61–70 of 72 posts
Hm, the Mac version of LibTorch is suddenly unavailable!? [0] I swear it was available for download until a few days ago... [0] https://pytorch.org/get-started/locally/
Edit: fixed links should be going live in a few mins, via: https://github.com/pytorch/pytorch.github.io/pull/141
Earlier quoted context omitted.
Why do closures suck? What are they missing?
I like to pass closures as return values or as function parameters. In my experience this is folly in c++.
$ cat test.cpp
#include
#include
template
auto pass_func(F func) {
return func();
}
auto return_func() {
std::string test = "Hello World!";
return [=](){ return test; };
}
int main() {
auto func = return_func();
std::cout Earlier quoted context omitted.
Wow, that’s actually really awesome. I really missed a good languages-other-than-Python story in Tensorflow. Now I feel a little inspired to try out ML again...
I’m really excited to use it in C++. When it’s in Python, it becomes much harder to embed into other programs and is susceptible to Python version issues when sharing code. This reduces barriers to use in addition to improving performance and portability.
Earlier quoted context omitted.
I love PyTorch, but my experience with jits embedded in Python (eg. Numba) has been everything but simple, nevermind trivial. I'll really have to try it to believe it.
I’ve had the opposite experience with numba in production. It works almost flawlessly, very easy to reason about the generated code and inspect annotations, easy to debug.
Earlier quoted context omitted.
I’ve had the opposite experience with numba in production. It works almost flawlessly, very easy to reason about the generated code and inspect annotations, easy to debug.
Check out github.com/google/jax, it’s NumPy on the GPU with automatic differentiation, JIT and autobatching.
Earlier quoted context omitted.
I'm hoping that this results in a nice, high-level API for https://github.com/fragcolor-xyz/nimtorch as well, which AFAIK has been wrapping the low-level Aten API. I've been keeping my eye on that project, and been really excited about it.
Likewise, I hope this leads to a nice wrapper API for Julia.
Earlier quoted context omitted.
I like to pass closures as return values or as function parameters. In my experience this is folly in c++.
Here's an example of doing both: $ cat test.cpp #include #include template auto pass_func(F func) { return func(); } auto return_func() { std::string test = "Hello World!"; return [=](){ return test; }; } int main() { auto func = return_func(); std::cout What's hard about it?
Earlier quoted context omitted.
Here's an example of doing both: $ cat test.cpp #include #include template auto pass_func(F func) { return func(); } auto return_func() { std::string test = "Hello World!"; return [=](){ return test; }; } int main() { auto func = return_func(); std::cout What's hard about it?
You didn't pass a function to std::cout, you called it and passed its return value.
Earlier quoted context omitted.
You didn't pass a function to std::cout, you called it and passed its return value.
The way I interpret the GP, they're talking about passing closures as a function argument. In the example I pass func (a closure) to pass_func. Not sure what std::cout has to do with it?