mixup | mixup : Beyond Empirical Risk Minimization

Multiple source of dotnet runtime and sdk had been installed. This resulting multiple conflicting binary path and assembly caching crash.

Step 1: Do a clean installation of CentOS

Step 2: Install required runtime version, for example:

There is no built-in functionality to prevent code from running more than once. You need some kind of external state to know whether it actually did run.

You can't just remember whether the function itself has been seen before, because you use a function expression ("lambda") here, and every evaluation of that creates a new function object which is not even equal to other function objects created by the same expression.

So, you need something to represent the location of the call.

I guess you could hack up something using stack traces. I will not recommend that (very expensive for very little advantage).

So, I'd recommend something like:

The answer is in the text you link:

The method takes another Point as a parameter, which might have different types from the self Point we’re calling mixup on. The method creates a new Point instance with the x value from the self Point (of type T) and the y value from the passed-in Point (of type W).

The types V and W may be different than T and U. mixup takes a Point (which is self) and a Point, and returns a Point, replacing the y:U property with a y:W property. The example code shows what it's doing. Try removing the and you'll see the problem.

When you say "the return type and parameter generics are clearly defined," they're only defined because of that .

Rust has a nice trick called the New Type Idiom just for this. By wrapping a single item in a tuple struct, you can create a "strong" or "distinct" type wrapper.

This idiom is also mentioned briefly in the tuple struct section of the Rust docs.

The "New Type Idiom" link has a great example. Here is one similar to the types you are looking for:

"You could make your webserver follow symlinks and simply symlink your mounted drive into the document root. Search for "follow symlinks " on your favorite search engine" - @DanielW.

Solution

created a movies folder within the root webserver directory.

mkdir /var/www/html/movies/

Then i used the mount for Linux as im using WSL

mount --bind --verbose /mnt/d/Movies /var/www/html/movies

Note: this will work, although you might need to create a script so it can be mounted on-Boot

quite littrally did a search for "follow symlinks " and found the solution within a few links

when accessing https://localhost/movies/movieFolder/fileName.mp4 it now loads from /mnt/d/Movies instead of having the web rootDir in the Movies folder.

Thanks @DanielW. @tachoshy for your assistance :)

but part2 is just simple mixup and don't need gradient

It actually does! In order to compute the gradient flow and backpropagate successfully to part1 of your model (which is learnable, according to you) you need to compute the gradients on part2 as well. Even though there are no learnable parameters on part2 of your model.

What I'm assuming happened when you applied torch.no_grad() on part2 is that only part3 of your model was able to learn while part1 stayed untouched.

Edit

So can we conclude that torch.no_grad() should not be applied between 2 or more learnable blocks, otherwise it would drop the learning ability of blocks before this no_grad() part?

The reasoning is simple: to compute the gradient on part1 you need to compute the gradient on intermediate results, irrespective of the fact that you won't use those gradients to update the tensors on part2. So indeed, you are correct.

vRAM usage has nothing to do with how many train/val examples there are, but rather model, image size, and batch size. 1280x1280 is a massive image size - on a 16gb GPU you will probably only be able to train at 1 or 2 batch size.

Either use a lower resolution/smaller model, a GPU with more vRAM, or decrease your batch size.

Also try NVIDIA AMP

Try to install selenium in terminal with:

The workflow that you are trying is a perfect place to use a composite template. The recommendation is use one composite template per document. In one POST envelopes call you can swap out the documents on the templates with documents at runtime and apply server (saved) templates to the documents. You can add recipients, drop recipients by not including them, and also add tabs or write values to tabs at runtime. See: https://www.docusign.com/blog/dsdev-from-the-trenches-composite-templates https://www.docusign.com/blog/dsdev-why-use-composite-templates