nmt | TensorFlow Neural Machine Translation Tutorial | Translation library

Unfortunately (?), the easiest way to know for sure what those categories map to is to look at OpenJDK source code. The NMT tag you are looking for is mtServiceability. This would show that "serviceability" are basically diagnostic interfaces in JDK/JVM: JVMTI, heap dumps, etc.

But the same kind of thing is clear from observing that stack trace sample you are showing mentions ThreadStackTrace::dump_stack_at_safepoint -- that is something that dumps the thread information, for example for jstack, heap dump, etc. If you have a suspicion for the memory leak in that code, you might try to build a MCVE demonstrating it, and submitting the bug against OpenJDK, or showing it to a fellow OpenJDK developer. You probably know better what your application is doing to cause thread dumps, focus there.

That being said, I don't see any obvious memory leaks in StackFrameInfo, neither can I reproduce any leak with stress tests, so maybe what you are seeing is "just" thread dumping over the larger and larger thread stacks. Or you capture it when thread dump is happening. Or... It is hard to say without the MCVE.

Update: After playing with MCVE, I realized that it reproduces with 17.0.1, but not with either mainline development JDK, or JDK 18 EA, or JDK 17.0.2 EA. I tested with 17.0.2 EA before, so was not seeing it, dang. Bisection between 17.0.1 and 17.0.2 EA shows it was fixed with JDK-8273902 backport. 17.0.2 releases this week, so the bug should disappear after you upgrade.

In SQL Server DB if your subquery is written after the select command where we wrote field name list, then your subquery must be return only one record and only one field, else you will get an error. In your script, you wrote subquery before the from command, after this Loan_Num,. I did a little research on your subquery. Your subquery will return more than 1 record in most cases. The reason is that you wrote group by nml.hprop and after the where command you wrote this condition nml.hprop in (2380, 3348). I would have written this query for you myself, but I don't know your business logic and what need you. If your subquery must return more than 1 record, so you must join this subquery to the main query, using inner join or left join, you can not write this subquery on the field list.

The idea of using split is correct but assigning directly in this way in a for loop will not change the list elements.

You should also avoid using split as a variable name when it is already the name of an inbuilt method.

A list comprehension will do the job:

Marian is an open-source tool for training and serving neural machine translation, mostly developed at the University of Edinburgh, Adam Mickiewicz University in Poznań and at Microsoft. It is implemented in C++ and is heavily optimized for MT, unlike PyTorch-based Huggingface Transformers that aim for generality rather than efficiency in a specific use case.

The NLP group at the University of Helsinki trained many translation models using Marian on parallel data collected at Opus, and open-sourced those models. Later, they also did a conversion of the trained model into Huggingface Transformers and made them available via the Huggingface Hub.

MarianMT is a class in Huggingface Transformers for imported Marian models. You can train a model in Marian and convert it yourself. OpusMT models are Marian models trained on the Opus data in Helsinki converted to the PyTorch models. If you search the Huggingface Hub for Marian, you will find other MarianMT models than those from Helsinki.

I could not figure out how to do this without resorting to building it manually. If someone else comes up with the correct way to do this with Streams, I will accept that as the answer.

Have revised this solution, thanks to @Holger for the following tips:

• entrySet().forEach(…) -> functional style: forEach((k,v) -> ...)
• computeIfAbsent
• map.keySet().size() == 0 -> abridged map.isEmpty()

Sourav. I was able to replicate the issue, when running your code the result was:

According to the documentation: layers.Embedding: the largest integer in the input should be smaller than the vocabulary size / input_dim.

input_dim: Integer. Size of the vocabulary, i.e. maximum integer index + 1.

That's why

Buying new PN532 NFC RFID Module solved the issue.