_dls(path):
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dls = TabularDataLoaders.from_csv(path/'adult.csv', path=path, y_names="salary",
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cat_names = ['workclass', 'education', 'marital-status', 'occupation',
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'relationship', 'race'],
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cont_names = ['age', 'fnlwgt', 'education-num'],
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procs = [Categorify, FillMissing, Normalize])
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return dls
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@call_parse
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def main(
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epochs:Param("Number of epochs", int)=5,
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fp16: Param("Use mixed precision training", store_true)=False,
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dump: Param("Print model; don't train", int)=0,
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runs: Param("Number of times to repeat training", int)=1,
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):
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"Training of Tabular data 'ADULT_SAMPLE'."
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path = rank0_first(untar_data,URLs.ADULT_SAMPLE)
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dls = get_dls(path)
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pr(f'epochs: {epochs};')
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for run in range(runs):
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pr(f'Run: {run}')
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learn = tabular_learner(dls, metrics=accuracy)
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if dump: pr(learn.model); exit()
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if fp16: learn = learn.to_fp16()
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n_gpu = torch.cuda.device_count()
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ctx = learn.distrib_ctx if num_distrib() and n_gpu else learn.parallel_ctx
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with ctx(): learn.fit_one_cycle(epochs)
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