Tensorflow sucks • Hacker News Board

Re: Tensorflow sucks

#131

Post by zo7 » Tue, Oct 10, 2017, 4:14 PM UTC

dsl wrote: post #68

zo7 wrote: post #41

I think the author raises a good point about Google envy. TensorFlow is not the most intuitive or flexible library out there, and it is very over-engineered if you're not doing large-scale distributed training. The main reason why everyone talks it up so much is because Google heavily marketed it from the outset, and everyone automatically assumes Google == Virtuoso Software Design because they couldn't make it throu…

Or maybe it is built by a company that is doing large-scale distributed training, and they open sourced it not to cater to every need, but to help others trying to do the same thing they are. Companies are under no obligation to make sure their open source is well suited for others use cases.

That was kinda my point, it's not the be-all deep learning library because they made it for their own use case, but its towering popularity (as in 10x the number of stars of other popular libraries) is not genuine.

Also I highly doubt that the main reason Google open sourced it was to be charitable.

Re: Tensorflow sucks

#132

Post by candiodari » Tue, Oct 10, 2017, 7:50 PM UTC

KirinDave wrote: post #83

ffriend wrote: post #14

Earlier quoted context omitted.

> I don’t understand their vendetta against the graph, which is a powerful abstraction that lets you choose different backend [...] You don't really need a graph to support different backends. One popular approach is to have different array implementations (e.g. CPU and GPU arrays). > [...] and let’s tensorboard show you an awesome view of your computation At the end of the post the author shows his API that lets you…

> You don't really need a graph to support different backends. One popular approach is to have different array implementations (e.g. CPU and GPU arrays). And now you can ( waves arms ) write it twice! Alternatively, you can make the interfaces between various impls be exactly the same but rename them so they're purpose-named. Then you've written Graph, for the most part.

Except your graph is symbolic, and good luck getting a breakpoint to fire when the calculation is happening ... Or if you don't like debugging, the problem manifests itself with merely printing values too.

Re: Tensorflow sucks

#133

Post by iluvmylife » Fri, Oct 13, 2017, 1:26 PM UTC

Tensorflow does do really well when it comes to serving models in production. Tensorflow serving or TensorRT 3 are fairly throughput efficient and low latency. PyTorch, for instance, does not have a good serving solution (I guess that's where Caffe2 is useful)