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More like... most of the scientific Python stack has settled on a third party library, NumPy, to provide support for numerical computing. That includes providin
by cwyers 6y ago
More like... most of the scientific Python stack has settled on a third party library, NumPy, to provide support for numerical computing. That includes providing data types like arrays. Tensorflow doesn't use NumPy's data types, so if you want to use Tensorflow Probability, you need to convert everything you've done in NumPy (or things that are built on top of NumPy, like Pandas) to TensorFlow, and use TensorFlow's functions/methods to manipulate them.
(The same is true if you use something that's built on top of Stan or JAGS or BUGS or something else.)
In Julia's Turing.jl, everything is built around data structures that are first-class parts of Julia, so there's no need to have special Turing.jl versions of, say, probability distributions.
- The_rationalist 6y agohttps://www.tensorflow.org/guide/tf_numpy https://www.tensorflow.org/guide/tf_numpy
- dd82 6y agoyes, and if you go to that link, you can see its experimental, for TF 2.4 _and_ the notebook on github was created Aug 3, 2020. So that is relatively new and most likely has a ways to go before further adoption. Its good that option exists, but doesn't seem very backwards compatible. That doesn't negate the points made in the parent.
- The_rationalist 6y agoIt will mature before the Julia ecosystem mature so the value proposition of Julia is out weighted by it's lack of ecosystem. Had they transparant interoperability with Python like Kotlin is for Java, I would consider it. They could at least rewrite their vm to use the truffle framework from graalvm so that they would become interoperable with Python on graal which in a few years will mature.
- amkkma 6y agoWhat do you know about the Julia ecosystem?