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

arXiv: https://arxiv.org/abs/1712.05902 · HTML: https://mlsanity.com/paper/1712.05902/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2017.

## Built on by (most cited first, 30 of 34)

- [Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks](https://mlsanity.com/paper/2202.10571/) (2022)
- [Dataset Condensation with Contrastive Signals](https://mlsanity.com/paper/2202.02916/) (2022)
- [Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing](https://mlsanity.com/paper/2104.14754/) (2021)
- [Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference](https://mlsanity.com/paper/2110.10031/) (2021)
- [Keep Me Updated! Memory Management in Long-term Conversations](https://mlsanity.com/paper/2210.08750/) (2022)
- [Visual Style Prompting with Swapping Self-Attention](https://mlsanity.com/paper/2402.12974/) (2024)
- [SSMix: Saliency-Based Span Mixup for Text Classification](https://mlsanity.com/paper/2106.08062/) (2021)
- [Towards Calibrated Robust Fine-Tuning of Vision-Language Models](https://mlsanity.com/paper/2311.01723/) (2023)
- [Scaling Law for Recommendation Models: Towards General-purpose User Representations](https://mlsanity.com/paper/2111.11294/) (2021)
- [Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models](https://mlsanity.com/paper/2205.00176/) (2022)
- [Saving Dense Retriever from Shortcut Dependency in Conversational Search](https://mlsanity.com/paper/2202.07280/) (2022)
- [DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation](https://mlsanity.com/paper/2410.03782/) (2024)
- [HyperCLOVA X Technical Report](https://mlsanity.com/paper/2404.01954/) (2024)
- [NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation](https://mlsanity.com/paper/2105.14454/) (2021)
- [3D-aware Blending with Generative NeRFs](https://mlsanity.com/paper/2302.06608/) (2023)
- [Response Generation with Context-Aware Prompt Learning](https://mlsanity.com/paper/2111.02643/) (2021)
- [Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection](https://mlsanity.com/paper/2303.17285/) (2023)
- [Feature Statistics Mixing Regularization for Generative Adversarial Networks](https://mlsanity.com/paper/2112.04120/) (2021)
- [Contrastive Fine-grained Class Clustering via Generative Adversarial Networks](https://mlsanity.com/paper/2112.14971/) (2021)
- [NegMerge: Sign-Consensual Weight Merging for Machine Unlearning](https://mlsanity.com/paper/2410.05583/) (2024)
- [Generator Knows What Discriminator Should Learn in Unconditional GANs](https://mlsanity.com/paper/2207.13320/) (2022)
- [BallGAN: 3D-aware Image Synthesis with a Spherical Background](https://mlsanity.com/paper/2301.09091/) (2023)
- [Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning](https://mlsanity.com/paper/2103.00771/) (2021)
- [StyleKeeper: Prevent Content Leakage using Negative Visual Query Guidance](https://mlsanity.com/paper/2510.06827/) (2025)
- [Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering](https://mlsanity.com/paper/2104.07242/) (2021)
- [Sequential Data Generation with Groupwise Diffusion Process](https://mlsanity.com/paper/2310.01400/) (2023)
- [Frequency Selective Augmentation for Video Representation Learning](https://mlsanity.com/paper/2204.03865/) (2022)
- [Memory Efficient Patch-based Training for INR-based GANs](https://mlsanity.com/paper/2207.01395/) (2022)
- [Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning](https://mlsanity.com/paper/2212.03760/) (2022)
- [Unsupervised Representation Learning for Binary Networks by Joint Classifier Learning](https://mlsanity.com/paper/2110.08851/) (2021)

## Abstract (arXiv)

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.