dask | Parallel computing with task scheduling | Machine Learning library
kandi X-RAY | dask Summary
kandi X-RAY | dask Summary
Parallel computing with task scheduling
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Top functions reviewed by kandi - BETA
- Calculates the ordering of a dsk .
- Converts an array into a block item index .
- Tests a tensor .
- Fires a low - level fringe .
- Map an array of blocks to a map .
- Apply a Gufunc to a function .
- Generate histogramdd for a given sample .
- Read Parquet from path .
- Performs a blockwise operation .
- Convert an array to a dask array .
dask Key Features
dask Examples and Code Snippets
Lines of Code : 3License : Permissive (BSD-3-Clause)
Copy git log $(git describe --tags --abbrev=0)..HEAD --pretty=format:"- %s \`%an\`_" > change.md && sed -i -e 's/(#/(:pr:`/g' change.md && sed -i -e 's/) `/`) `/g' change.md
git commit -a -m "bump version to YYYY.M.X"
git tag -a YYYY See :doc:`dataframe`.
>>> index = pd.date_range("2021-09-01", periods=2400, freq="1H")
... df = pd.DataFrame({"a": np.arange(2400), "b": list("abcaddbe" * 300)}, index=index)
... ddf = dd.from_pandas(df, npartitions=10)
... ddf
Dask DataFram import pandas as pd
dtypes = {
"y": "int",
"z": "int",
"a": "int",
"b": "int",
"c": "object",
"total_x": "f64",
}
meta = pd.DataFrame(columns=dtypes.keys())
import dask
import pandas as pd
Dask Series Structure:
npartitions=2
0 int64
5 ...
9 ...
Name: data1x2, dtype: int64
Dask Name: getitem, 15 tasks
import dask
from dask import dataframe as dd
import dask_geopandas
BM = dd.read_csv(BM_path, skiprows=2,names=["X","Y","Z","Lith"])
BM["geometry"] = dask_geopandas.points_from_xy(BM,"X","Y","Z")
gdf = dask_geopandas.from_dask_dataframe(BMddf = dd.from_pandas(pdf, npartitions=3)
ddf.to_parquet('C:\\temp\\OLD_FILE_NAME', engine='pyarrow', overwrite=True)
ddf2 = dd.read_parquet('C:\\temp\\OLD_FILE_NAME')
ddf2['new_column'] = 1
ddf2.to_parquet('C:\\temp\\NEW_FILE_NAME', engi python-version: 3.8
- name: install black
run: |
- pip install black==20.8b1
+ pip install black==22.3.0
- name: run black
run: |
black . --check --line-length 100
df["x"] = df["y"].isin(a_list).map({False: "No", True: "Yes"})
import dask
df = dask.datasets.timeseries(seed=123)
df["x"] = df["name"].isin(["Bob", "Tim"]).map({False: "No", True: "Yes"})
print(df.head(10))
# In [9]: tinydf = pd.DataFrame({"col1": [11, 21], "col2": [12, 22]})
...: for i in range(1000):
...: tinydf.to_parquet(f"myfile_{i}.parquet")
In [10]: df = dask.dataframe.read_parquet([f"myfile_{i}.parquetCommunity Discussions
Trending Discussions on dask
QUESTION
Problem
I'm trying to clip a very large block model (5.8gb CSV file) containing centroid x, y, and z coordinates with an elevation raster. I'm trying to obtain only the blocks lying just above the raster layer.
I usually do this in ArcGIS by clipping my block model points to the outline of my raster and then extracting the raster values to the block model points. For large datasets this takes an ungodly amount of time (yes, that's a technical term) in ArcGIS.
How I want to solve it
I want to speed this up by importing the CSV to Python. Using Dask, this is quick and easy:
...ANSWER
Answered 2022-Mar-31 at 20:54The optimal way of linking dask and geopandas is the dask-geopandas package.
QUESTION
if running our lint checks with the python black pkg. an error comes up
ImportError: cannot import name '_unicodefun' from 'click' (/Users/robot/.cache/pre-commit/repo3u71ccm2/py_env-python3.9/lib/python3.9/site-packages/click/__init__.py)
related issues:
https://github.com/psf/black/issues/2976
https://github.com/dask/distributed/issues/6013
ANSWER
Answered 2022-Mar-30 at 08:58This has been fixed by Black 22.3.0. Versions before that won't work with click 8.1.0.
https://github.com/psf/black/issues/2964
E.g.: black.yml
QUESTION
I have the following workflow.
...ANSWER
Answered 2022-Mar-16 at 04:54The new divisions are chosen so that the total memory of the files in each partition doesn't exceed 1000 MB.
If the main consideration for repartitioning is memory, it might be a good idea to use .repartition(partition_size='1000MB'). The script looks like:
QUESTION
The documentation shows the following formula in case of "auto" mode :
$ dask-worker .. --memory-limit=auto # TOTAL_MEMORY * min(1, nthreads / total_nthreads)
My CPU spec :
...ANSWER
Answered 2022-Mar-16 at 14:05I suspect nthreads refers to how many threads this particular worker has available to schedule tasks on while total_nthreads refers to the total number of threads available on your system.
The dask-worker CLI command has the same defaults as LocalCluster (see GitHub issue). Assuming the defaults for LocalCluster spin up n workers where n is the number of available cores on your system and assign m threads to each worker where m is the number of threads per core:
QUESTION
I'm new to dask and am trying to implement some post-processing tasks when workers shutdown. I'm currently using an EC2Cluster with n_workers=5
The cluster is created each time I need to run my large task. The task outputs a bunch of files which I want to send to AWS S3.
How would I implement a "post-processing" function that would run on each worker to send any logs and outputs to my AWS S3?
Thanks in advance
...ANSWER
Answered 2022-Mar-12 at 00:15You can use Python’s standard logging module to log whatever you'd like as the workers are running and then use the worker plugin you wrote to save these logs to an S3 bucket on teardown (check out the docs on logging in Dask for more details). Here's an example:
QUESTION
I have a dask architecture implemented with five docker containers: a client, a scheduler, and three workers. I also have a large dask dataframe stored in parquet format in a docker volume. The dataframe was created with 3 partitions, so there are 3 files (one file per partition).
I need to run a function on the dataframe with map_partitions, where each worker will take one partition to process.
My attempt:
...ANSWER
Answered 2022-Mar-11 at 13:27The python snippet does not appear to use the dask API efficiently. It might be that your actual function is a bit more complex, so map_partitions cannot be avoided, but let's take a look at the simple case first:
QUESTION
I have this python code that uses the apscheduler library to submit processes, it works fine:
ANSWER
Answered 2022-Mar-07 at 01:32Dask distributed has a fire_and_forget method which is an alternative to e.g. client.compute or dask.distributed.wait if you want the scheduler to hang on to the tasks even if the futures have fallen out of scope on the python process which submitted them.
QUESTION
I am trying to do a regular import in Google Colab.
This import worked up until now.
If I try:
ANSWER
Answered 2021-Oct-15 at 21:11Found the problem.
I was installing pandas_profiling, and this package updated pyyaml to version 6.0 which is not compatible with the current way Google Colab imports packages.
So just reverting back to pyyaml version 5.4.1 solved the problem.
For more information check versions of pyyaml here.
See this issue and formal answers in GitHub
##################################################################
For reverting back to pyyaml version 5.4.1 in your code, add the next line at the end of your packages installations:
QUESTION
Below code uses appx 1 sec to execute on an 8-CPU system. How to manually configure number of CPUs used by dask.compute eg to 4 CPUs so the below code will use appx 2 sec to execute even on an 8-CPU system?
ANSWER
Answered 2022-Feb-22 at 14:23There are a few options:
- specify number of workers at the time of cluster creation
QUESTION
For the following directory structure
...ANSWER
Answered 2022-Jan-30 at 11:35IIUC, you can use:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
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Install dask
You can use dask like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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