[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119054-en":3,"doc-seo-119054-105":29,"detail-sidebar-cat-0-en-105":89},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":11},119054,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","JetTrain - IDE-Native Machine Learning Experiments","Integrated development environments (IDEs) are widely used for writing and debugging code, yet launching machine learning (ML) experiments remains awkward and often requires switching contexts to remote systems. JetTrain addresses this gap by integrating an IDE-native workflow that delegates specific tasks to remote compute on demand. Users can develop locally and then seamlessly run experiments remotely, improving throughput and lowering the barrier for training and fine-tuning workflows.","JetTrain: IDE-Native Machine Learning Experiments  \nArtem Trofimov  \nJetBrains Berlin, Germany artem.trofi[mov@jetbrains.com](mov@jetbrains.com)  \nMikhail Kostyukov  \nJetBrains Amsterdam, Netherlands [mikhail.kostyukov@jetbrains.com](mikhail.kostyukov@jetbrains.com)  \nSergei Ugdyzhekov  \nJetBrains Munich, Germany [sergei.ugdyzhekov@jetbrains.com](sergei.ugdyzhekov@jetbrains.com)  \narXiv :2402 . 10857v1 [ cs . SE] 16 Feb 2024  \nNatalia Ponomareva  \nJetBrains Berlin, Germany  \n[natalia.ponomareva@jetbrains.com](natalia.ponomareva@jetbrains.com)  \nIgor Naumov  \nJetBrains Belgrade, Serbia [igor.naumov@jetbrains.com](igor.naumov@jetbrains.com)  \nMaksim Melekhovets  \nJetBrains Berlin, Germany  \n[maksim.melekhovets@jetbrains.com](maksim.melekhovets@jetbrains.com)  \nABSTRACT  \nIntegrated development environments (IDEs) are prevalent codewriting and debugging tools. However, they have yet to be widely adopted for launching machine learning (ML) experiments. This work aims tofill this gap by introducingJetTrain, anIDE-integrated tool that delegates specific tasks from an IDE to remote computational resources. A user can write and debug code locally and then seamlessly run it remotely using on-demand hardware. We argue that this approach can lower the entry barrier for ML training problems and increase experiment throughput.  \nKEYWORDS  \nIntegrated Development Environment, Machine Learning, MLOps  \n1 INTRODUCTION  \nOne of the core parts of a machine learning workflow is training. Training is adjusting model internal parameters (like weights ina neural network) to minimize errors in predictions or decisions. Multiple training runs form an experiment that checks some hypothesis about a model improvement.  \nTraining or fine-tuning modern machine learning models requires complex hardware, especially in the LLM era [4] . Thus, ML engineers use various computational resources for code writing and experiment launching. This leads to overcomplicated ML experimentation tools requiring context switching [8] .  \nThis work proposes a novel IDE-integrated approach to launching ML experiments called JetTrain. We hypothesize that it can lower the entry barrier for users familiar with IDE and decrease the adverse effects of context switching. We overview existing interfaces for launching ML experiments in Section 2 . Our approach is introduced in Section 3, and the challenges are discussed in Section 4 .  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific [permission and/or a fee. Request permissions from permissions@acm.org](permission and/or a fee. Request permissions from permissions@acm.org).  \nIDE ’24, April 20, 2024, Lisbon, Portugal  \n© 2024 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 979-8-4007-0580-9/24/04...$15.00  \n[https://doi.org/10.1145/3643796.3648455](https://doi.org/10.1145/3643796.3648455)  \n2 MOTIVATION  \nThere are multiple interfaces to launch ML experiments on remote hardware. In this section, we discuss widely adopted approaches and highlight their advantages and limitations.  \nSecure Shell (SSH) connection to rented virtual machines (VMs) or on-premise servers is the most straightforward approach to launch experiments. An ML engineer should install all required libraries, download data, and run a locally prepared code. Using remote development features in an IDE for these purposes is even possible. Nonetheless, this approach exhibits limited scalability and is ineffective in cost.  \nJupyter Notebooks provide complete control over code execution","cbCaiv0QYUoB6pbH","https://ap.wps.com/l/cbCaiv0QYUoB6pbH","pdf",86127,1,3,"English","en",105,"# Abstract\n# Introduction\n# Motivation\n## Secure Shell (SSH) based launching\n## Jupyter Notebooks\n## Pipeline tools\n## Task scheduling tools\n## Gap and direction","[{\"question\":\"What problem does JetTrain target in machine learning workflows?\",\"answer\":\"JetTrain targets the difficulty of launching ML experiments from within IDEs, which often forces users to switch contexts and adopt overly complex tooling.\"},{\"question\":\"How does JetTrain work at a high level?\",\"answer\":\"JetTrain integrates with an IDE and delegates specific tasks to remote computational resources on demand, allowing local code writing and debugging before remote execution.\"},{\"question\":\"What limitations of existing tools does the paper highlight?\",\"answer\":\"The paper contrasts SSH and Jupyter for onboarding and control but limited scalability or cost efficiency, and it notes pipeline and task-scheduling tools may lack debugging, increase entry barriers, or push production complexity into experimentation.\"}]","JetTrain - 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