Log fast.ai metrics to neptune

Prerequisites

Integration with fast.ai framework is introduced as a part of Neptune-contrib - open source project curated by Neptune team.

Please install it before you continue. Check the docs if you need more info.

pip install neptune-contrib
pip install neptune-contrib[monitoring]

Create your databunch

from fastai.vision import *
path = untar_data(URLs.MNIST_TINY)

data = ImageDataBunch.from_folder(path, ds_tfms=(rand_pad(2, 28), []), bs=64)
data.normalize(imagenet_stats)

Create the learner find your optimal learning rate and plot it

learn = cnn_learner(data, models.resnet18, metrics=accuracy)
learn.lr_find()
learn.recorder.plot()

Create an experiment and add neptune_monitor callback

import neptune
from neptunecontrib.monitoring.fastai import NeptuneMonitor

neptune.init(project_qualified_name='USER_NAME/PROJECT_NAME')

with neptune.create_experiment(params={'lr': 1e-2}):
    learn.callbacks.append(NeptuneMonitor())
    learn.fit_one_cycle(20, 1e-2)

Monitor your fast.ai training in Neptune

Now you can watch your fast.ai model training in neptune!

Full fast.ai monitor script

Simply copy and paste it to fastai_example.py and run.

from fastai.vision import *
import neptune
from neptunecontrib.monitoring.fastai import NeptuneMonitor

neptune.init(project_qualified_name='USER_NAME/PROJECT_NAME')

path = untar_data(URLs.MNIST_TINY)

data = ImageDataBunch.from_folder(path, ds_tfms=(rand_pad(2, 28), []), bs=64)
data.normalize(imagenet_stats)

learn = cnn_learner(data, models.resnet18, metrics=accuracy)
learn.lr_find()
learn.recorder.plot()

with neptune.create_experiment(params={'lr': 1e-2}):
    learn.callbacks.append(NeptuneMonitor())
    learn.fit_one_cycle(20, 1e-2)