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Glue: log in Valohai-compatible JSON format too

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Juha Kiili 2019-11-21 12:35:25 +02:00 committed by Aarni Koskela
parent 0cdfcca24b
commit 2cf3447e0a
1 changed files with 12 additions and 3 deletions
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15
examples/run_glue.py
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@ -22,6 +22,7 @@ import glob
import logging
import os
import random
import json
import numpy as np
import torch
@ -171,13 +172,21 @@ def train(args, train_dataset, model, tokenizer):
if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics
logs = {'step': global_step}
if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(args, model, tokenizer)
for key, value in results.items():
tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
eval_key = 'eval_{}'.format(key)
tb_writer.add_scalar(eval_key, value, global_step)
logs[eval_key] = str(value)
logging_loss = tr_loss
loss_scalar = (tr_loss - logging_loss) / args.logging_steps
learning_rate_scalar = scheduler.get_lr()[0]
tb_writer.add_scalar('lr', learning_rate_scalar, global_step)
tb_writer.add_scalar('loss', loss_scalar, global_step)
logs['learning_rate'] = learning_rate_scalar
logs['loss'] = loss_scalar
print(json.dumps(logs))
if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
# Save model checkpoint