gpr | General Purpose Raw image format | Computer Vision library

You did not provide the type of publication, so you use just a basic Publication. from() is a function of MavenPublication, so you need to explicitly specify that you need a MavenPublication:

The fact that you see throttling even for narrow SIMD instructions is a side-effect of a behavior I call implicit widening.

Basically, on modern Intel, if the upper 128-255 bits are dirty on any register in the range ymm0 to ymm15, any SIMD instruction is internally widened to 256 bits, since the upper bits need to be zeroed and this requires the full 256-bit registers in the register file to be powered and probably the 256-bit ALU path as well. So the instruction acts for the purposes of AVX frequencies as if it was 256-bit wide.

Similarly, if bits 256 to 511 are dirty on any zmm register in the range zmm0 to zmm15, operations are implicitly widened to 512 bits.

For the purposes of light vs heavy instructions, the widened instructions have the same type as they would if they were full width. That is, a 128-bit FMA which gets widened to 512 bits acts as "heavy AVX-512" even though only 128 bits of FMA is occurring.

This applies to all instructions which use the xmm/ymm registers, even scalar FP operations.

Note that this doesn't just apply to this throttling period: it means that if you have dirty uppers, a narrow SIMD instruction (or scalar FP) will cause a transition to the more conservative DVFS states just as a full-width instruction would do.

this is not a GPflow issue but a subtlety of TensorFlow's eager vs graph mode: In eager mode (which is the default behaviour when you interact with tensors "manually" as in calling the kernel) K_pre.shape works just as expected. In graph mode (which is what happens when you wrap code in tf.function(), this generally does not always work (e.g. the shape might depend on tf.Variables with None shapes), and you have to use tf.shape(K_pre) instead to obtain the dynamic shape (that depends on the actual values inside the variables). GPflow's Scipy class by default wraps the loss&gradient computation inside tf.function() to speed up optimization. If you explicitly turn this off by passing compile=False to the minimize() call, your code example runs fine. If you replace the .shape attributes with tf.shape() calls to fix it properly, it likewise will run fine.

So correlated sub queries can normally be turn into some form of join in you case the primary things you are doing is matching on two columns and selecting what appears randomly (if you have more than 1 row in active=false state) a row, this can be turned into a row_number() and using QUALITY as the equivalent as LIMIT.

Thus:

Thanks to the comments, I successfully compiled and ran an Ada program calling C code which calls CUDA code. These are the files I edited :

kernel.cuh

Your main program is in Ada, so you should tell your compiler to link Ada with -lm, even if the call is made from C:

Your model doesn't predict anything:

Pandas sees the first column as 'Occupation' not Occupation.

use this:-

As suggested by San Mason, adding noise actually works! Otherwise, while you do it manually (in the custom code), set the initial noise to reasonably low and have multiple restarts with different initializations then you will get values close by. By the way, noiseless data seems to be creating a stationary ridge in the space of hyperparameters (like Fig. 1.6 in Surrogates GP book). Note that scikit-learn noise is sigma_n^2 for your custom function. Below are the snippets of noisy and noise-less cases.