kaldi-asr/kaldi

nnet3 : Reduce unnecessary outputs (file or email) during training

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#1,349 opened on 2017年1月17日

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 (2 comments) (0 reactions) (0 assignees)Shell (15,392 stars) (5,359 forks)batch import
enhancementhelp wantednnet3stale

説明

(Created to ensure we keep track of a low priority issue.)

Status update emails are sent after every iteration if an email id is specified during training. However status information uses output of train/validation accuracy/log-probability computation jobs which are run in the background. In many cases these might be very slow to compute, depending on the model, as these are run on the CPU. This would mean that there are uninformative status mails which do not provide any info other than training time.

e.g.

 %Iter   duration        train_loss      valid_loss      difference
Total training time is 0:59:53

コントリビューターガイド

技術スタック
cppshell
領域
machine learning
Issue 種別
feature
難度新しい貢献者にとっての実装難度です。1 は非常に小さな変更、5 は専門的な作業です。
3
推定時間経験ある貢献者が調査、実装、テスト、pull request 準備にかけるおおよその時間範囲です。
half day
活動状況Issue が今どれだけ取り組みやすいかを示します。新しい、活発、古い、ブロック中、またはメンテナー入力待ちなどです。
stale
明確さ期待される変更、受け入れ条件、次の手順がどれだけ明確かを示します。
mostly clear
前提条件
Understanding of Kaldi training scriptsBasic shell scripting
初心者向け度初回貢献者にどれだけ取り組みやすいかを 1-100 で推定したスコアです。
40
調査方針
Investigate the training scripts in 'steps/nnet3/' to understand how status emails are generated. Look for the logic that decides whether to send email and how the validation accuracy is computed. Consider adding a condition to skip sending if validation results are not ready or are uninformative. Check existing comments or related issues for previous discussion.