fnet | FNET port for chibios | Runtime Evironment library

You need to select the div's inside resultSet, try changing from this:

If you want fast scraping https://scrapy.org is a lot faster(it just scrapes things and then process it), sometime you can even use requests, only use selenium for JavaScript heavy things as its slow.

Also a simple request based spider, to serve as an example (this one that requires auth:

Your target element is inside a frame:

Something like this

The only difference is that in second approach you'll have to keep much more stuff in memory - until you call backward you'll have all unrolled parameters fnet.parameters(time=T) (along with intermediate computation tensors) for each of task_num iterations as part of the graph for the aggregated meta_loss. If you call backward on every task then you only need to keep full set of unrolled parameters (and other pieces of the graph) for one task.

So to answer your question's title: because in this case the memory footprint is task_num times bigger.

In a nutshell what you're doing is similar to comparing loopA(N) and loopB(N) in the following code. Here loopA will get as much memory as it can and OOM with sufficiently large N, while loopB will use about same amount of memory for any large N:

Instead of using selenium to download the file I prefer to us BeautifulSoup. Change your table to the one below, to get the html

Please try below code to download your pdf

Untested but you can try something along the lines of the following:

If you have a nn.Linear layer in your net, you cannot decide "on-the-fly" what the input size for this layer would be.
In your net you compute num_flat_features for every x and expect your self.fc1 to handle whatever size of x you feed the net. However, self.fc1 has a fixed size weight matrix of size 400x120 (that is expecting input of dimension 16*5*5=400 and outputs 120 dim feature). In your case the size of x translated to 7744 dim feature vector that self.fc1 simply cannot handle.

If you do want your network to be able to handle any size x, you can have a parameter-free interpolation layer resizing all x to the right size before self.fc1: