This there a way to get the bitmask of the clocks_throttle_reasons.active into plain english? Is there perhaps a list somewhere? Please find my command below
nvidia-smi --query-gpu=clocks_throttle_reasons.active --format=csv
The above code returns the follow 0x0000000000000004 bitmask which looking at my data suggests that its a high power draw issue.
Thanks in advance!
Related
How APPROX_COUNT_DISTINCT actually works to get better performance and why the count we get from this function is not accurate?
Does it use HASH GROUP BY internally?
From https://db-blog.web.cern.ch/blog/luca-canali/2014-08-scaling-cardinality-estimates-12102 we can find that it uses HyperLogLog algorithm and it also gives a link to its' description on Alex Fatkulin's blog: http://afatkulin.blogspot.com/2013/11/hyperloglog-in-oracle.html
PS. RIP Alex... Hopefully his articles are still alive...
How can I specify in the construction of the HistFactory the signal and background to be 2-dimensional distributions?
I have understood than in RooStats you need to change the TH1 to a TH2.
At the moment to write my model in the json file can I use a ndarray to do something similar?.
Which is the correct way to do this?
I hope someone can help me and thank you in advance.
Currently the best way is to unroll the distributions e.g.
{'data': 2darray.ravel().tolist()}
Since mathematically it doesn't make any difference.
If you want to convert from XML+ROOT this is not yet supported (but could be). If so, please open an issue on GitHub.
Thanks for using pyhf!
I am using tutorial from here.
I use mask_rcnn_inception_v2 detection model with my own dataset. I want to add PNG mask, i use some applications to do it. but I wonder how i put this data to be used in detection. I see the mention anywhere.
How to implement the PNG mask in object detection ? (where i put it, how to use it)
Do you know how to launch the evaluation and training in same time on tensorboard i see it is possible.
generally where i can ask all Tensorflow general question as configuration file explanations
On Github Tensorflow it is specified we have to ask question here because not a Tensorflow issue and great community here with some great guys!
thanks to guy on github he points a missing configuration in pipeline.config
number_of_stages: 3
and it changes all the result i can see the mask now. youpi !
For any further information, there's a good explanation here:
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/instance_segmentation.md
It explains how to prepare you masks and what to modify.
I know this might be rather a simple issue to ask for and we can also set the barcode format to be scanned by Zxing, like this:
(1)intent.putExtra("SCAN_MODE", "QR_CODE_MODE"); //or any other format
if we do this:
(2)intent.putExtra("SCAN_MODE", "SCAN_MODE"); //for all modes`
While doing the #2 mentioned right above this line, the scanner sometimes seems to scan part of the barcode and picks up wrong information. For example if I try to simply scan a UPC barcode, 98% of the times it works beautifully, but sometimes it just returns me a wrong barcode. I think I know whats happening here, I have an idea up in my head, but what is the exact technical explanation for this? (Anyone familiar with barcodes can help) Thanks in advance guys.
SCAN_MODE is not a valid value. It is ignored and you are scanning for all formats.
It is not reading the wrong information from a barcode; it is finding a 'phantom' barcode among all those white and black lines, of another format. The usual culprit is UPC-E, which is the easiest to accidentally see.
This is why it is far better to restrict the scan to the format you are interested in with a correct value of SCAN_MODE.
I'm looking for a library or technique to detect the input language of blocks of text provided by users. Online lookups (like Google translate) won't work for this task as I'm writing an app which must run offline.
Thanks.
Here are two more n-gram-based gems you might want to try. They work offline.
https://github.com/echen/unsupervised-language-identification, optimized for separating english and other languages (has a live demo)
https://github.com/feedbackmine/language_detector, less specialized, will detect more languages. Some languages may need some extra training — I found it to be not precise enough for German text.
For anyone interested, I've found http://rubygems.org/gems/kenwaln-whatlanguage, which is performing excellently.
I'm using CLD which I really like, succinct and easy to use. Give it a try.
A quick demo of WhatLanguage in Ruby:
http://www.youtube.com/watch?v=lNqZ2cqOReo&list=UUJ_3fstMOH-g4yBxtvgAWkw&index=0&feature=plcp