So, I missed blog post 9. This is me acknowledging that for consistency.
Anyway, the past couple of weeks have been incredibly productive for ContentsMayBeHot. Matthew has finished collecting all the replay data, we have refactored our project to reduce complexity, we have improved the runtime of our code, and finally we have started seriously training our model.
Matthew implemented multi-threading for the model loading. Which reduced our load time between files from about 3-5 Seconds to 1 Second or less. Which allows us to fully train a model in much less time!
While Matthew did this I reduced the code duplication in our project. This way, if we needed to change how we loaded our training data, we didn't have to change it in multiple places. This just allows us to make hot-fixes much more efficiently.
We also started working on some unittests for our project using pytest. These tests were written because of a requirement for another class, but we thought it would be useful to mention these tests.
Now that we are in the extreme-final steps of our project, my primary role has been actually training the data. The results of which are already on our github.
Matthew and I will be moving to focusing on our paper and our poster. While we will be helping one another, Matthew will be taking the lead for the paper while I will be taking the lead for the poster. I personally hope to get the poster done within the next couple of days.
Anyway, the past couple of weeks have been incredibly productive for ContentsMayBeHot. Matthew has finished collecting all the replay data, we have refactored our project to reduce complexity, we have improved the runtime of our code, and finally we have started seriously training our model.
The Changes
Matthew implemented multi-threading for the model loading. Which reduced our load time between files from about 3-5 Seconds to 1 Second or less. Which allows us to fully train a model in much less time!
While Matthew did this I reduced the code duplication in our project. This way, if we needed to change how we loaded our training data, we didn't have to change it in multiple places. This just allows us to make hot-fixes much more efficiently.
We also started working on some unittests for our project using pytest. These tests were written because of a requirement for another class, but we thought it would be useful to mention these tests.
Next Steps
Now that we are in the extreme-final steps of our project, my primary role has been actually training the data. The results of which are already on our github.
Matthew and I will be moving to focusing on our paper and our poster. While we will be helping one another, Matthew will be taking the lead for the paper while I will be taking the lead for the poster. I personally hope to get the poster done within the next couple of days.
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