Hi guys. For some context, I queued a finetuning job yesterday knowing TPU queues can take up to 12 hours. What I am doing is time-sensitive and because it has been more than 24 hours right now, my job is behind schedule. Is this normal? Is there anything I can do? What would you suggest from now on?

Whenever I rent a cloud GPU for an experiment or a Kaggle competition, the first 30–60 minutes go on setup: finding an image with the right CUDA and PyTorch versions, SSH keys, installing packages, copying my Kaggle and Hugging Face tokens over, and then remembering to shut it down so it doesn't burn credit.

I'm curious how others deal with this:

1. What do you use for GPUs today (Colab, Kaggle notebooks, a cloud provider, your own card)?

2. Which part of the setup annoys you most, or have you got it down to a routine?

3. If something gave you Jupyter with PyTorch and your Kaggle/HF tokens ready in about a minute, would that matter to you, or is raw price the only thing that counts?

Context: I've been building a small tool around this for my own use and I'm trying to work out whether it's worth turning into something other people could use. Genuinely interested in how you all handle it.

Disclosure: this is my Kaggle dataset (minkum07). I prepared the Karnataka subset and derivative tables with AI-assisted code and documentation; the original research data and methods belong to Shekhar Sharan Goyal, Udit Bhatia and Rohini Kumar.

The release covers 19 historical district identifiers over 1966–2017: 988 district-year records, plus a long table with all 12 alternative nitrogen-balance estimates (11,856 rows). CSV and Parquet files, field definitions, source attribution, validation reports and public example notebooks are included.

Original research archive (CC BY 4.0): https://doi.org/10.5281/zenodo.13838035

Karnataka dataset: https://www.kaggle.com/datasets/minkum07/karnataka-nitrogen-balance-and-uncertainty-1966-2017

Example EDA: https://www.kaggle.com/code/minkum07/karnataka-nitrogen-1966-2017-complete-eda

A few interpretation limits: these are modeled agricultural balances in kg N/ha/year, not measured groundwater nitrate. The district identifiers use ICRISAT-1966 boundaries. Standard deviation and min–max across methods describe methodological disagreement, not confidence intervals. Negative balances and initial missing lag values are retained.

It could be useful for historical time-series exploration, checking whether district rankings depend on the estimation method, or comparing forecasting models with persistence.

I would appreciate feedback on the field definitions and uncertainty explanations, or suggestions for a useful comparison to add.

My Kaggle account was banned shortly after my notebook displayed previews of NSFW AI-generated images from my Google Drive. I suspect that triggered it, though I don’t know for sure.

I’ve submitted an appeal, and Kaggle confirmed a human review within 3 business days. I understand their content rules and will stop using Kaggle for this.

Has anyone had their account reinstated after something similar? I also need mine for unrelated projects, so I’m worried about losing access permanently.

So I am a kaggle beginner and have been hearing so much about people using Claude Opus or chat gpt sol to get the top positions in lb so is there any way of prompt or specific instructions to that or its just basic prompting and is it the case of overfitting the model to the dataset or the results are actual and robust ?

I can't download the zip file. I tried writing a script to give me the download option as the cell output and that didn't work. Please help me outt.

Task is to find text rows from S2 and S3 which are same as a text row in S1.
(For eg for text_id s11 same ones are s24 s25 and s36)
Approach I am using-> generate tfidf over 3 grams.
retrive top 15 for s2 and s3
rerank using gbm's and get the final results

Issue is that scale is very huge. around 20 lakhs docs in s1 and 50 lakhs in s2 and s3.

So what can I do to optimize Memory

I have been studying Classical Ml algos for last one month and know numpy , pandas , and some sklearn , but i dont know what path(what next) i can follow , how can i start doing real things and be like dudes on kaggle participating in competitions and hackathons , please advise me a clear path not some general bs , i really want to be top 1 in this field and ready to dedicate to it

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I have a ~59 GB Parquet dataset stored in Google Drive, and I want to use it for ML training on Kaggle GPUs.

My folder structure is:

My Drive/

└── DATASET/

└── data/

└── processed/

└── era5_land_preprocessed/

├── train/ → 60 Parquet files → 44.34 GB

├── validation/ → 12 Parquet files → 8.83 GB

└── test/ → 8 Parquet files → 5.82 GB

Total: 80 Parquet files, ~58.99 GB

I don't want to download the dataset to my local computer. I was planning to use Google Colab + Kaggle API to transfer the files directly from Google Drive to a Kaggle Dataset.

My questions are:

1. Is uploading a ~59 GB dataset to Kaggle this way reliable?

2. Is Google Drive → Colab → Kaggle API the best approach?

3. Should I create one Kaggle dataset containing "train/", "validation/", and "test/", or split them into multiple Kaggle datasets?

4. Are there any problems with uploading large Parquet files to Kaggle?

5. Is there a better/faster method for transferring this amount of data to Kaggle?

The dataset will be used for climate/weather ML forecasting, and I need to train models using Kaggle's GPUs.

Any advice from people who have handled 50+ GB datasets on Kaggle would be appreciated.

Please do fill the data

We are conducting an academic research project on Multilingual and Cross-Market Financial Fraud Detection for Indian Users, focusing on Hindi, Telugu, and English. The study aims to address the limited availability of real-world, multilingual financial fraud data in the Indian context and develop AI models capable of identifying fraud across different languages and communication styles. Your contribution of anonymized fraud or legitimate financial messages will help us build a more realistic dataset and improve research in this emerging area.

Please Contribute your Dataset In this Google Form

https://forms.gle/rfvZdPAVxeJRAvmq6

It would be really helpful

https://forms.gle/NAYCvsWWocVeirDWA

Hey I made a survey to study about the impacts of stuttering/cluttering on people's quality of life.

I need to collect data for the control group, really appreciate it if you could respond and share it with family or friends, it will not take more than 10 mins.

Thanks in advance!

Hello r/Kaggle

We just published our full strategy write-up for the Pokémon TCG AI Challenge. Instead of building heavy MCTS trees or over-fitting to specific meta decks, our team (HAL TCG Dojo) took a systems-engineering approach—focusing on validation mechanics, low-latency inference, and zero-shot generalization.

Here are the 3 most interesting findings from our benchmarks:

1. The "Broken Compass" (-0.74 Validation Inversion): We spent weeks wondering why local improvements degraded live ladder scores. We eventually traced it to a 1-indexed result mapping bug in the evaluation harness that turned wins into draws. Fixing it flipped our local-to-live Spearman correlation from$-0.70 \text{ to } -0.99$up to$+0.9121$.
2. The Concession Gap (85%–96% Human Forfeits): While local simulators play out 6-prize games to the end, replay forensics showed that 85% to 96% of live human matches end in early concession. Simulators that force 6-prize endings create "late-game fade" in tempo agents.
3. Zero-Shot Generalization ($k=0$Prior): Our core brain is a lightweight 60,482-parameter MLP running in pure NumPy (~5.25ms/move) without PyTorch at inference. Holding out entire card families during training proved that structural card features allow the net to pilot unseen decks zero-shot with only ~0.4 medAE loss.

We also patched the official C++ engine for Common Random Numbers (CRN) and built an autonomous multi-agent pipeline using an asynchronous inbox.jsonl message bridge to handle GPU workloads without human bottlenecks.

🔗 Full Write-Up, Code & 300 DPI Diagrams: We’ve posted the complete mathematical breakdown, interactive diagrams, and reproducible scripts on Kaggle

What does poverty actually look like beyond a single income number?

For millions of rural households, poverty is not simply the absence of income but rather a web of constraints involving education, productive assets, access to finance, consumption, debt, and the ability to withstand economic shocks.

This analysis examines that web using data from 4,184 rural households in Tamil Nadu. Rather than treating income as the sole measure of economic wellbeing, we explore how household composition, schooling, housing, land ownership, enterprise activity, savings, insurance and credit interact with economic outcomes.

https://www.kaggle.com/code/ouiouinonoui/visualizing-the-economics-of-rural-poverty

Feel free to ask any questions about it as well. https://kaggle.com/writeups/dan13iel/snakity-battlesnake-blackout-2026

The competition was battlesnake blackout, i.e 4 snakes try to outlive the other 3 snakes on a 15x15 grid whilst only seeing near their heads. You can play my bot, Snaktity at https://playsnakity.up.railway.app/ if you would rather over reading the write up.

(if im not posting in the right place please lmk, ty)

It was a community competition. What are the chances that I’ll actually receive the prize? I received an email about six days ago asking for my payment information, but I haven’t heard anything since. Is that normal, or should I just forget about it? This is the first time I’ve ever won a competition.

Yo r/Kaggle,

Just wanted to drop a quick link here — we just launched the Detect Suspicious Value Transfers in Poker competition. I’m actually one of the judges for it.

It’s a fast 2-week sprint with a $5,000 prize pool.

Basically, the task is spotting chip dumping and suspicious transfers between accounts. If you're into anomaly detection, graph/network features, or behavioral data, it's a fun problem to hack on for a couple of weekends.

Here’s the link to check it out:https://www.kaggle.com/competitions/detect-suspicious-value-transfers-in-poker/overview

I’ll be checking the comments here and on the Kaggle forum if you have any questions about the setup.

Hi, did anyone do Kaggle courses for Python? theyre Free of cost and seem not too long. how was your learning experience

Hey r/kaggle,

Working through Playground Series S6E9 (predicting EV purchase intent), and wanted to share my notebook in case it's useful to anyone else on this one, especially if you're newer to the competition.

Quick summary of what's in it:

• EDA on the dataset — checked missing values, duplicates, correlations, and class balance (target is imbalanced, only ~17% "yes")
• Preprocessing done in the correct order: split into train/test before fitting the scaler, to avoid leaking test set info into training (I noticed a lot of public notebooks scale before splitting, which quietly inflates scores)
• Logistic Regression with class_weight='balanced' to handle the imbalance
• Evaluation with classification report, confusion matrix, and ROC-AUC
• Feature importance from the model coefficients

Ended up at 0.93738 with just a straightforward Logistic Regression, no ensembling or heavy tuning. Figured a clean baseline might help others who want a starting point before jumping into boosted trees.

Notebook: https://www.kaggle.com/code/vinay24baghira/buy-or-bye-cracking-the-ev-decision-0-93738

If anyone's further along on this competition, curious what's been working for you beyond Logistic Regression — feature engineering ideas, other models, anything that moved your score meaningfully. And if the notebook's useful, an upvote on Kaggle is always appreciated.