profiler | A profiling and performance analysis tool for TensorFlow | Monitoring library

TensorFlow 2.8 doesn't support CUDA 11.6. but requires 11.2 see docs

Seems you need to get in touch with the VM's owner to update the dependencies

Looking at the result of Cachegrind, it doesn't look like the memory is your bottleneck. The NN has to be stored in memory anyway, but if it's too large that your program's having a lot of L1 cache misses, then it's worth thinking to try to minimize L1 misses, but 1.7% of L1 (data) miss rate is not a problem.

So you're trying to make this run fast anyway. Looking at your code, what's happening at the most inner loop is very simple (load-> multiply -> add -> store), and it doesn't have any side effect other than the final store. This kind of code is easily parallelizable, for example, by multithreading or vectorizing. I think you'll know how to make this run in multiple threads seeing that you can write code with some complexity, and you asked in comments how to manually vectorize the code.

I will explain that part, but one thing to bear in mind is that once you choose to manually vectorize the code, it will often be tied to certain CPU architectures. Let's not consider non-AMD64 compatible CPUs like ARM. Still, you have the option of MMX, SSE, AVX, and AVX512 to choose as an extension for vectorized computation, and each extension has multiple versions. If you want maximum portability, SSE2 is a reasonable choice. SSE2 appeared with Pentium 4, and it supports 128-bit vectors. For this post I'll use AVX2, which supports 128-bit and 256-bit vectors. It runs fine on your CPU, and has reasonable portability these days, supported from Haswell (2013) and Excavator (2015).

The pattern you're using in the inner loop is called FMA (fused multiply and add). AVX2 has an instruction for this. Have a look at this function and the compiled output.

I found what I was looking for in odbc package documentation : direct execution.

The odbc package uses Prepared Statements to compile the query once and reuse it, allowing large or repeated queries to be more efficient. However, prepared statements can actually perform worse in some cases, such as many different small queries that are all only executed once. Because of this the odbc package now also supports direct queries by specifying immediate = TRUE.

This will use a prepared statement:

This issue was fixed by Google (10 February 2022).

You can now update Android Studio normally.

Thank you all for helping to bring this problem to Google's attention.

This issue was fixed by Google (10 February 2022).

You can now update Android Studio normally.

I had the same problem after updating Android Studio to Bumble Bee. Please set acceptable encodings in the request header. ("Accept-Encoding", "identity") It works for me.

In generally, render should take about 16 milliseconds. Any longer than that and things start feeling really janky. I would recommend this article on performance in react. He is explaining more about profiling and performance in react.
performance with react(with forms) : https://epicreact.dev/improve-the-performance-of-your-react-forms/
profiling article: https://kentcdodds.com/blog/profile-a-react-app-for-performance

Microsoft.Data.SqlClient 4.0 is using ENCRYPT=True by default. Either you put a certificate on the server (not a self signed one) or you put

TrustServerCertificate=Yes;

on the connection string.

From what you have shared here I assume the delay is in network latency.

Dev Java App to Dev Server takes 6 seconds.

Azure Kubernetes App to Azure SQL Database takes 30 ms. Which I think is reasonable if you include the network latency between app and server. I dont think you will get result in 2 ms. If you get, please let me know ;)

Try to check network latency in some ways like doing on SELECT 1 query and calculate turnaround time.

interesting read:
https://azure.microsoft.com/en-in/blog/testing-client-latency-to-sql-azure/
https://docs.microsoft.com/en-us/archive/blogs/igorpag/azure-network-latency-sql-server-optimization
https://github.com/RicardoNiepel/azure-mysql-in-aks-sample There is a way to measure latency network in java