auto-tune | music auto-tagging library | Music Player library

• Read data from Modbus Server precisely every 0.05 seconds.

The bigger picture is that I am creating a PyQt5 app through which I want to save and plot the Modbus data. So later I can use them for PID auto-tuning. PID auto-tuner requires that the data are measured with precision of at least 0.05 seconds. And also data points need to be spread equally like this: M wait 0.05s then M wait 0.05s then M and not like this: M wait 0.08s then M wait 0.03s then M (M = measure data).

• I have tried implementing Threading.Timer to read data every 0.05 seconds.

• The problem is that the precision of the timer is too low.

This is the code on which I were testing the Threading.Timer precision:

So I tried to use Captum with PyTorch Lightning. I am having issues when passing the Module to Captum, since it seems to do weird reshaping of the tensors. For example in the below minimal example, the lightning code works easy and well. But when I use IntegratedGradient with "n_step>=1" I get an issue. The code of the LighningModule is not that important I would say, I wonder more at the code line at the very bottom.

Does anyone know how to work around this?

I have a primary db and a secondary geo-replicated db. On the primary, the server atuomatic tuning is turned on.

On the replica when I try to do the same I encounter the following issues.

The database is inheriting settings from the server, but the server is in the unspecified state. Please specify the automatic tuning state on the server.

And

Automated recommendation management is disabled because Query Store has reached its capacity limit and is not collecting new data. Learn more about the retention policies to maintain Query Store so new data can be collected.

However, on the server, tuning options are on so I don't understand that "unspecified state". Moreover, why I look at the Query Store set up in both databases properties in SSMS they are exactly the same with 9MB of space available out of 10MB.

Note: both databases are setup on the 5 DTUs basic pricing plan.

UPDATE

While the primary db Query store Operation Mode is Read Write, the replica is Read Only. It seems I cannot change it (I couldn't from the properties dialog of the db in SSMS).

Fair enough but how can the same query be 10 times faster on primary than on replica. Aren't optimizations copied accross?

UPDATE 2

Actually Query Store are viewable on SSMS and I can see that they are identical in both db. I think the difference in response times that I observe is not related.

UPDATE 3

I marked @vCillusion's post as the answer as he/she deserves credits. However, it's too detailed with regards to the actual issue.

My replica is read-only and as such cannot be auto-tuned as this would require writing in the query store. Azure not being able to collect any data into the read only query store led to a misleading (and wrong) error message about the query store reaching its capacity.

Auto-tuner for a car application: The application may change depending on model of the car, so naturally the objective function is going to change as well. The problem is to tune the parameters to the optimum ones for the specific car model. Input: car model, output: optimum paramters for the application for the specific car model. I want to solve this with optimization

I'm trying to minimize a complex nonlinear function, constrained with two nonlinear constraints, one inequality and one equality constraint. The problem is not bounded per se but i've put bounds on the parameters anyway to help speed up the optimization since I know more or less where the correct parameters lie. Parameters are: [x0,x1,x2,x3]

I've used the scipy.optimize.minimize() function with SLSQP method and found good results when the problem is bounded correctly. Although, the scipy.optimize.minimize() function is a local optimizer and solves QP problems which I don't think that my problem is. I've therefore started using a global optimization method with mystic,(mystic.differential_evolution). Since I'm not an expert in global optimization I naturally have some questions.

If I choose the bounds too wide the optimizer (mystic.differential_evolution) will stop iterating after a while and print:

STOP("ChangeOverGeneration with {'tolerance': 0.005, 'generations': 1500}")

When I run the solution that the optimizer found, I see that the result is not as good as if I were to lower(shrink) the bounds. Obviously the global optimizer has not found the global optimum, yet it stopped iterating. I know that there are a multiple parameter sets that yield the same global minimum.

Since the objective function may change with the car model, I want the bounds to remain relativley broad in case the global optimum changes which would change the correct parameters.

1. How do I tune the settings of the optimizer to get it to keep searching and find the global optimum?
2. Is the npop = 10*dim rule a good approach to the problem?
3. Can I make broaden the horizon of the optimizers search algorithm to get it to find the optimal parameters which it missed?

I want to add some synonyms and aliases for text searches via the AWS CloudSearch console. I followed the instructions in Configuring Text Analysis Schemes for Amazon CloudSearch, but a test search still doesn't match on my alias.

I configured a scheme called default along with the following synonym JSON: