cudf | cuDF - GPU DataFrame Library | GPU library
kandi X-RAY | cudf Summary
kandi X-RAY | cudf Summary
Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.
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cudf Examples and Code Snippets
License : Strong Copyleft (CC BY-SA 4.0)
Copy# CUDA 9.2
conda install -c nvidia -c rapidsai -c numba -c conda-forge -c defaults cudf
# CUDA 10.0
conda install -c nvidia/label/cuda10.0 -c rapidsai/label/cuda10.0 -c numba -c conda-forge -c defaults cudf
import cudf
a = cudf.DataFrame({"col1": [1, 1, 1, 2, 2, 2], "col2": [1, 2, 1, 1, 2, 1], "dt": [10000000, 2000000, 3000000, 100000, 2000000, 40000000]})
a['dt'] = a['dt'].astype('datetime64[ns]')
print(a)
a['dt'] = a['dt'].astype('datetimeimport cudf
a = cudf.DataFrame([[0.1, 0.2, 0.3, 0.4], [0.1, 0.2, 0.3, 0.4]])
b = cudf.DataFrame([[0.1, 0.2], [0.1, 0.2]])
res = cudf.DataFrame.from_gpu_matrix(
a.values.T.dot(b.values)
)
print(res)
0 1
0 0.02 0.04
1 0.04import cudf
df1 = cudf.DataFrame({'a': [0, 1, 2, 3],'b': [0.1, 0.2, 0.3, 0.4]})
df2 = cudf.DataFrame({'a': [4, 5, 6, 7],'b': [0.1, 0.2, None, 0.3]}) #your new elements
df3= df1.a.append(df2.a)
df3
0 0
1 1
2 import cudf
x = cudf.DataFrame({'x': [1, 2, 3]})
x_usage = x.memory_usage(deep=True)
print(x_usage)
x 24
Index 0
dtype: int64
import pynvml
pynvml.nvmlInit()
handle = pynvml.nvimport cudf
df = cudf.DataFrame({'a':[0,1,2,3,4]})
df['new'] = df['a'] >= 3
df['new'] = df['new'].astype('int') # could use int8, int32, or int64
# could also do (df['a'] >= 3).astype('int')
df
a new
0 0 0
1 1 0
2 import cudf
df = cudf.DataFrame({'basement_flag': [1, 1, 1, 0],
'basementsqft': [400,750,500,0],
'fireplace_count': [2, None, None, 1], #<-- added a None to illustrate the targeted nature of theSELECT c.ClientID,
c.LastName,
c.FirstName,
c.MiddleName,
CASE
WHEN cudf.UserDefinedFieldFormatULink = '93fb3820-38aa-4655-8aad-a8dce8aede' THEN
cudf.UDF_ReportValue AS 'DA Status'
WHEN cudf.UserDefinedSELECT
c.ClientID
, c.LastName
, c.FirstName
, c.MiddleName
, CASE WHEN cudf.UserDefinedFieldFormatULink = '93fb3820-38aa-4655-8aad-a8dce8aede' THEN cudf.UDF_ReportValue --AS 'DA Status'
WHEN cudf.UserDefinedFieldFormatULink = '2144a7Community Discussions
Trending Discussions on cudf
QUESTION
I am trying to install cuDF on Google Colab for hours. One of the requirements I should install cuDF with GPU Tesla T4. While google colab gives me every time GPU Tesla K80 and I cannot install cuDF. I tried this snippet of code to check what type of GPU I have every time:
...ANSWER
Answered 2022-Mar-10 at 22:05The K80 use Kepler GPU architecture, which is not supported by RAPIDS. Colab itself no longer can run the latest versions of RAPIDS. You can try SageMaker Studio Lab for your Try it Now experience. https://github.com/rapidsai-community/rapids-smsl.
QUESTION
I am new to the RAPIDS AI world and I decided to try CUML and CUDF out for the first time. I am running UBUNTU 18.04 on WSL 2. My main OS is Windows 11. I have a 64 GB RAM and a laptop RTX 3060 6 GB GPU.
At the time I am writing this post, I am running a TSNE fitting calculation over a CUDF dataframe composed by approximately 26 thousand values, stored in 7 columns (all the values are numerical or binary ones, since the categorical ones have been one hot encoded). While classifiers like LogisticRegression or SVM were really fast, TSNE seems taking a while to output results (it's been more than a hour now, and it is still going on even if the Dataframe is not so big). The task manager is telling me that 100% of GPU is being used for the calculations even if, by running "nvidia-smi" on the windows powershell, the command returns that only 1.94 GB out of a total of 6 GB are currently in use. This seems odd to me since I read papers on RAPIDS AI's TSNE algorithm being 20x faster than the standard scikit-learn one.
I wonder if there is a way of increasing the percentage of dedicated GPU memory to perform faster computations or if it is just an issue related to WSL 2 (probably it limits the GPU usage at just 2 GB).
Any suggestion or thoughts? Many thanks
...ANSWER
Answered 2022-Jan-13 at 23:57The task manager is telling me that 100% of GPU is being used for the calculations
I'm not sure if the Windows Task Manager will be able to tell you of GPU throughput that is being achieved for computations.
"nvidia-smi" on the windows powershell, the command returns that only 1.94 GB out of a total of 6 GB are currently in use
Memory utilisation is a different calculation than GPU throughput. Any GPU application will only use as much memory as is requested, and there is no correlation between higher memory usage and higher throughput, unless the application specifically mentions a way that it can achieve higher throughput by using more memory (for example, a different algorithm for the same computation may use more memory).
TSNE seems taking a while to output results (it's been more than a hour now, and it is still going on even if the Dataframe is not so big).
This definitely seems odd, and not the expected behavior for a small dataset. What version of cuML are you using, and what is your method argument for the fit task? Could you also open an issue at www.github.com/rapidsai/cuml/issues with a way to access your dataset so the issue can be reproduced?
QUESTION
I installed WSL 2 (5.10.60.1-microsoft-standard-WSL2) under Windows 21H2 (19044.1348) and using NVidia driver 510.06 with a pascal GPU (1070). I use the default ubuntu version in WSL (20.04.3 LTS) I tried both docker and anaconda versions. I can run the Jupiter Notebook and import the library's. you can also create a cudf Datagramme. but writing to it or ding anything else gives a memory error.
...ANSWER
Answered 2021-Nov-23 at 00:25Sadly, RAPIDS on WSL2 only runs on Pascal GPUs with RAPIDS 21.08, but not 21.10 or later. Please try 21.08. It was still experimental with those versions, so YMMV.
QUESTION
The rapids.ai cudf type is somewhat compatible with pandas, but here is a strange incompatibility. cudf.Series has a .diff() method, but a cudf.DataFrame does not appear to. This is super-annoying (consider, for example, a data frame of stock prices, with columns corresponding to instruments). There are, of course, kludgy ays to get around this (converting to pandas data frame and back comes to mind), but I wonder what the canonical way is. Any advice?
ANSWER
Answered 2021-Oct-09 at 18:44cuDF Python covers a large segment of the pandas API, but there are some gaps (as you've run into here).
Today, the easiest way to run diff on every column and return a dataframe would be the following:
QUESTION
I can use tqdm progress bar in pandas for example:
ANSWER
Answered 2021-Jul-31 at 18:57Until progress_apply is available, you would have to implement an equivalent yourself (e.g. using apply_chunks). Just a sketch of the code:
QUESTION
Getting this error while converting array to cuPy array: TypeError: Implicit conversion to a host NumPy array via array is not allowed, To explicitly construct a GPU array, consider using cupy.asarray(...) To explicitly construct a host array, consider using .to_array()
...ANSWER
Answered 2021-Jul-27 at 13:28Converting a cuDF series into a CuPy array with cupy.asarray(series) requires CuPy-compatible data types. You may want to double check your Series is int, float, or bool typed, rather than string, decimal, list, or struct typed.
QUESTION
I just loaded the csv file with cudf (rapidsai) to reduce the time it takes.
An issue comes up when I try to search index with an condition where df['X'] = A.
here is my code example:
...ANSWER
Answered 2021-Jul-08 at 02:31cuDF is trying to dispatch from numpy.where to cupy.where via the array function protocol. For one reason or another, cuDF is not able to successfully run the dispatched function in this case.
In general, the recommendation would be to explicitly use CuPy rather than numpy here.
QUESTION
I have a DF that I want to use Groupby + Shift. I can do this in pandas, but I cannot do it in cuDF because it is not implemented yet: see the issue Issue #7183. The feature request was long ago, so it seems like they will not implement this in the near future. Is there any alternative way?
...ANSWER
Answered 2021-May-28 at 00:10UPDATE: RAPIDS just finished merging groupby.shift() into cudf.
Please try it out in the 21.06 nightlies!
Previous Post: This is currently planned to be implemented in 0.20.
QUESTION
How to create unique id column in dsak cudf dataframe across all the partitions So far I am using following technique, but if I increase data to more than 10cr rows it is giving me memory error.
...ANSWER
Answered 2021-May-19 at 08:43The reason why you are running into memory error is this step:
QUESTION
I've been trying to run RAPIDS on Google Colab pro, and have successfully installed the cuml and cudf packages, however I am unable to run even the example scripts.
TLDR;Anytime I try to run the fit function for cuml on Google Colab I get the following error. I get this when using the demo examples both for installation and then for cuml. This happens for a range of cuml examples (I first hit this trying to run UMAP).
...ANSWER
Answered 2021-May-06 at 17:13Colab retains cupy==7.4.0 despite conda installing cupy==8.6.0 during the RAPIDS install. It is a custom install. I just had success pip installing cupy-cuda110==8.6.0 BEFORE installing RAPIDS, with
!pip install cupy-cuda110==8.6.0:
I'll be updating the script soon so that you won't have to do it manually, but want to test a few more things out. Thanks again for letting us know!
EDIT: script updated.
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