Word is that internally at Google, among a few teams, and then also externally, Trax/Jax are putting up real competition to Tensorflow. Some teams have moved off of tensorflow entirely. Combined with the better research capabilities of PyTorch, the future of tensorflow is not bright. Given that, Tensorflow still provides the highest performance with regards to production usage, and has tons of legacy code strewn throughout the web.
I would argue that this is not the fault of Tensorflow, but rather the hazard of being the first implementation in an extremely complex space. Seems like usually there needs to be some sacrificial lamb in software domains. Somewhat like Map/Reduce was quickly replaced by Spark, which has no real competitors.
I would argue that this is not the fault of Tensorflow, but rather the hazard of being the first implementation in an extremely complex space. Seems like usually there needs to be some sacrificial lamb in software domains. Somewhat like Map/Reduce was quickly replaced by Spark, which has no real competitors.