Supamodel -- monitor ML training sessions on your iPhone

Hacker News

As someone who has been training ML models for over 5 years now, I have always felt the need to monitor how my experiments are doing on my phone. Did it crash? Has the loss saturated?? How’s the accuracy doing?? Should I cut my losses and start training with another set of hyperparams or let this experiment converge more?? Am I wasting GPU hours due to a dataloader bottleneck? Given that the size of datasets and model params has only gotten bigger, the time to figure out a nice converging architecture has gotten longer. Plus I am easily paying $5-$12 per hour for GPUs , and hence if there’s something fishy going on with my training sesh, I want to be notified about it as quickly as possible, even if I am at the gym, on the train or on a date I also want to stop and start experiments remotely by changing a few hyper-params so that I waste no time. What I want to know is how many others on here have felt a similar need. Idk maybe its just my workaholic arse? So I built a dead simple app that lets you monitor your experiments from your iPhone. I am calling it Supamodel. Here is how it works: 1. Install the app from app store: https://apps.apple.com/us/app/supamodel/id6517357968 2. Sign in with Google or email/pwd 3. Click on the menu button top left, and copy the API key 4. use it to login to supamodel in code: import supamodel supamodel.login("API_KEY") Now you can log any metric using supamodel.log and it will show up in the app. It automatically logs GPU usage too. You can also set alerts for the metrics you log so that if they fall below or exceed a certain number you get a push notification. Give it a try, and if you have any questions or feedback please let me know in the comments. link to python package: https://github.com/sarangzambare/supamodel-py Thanks!

Visit product View on Hacker NewsJul 12, 2024

Share card

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, google · Missing: mac, agents, macos

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, google, way · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Runlog54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ML training monitor

Indie Hackers1ai

Monitor ML model training on mobile42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Monitor ML model training on mobile

Hacker News7

Training classifiers with Create ML – fruits, flowers, xray, sentiment56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Training classifiers with Create ML – fruits, flowers, xray, sentiment

Hacker News5

Slack bot to monitor/stop/restart ML model training remotely59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Slack bot to monitor/stop/restart ML model training remotely

Hacker News2

Wooclap, an interactive platform for your classes and training sessions29%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Wooclap, an interactive platform for your classes and training sessions

Hacker News5

Learn radiology via interactive training sessions46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Learn radiology via interactive training sessions

Hacker News1

Training ML model on 4 datasets at once52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Training ML model on 4 datasets at once

Hacker News2

Graphsignal – ML profiler to speed up training and inference65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Graphsignal – ML profiler to speed up training and inference

Hacker News16

Live Twitch Demo at 2pm – Monitor ML Models in Sagemaker with Fiddler59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Live Twitch Demo at 2pm – Monitor ML Models in Sagemaker with Fiddler

Hacker News1

An annotation tool for ML and NLP63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

An annotation tool for ML and NLP

Hacker News76

Browse all products Analyze your launch