cls | : Processing | Frontend Framework library

While I do not know how to deal with this problem directly, I had a somewhat similar issue(and solved). The difference is:

• I use fairseq
• I can run my code on google colab with 1 GPU
• Got RuntimeError: unable to mmap 280 bytes from file : Cannot allocate memory (12) immediately when I tried to run it on multiple GPUs.

From the other people's code, I found that he uses python -m torch.distributed.launch -- ... to run fairseq-train, and I added it to my bash script and the RuntimeError is gone and training is going.

So I guess if you can run with 21000 samples, you may use torch.distributed to make whole data into small batches and distribute them to several workers.

The command you want to build, will have to be built using a task. Once built in a task, you can then bind it to which ever key configuration you like. Before I answered this I built a simple example to help demonstrate what I just said.

Create a tasks.json file in the .vscode directory. You can use this command from your projects root:

Tasks are like creating complicated keybinding (well sort'a), its more like a complex keybinding that took steroids, and can do a bunch of stuff keybindings cannot do. All BS aside, it is an extremely powerful tool. VSCode users that are not using it, are missing out on one of the most powerful features that VSCode offers. Anyhow, this is how you create a task that, in-a-nutshell, defines and executes a shell command.

You can make the entire class inherit torch.nn.Module like so:

Be careful cls is typically used for classmethods for example like this:

You need to sign the transaction before sending with your account that has ETH balance.

You need to use Signing middleware.

Your __repr__ calls your __str__, which calls the super's __str__, which defers to repr, which calls your __repr__, which is an infinite recursion. You could call super().__repr__ in your __repr__ method, instead of calling str(self).

You can sort of reverse engineer the serialization process by simply looking at how they decode it.

It is apparent that the operations b64decode -> decompress -> loads are happening. Furthermore, the object that is pickle loaded clearly seems to be a list of lists, numpy arrays, or a mix of other python objects.

From this, if we arrange the operations in opposite, then maybe dumps -> compress and b64encode may have been used?

I realize this may create more trouble than it solves, but if you don't plan to derive anything from MappingMeta, this seems to get around the problem (in that your code above runs).