Scalable machine learning models for predicting quantum transport in disordered 2D hexagonal materials

· · 来源:tutorial资讯

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In the live game, every API call that affected the player’s inventory triggered a write to the corresponding record in our Azure Cosmos database. From a player’s perspective, the game is constantly saving their progress. To achieve parity in the offline game, we exposed two functions in the AOT DLL for getting and setting a player’s inventory (equivalent to the Cosmos DB inventory document). When the game first starts up, the local save file on disk is read and the inventory is loaded into the DLL’s memory. As the various serverless HTTP operations occur throughout gameplay the DLL’s in-memory inventory state gets updated. After these operations, if the inventory was changed, the client fetches the new full inventory state from the DLL and saves it back to disk.

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