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I use Emacs to write blog. In the recent update, I found M-RET no longer behave as leader key in org mode, but behave as org-meta-return. And even more strange is that in other mode, it behave as leader key. And M-RET also works in terminal in org mode.

First zip all of the dependencies into zip file like this. Then you can use one of the following methods to import it. |-- kk.zip | |-- kk.py Using –py-files in spark-submit #When submit spark job, add --py-files=kk.zip parameter. kk.zip will be distributed with the main scrip file, and kk.

These days I use InfluxDB to save some time series data. I love these features it provides: High Performance #According to to it’s hardware guide, a single node will support more than 750k point write per second, 100 moderate queries per second and 10M series cardinality.

Python supports multiple inheritance, its class can be derived from more than one base classes. If the specified attribute or methods was not found in current class, how to decide the search sequence from superclasses? In simple scenario, we know left-to right, bottom to up.

defer is a useful function to do cleanup, as it will execute in LIFO order before the surrounding function returns. If you don’t know how it works, sometimes the execution result may confuse you. How it Works and Why Value or Pointer Receiver Matters #I found an interesting code on Stack Overflow:

Before talking about SimHash, let’s review some other methods which can also identify duplication. Longest Common Subsequence(LCS) #This is the algorithm used by diff command. It is also edit distance with insertion and deletion as the only two edit operations.

Here is the main logic for jaeger agent and jaeger collector. (Based on jaeger 1.13.1) Jaeger Agent #Collect UDP packet from 6831 port, convert it to model.Span, send to collector by gRPC Jaeger Collector #Process gRPC or process packet from Zipkin(port 9411).

Thanks for the articles I list at the end of this post, I understand how transformers works. These posts are comprehensive, but there are some points that confused me. First, this is the graph that was referenced by almost all of the post related to Transformer.

\(s_t\) and \(h_i\) are source hidden states and target hidden state, the shape is (n,1). \(c_t\) is the final context vector, and \(\alpha_{t,s}\) is alignment score. \[\begin{aligned} c_t&=\sum_{i=1}^n \alpha_{t,s}h_i \\ \alpha_{t,s}&= \frac{\exp(score(s_t,h_i))}{\sum_{i=1}^n \exp(score(s_t,h_i))} \end{aligned}\] Global(Soft) VS Local(Hard) #Global Attention takes all source hidden states into account, and local attention only use part of the source hidden states.

Load separate files #data.Field parameters is here. When calling build_vocab, torchtext will add <unk> in vocabulary list. Set unk_token=None if you want to remove it. If sequential=True (default), it will add <pad> in vocab. <unk> and <pad> will add at the beginning of vocabulary list by default.