MLFlow Tutorial Part 1: Experiment Tracking

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This tutorial will show you the basics of experiment tracking with MLFlow for TensorFlow, Sklearn, and other frameworks. Learn how to structurise your experiments, log everything you want, and save the best models for later use. You can code along or simply pull the notebook and read it at your own pace.
Links:
Github repo - github.com/aru...
FT Transformer Blog - / improving-tabtransform...

Опубликовано:

20 окт 2024

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Комментарии : 26

@RicardoLastra-e6r Месяц назад

Great example on how to use MLFlow

@hndr91 10 месяцев назад

What a clear explanation, thanks man!

@atruberts 10 месяцев назад

Glad it helped!

@peegee101 Год назад

Love the haircut! Nicely done!

@atruberts Год назад

Thank you so much 😀

@fkeb37e9w0 8 месяцев назад

I am running mlflow server with local host inside a vm and using the same as tracking uri, but when I do start_run() I get an error of 400 or 403. How do I resolve this.

@tejasmanchi Год назад

Great content man ..why am i not able to see the compare button in the ui ..using 2.7.1 version in windows chrome

@AbdolaMike Год назад

Loved the video. you got my sub!

@atruberts Год назад

Awesome, thank you! Sorry for taking so long to reply, I'm planning to be more active now 🤞

@trangmun2851 9 месяцев назад

Amazing thankyou so much!

@СемёнСемёныч-е4д Год назад

Good stuff!

@miguelovallevillamil4953 Год назад

Amazing content

@atruberts Год назад

Thank you so much! More is coming soon :)

@brahyamalmonteruiz9984 8 месяцев назад

excellent video, but the audio was too low

@NewDataTime 10 месяцев назад

Thank you

@atruberts 10 месяцев назад

You're welcome, glad you enjoyed it!

@DoubleJMc Год назад

nice!

@atruberts Год назад

Thanks buddy! I hope it's useful 😉

@AvijeetPandey Год назад

Hi Antons, thanks for the helpful video. However, we only have access to R/Jupyter notebook inside a virtual environment at my workplace. Hence, all the ports are blocked. So, spawning that cool UI/backend server is not an option. Can we still use Mlflow tracking with just jupyter notebooks and local filesystem(maybe txt files)? Would be cool if you make a part 2 for the same.. Thanks

@atruberts Год назад

Hi Avijeet, I'm glad you liked it! Hmm, in theory everything that your experiments generate are stored locally, either in mlruns folder, or in the .db you've specified. You'll need to find out where exactly the parameters/metrics you need are stored, but once you have their location it'll be quite easy to access them and compare between each other.

@atruberts Год назад

Just out of curiosity, what does it tell you when you try to run mlflow ui command?

@AvijeetPandey Год назад

@@atruberts no issues when running the ui command, it gives me a URL with some port address, but unable to open that URL in browser due to IT policies, just like standard Web address block notice

@AvijeetPandey Год назад

@@atruberts OK, so SQLite backend is not necessary

@НикитаФурин-о4н Год назад

I guess you can use ngrok to access your local port externally. For example, it works in Google colab to open mlflow ui. Although it seems less secure than using ui locally. You can find examples of using ngrok in zenml notebooks.