dask

I noticed our release version anchor links in the changelog don't actually reference a specific released version. If I go to the changelog and click on the 2021.12.0 link, I'm redirected to https://docs.dask.org/en/stable/changelog.html#id1 when, naively, I would have expected this link to look like https://docs.dask.org/en/stable/changelog.html#2021.12.0 (or something similar). As you move down

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

The following doesn't work

import xarray as xr

da = xr.DataArray([[1,2],[1,2]], dims=("x", "y"))
da.stack(flat=...)

Describe the solution you'd like
This could be equivalent to

da.s

Describe the bug

Failed to execute Series.drop_duplicates.

In [75]: a = md.DataFrame(np.random.rand(10, 2), columns=['a', 'b'], chunk_size=2)                  

In [76]: a['a'].drop_duplicates().execute()               

The stumpy.snippets feature is now completed in #283 which follows this work:

• The original published paper
• The supporting website with data examples

We have a rough notebook t

As a dask maintainer, I want to trust the code coverage report.

Our coverage badge is a bit misleading showing coverage below 90%. This is due to us not collecting coverage in a few places. Also, we simply have a few modules which are only there for debugging and/or historical reasons

The most relevant parts (scheduler, worker, etc.) do have quite good coverage. I believe the <90% batch does

One option to check for duplicate keys in the YAML files loaded/used by Satpy would be to add a custom constructor/loader as described in this gist:

https://gist.github.com/pypt/94d747fe5180851196eb

This wouldn't help the pre-commit in this PR, but at least the pre-commit is checking syntax.

_Originally posted by @djhoese in pytroll/satpy#1935 (comment)

Does HyperGBM's make_experiment return the best model?
How does it work on paramter tuning? It's say that, what's its seach space (e.g. in XGboost)???

Is your feature request related to a problem? Please describe.
Look at here

If taking just one row with our sorting, we may use GROUP BY and FIRST to solve this problem, it can be a lot faster. Let's add this special handling.

Code Sample, a minimal, complete, and verifiable piece of code

from pyresample.boundary import Boundary
b = Boundary(my_lons, my_lats)
print(b.contour_poly.area())

Problem description

The above code doesn't fail if the provided lons/lats are 2D (not sure on 3D+), but the class and all functions/utilities underneath it assume 1D arrays. The end results are incor

The ML implementation is still a bit experimental - we can improve on this:

• SHOW MODELS and DESCRIBE MODEL
• Hyperparameter optimizations, AutoML-like behaviour
• @romainr brought up the idea of exporting models (#191, still missing: onnx - see discussion in the PR by @rajagurunath)
• and some more showcases and examples

from dask_jobqueue import SLURMCluster 
cluster = SLURMCluster(cores=1, memory='1GB') 
print(cluster.job_script()) 

#!/usr/bin/env bash

#SBATCH -J dask-worker
#SBATCH -n 1
#SBATCH --cpus-per-task=1
#SBATCH --mem=954M
#SBATCH -t 00:30:00

/home/lesteve/miniconda3/bin/python -m distributed.cli.dask_worker tcp://192.168.0.11:44065 --nthreads 1 --memory-limit 1000.00MB -

Problem description

Reading a dataset with eager's read functionality raises a ValueError when providing columns.

Example code (ideally copy-pastable)

import pandas as pd

from tempfile import TemporaryDirectory
from functools import partial
from storefact import get_store_from_url

from kartothek.io.eager import store_dataframes_as_dataset, read_dataset_as_data

Example for numerical weather prediction

to be added to initialised datasets

Data sources (to) implement(ed):

• GEFS https://www.ncei.noaa.gov/thredds/catalog/model-gefs-003/202008/20200831/catalog.html
• DWD https://opendata.dwd.de/weather/nwp/

relates to #600

In determining the correct reader for the file provided we currently have two options (as of #224).

1. Providing reader param to AICSImage (i.e. img = AICSImage("s3://some-file.ext", reader=readers.lif_reader.LifReader)
2. Not providing a reader, and AICSImage looping over all SUPPORTED_READERS.

Option 1 is the fastest + safest method for loading a file into AICSImage (without using

Currently all of the metrics computed are independent of a target variable or column, but if lens.summarise took the name of a column as the target variable, the output of some metrics could be more interpretable even if the target variable is not used in any kind of predictive modelling.

A good example of this could be PCA (see #14), which could plot the different categories of the target va

Without thinking I put resampling="bilinear" and got an error when I called .compute()

Traceback (most recent call last):
  File "carajas.py", line 92, in <module>
    band_medianNP = band_median.compute()
  File "/home/ubuntu/anaconda3/envs/richard/lib/python3.8/site-packages/xarray/core/dataarray.py", line 899, in compute
    return new.load(**kwargs)
  File "/home/ubuntu/anaco

As per @krfricke, we should create a single RayCommunication actor class that holds both an asyncio Queue and asyncio Event. This way we only need one remote actor for all communication between training workers and the driver, and we don't have to be dependent on ray.util.Queue