Deep learning and Machine Learning API - Refactoring
I have 10 repos open source in github, I need someone to review and read the codes of the repos and create a unified environments for all frameworks to support multiple environments (crypto, margin trading, stocks etc) and be able to generate prediction with rest api endpoints - better withdjango, the idea is to send JSON to the API and the training process to get started according to config.
Frameworks are in the trading space and deep learning - only skilled developers with machine / deep learning please.
Some use gym, some use tensorflow and some pytorch, you will need to create and choose the best algorithm for my needs.
Goals:
- Choose the best looking framework with most features. (cpu, gpu, multi asset, signal asset etc)
- Add ideas from other notebooks and create a unified framework that supports more features from the other notebooks.
- be able to replace the features in the features dataframe - eg config with json
- create a signal source to train agents according to user's CONFIG
- Improve code to be lightweight and savey on training resources - suggest ideas , maybe preprocess with numba etc.
- Think of ways out of the box how to train at shortest time with best performance.
- work inside my github repo
- create some visualization of the training of cumulative reward and episode length
optional:
later, I am developing this into a django web app and this API should be accessed with website only. c an consider building the whole website with full features after I see this project quality.
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And how much do you value it at the lowest rate?
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