Problem:
catboost version: 0.23.2
Operating System: all
Tutorial: https://github.com/catboost/tutorials/blob/master/custom_loss/custom_metric_tutorial.md

Impossible to use custom metric (С++).

Code example

from catboost import CatBoost
train_data = [[1, 4, 5, 6],
 

Heston model has accurate density approximations for European option prices, which are of interest.

The module implementing this method should live under tf_quant_finance/volatility/heston_approximation.py. It should support both European option puts and calls approximations. Tests should be in heston_approximation_test.py in the same folder.

The standard accelerate test suite, used by all the backends, can be quite slow. Several of the tests are significantly slower than the others, for example segmented folds and scans, which I believe is because the reference implementations are very inefficient. Writing some more efficient reference implementations (e.g. using Data.Vector.Unboxed) should help speed things up.

Examples to be removed:

• space_invaders/et_space_invaders.py
• space_invaders/minimal_space_invaders.py
• space_invaders/random_baseline.py
• asteroids/random_network_baseline.py
• datasets/davis.py
• datasets/spoken_mnist.py
• mnist/batch_conv_mnist.py
• mnist/minimal_mnist.py
• mnist/minimal_resevoir.py
• mnist/minimal_supervised_mnist.py
• [

Exchange a package that is marked as deprecated.
Seen in CI as warning in the apt install step with CUDA 10.1.243.

To Do:

• double check this works with CUDA 9.2 (current docker image; check minimal version constraint for Nvidia NGC) or...
• update Docker image to newer CUDA

load and store functions are not implemented for vec class now.

Just an FYI whilst I was trawling through the ROCm GitHub page:

https://rocmdocs.amd.com/en/latest/Programming_Guides/Programming-Guides.html#