6 devlogs
40h 46m 14s
Machine learning model for stock trading.
I completed and transferred over data from my 8th training run where I was trying out different convolutional dilations. The image shows the mlflow graphs of the results and some of my conclusions. I also switched the database backend to use postgresql instead of sqlite (this took a really long time due to like linux directory permissions and stuff) because sqlite was running really slow for all my data.
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I analyzed the results from my 6th large training run series and setup the parameters for the 7th.
I also wrote some code to allow for easier sequential training runs of so-called “sweeps” - so I could send one job that ran multiple sweeps for organizational purposes rather than having to manually start them after previous ones completed. At the end, I started the 7th training run series too (image is a gpu status from terminal).
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Switched to using mlflow for enhanced logging and visualization. Also started using using torch.compile and tensor-float 32 to make training significantly faster. Sorting out torch compile Dynamo speculation divergence issues took a while.
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Finished gigantic training run totaling 256 different models, and compiled/uploaded data from that.
Also fixed by source code to retry after hitting gpu out of memory errors.
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New version of model with enhanced feature set is being gpu-trained right now.
Will take around 2 days to complete.
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I’m working on my first project! This is so exciting. I can’t wait to share more updates as I build.
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