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Miscellaneous libraries to improve your code's performance¶

Over the course of years, Python ecosystem has developed more and more solutions to combat the relatively slow performance of the language. As seen in the previous lessons, the speed of computation matters quite a lot while using a technology in scientific setting. This lesson introduces a few such popular libraries that have been widely adopted in the scientific use of Python.

Compiling Python code¶

We know that Python is an interpreted and not a compiled language, but is there a way to compile Python code? There are a few libraries/frameworks that lets you just in time (JIT) or ahead of time (AOT) compile Python code. Both the techniques allow users to compile their Python code without using any explicit low level langauge's bindings, but both the techniques are different from each other.

Just in time compilers compile functions/methods at runtime whereas ahead of time compilers compile the entire code before runtime. AOT can do much more compiler optimizations than JIT, but AOT compilers produce huge binaries and that too only for specific platforms. JIT compilers have limited optimization routines but they produce small and platform independent binaries.

JIT and AOT compilers for Python include, but are not limited to -

Numba: a Just-In-Time Compiler for Numerical Functions in Python
mypyc: compiles Python modules to C extensions using standard Python type hints
JAX: a Python library for accelerator-oriented array computation and program transformation

Although all of them were built to speed-up Python code, each one of them is a bit different from each other when it comes to their use-case. We will specifically look into Numba in this lesson.

Numba¶

Numba is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code. The good thing about Numba is that it works on plain Python loops and one doesn't need to configure any compilers manually to JIT compile Python code. Another good thing is that it understands NumPy natively, but as detailed in its documentation, it only understands a subset of NumPy functionalities.

Numba provides users with an easy to use function decorator - jit. Let's start by importing the libraries we will use in this lesson.

In [1]:

import math

from numba import jit, njit
import numpy as np

We can mark a function to be JIT compiled by decorating it with numba's @jit. The decorator takes a nopython argument that tells Numba to enter the compilation mode and not fall back to usual Python execution.

Here, we are showing a usual python function, and one that's decorated. We don't need to duplicate and change its name when using numba, but we want to keep both of them here to compare their execution times.

In [2]:

def f(x):
    return np.sqrt(x)

@jit(nopython=True)
def jit_f(x):
    return np.sqrt(x)

It looks like the jit decorator should make our Numba compile our function and make it much faster than the non-jit version. Let's test this out.

Note that the first function call is not included while timing because that is where Numba compiles the function. The compilation at runtime is called just in time compilation and resultant binaries are cached for the future runs.

In [3]:

data = np.random.uniform(low=0.0, high=100.0, size=1_000)

In [4]:

%%timeit
f(data)

2.04 μs ± 1.58 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

In [5]:

%%timeit -n 1 -r 1
_ = jit_f(data)  # compilation and run

436 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

In [6]:

%%timeit
jit_f(data)  # just run

3.62 μs ± 2.79 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

Surprisingly, the JITted function was slower than plain Python and NumPy implementation! Why did this happen? Numba does not provide valuable performance gains over pure Python or NumPy code for simple operations and small dataset. The JITted function turned out to be slower than the non-JIT implementation because of the compilation overhead. Note that the result from the compilation run could be very noisy and could give a higher than real value, as mentioned in the previous lessons. Let's try increasing the size of our data and perform a non-NumPy list comprehension on the data.

The jit decorator with nopython=True is so widely used there exists an alias decorator for the same - @njit

In [7]:

data = np.random.uniform(low=0.0, high=100.0, size=1_000_000)

In [8]:

def f(x):
    return [math.sqrt(elem) for elem in x]

@njit
def jit_f(x):
    return [math.sqrt(elem) for elem in x]

In [9]:

%%timeit
f(data)

87.9 ms ± 1.58 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [10]:

%%timeit -n 1 -r 1
_ = jit_f(data)  # compilation and run

97.7 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

In [11]:

%%timeit
jit_f(data)  # just run

37.1 ms ± 917 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)

That was way faster than the non-JIT function! But, the result was still slower than the NumPy implementation. NumPy is still good for relatively simple computations, but as the complexity increases, Numba functions start outperforming NumPy implementations.

Let's go back to our plain Python mandelbrot code from the previous lessons and JIT compile it -

In [12]:

@njit
def mandel1(position, limit=50):
    
    value = position
    
    while abs(value) < 2:
        limit -= 1        
        value = value**2 + position
        if limit < 0:
            return 0
        
    return limit

xmin = -1.5
ymin = -1.0
xmax = 0.5
ymax = 1.0
resolution = 300
xstep = (xmax - xmin) / resolution
ystep = (ymax - ymin) / resolution
xs = [(xmin + (xmax - xmin) * i / resolution) for i in range(resolution)]
ys = [(ymin + (ymax - ymin) * i / resolution) for i in range(resolution)]

In [13]:

%%timeit -n 1 -r 1
data = [[mandel1(complex(x, y)) for x in xs] for y in ys]  # compilation and run

136 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

In [14]:

%%timeit
data = [[mandel1(complex(x, y)) for x in xs] for y in ys]  # just run

54.9 ms ± 156 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)

The compiled code already beats our fastest NumPy implementation! It is not necessary the compiled code will perform better than NumPy code, but it usually gives performance gains for signifantly large computations. As always, it is good to measure the performance to check if there are any gains.

Let's try JITting our NumPy code.

In [15]:

@njit
def mandel_numpy(position,limit=50):
    value = position
    diverged_at_count = np.zeros(position.shape)
    while limit > 0:
        limit -= 1
        value = value**2 + position
        diverging = value * np.conj(value) > 4
        first_diverged_this_time = np.logical_and(diverging, diverged_at_count == 0)
        diverged_at_count[first_diverged_this_time] = limit
        value[diverging] = 2
        
    return diverged_at_count

ymatrix, xmatrix = np.mgrid[ymin:ymax:ystep, xmin:xmax:xstep]
values = xmatrix + 1j * ymatrix

In [16]:

%%timeit
mandel_numpy(values)

---------------------------------------------------------------------------
TypingError                               Traceback (most recent call last)
Cell In[16], line 1
----> 1 get_ipython().run_cell_magic('timeit', '', 'mandel_numpy(values)\n')

File /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/IPython/core/interactiveshell.py:2543, in InteractiveShell.run_cell_magic(self, magic_name, line, cell)
   2541 with self.builtin_trap:
   2542     args = (magic_arg_s, cell)
-> 2543     result = fn(*args, **kwargs)
   2545 # The code below prevents the output from being displayed
   2546 # when using magics with decorator @output_can_be_silenced
   2547 # when the last Python token in the expression is a ';'.
   2548 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False):

File /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/IPython/core/magics/execution.py:1209, in ExecutionMagics.timeit(self, line, cell, local_ns)
   1207 for index in range(0, 10):
   1208     number = 10 ** index
-> 1209     time_number = timer.timeit(number)
   1210     if time_number >= 0.2:
   1211         break

File /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/IPython/core/magics/execution.py:173, in Timer.timeit(self, number)
    171 gc.disable()
    172 try:
--> 173     timing = self.inner(it, self.timer)
    174 finally:
    175     if gcold:

File <magic-timeit>:1, in inner(_it, _timer)

File /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/numba/core/dispatcher.py:424, in _DispatcherBase._compile_for_args(self, *args, **kws)
    420         msg = (f"{str(e).rstrip()} \n\nThis error may have been caused "
    421                f"by the following argument(s):\n{args_str}\n")
    422         e.patch_message(msg)
--> 424     error_rewrite(e, 'typing')
    425 except errors.UnsupportedError as e:
    426     # Something unsupported is present in the user code, add help info
    427     error_rewrite(e, 'unsupported_error')

File /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/numba/core/dispatcher.py:365, in _DispatcherBase._compile_for_args.<locals>.error_rewrite(e, issue_type)
    363     raise e
    364 else:
--> 365     raise e.with_traceback(None)

TypingError: Failed in nopython mode pipeline (step: nopython frontend)
No implementation of function Function(<built-in function setitem>) found for signature:
 
 >>> setitem(array(float64, 2d, C), array(bool, 2d, C), int64)
 
There are 16 candidate implementations:
     - Of which 14 did not match due to:
     Overload of function 'setitem': File: <numerous>: Line N/A.
       With argument(s): '(array(float64, 2d, C), array(bool, 2d, C), int64)':
      No match.
     - Of which 2 did not match due to:
     Overload in function 'SetItemBuffer.generic': File: numba/core/typing/arraydecl.py: Line 221.
       With argument(s): '(array(float64, 2d, C), array(bool, 2d, C), int64)':
      Rejected as the implementation raised a specific error:
        NumbaTypeError: Multi-dimensional indices are not supported.
  raised from /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/numba/core/typing/arraydecl.py:91

During: typing of setitem at /tmp/ipykernel_16741/2672903129.py (10)

File "../../../../../../tmp/ipykernel_16741/2672903129.py", line 10:
<source missing, REPL/exec in use?>

During: Pass nopython_type_inference

That does not work. The error might be solvable or it might just be out of Numba's scope. Numba does not distinguish between plain Python and NumPy; hence both the implementations are broken down to the same machine code. Therefore, for Numba, it makes sense to write complex computations with the ease of Python loops and lists than with NumPy functions. Moreover, Numba only understands a subset of Python and NumPy so it is possible that a NumPy snippet does not work but a simplified Python loop does.

Let's make minor adjustments to fix the NumPy implementation and measure its performance. We flatten the NumPy arrays and consider only the real part while performing the comparison.

In [17]:

@njit
def mandel_numpy(position,limit=50):
    value = position.flatten()
    diverged_at_count = np.zeros(position.shape).flatten()
    while limit > 0:
        limit -= 1
        value = value**2 + position.flatten()
        diverging = (value * np.conj(value)).real > 4
        first_diverged_this_time = (np.logical_and(diverging, diverged_at_count == 0))
        diverged_at_count[first_diverged_this_time] = limit
        value[diverging] = 2

    return diverged_at_count.reshape(position.shape)

ymatrix, xmatrix = np.mgrid[ymin:ymax:ystep, xmin:xmax:xstep]
values = xmatrix + 1j * ymatrix

In [18]:

%%timeit -n 1 -r 1
mandel_numpy(values)  # compilation and run

488 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

In [19]:

%%timeit
mandel_numpy(values)  # just run

28.7 ms ± 104 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)

The code performs similar to the plain Python example!

Numba also has functionalities to vectorize, parallelize, and strictly type check the code. All of these functions boost the performance even further or helps Numba to avoid falling back to the "object" mode (nopython=False). Refer to Numba's documentation for a complete list of features.

Numba support in Scientific Python ecosystem¶

Most of the scientific libraries nowadays ship Numba support with them. For example, Awkward Array is a library that lets users perfor JIT compilable operations on non-uniform data, something that NumPy misses. Similarly, Akimbo provides JIT compilable processing of nested, non-uniform data in dataframes. There are other niche libraries, such as, vector that provides Numba support for high energy physics vector.

Compiled Python bindings¶

Several Python libraries/frameworks are written in a compiled language but provide Python bindings for their compiled code. This code does not have to be explicitly marked to be compiled by developers. A few Python libraries that have their core written in compiled languages but provide Python bindings -

Cython: superset of Python that supports calling C functions and declaring C types on variables and class attributes
Scipy: wraps highly-optimized scientific implementations written in low-level languages like Fortran, C, and C++
Polars: dataframes powered by a multithreaded, vectorized query engine, written in Rust
boost-histogram: Python bindings for the C++14 Boost::Histogram library
Awkward Array: compiled and fast manipulation of JSON-like data with NumPy-like idioms
Astropy: astronomy and astrophysics core library

JIT or AOT compiling libraries and the libraries providing Python bindings for a compiled language are not mutually exclusive. For instance, PyTorch and Tensorflow offer users "eager" and "graph" executation. Eager execution builds computational graph at runtime, making it slow, easy to debug, and JIT compilable. On the other hand, "graph" execution builds the computational graph at the kernel level, making it fast, hard to debug, and with no need to JIT compile. Similarly, Awkward Array provides Python bindings for array creation, but high-level operations on these arrays can be JIT compiled.

We will specifically look into Cython in the next lesson.

Distributed and parallel computing with Python¶

Along with the speed of execution of a program, scientific computation usually requires a large amount of memory because of the massive nature of data produced at scientific experiments. This issue can be addressed by employing distributed and parallel computing frameworks to spread the computation across multiple machines or multiple cores in a single machine. Parallel computing at a smaller level can be achieved by distributing a task over multiple threads, but Python's GIL (Global Interpreter Lock) blocks the interpretor from doing this for computational tasks. There are ongoing efforts, such as the implementation of PEP 703 to make GIL optional, but Numba allows users to bypass GIL by pass nogil=True to @jit.

At a larger scale, Dask can be used to parallelize computation over multiple machine or "nodes". Dask can even parallelize the code marked with @numba.jit(nogil=True) to multiple threads in a machine, but it does not itself bypass the GIL.

Dask¶

Dask is a Python library for parallel and distributed computing. Dask supports arrays, dataframes, Python objects, as well as tasks on computer clusters and nodes. The API of Dask is very similar to that of NumPy, Pandas, and plain Python, allowing users to quickly parallelize their implementations. Let's create a dummy dataframe using dask.

In [20]:

import dask

df = dask.datasets.make_people()  # returns a dask "bag"
df = df.to_dataframe()

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
File /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/dask/_compatibility.py:114, in import_optional_dependency(name, extra, min_version, errors)
    113 try:
--> 114     module = importlib.import_module(name)
    115 except ImportError as err:

File /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/importlib/__init__.py:90, in import_module(name, package)
     89         level += 1
---> 90 return _bootstrap._gcd_import(name[level:], package, level)

File <frozen importlib._bootstrap>:1387, in _gcd_import(name, package, level)

File <frozen importlib._bootstrap>:1360, in _find_and_load(name, import_)

File <frozen importlib._bootstrap>:1324, in _find_and_load_unlocked(name, import_)

ModuleNotFoundError: No module named 'pyarrow'

The above exception was the direct cause of the following exception:

ImportError                               Traceback (most recent call last)
Cell In[20], line 4
      1 import dask
      3 df = dask.datasets.make_people()  # returns a dask "bag"
----> 4 df = df.to_dataframe()

File /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/dask/bag/core.py:1609, in Bag.to_dataframe(self, meta, columns, optimize_graph)
   1560 """Create Dask Dataframe from a Dask Bag.
   1561 
   1562 Bag should contain tuples, dict records, or scalars.
   (...)
   1605 0  Charlie      300
   1606 """
   1607 import pandas as pd
-> 1609 import dask.dataframe as dd
   1611 if meta is None:
   1612     head = self.take(1, warn=False)

File /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/dask/dataframe/__init__.py:21
     19 import dask.dataframe._pyarrow_compat
     20 from dask.base import compute
---> 21 from dask.dataframe import backends, dispatch
     22 from dask.dataframe.dask_expr import (
     23     DataFrame,
     24     Index,
   (...)
     51     to_timedelta,
     52 )
     53 from dask.dataframe.groupby import Aggregation

File /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/dask/dataframe/backends.py:14
     12 from dask.array.percentile import _percentile
     13 from dask.backends import CreationDispatch, DaskBackendEntrypoint
---> 14 from dask.dataframe._compat import PANDAS_GE_220, is_any_real_numeric_dtype
     15 from dask.dataframe.dispatch import (
     16     categorical_dtype_dispatch,
     17     concat,
   (...)
     34     union_categoricals_dispatch,
     35 )
     36 from dask.dataframe.extensions import make_array_nonempty, make_scalar

File /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/dask/dataframe/_compat.py:11
      9 import_optional_dependency("pandas")
     10 import_optional_dependency("numpy")
---> 11 import_optional_dependency("pyarrow")
     13 import numpy as np
     14 import pandas as pd

File /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/dask/_compatibility.py:117, in import_optional_dependency(name, extra, min_version, errors)
    115 except ImportError as err:
    116     if errors == "raise":
--> 117         raise ImportError(msg) from err
    118     return None
    120 # Handle submodules: if we have submodule, grab parent module from sys.modules

ImportError: Missing optional dependency 'pyarrow'.  Use pip or conda to install pyarrow.

In [21]:

df

Out[21]:

dask.bag<mimesis, npartitions=10>

The computation gave us a dask object and not the actual answer. Why is that? Displaying the dataframe just displays the metadata of the variable, and not any data. This is because of the "lazy" nature of dask. Dask has "lazy" execution, which means that it will store the operations on the data and create a task graph for the same, but will not execute the operations until a user explicitly asks for the result. The metadata specifies npartitions=10, which means that the dataframe is split into 10 parts that will be accessed parallely. We can explicitly tell dask to give us the dataframe values using .compute().

In [22]:

df.compute()

Out[22]:

[{'age': 52,
  'name': ('Lorriane', 'Bridges'),
  'occupation': 'Recorder',
  'telephone': '+15859403243',
  'address': {'address': '835 Mabel Court', 'city': 'Rockledge'},
  'credit-card': {'number': '5150 5846 0604 0798',
   'expiration-date': '06/23'}},
 {'age': 40,
  'name': ('Fransisca', 'Donaldson'),
  'occupation': 'Housing Supervisor',
  'telephone': '+13520855820',
  'address': {'address': '799 Nautilus Mall', 'city': 'Lemon Grove'},
  'credit-card': {'number': '3723 206048 29749', 'expiration-date': '11/17'}},
 {'age': 12,
  'name': ('Phebe', 'Hurst'),
  'occupation': 'Breeder',
  'telephone': '+17041543289',
  'address': {'address': '127 Ord Bridge', 'city': 'Grand Prairie'},
  'credit-card': {'number': '3767 036249 77067', 'expiration-date': '12/20'}},
 {'age': 83,
  'name': ('Lisandra', 'Glass'),
  'occupation': 'Window Cleaner',
  'telephone': '+1-231-364-1822',
  'address': {'address': '99 Sonoma Mews', 'city': 'Morristown'},
  'credit-card': {'number': '4825 8445 8625 4977',
   'expiration-date': '02/18'}},
 {'age': 49,
  'name': ('Herschel', 'Best'),
  'occupation': 'Fund Raiser',
  'telephone': '+1-228-067-3772',
  'address': {'address': '1023 Erkson Highway', 'city': 'West Springfield'},
  'credit-card': {'number': '2638 4924 1250 1996',
   'expiration-date': '07/25'}},
 {'age': 47,
  'name': ('Deandrea', 'Myers'),
  'occupation': 'Tunneller',
  'telephone': '+16783343801',
  'address': {'address': '377 Anza Route', 'city': 'Bartlesville'},
  'credit-card': {'number': '2238 8255 5566 7609',
   'expiration-date': '01/17'}},
 {'age': 112,
  'name': ('Reinaldo', 'Levine'),
  'occupation': 'Parts Man',
  'telephone': '+1-860-048-3907',
  'address': {'address': '562 Trenton Freeway', 'city': 'Pflugerville'},
  'credit-card': {'number': '3499 378562 07611', 'expiration-date': '06/20'}},
 {'age': 71,
  'name': ('Garland', 'Frost'),
  'occupation': 'Hawker',
  'telephone': '+19403031635',
  'address': {'address': '966 Kinzey Court', 'city': 'Waukesha'},
  'credit-card': {'number': '3457 034246 61356', 'expiration-date': '11/18'}},
 {'age': 116,
  'name': ('Wilbert', 'Robertson'),
  'occupation': 'Medical Technician',
  'telephone': '+19121265034',
  'address': {'address': '1062 Laidley Ranch', 'city': 'Maryville'},
  'credit-card': {'number': '5379 8139 4092 5470',
   'expiration-date': '10/25'}},
 {'age': 39,
  'name': ('Carie', 'Hahn'),
  'occupation': 'Night Watchman',
  'telephone': '+19521458055',
  'address': {'address': '125 Hampshire Pine', 'city': 'DeSoto'},
  'credit-card': {'number': '4381 6378 5154 7513',
   'expiration-date': '08/23'}},
 {'age': 52,
  'name': ('Kelvin', 'Clayton'),
  'occupation': 'Therapist',
  'telephone': '+1-276-020-0586',
  'address': {'address': '607 Robinson Stravenue', 'city': 'Alexandria'},
  'credit-card': {'number': '4546 2059 6852 5285',
   'expiration-date': '02/17'}},
 {'age': 102,
  'name': ('Terina', 'Tyler'),
  'occupation': 'Cargo Operator',
  'telephone': '+14059662484',
  'address': {'address': '795 Lassen Court', 'city': 'Warwick'},
  'credit-card': {'number': '4410 4300 2879 4589',
   'expiration-date': '11/17'}},
 {'age': 7,
  'name': ('Domenic', 'Franklin'),
  'occupation': 'Leaflet Distributor',
  'telephone': '+1-914-574-9175',
  'address': {'address': '1126 Legion Of Honor Mall', 'city': 'Holyoke'},
  'credit-card': {'number': '4535 7964 7260 2431',
   'expiration-date': '07/20'}},
 {'age': 107,
  'name': ('Verlene', 'Simmons'),
  'occupation': 'Massage Therapist',
  'telephone': '+1-219-450-8421',
  'address': {'address': '1140 Paloma Crossing', 'city': 'Irving'},
  'credit-card': {'number': '3487 339868 48312', 'expiration-date': '08/23'}},
 {'age': 49,
  'name': ('Johnnie', 'Mendoza'),
  'occupation': 'Business Proprietor',
  'telephone': '+1-440-939-9921',
  'address': {'address': '526 Stanton Crescent', 'city': 'Corona'},
  'credit-card': {'number': '3408 174721 93495', 'expiration-date': '12/17'}},
 {'age': 7,
  'name': ('Ellis', 'Hardin'),
  'occupation': 'Neurologist',
  'telephone': '+1-401-206-2324',
  'address': {'address': '858 Holladay Heights', 'city': 'Garden Grove'},
  'credit-card': {'number': '3719 527437 17228', 'expiration-date': '07/20'}},
 {'age': 37,
  'name': ('Jennine', 'Horn'),
  'occupation': 'Bill Poster',
  'telephone': '+19208878874',
  'address': {'address': '1198 De Soto Hills', 'city': 'Bessemer'},
  'credit-card': {'number': '3433 016046 56312', 'expiration-date': '08/23'}},
 {'age': 6,
  'name': ('Omar', 'Rowe'),
  'occupation': 'Medal Dealer',
  'telephone': '+1-938-745-4739',
  'address': {'address': '269 Manzanita Bend', 'city': 'Bowie'},
  'credit-card': {'number': '4443 0274 8086 7365',
   'expiration-date': '10/20'}},
 {'age': 62,
  'name': ('Evia', 'Williams'),
  'occupation': 'Painter',
  'telephone': '+1-713-518-4512',
  'address': {'address': '1273 Ordway Landing', 'city': 'Asheville'},
  'credit-card': {'number': '3727 965475 92413', 'expiration-date': '03/24'}},
 {'age': 76,
  'name': ('Weston', 'Wilkinson'),
  'occupation': 'Therapist',
  'telephone': '+1-774-125-1665',
  'address': {'address': '633 Comerford Viaduct', 'city': 'Cupertino'},
  'credit-card': {'number': '5123 7082 1740 8193',
   'expiration-date': '11/24'}},
 {'age': 41,
  'name': ('Terry', 'Woodard'),
  'occupation': 'Proprietor',
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  'telephone': '+1-408-390-9991',
  'address': {'address': '928 Staples Manor', 'city': 'Conway'},
  'credit-card': {'number': '4674 6252 5370 1800',
   'expiration-date': '03/17'}},
 {'age': 102,
  'name': ('Wilton', 'Aguilar'),
  'occupation': 'Building Estimator',
  'telephone': '+1-831-797-9186',
  'address': {'address': '1340 Holly Park Pass', 'city': 'Edmonds'},
  'credit-card': {'number': '4859 0557 5616 2432',
   'expiration-date': '11/20'}},
 {'age': 26,
  'name': ('Leoma', 'Macdonald'),
  'occupation': 'Greengrocer',
  'telephone': '+1-313-470-5029',
  'address': {'address': '242 Pelton Junction', 'city': 'Lenoir'},
  'credit-card': {'number': '3782 581806 13893', 'expiration-date': '01/25'}},
 {'age': 100,
  'name': ('Mitchel', 'Valencia'),
  'occupation': 'Shipwright',
  'telephone': '+1-938-723-1266',
  'address': {'address': '471 Stoneybrook Road', 'city': 'Oregon'},
  'credit-card': {'number': '4266 9824 4159 9222',
   'expiration-date': '08/20'}},
 {'age': 41,
  'name': ('Rocco', 'Hayden'),
  'occupation': 'Plastics Engineer',
  'telephone': '+12701339961',
  'address': {'address': '123 Behr Center', 'city': 'Forest Lake'},
  'credit-card': {'number': '3474 237205 18063', 'expiration-date': '04/19'}},
 {'age': 66,
  'name': ('Lewis', 'Hewitt'),
  'occupation': 'Forest Ranger',
  'telephone': '+15713761224',
  'address': {'address': '1312 Caire Bridge', 'city': 'Tampa'},
  'credit-card': {'number': '4549 6001 3789 8315',
   'expiration-date': '04/24'}},
 {'age': 94,
  'name': ('Noe', 'Newman'),
  'occupation': 'Horse Riding Instructor',
  'telephone': '+18700836148',
  'address': {'address': '913 Leavenworth Estate', 'city': 'Pleasantville'},
  'credit-card': {'number': '3787 882106 71396', 'expiration-date': '01/18'}},
 {'age': 60,
  'name': ('Shoshana', 'Frye'),
  'occupation': 'Golf Club Professional',
  'telephone': '+1-906-889-9752',
  'address': {'address': '66 Persia Motorway', 'city': 'Amarillo'},
  'credit-card': {'number': '5148 9403 4855 1135',
   'expiration-date': '05/20'}},
 {'age': 14,
  'name': ('Brock', 'Shannon'),
  'occupation': 'Administration Manager',
  'telephone': '+1-480-905-4667',
  'address': {'address': '793 Charles J Brenham Lane',
   'city': 'South Pasadena'},
  'credit-card': {'number': '3703 490139 55607', 'expiration-date': '03/25'}},
 {'age': 67,
  'name': ('Gary', 'Banks'),
  'occupation': 'Social Worker',
  'telephone': '+18126510954',
  'address': {'address': '1215 Flora Expressway', 'city': 'Mishawaka'},
  'credit-card': {'number': '3487 329872 68314', 'expiration-date': '07/25'}},
 {'age': 105,
  'name': ('Mitchell', 'Ewing'),
  'occupation': 'Builder',
  'telephone': '+1-412-296-3510',
  'address': {'address': '364 Osgood Garden', 'city': 'La Puente'},
  'credit-card': {'number': '3702 246834 10664', 'expiration-date': '06/22'}},
 {'age': 12,
  'name': ('Donnette', 'Curry'),
  'occupation': 'Reflexologist',
  'telephone': '+1-207-677-3917',
  'address': {'address': '255 Robert C Levy Spur', 'city': 'Blythe'},
  'credit-card': {'number': '2322 4786 5133 5734',
   'expiration-date': '03/16'}},
 {'age': 31,
  'name': ('Drusilla', 'Pugh'),
  'occupation': 'Horse Trader',
  'telephone': '+1-239-614-0309',
  'address': {'address': '955 Saint George Street', 'city': 'Owasso'},
  'credit-card': {'number': '4864 0977 0543 4739',
   'expiration-date': '06/22'}},
 {'age': 25,
  'name': ('Theo', 'Meyer'),
  'occupation': 'Pipe Inspector',
  'telephone': '+1-239-674-5036',
  'address': {'address': '865 Blair Park', 'city': 'West Chester'},
  'credit-card': {'number': '4705 5994 3797 2178',
   'expiration-date': '02/24'}},
 {'age': 85,
  'name': ('Bradley', 'Mullen'),
  'occupation': 'Stud Hand',
  'telephone': '+1-364-629-8062',
  'address': {'address': '357 Berkeley Street', 'city': 'Onalaska'},
  'credit-card': {'number': '4477 2970 5353 3293',
   'expiration-date': '08/19'}},
 {'age': 91,
  'name': ('Marty', 'Hyde'),
  'occupation': 'Coroner',
  'telephone': '+1-662-322-7382',
  'address': {'address': '1241 Esmeralda Creek', 'city': 'Portsmouth'},
  'credit-card': {'number': '2344 2689 6597 9341',
   'expiration-date': '09/21'}},
 {'age': 100,
  'name': ('Demarcus', 'Richmond'),
  'occupation': 'Playgroup Leader',
  'telephone': '+12539226036',
  'address': {'address': '1379 Nordhoff Viaduct', 'city': 'Bend'},
  'credit-card': {'number': '3755 099558 11796', 'expiration-date': '10/18'}},
 {'age': 34,
  'name': ('Albert', 'Howe'),
  'occupation': 'Chicken Chaser',
  'telephone': '+18451545436',
  'address': {'address': '220 Hattie Trace', 'city': 'Maple Heights'},
  'credit-card': {'number': '3477 513679 93623', 'expiration-date': '04/22'}},
 {'age': 3,
  'name': ('Cyrus', 'May'),
  'occupation': 'Hotelier',
  'telephone': '+1-816-613-1339',
  'address': {'address': '1122 Drummond Route', 'city': 'Seal Beach'},
  'credit-card': {'number': '5158 0071 2404 8178',
   'expiration-date': '11/18'}},
 {'age': 113,
  'name': ('Daron', 'Sutton'),
  'occupation': 'Driver',
  'telephone': '+1-307-043-3632',
  'address': {'address': '1285 Northern Center', 'city': 'Cocoa'},
  'credit-card': {'number': '4641 7448 9369 3036',
   'expiration-date': '09/22'}},
 {'age': 65,
  'name': ('Yee', 'Kline'),
  'occupation': 'Project Worker',
  'telephone': '+1-240-098-3627',
  'address': {'address': '949 Acacia Loop', 'city': 'Raymore'},
  'credit-card': {'number': '3732 757063 51035', 'expiration-date': '05/18'}},
 {'age': 108,
  'name': ('Keven', 'Avery'),
  'occupation': 'Administration Assistant',
  'telephone': '+1-305-871-0963',
  'address': {'address': '389 Texas Parkway', 'city': 'West Mifflin'},
  'credit-card': {'number': '3798 618897 28331', 'expiration-date': '01/24'}},
 {'age': 63,
  'name': ('Jae', 'Duffy'),
  'occupation': 'Botanist',
  'telephone': '+14016042697',
  'address': {'address': '1016 Iris Pike', 'city': 'Barstow'},
  'credit-card': {'number': '3496 724897 18821', 'expiration-date': '06/16'}},
 {'age': 74,
  'name': ('Cary', 'Clay'),
  'occupation': 'Restaurant Manager',
  'telephone': '+1-339-804-4499',
  'address': {'address': '1012 Young Shore', 'city': 'Missouri City'},
  'credit-card': {'number': '3715 342809 31808', 'expiration-date': '09/24'}},
 {'age': 59,
  'name': ('Reynaldo', 'Baldwin'),
  'occupation': 'Postmaster',
  'telephone': '+12282371460',
  'address': {'address': '193 Knollview Pike', 'city': 'La Verne'},
  'credit-card': {'number': '5403 3523 5381 4707',
   'expiration-date': '03/18'}},
 {'age': 105,
  'name': ('Quentin', 'Horton'),
  'occupation': 'Lawyer',
  'telephone': '+1-808-666-8088',
  'address': {'address': '514 Majestic Court', 'city': 'Atlantic City'},
  'credit-card': {'number': '3701 423880 33138', 'expiration-date': '09/22'}},
 {'age': 67,
  'name': ('Lawerence', 'Lott'),
  'occupation': 'Librarian',
  'telephone': '+1-623-461-3132',
  'address': {'address': '690 Century Field', 'city': 'Cooper City'},
  'credit-card': {'number': '3430 269089 45836', 'expiration-date': '04/19'}},
 {'age': 2,
  'name': ('Breann', 'Bruce'),
  'occupation': 'Tractor Driver',
  'telephone': '+1-310-110-9334',
  'address': {'address': '491 Merriam Spur', 'city': 'Howard'},
  'credit-card': {'number': '2611 6337 2543 9595',
   'expiration-date': '11/19'}},
 {'age': 4,
  'name': ('Rosana', 'Spears'),
  'occupation': 'Caterer',
  'telephone': '+1-507-265-8157',
  'address': {'address': '921 Dorman Pass', 'city': 'Oxford'},
  'credit-card': {'number': '4001 9619 3883 0258',
   'expiration-date': '12/22'}},
 {'age': 45,
  'name': ('Marc', 'Brady'),
  'occupation': 'Lecturer',
  'telephone': '+16190627892',
  'address': {'address': '174 Marshall Grove', 'city': 'East Providence'},
  'credit-card': {'number': '5585 3673 5761 7732',
   'expiration-date': '07/21'}},
 {'age': 2,
  'name': ('Domonique', 'Cohen'),
  'occupation': 'Telemarketeer',
  'telephone': '+16191214137',
  'address': {'address': '722 Greenwich Ferry', 'city': 'Coon Rapids'},
  'credit-card': {'number': '3421 554077 75856', 'expiration-date': '10/23'}},
 {'age': 1,
  'name': ('Moises', 'Delaney'),
  'occupation': 'Machine Tool',
  'telephone': '+16024002575',
  'address': {'address': '943 Quint Garden', 'city': 'Blue Island'},
  'credit-card': {'number': '5116 1145 1714 9018',
   'expiration-date': '08/23'}},
 {'age': 21,
  'name': ('Chas', 'Cortez'),
  'occupation': 'Waitress',
  'telephone': '+19274651884',
  'address': {'address': '993 Hillside Bend', 'city': 'Lawrence'},
  'credit-card': {'number': '3446 097905 82301', 'expiration-date': '12/18'}},
 {'age': 116,
  'name': ('Carmina', 'Tanner'),
  'occupation': 'Laminator',
  'telephone': '+1-970-834-6892',
  'address': {'address': '1115 Holland Walk', 'city': 'Cottonwood Heights'},
  'credit-card': {'number': '4287 5397 8896 9569',
   'expiration-date': '12/18'}},
 {'age': 49,
  'name': ('Vertie', 'Garrison'),
  'occupation': 'Priest',
  'telephone': '+1-214-127-8915',
  'address': {'address': '1163 Jason Cove', 'city': 'North Las Vegas'},
  'credit-card': {'number': '4252 5406 7137 8513',
   'expiration-date': '11/23'}},
 {'age': 110,
  'name': ('Cristopher', 'Dickson'),
  'occupation': 'Postwoman',
  'telephone': '+14406061592',
  'address': {'address': '776 Child Ferry', 'city': 'Batavia'},
  'credit-card': {'number': '3474 108451 95075', 'expiration-date': '01/24'}},
 {'age': 54,
  'name': ('Fredda', 'Ramsey'),
  'occupation': 'Servant',
  'telephone': '+1-973-065-7488',
  'address': {'address': '1112 Beacon Expressway', 'city': 'Agawam'},
  'credit-card': {'number': '2512 1500 0385 5186',
   'expiration-date': '03/22'}},
 {'age': 119,
  'name': ('Jerold', 'Hartman'),
  'occupation': 'Wood Cutter',
  'telephone': '+1-302-708-3864',
  'address': {'address': '282 Larch Avenue', 'city': 'Sheridan'},
  'credit-card': {'number': '2665 9152 7774 5775',
   'expiration-date': '11/24'}},
 {'age': 99,
  'name': ('Hilma', 'Delaney'),
  'occupation': 'Consultant',
  'telephone': '+15755437976',
  'address': {'address': '155 Arballo Road', 'city': 'San Diego'},
  'credit-card': {'number': '3402 014184 32210', 'expiration-date': '02/25'}},
 {'age': 74,
  'name': ('Domenic', 'Alvarez'),
  'occupation': 'Aircraft Maintenance Engineer',
  'telephone': '+17274027628',
  'address': {'address': '454 Jose Sarria Freeway', 'city': 'New Iberia'},
  'credit-card': {'number': '3492 588569 52662', 'expiration-date': '09/21'}},
 {'age': 63,
  'name': ('Julene', 'Garza'),
  'occupation': 'Purchase Clerk',
  'telephone': '+13206552694',
  'address': {'address': '1245 Mission Rock Lake', 'city': 'Dalton'},
  'credit-card': {'number': '5379 7030 9756 4539',
   'expiration-date': '03/21'}},
 {'age': 70,
  'name': ('Raul', 'Burris'),
  'occupation': 'Welfare Officer',
  'telephone': '+19131403832',
  'address': {'address': '789 Harlow Estate', 'city': 'Springfield'},
  'credit-card': {'number': '3768 845141 22616', 'expiration-date': '09/18'}},
 {'age': 117,
  'name': ('Reuben', 'Conner'),
  'occupation': 'Masseur',
  'telephone': '+14079147545',
  'address': {'address': '659 Clinton Parkway', 'city': 'Simi Valley'},
  'credit-card': {'number': '5565 2168 6658 2719',
   'expiration-date': '05/23'}},
 {'age': 42,
  'name': ('Hugh', 'Love'),
  'occupation': 'Pest Controller',
  'telephone': '+19573055366',
  'address': {'address': '1215 Sharon Crossing',
   'city': 'San Juan Capistrano'},
  'credit-card': {'number': '4969 9718 1840 2425',
   'expiration-date': '05/24'}},
 {'age': 18,
  'name': ('Colby', 'Jarvis'),
  'occupation': 'Furrier',
  'telephone': '+17694304965',
  'address': {'address': '470 Fremont Circle', 'city': 'Killeen'},
  'credit-card': {'number': '3407 244035 38527', 'expiration-date': '11/22'}},
 {'age': 27,
  'name': ('Mel', 'Carter'),
  'occupation': 'Journalist',
  'telephone': '+1-315-230-7020',
  'address': {'address': '140 Cesar Chavez On Alley', 'city': 'Cupertino'},
  'credit-card': {'number': '3421 684144 17321', 'expiration-date': '10/22'}},
 {'age': 91,
  'name': ('Tesha', 'Best'),
  'occupation': 'Shop Keeper',
  'telephone': '+1-865-662-5526',
  'address': {'address': '1230 Van Ness Place', 'city': 'Socorro'},
  'credit-card': {'number': '5192 6350 9952 6220',
   'expiration-date': '11/24'}},
 {'age': 80,
  'name': ('Janay', 'Aguilar'),
  'occupation': 'Insurance Staff',
  'telephone': '+19106498994',
  'address': {'address': '88 Santa Ysabel Park', 'city': 'Sugar Hill'},
  'credit-card': {'number': '3756 223611 39858', 'expiration-date': '08/19'}},
 {'age': 66,
  'name': ('Arianna', 'Hardy'),
  'occupation': 'Magician',
  'telephone': '+1-814-921-6555',
  'address': {'address': '1127 Fresno Point', 'city': 'Mauldin'},
  'credit-card': {'number': '3727 144995 55767', 'expiration-date': '01/24'}},
 {'age': 44,
  'name': ('Stephen', 'Landry'),
  'occupation': 'Technical Assistant',
  'telephone': '+19732806756',
  'address': {'address': '47 Isis Park', 'city': 'Evans'},
  'credit-card': {'number': '3729 808032 98531', 'expiration-date': '09/25'}},
 {'age': 107,
  'name': ('Jake', 'Warner'),
  'occupation': 'Thatcher',
  'telephone': '+1-754-557-0413',
  'address': {'address': '701 La Ferrera Expressway',
   'city': 'Temple Terrace'},
  'credit-card': {'number': '3435 183250 88414', 'expiration-date': '08/23'}},
 {'age': 3,
  'name': ('Jeremiah', 'Clark'),
  'occupation': 'Warehouse Manager',
  'telephone': '+16622608998',
  'address': {'address': '1144 Judah Point', 'city': 'Valparaiso'},
  'credit-card': {'number': '4277 7935 8758 6197',
   'expiration-date': '12/16'}},
 {'age': 65,
  'name': ('Jessi', 'Gibbs'),
  'occupation': 'Forester',
  'telephone': '+19792584179',
  'address': {'address': '1393 Bryant Street', 'city': 'Yukon'},
  'credit-card': {'number': '2254 7629 7809 7099',
   'expiration-date': '12/25'}},
 {'age': 97,
  'name': ('Breana', 'Berry'),
  'occupation': 'Marketing Coordinator',
  'telephone': '+1-973-559-8223',
  'address': {'address': '1149 Gillette Freeway', 'city': 'Duncanville'},
  'credit-card': {'number': '4539 2041 3313 2050',
   'expiration-date': '05/16'}},
 {'age': 32,
  'name': ('Hank', 'Bentley'),
  'occupation': 'Health Advisor',
  'telephone': '+15670531927',
  'address': {'address': '25 Crestlake Bend', 'city': 'Cedar Park'},
  'credit-card': {'number': '4125 1727 4791 6012',
   'expiration-date': '07/22'}},
 {'age': 97,
  'name': ('Shane', 'Pate'),
  'occupation': 'Gamekeeper',
  'telephone': '+1-540-248-1726',
  'address': {'address': '302 Garlington Park', 'city': 'Chesterfield'},
  'credit-card': {'number': '3498 141356 56456', 'expiration-date': '01/17'}},
 {'age': 51,
  'name': ('Felton', 'Acevedo'),
  'occupation': 'Radio Operator',
  'telephone': '+19032508739',
  'address': {'address': '833 Still Trace', 'city': 'Duncan'},
  'credit-card': {'number': '4638 1727 6481 0650',
   'expiration-date': '09/22'}},
 {'age': 71,
  'name': ('Noble', 'Webb'),
  'occupation': 'Television Director',
  'telephone': '+13194644378',
  'address': {'address': '271 Richards Manor', 'city': 'Wilkes-Barre'},
  'credit-card': {'number': '5343 4768 6558 4178',
   'expiration-date': '01/16'}},
 {'age': 97,
  'name': ('Carmen', 'Goodwin'),
  'occupation': 'Postman',
  'telephone': '+1-828-943-6964',
  'address': {'address': '64 Juniper Canyon', 'city': 'Lynchburg'},
  'credit-card': {'number': '3408 385690 82680', 'expiration-date': '03/16'}},
 {'age': 103,
  'name': ('Willena', 'Ballard'),
  'occupation': 'Paediatrician',
  'telephone': '+1-573-297-6067',
  'address': {'address': '396 Legion Of Honor Place', 'city': 'Owatonna'},
  'credit-card': {'number': '4944 6589 7998 7563',
   'expiration-date': '05/22'}},
 {'age': 61,
  'name': ('Rubin', 'Hyde'),
  'occupation': 'Window Dresser',
  'telephone': '+1-801-455-3819',
  'address': {'address': '62 Cosmo Gardens', 'city': 'Elk Grove'},
  'credit-card': {'number': '4207 0540 4691 6706',
   'expiration-date': '06/22'}},
 {'age': 0,
  'name': ('Aron', 'Parsons'),
  'occupation': 'Health Planner',
  'telephone': '+1-636-876-7955',
  'address': {'address': '680 Elmwood Creek', 'city': 'Washington'},
  'credit-card': {'number': '5368 8897 7576 1498',
   'expiration-date': '09/25'}},
 {'age': 18,
  'name': ('Mariko', 'Hartman'),
  'occupation': 'Health Service',
  'telephone': '+1-779-928-8482',
  'address': {'address': '1310 Raymond Pike', 'city': 'Howard'},
  'credit-card': {'number': '5346 3684 1946 6332',
   'expiration-date': '02/23'}},
 {'age': 114,
  'name': ('Christene', 'Villarreal'),
  'occupation': 'Technical Engineer',
  'telephone': '+15622052316',
  'address': {'address': '694 Peter Yorke Park', 'city': 'Cookeville'},
  'credit-card': {'number': '5513 3727 6115 3149',
   'expiration-date': '03/22'}},
 {'age': 80,
  'name': ('Belkis', 'Snow'),
  'occupation': 'Steward',
  'telephone': '+14807513008',
  'address': {'address': '310 Pollard Bypass', 'city': 'Palm Bay'},
  'credit-card': {'number': '3491 707787 44350', 'expiration-date': '04/24'}},
 {'age': 114,
  'name': ('Tyrone', 'Guzman'),
  'occupation': 'Progress Chaser',
  'telephone': '+16468638853',
  'address': {'address': '204 Christmas Tree Point Freeway',
   'city': 'Hopkins'},
  'credit-card': {'number': '4043 4968 0330 1465',
   'expiration-date': '01/17'}},
 {'age': 18,
  'name': ('Sharie', 'Pugh'),
  'occupation': 'Showman',
  'telephone': '+17476666218',
  'address': {'address': '963 Mirando Arcade', 'city': 'Midvale'},
  'credit-card': {'number': '4019 0440 9589 0017',
   'expiration-date': '11/22'}},
 {'age': 49,
  'name': ('Bret', 'Finch'),
  'occupation': 'Floor Layer',
  'telephone': '+1-424-111-1501',
  'address': {'address': '244 Vista Verde Extension', 'city': 'Hopewell'},
  'credit-card': {'number': '5104 7147 2935 4647',
   'expiration-date': '11/21'}},
 ...]

We can now perform pandas like operation on our dataframe and let dask create a task graph for the same.

In [23]:

new_df = df.groupby("occupation").age.max()
new_df

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[23], line 1
----> 1 new_df = df.groupby("occupation").age.max()
      2 new_df

AttributeError: 'Bag' object has no attribute 'age'

Instead of computing, let's visualize the task graph.

In [24]:

dask.visualize(new_df, filename="visualization.png")

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[24], line 1
----> 1 dask.visualize(new_df, filename="visualization.png")

NameError: name 'new_df' is not defined

We can see that the task graph starts with 10 independent branches because our dataframe was split into 10 partitions at the start. Let's compute the answer.

In [25]:

new_df.compute()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[25], line 1
----> 1 new_df.compute()

NameError: name 'new_df' is not defined

Similarly, one can peform such computations on arrays and selected Python data structures.

Dask support in Scientific Python ecosystem¶

Similar to the adoption of Numba in the scientific Python ecosystem, dask is being adopted at an increasing rate. Some examples include dask-awkward for ragged data computation, dask-histogram for parallel filling of histograms dask support in cuDF, for parallelizing CUDA dataframes, and dask-sql, a distributed SQL Engine.

GPU accelerated computing in Python¶

The last method for speeding up Python that we will look at is running your code on GPUs instead of CPUs. GPUs are specifically built to speed up computational tasks such as doing linear algebra or experimenting with computer graphics. Support for GPUs can be provided by writing custom kernels or by using Pythonic GPU libraris such as CuPy.

CuPy¶

CuPy is an array-based library enabling Python code to be scaled to GPUs. CuPy's API is very similar to NumPy and SciPy, and it was built as their extension to GPUs. There are a few differences in the API, and even a few missing functions, but it is actively developed and in a stable state. CuPy requires CUDA or AMD ROCm to be installed on your machine. After the installation, majority of the NumPy and SciPy scripts can be converted to CuPy by just replacing numpy and scipy with cupy.

import cupy as cp

print(cp.cuda.runtime.getDeviceCount())  # check if CuPy identifies the CUDA GPU

x = cp.arange(6).reshape(2, 3).astype('f')
y = cp.arange(6).reshape(2, 3).astype('f')

result = x + y

The code is not executed in this lesson as the service where these lessons are built do not posses a GPU.

CuPy support in Scientific Python ecosystem¶

Similar to Numba and Dask, CuPy is being adopted in multiple scientific libraries to provide GPU capabilities to the users. Dask along with its exosystem libraries, such as dask-image provide CuPy and GPU support. Similarly, packages like Awkward Array, cupy-xarray, and pyxem use CuPy internally to offer GPU support.

Writing custom GPU kernels and binding them to Python¶

Some Python libraries write their own custom kernels to provide GPU support to the users. For instance, cuDF uses libcudf, a C++/CUDA dataframe library to provide GPU support for dataframes in Python. Similarly, Tensorflow and PyTorch have their own GPU kernels and they do not depend on CuPy to run their code on GPUs. One can even write custom GPU kernels in CuPy, and libraries like Awkward Array leverage that for ragged data computation.