Use web API¶
Send training logs via web API.
Start ChainerUI server¶
$ chainerui server
Open http://localhost:5000/ . To stop, press Ctrl+C
on the console. When use original host or port, see command option.
Or, use ChainerUI’s docker container to run ChainerUI server, see docker start.
Customize training loop¶
Setup example from a brief MNIST example:
import chainerui
def main():
args = parser.parse_args()
# [ChainerUI] To use ChainerUI web client, must initialize
# args will be shown as parameter of this experiment.
chainerui.init(conditions=args)
# Set up a neural network to train
# Classifier reports softmax cross entropy loss and accuracy at every
# iteration, which will be used by the PrintReport extension below.
# [ChainerUI] plot loss and accuracy reported by this link
model = L.Classifier(MLP(args.unit, 10))
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
# [ChainerUI] set log reporter on the extention
trainer.extend(extensions.LogReport(
postprocess=chainerui.log_reporter()))
Note
User doesn’t have to execute $ chainerui project create
command. chainerui.init()
add a project using current directory on the first running. Project name can be customized using project_name
option. Training results wil be created every running. Result name is set timestamp automatically and can be customized via web UI.