transformers | normalizing property names and types from an external data

The error states that you do not have a variable called sentences in the scope. I believe the tutorial presumes you already have a list of sentences and are tokenizing it.

Have a look at the documentation The first argument can be either a string or list of string or list of list of strings.

Your current flow does not return a value, you are simply logging the message.

A terminating .log() ends the flow.

Delete the .log() element so the result of the transform will automatically be routed back to the gateway.

Or add a .bridge() (a bridge to nowhere) after the log and it will bridge the output to the reply channel.

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.

You can add a manifest to your task since it is type Jar. Specifying an entrypoint with the Main-Class attribute should make your Jar executable.

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

You cannot use the export_text function on the whole pipeline as it only accepts Decision Tree objects, i.e. DecisionTreeClassifier or DecisionTreeRegressor. Only pass the fitted estimator of your pipeline and it will work:

You can create a new message from transformed and remove headers you don't need

desertnaut already teased the answer in his comment. I shall just explicate and complete:

When you want to cross-validate several data processing steps together with an estimator, the best way is to use Pipeline objects. According to the user guide, a Pipeline serves multiple purposes, one of them being safety:

Pipelines help avoid leaking statistics from your test data into the trained model in cross-validation, by ensuring that the same samples are used to train the transformers and predictors.

With your definitions like above, you would wrap your transformations and classifier in a Pipeline the following way:

If the Movie object needs to be shared between Actors, another way to do this is to use std::vector> instead of std::vector or std::vector.

The reason why std::vector would be difficult is basically what you've discovered. The Movie object is separate from another Movie object, even if the Movie has the same name.

Then the reason why std::vector would be a problem is that yes, you can now "share" Movie objects, but the maintenance of keeping track of the number of shared Movie objects becomes cumbersome.

In comes std::vector> to help out. The std::shared_ptr is not just a pointer, but a smart pointer, meaning that it will be a reference-counted pointer that will destroy itself when all references to the object go out of scope. Thus no memory leaks, unlike if you used a raw Movie* and mismanaged it in some way.