NSML: A Machine Learning Platform That Enables You to Focus on Your Models

By Nako Sung, Minkyu Kim, Hyunwoo Jo, Youngil Yang, Jingwoong Kim, Leonard Lausen, Youngkwan Kim, Gayoung Lee, Donghyun Kwak, Jung-Woo Ha and 1 more. 2017

89 citations recorded by the source corpus.

Machine learning libraries such as TensorFlow and PyTorch simplify model implementation. However, researchers are still required to perform a non-trivial amount of manual tasks such as GPU allocation, training status tracking, and comparison of models with different hyperparameter settings. We propose a system to handle these tasks and help researchers focus on models. We present the requirements of the system based on a collection of discussions from an online study group comprising 25k members. These include automatic GPU allocation, learning status visualization, handling model parameter snapshots as well as hyperparameter modification during learning, and comparison of performance metrics between models via a leaderboard. We describe the system architecture that fulfills these requirements and present a proof-of-concept implementation, NAVER Smart Machine Learning (NSML). We test the system and confirm substantial efficiency improvements for model development.

Subfields: Artificial Intelligence

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Citations in the corpus

Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference (2021, methodology): “All experiments were performed based on NAVER Smart Machine Learning (NSML) platform (Kim et al., 2018; Sung et al., 2017).”
BioBERT: a pre-trained biomedical language representation model for biomedical text mining (2019, methodology): “We used 8 NVIDIA V100 (32GB) GPUs for the pre-training (131,072 words/batch) using NSML.”
Phase-aware Speech Enhancement with Deep Complex U-Net (2019, methodology): “All experiments were implemented and fine-tuned with NAVER Smart Machine Learning (NSML) platform (Sung et al., 2017; Kim et al., 2018).”
In defence of metric learning for speaker recognition (2020, methodology): “Our implementation is based on the PyTorch framework [41] and trained on the NAVER Smart Machine Learning (NSML) platform [42].”
Character Region Awareness for Text Detection (2019, methodology): “All experiments are performed with NAVER Smart Machinie Learning (NSML) platform [14, 34].”
A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization (2019, methodology): “All computations were done on NAVER Smart Machine Learning (NSML) platform (Sung et al., 2017; Kim et al., 2018).”
DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder (2018, methodology): “All the models are implemented with Pytorch 0.4.0 3 , and ﬁne-tuned with NAVER Smart Machine Learning (NSML) platform (Sung et al. , 2017; Kim et al. , 2018).”
Clova Baseline System for the VoxCeleb Speaker Recognition Challenge 2020 (2020, methodology): “Our implementation is based on the PyTorch framework [19] and trained on the NAVER Smart Machine Learning (NSML) platform [20].”