🐛 Bug

During our hackathon today, I ran into this weird error highlighting.

import torch
from typing import Dict

@torch.jit.script
def missing_index(x: Dict[str, int]) -> int:
    return x['dne']

missing_index({'item': 20, 'other_item': 120})

This code outputs:

     9 
     10 
---> 11 missing_index({'item': 20, 'other_item': 120})

RuntimeError: The fo

At this moment relu_layer op doesn't allow threshold configuration, and legacy RELU op allows that.
We should add configuration option to relu_layer.

Problem: the approximate method can still be slow for many trees
catboost version: master
Operating System: ubuntu 18.04
CPU: i9
GPU: RTX2080

Would be good to be able to specify how many trees to use for shapley. The model.predict and prediction_type versions allow this. lgbm/xgb allow this.

Our users are often confused by the output from programs such as zip2john sometimes being very large (multi-gigabyte). Maybe we should identify and enhance these programs to output a message to stderr to explain to users that it's normal for the output to be very large - maybe always or maybe only when the output size is above a threshold (e.g., 1 million bytes?)

Hi ,

I have tried out both loss.backward() and model_engine.backward(loss) for my code. There are several subtle differences that I have observed , for one retain_graph = True does not work for model_engine.backward(loss) . This is creating a problem since buffers are not being retained every time I run the code for some reason.

Please look into this if you could.

Is your feature request related to a problem? Please describe.
I would be useful to have the ability to make a reverse copy of a column and/or table.
I am thinking of std::reverse (https://en.cppreference.com/w/cpp/algorithm/reverse) . But not necessarily using iterators, since that might make things more complicated.

Describe the solution you'd like
The following APIs could be

Current implementation of join can be improved by performing the operation in a single call to the backend kernel instead of multiple calls.

This is a fairly easy kernel and may be a good issue for someone getting to know CUDA/ArrayFire internals. Ping me if you want additional info.

We would like to forward a particular 'key' column which is part of the features to appear alongside the predictions - this is to be able to identify to which set of features a particular prediction belongs to. Here is an example of predictions output using the tensorflow.contrib.estimator.multi_class_head:

{"classes": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],
 "scores": [0.068196

Problem

Cub allows itself to place into a namespace via CUB_NS_PREFIX and CUB_NS_POSTFIX, such that multiple shared libraries can each utilize their own copy of it (and thus different versions can safely coexist). Static variables used for caching could otherwise cause problems (e.g., https://github.com/NVIDIA/cub/blob/main/cub/util_device.cuh#L212).

Thrust however depends on cub and

How do I get TOP-K evaluation indicators for multi-classification? For example, TOP-3 accuracy.