Feature idea summary

Cgroups plugin supports only proportional and max Block IO policies. We should support BFQ scheduler as well. Disk stats for the scheduler are in blkio.bfq.io_service_bytes and blkio.bfq.io_serviced files.

Relevant telegraf.conf:

[[inputs.kinesis_consumer]]
  region = "eu-west-1"
  streamname = "telegrafStream"
  data_format = "influx"

System info:

Telegrad 1.17.2-alpine (running in Fargate)

Steps to reproduce:

it's becoming more time-consuming and error-prone to manually re-test all the demos following internal refactorings and API adjustments.

now that the API is fleshed out a bit, it's possible to test a large amount of code (non-granularly) without having to simulate all interactions via Puppeteer or similar.

a lot of code can already be regression-tested by simply running all the demos and val

Is your feature request related to a problem? Please describe.
NA

Describe the solution you'd like
I thought I'd ask first, before submitting a PR—@MatthewMiddlehurst because it's your code, @kachayev and @RavenRudi because you are working on related PRs—would it be helpful to add [MiniRocket](https://github.com/alan-turing-institute/sktime/blob/main/sktime/transformations/panel/rocke

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提建议？
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提修复PR？
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提Feature？
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Create

create table tt (dts timestamp, nts timestamp) timestamp(dts)

Insert

insert into tt 
select timestamp_sequence(1577836800000000L, 10L), timestamp_sequence(1577836800000000L, 10L) 
from long_sequence(2L)

Select

select 'nts', min(nts) from tt where nts > '2020-01-01T00:00:00.000000Z'

Expected
nts in the first column

Actual
timestamp

Use case:
Right now one can only use date_trunc() to easily define time buckets. date_trunc() only supports predefine time intervals like 1 minute, 1 hour, etc. . In time-series use cases it is often necessary to define different time bucket sizes like e.g. '5 minutes' or '20 minutes'

a workaround for this is the - error prone - integer division on the timestamp e.g.

S

Description

(A clear and concise description of what the feature is.)

• util.cumsum implementation https://github.com/awslabs/gluon-ts/blob/master/src/gluonts/mx/util.py#L326 does not scale under mx.ndarray
• cumsum is 2-5 times slower than nd.cumsum under both mx.sym and mx.ndarray, and even fails for large 4-dim input

Sample test

Code

# import ...
def test_

Hi, Thanks for the awesome library!

So I am running a Kmeans on lots of different datasets, which all have roughly four shapes, so I initialize with those shapes and it works well, except for just a few times. There are a few datasets that look different enough that I end up with empty clusters and the algorithm just hangs ("Resumed because of empty cluster" again and again).

I conceptually

similar to scikit model.get_params