tgt | user-space iSCSI target daemon | Storage library

I am trying to come up with a script to copy folders from one server to another. I might be going about this wrong, but I'm try to copy the directories from one server into an array, copy the directories from the second server into an array, compare them and then create the folders needed in the server that doesn't have them:

I have have newly installed and created spark, scala, SBT development environment in intellij but when i am trying to compile SBT, getting unresolved dependencies error.

below is my SBT file

Requirement: Generate new ID from the MAX ID for those Name doesn't exist in the Target table and has count >1

Below is the Source data, The yellow highlighted are new rows, Those with count >1 are incremented with a new ID, and those with count =1 defaults to FM00000001

The expected result is highlighted in yellow in the Target table

I have generated the existing ID manually for one time , as I have to automate daily jobs so I need to generate incremental ID from MAX ID for those count >1

Previously I've reported it into kafkacat tracker but the issue has been closed as related to cyrus-sasl/krb5.

I’m currently trying to train a Spanish to English model using yaml scripts. My data set is pretty big but just for starters, I’m trying to get a 10,000 training set and 1000-2000 validation set working well first. However, after trying for days, I think I need help considering that my validation accuracy goes down the more I train while my training accuracy goes up.

My data comes from the ES-EN coronavirus commentary data set from ModelFront found here https://console.modelfront.com/#/evaluations/5e86e34597c1790017d4050a. I found the parallel sentences to be pretty accurate. And I’m using the first 10,000 parallel lines from the dataset, skipping sentences that contain any digits. I then take the next 1000 or 2000 for my validation set and the next 1000 for my test set, only containing sentences without numbers. Upon looking at the data, it looks clean and the sentences are lined up with each other in the respective lines.

I then use sentencepiece to build a vocabulary model. Using the spm_train command, I feed in my English and Spanish training set, comma separated in the argument, and output a single esen.model. In addition, I chose to use unigrams and a vocab size of 16000

As for my yaml configuration file: here is what I specify

My source and target training data (the 10,000 I extracted for English and Spanish with “sentencepiece” in the transforms [])

My source and target validation data (2,000 for English and Spanish with “sentencepiece” in the transforms [])

My vocab model esen.model for both my Src and target vocab model

Encoder: rnn Decoder: rnn Type: LSTM Layers: 2 bidir: true

Optim: Adam Learning rate: 0.001

Training steps: 5000 Valid steps: 1000

Other logging data.

Upon starting the training with onmt_translate, my training accuracy starts off at 7.65 and goes into the low 70s by the time 5000 steps are over. But, in that time frame, my validation accuracy goes from 24 to 19.

I then use bleu to score my test set, which gets a BP of ~0.67.

I noticed that after trying sgd with a learning rate of 1, my validation accuracy kept increasing, but the perplexity started going back up at the end.

I’m wondering if I’m doing anything wrong that would make my validation accuracy go down while my training accuracy goes up? Do I just need to train more? Can anyone recommend anything else to improve this model? I’ve been staring at it for a few days. Anything is appreciated. Thanks.

!spm_train --input=data/spanish_train,data/english_train --model_prefix=data/esen --character_coverage=1 --vocab_size=16000 --model_type=unigram

I did a pure CSS/JS piano keyboard using the AudioContext object but I have two problems related to the playTone function:

1. On Chrome/android (v.89.0.4389.105/Android 10) it seems that the volume is halved at every key pressed: after a few notes played the volume is not audible anymore.

2. On Firefox (v.88/MacOS 10.15.7) I hear a crackle at the end of every key pressed.

On latest Chrome/MacOS, for comparison, it sounds good.

I had no error. Always refresh cache and local memory.

Resources for Verifying Translations:

[NCBI Protein Translation Tool][1] (Validation)

[Text Compare][2] (Verification)

[Solution Inspiration][3]

300 DNA chars -> 100 protein chars.

From the docs it says to create a transformer model like this: