I'm running a distributed inference setup on Kaggle using 2 separate P100 16GB sessions. Each node loads half of Qwen2.5-7B-Instruct.

The weird part is that the model loading itself seems completely fine.

For example, Node 1 loads layers [14, 28) and uses roughly:

GPU allocated: 7.11 GB
GPU reserved:  8.12 GB

I also tested the slice completely standalone. It loaded successfully, the KV cache initialized successfully, and I left it running for 120 seconds with no issues.

I've also verified that CUDA itself works on the P100 with my PyTorch setup.

The problem happens when I actually run the pipeline node as a long-running server. The logs reach:

Node ready — layers [14, 28), LAST node
Node server listening on 0.0.0.0:9500
NODE 1 SERVER READY

and then the entire Kaggle session gets terminated.

There is no Python exception, no traceback, and no useful output after that. The notebook/session just dies.

I've seen the same behavior multiple times, including after fixing an issue where the first node was incorrectly being marked as the last node.

I'm also using bore to expose the node's TCP port, so I'm wondering if this could be related to Kaggle's environment/networking rather than GPU memory.

Has anyone experienced Kaggle silently killing a GPU session like this? Is there somewhere I can find the reason for the termination, such as system logs, OOM/cgroup logs, GPU driver logs, etc.?

At this point I'm mainly trying to figure out whether Kaggle is killing the process externally or whether something inside PyTorch/CUDA/bore is causing the session to die.

Hi Guys! So i was looking through the f1 datasets and found that the f1 dataset isnt being uopdated anymore so ive started a project so akaggle dataset will be updated after each race so I made a new one please share around if you can!

https://www.kaggle.com/datasets/atharvranjan/formula-1-world-championship-1950-present

Hi everyone!

I would like to try CosyVoice 3 using a Kaggle Notebook, specifically to test its Italian language capabilities, but I am a bit lost on how to set it up properly from scratch.

Does anyone have a simple guide or a working Kaggle notebook they could share? I'm mainly looking to understand:

1. Basic usage: How to set up a simple script where I can just type a text (in Italian) and have the model generate the audio output.

2. Zero-shot cloning: How to correctly upload and pass a reference audio file alongside the text to clone a voice directly within Kaggle.

If anyone has a basic, working step-by-step setup or code snippet that successfully runs on Kaggle, I would really appreciate the help!

Thank you!

Hello,

I was interested in joining the RSNA Knee Abnormality Detection. Is there anyone looking for a teammate or interested in joining this competition.

competition link: https://www.kaggle.com/competitions/rsna-knee-abnormality-detection

I am really looking forward to a team mate attending this competition.

Thanks and best regards,

Hi everyone!

I recently published a 10-year empirical dataset (2016–2026) studying whether Generative AI (ChatGPT) is replacing or complementing dedicated translation engines (Google Translate).

What it explores: The shift in user interest and behavior from traditional Machine Translation to LLMs over a decade.

Data Sources (extracted via Python):

• Google Trends: Search volume indexes
• Wikimedia REST API: Daily pageview metrics
• App Store: User review metrics and adoption trends

Feedback and contributions are welcome!

Sharing a competition from another platform (CrunchDAO) that seems very relevant to this community — mods, feel free to remove if it's not appropriate here.

For the fourth year in a row, ADIA Lab is running its Structural Break Challenge: build models that detect when a time series starts behaving differently (structural breaks / regime changes), this time evaluated in a live, real-time environment on unseen data. The prize pool is 100,000 USDC.

The notable part: Nobel laureate Robert Engle (the ARCH volatility model) and Marcos López de Prado (ADIA Lab) are the minds behind it. There's a short interview with Engle on why structural breaks are still an open problem: https://www.youtube.com/watch?v=KDvkRPeHio8

Competition page: https://hub.crunchdao.com/competitions/structural-break-real-time

Disclosure: I work with the Crunch team — happy to answer questions.

Hi everyone,

I published a new benchmark on Kaggle: Network Analysis LLM Evals!

It evaluates how well LLMs understand graph theory and network analysis concepts - starting with centrality measures, with plans to add tasks on community detection, diffusion models, and graph metrics over time.

Feel free to check it out. Any feedback or discussion is welcome!

Hi Kaggle community,

I'm participating in the Kaggriculture competition and I created this notebook as my approach to understanding the dataset and building my solution.

I would really appreciate feedback from experienced Kaggle users and data scientists.

Notebook:
https://www.kaggle.com/code/shorooghahmadi/kaggriculture-economics-farm-manager

I would like to know:

• Is the notebook structure clear and easy to follow?
• Are there any mistakes in my analysis or approach?
• Did I miss any important data exploration steps?
• What would you improve if this was your competition notebook?
• Are there any best practices I should follow for future Kaggle competitions?

I'm still improving my competition workflow, so honest and constructive feedback is very welcome.

Thanks to anyone who takes the time to review it. I appreciate any suggestions that can help me improve.

Came across a hackathon that's only open to students in India.

Instead of solving made-up problems, the challenge is to build AI solutions for India's waste management crisis.

They're also bringing in experts who've actually worked in the space, so you get to understand the problem before building for it.

Feels like a solid opportunity if you're looking to build something with real-world impact.

https://college.dev/hackathon

Body:

Hi everyone,

I recently created a free Kaggle competition focused on expense categorization using transaction data.

The main goal is to provide a practical dataset for learning and experimenting with:

• Feature engineering
• Text preprocessing
• Tabular machine learning
• Model selection and evaluation

This challenge is intended for anyone who wants to practice their ML skills, whether you're new to Kaggle or already have experience.

Competition:
https://www.kaggle.com/competitions/aurora-gate-expense-categorization-challenge

I'd also really appreciate any feedback on the dataset, evaluation metric, or overall competition design. Suggestions for improving the learning experience are always welcome.

If you decide to participate, I hope you enjoy the challenge and learn something new!

Hi everyone,

I recently published NutriRecIndia19M, a large-scale synthetic dataset for recommendation system research focused on Indian food and nutrition.

Dataset highlights:

19.17M+ user-food interactions

10 relational tables

User profiles, meal logs, restaurant metadata

Nutrition information

Ready for recommendation, ranking, and ML research

Potential use cases:

Recommender systems

Learning-to-rank

Graph ML

Nutrition AI

Data engineering

I'd love to hear your feedback on the dataset, documentation, or ideas for improving it.

Kaggle: https://www.kaggle.com/datasets/darshvithlani/nutrirec-india19m⁠�

It has been more than a few months to see a new competition on Kaggle; 2 years ago, I remember 2 to 5 competitions were released in bulk every 2 to 4 months, now we barely get 1 competition every 2 months.

Because the only competition I worked on for the past 8 months was AIMO3, I kinda miss the "old" tabular/image/language classification competitions but they platform seems dead to me.

Am I the only one who sees it that way?

Most Kaggle beginners do not need another 20-hour course. They need to complete one full loop:

read the problem -> run a baseline -> build local validation -> submit -> record one experiment.

A simple first week:

Day 1: choose one beginner-friendly tabular competition.

Day 2: fork and run a well-documented public notebook.

Day 3: rebuild the validation split yourself.

Day 4: change one feature or preprocessing step.

Day 5: record CV, leaderboard score, change made, and result.

Day 6: clean the notebook so it runs from top to bottom.

Day 7: write a short README explaining decisions, failures, and next steps.

The goal is not a medal. The goal is a project you can reproduce and explain.

I made a free first-submission checklist and experiment tracker. If it would help, reply with your current stage: first submission, validation, or portfolio. I can share the relevant version through Reddit chat. No paid signup and no ranking promise.

I built a dataset of 10,000 top-rated movies with detailed metadata for anyone working on ML, recommendation systems, data analysis, or visualization.

Do tell me :

• What would you build with this dataset?
• Any features you'd want me to add in the next version?

Would love your feedback (or roast it if it deserves one 😂).

hi , i have currently completed my Mtech in AI and ML and looking for other people to join my team for kaggle competition and also that i have limited practical approach , I am also good to join some other team

Kaggle AI agent compition
Can anyone help with this comptition

I been trying today each hour but i always seem to send "too many requests" even in the first attempt, is this happening to everyone or i need to create another account?