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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)

2.02 μs ± 1.34 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

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

In [6]:

%%timeit
jit_f(data)  # just run

3.49 μs ± 4.41 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)

94.7 ms ± 1.14 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

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

In [11]:

%%timeit
jit_f(data)  # just run

38.1 ms ± 955 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

130 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

54.1 ms ± 169 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/3.12.11/x64/lib/python3.12/site-packages/IPython/core/interactiveshell.py:2565, in InteractiveShell.run_cell_magic(self, magic_name, line, cell)
   2563 with self.builtin_trap:
   2564     args = (magic_arg_s, cell)
-> 2565     result = fn(*args, **kwargs)
   2567 # The code below prevents the output from being displayed
   2568 # when using magics with decorator @output_can_be_silenced
   2569 # when the last Python token in the expression is a ';'.
   2570 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False):

File /opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/IPython/core/magics/execution.py:1226, in ExecutionMagics.timeit(self, line, cell, local_ns)
   1224 for index in range(0, 10):
   1225     number = 10 ** index
-> 1226     time_number = timer.timeit(number)
   1227     if time_number >= 0.2:
   1228         break

File /opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/IPython/core/magics/execution.py:184, in Timer.timeit(self, number)
    182 gc.disable()
    183 try:
--> 184     timing = self.inner(it, self.timer)
    185 finally:
    186     if gcold:

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

File /opt/hostedtoolcache/Python/3.12.11/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/3.12.11/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/3.12.11/x64/lib/python3.12/site-packages/numba/core/typing/arraydecl.py:91

During: typing of setitem at /tmp/ipykernel_17055/2672903129.py (10)

File "../../../../../../tmp/ipykernel_17055/2672903129.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

478 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 ± 25.1 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/3.12.11/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/3.12.11/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/3.12.11/x64/lib/python3.12/site-packages/dask/bag/core.py:1607, in Bag.to_dataframe(self, meta, columns, optimize_graph)
   1558 """Create Dask Dataframe from a Dask Bag.
   1559 
   1560 Bag should contain tuples, dict records, or scalars.
   (...)   1603 0  Charlie      300
   1604 """
   1605 import pandas as pd
-> 1607 import dask.dataframe as dd
   1609 if meta is None:
   1610     head = self.take(1, warn=False)

File /opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/dask/dataframe/__init__.py:24
     22 from dask._dispatch import get_collection_type
     23 from dask.base import compute
---> 24 from dask.dataframe import backends, dispatch
     25 from dask.dataframe.dask_expr import (
     26     DataFrame,
     27     Index,
   (...)     53     to_timedelta,
     54 )
     55 from dask.dataframe.groupby import Aggregation

File /opt/hostedtoolcache/Python/3.12.11/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/3.12.11/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/3.12.11/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': 90,
  'name': ('Brain', 'Watson'),
  'occupation': 'Parts Manager',
  'telephone': '+1-815-804-5223',
  'address': {'address': '440 Borica Manor', 'city': 'Peoria'},
  'credit-card': {'number': '3405 943016 82440', 'expiration-date': '06/20'}},
 {'age': 27,
  'name': ('Malcolm', 'Coffey'),
  'occupation': 'Garage Foreman',
  'telephone': '+1-314-552-7824',
  'address': {'address': '611 Hillway Boulevard', 'city': 'Broadview Heights'},
  'credit-card': {'number': '4750 6224 4881 3035',
   'expiration-date': '07/23'}},
 {'age': 7,
  'name': ('Nathanael', 'Madden'),
  'occupation': 'Physiologist',
  'telephone': '+15012782356',
  'address': {'address': '820 Wallace Expressway', 'city': 'Ferndale'},
  'credit-card': {'number': '4791 6852 7419 3062',
   'expiration-date': '04/25'}},
 {'age': 118,
  'name': ('Azalee', 'Morrison'),
  'occupation': 'Sportsperson',
  'telephone': '+1-480-798-2112',
  'address': {'address': '552 Michigan Loop', 'city': 'Yucca Valley'},
  'credit-card': {'number': '4660 4079 8395 8323',
   'expiration-date': '02/19'}},
 {'age': 40,
  'name': ('Mauro', 'Kline'),
  'occupation': 'Graphologist',
  'telephone': '+1-386-576-6058',
  'address': {'address': '208 Northgate Freeway', 'city': 'Visalia'},
  'credit-card': {'number': '4257 3167 2518 6391',
   'expiration-date': '10/17'}},
 {'age': 76,
  'name': ('Rocky', 'Durham'),
  'occupation': 'Pawnbroker',
  'telephone': '+1-906-683-6244',
  'address': {'address': '284 Lisbon Lane', 'city': 'Northglenn'},
  'credit-card': {'number': '2526 7495 4474 3332',
   'expiration-date': '01/23'}},
 {'age': 102,
  'name': ('Devon', 'Long'),
  'occupation': 'Sand Blaster',
  'telephone': '+1-737-415-4934',
  'address': {'address': '1039 Commercial Green', 'city': 'Stow'},
  'credit-card': {'number': '2480 2869 4071 0973',
   'expiration-date': '03/21'}},
 {'age': 38,
  'name': ('Mafalda', 'Jensen'),
  'occupation': 'Aerial Erector',
  'telephone': '+14458132106',
  'address': {'address': '14 Hiliritas Route', 'city': 'Mercer Island'},
  'credit-card': {'number': '3765 570080 28495', 'expiration-date': '04/24'}},
 {'age': 31,
  'name': ('Kraig', 'Gillespie'),
  'occupation': 'Florist',
  'telephone': '+1-641-072-1514',
  'address': {'address': '333 Pratt Walk', 'city': 'Hinesville'},
  'credit-card': {'number': '4537 0155 1430 0534',
   'expiration-date': '07/18'}},
 {'age': 111,
  'name': ('Jorge', 'Burris'),
  'occupation': 'Dealer',
  'telephone': '+1-445-547-1160',
  'address': {'address': '307 Smith Lane', 'city': 'Carbondale'},
  'credit-card': {'number': '2490 4631 5110 3853',
   'expiration-date': '07/23'}},
 {'age': 97,
  'name': ('Robert', 'Townsend'),
  'occupation': 'Gilder',
  'telephone': '+1-762-414-9211',
  'address': {'address': '1269 Grijalva Annex', 'city': 'Lake Worth'},
  'credit-card': {'number': '4926 2631 1725 0727',
   'expiration-date': '08/19'}},
 {'age': 6,
  'name': ('Carlita', 'Callahan'),
  'occupation': 'Thermal Engineer',
  'telephone': '+14692004375',
  'address': {'address': '401 Filbert Mall', 'city': 'Avenal'},
  'credit-card': {'number': '3452 185128 16481', 'expiration-date': '01/20'}},
 {'age': 11,
  'name': ('Oscar', 'Pierce'),
  'occupation': 'Sign Maker',
  'telephone': '+1-408-567-8979',
  'address': {'address': '1291 Saint George Trace', 'city': 'El Paso'},
  'credit-card': {'number': '4137 7595 8859 9679',
   'expiration-date': '08/20'}},
 {'age': 51,
  'name': ('Myles', 'Gill'),
  'occupation': 'Packaging',
  'telephone': '+16518219151',
  'address': {'address': '1185 Mirabel Walk', 'city': 'East Point'},
  'credit-card': {'number': '3750 706594 59143', 'expiration-date': '03/16'}},
 {'age': 105,
  'name': ('Emmie', 'Humphrey'),
  'occupation': 'Cinema Manager',
  'telephone': '+17342962855',
  'address': {'address': '360 Cook Glen', 'city': 'Cicero'},
  'credit-card': {'number': '4999 8029 8727 1453',
   'expiration-date': '04/17'}},
 {'age': 69,
  'name': ('Twanda', 'Howell'),
  'occupation': 'Progress Chaser',
  'telephone': '+1-208-147-0441',
  'address': {'address': '515 Harold Canyon', 'city': 'Visalia'},
  'credit-card': {'number': '3788 395987 50029', 'expiration-date': '08/17'}},
 {'age': 19,
  'name': ('Ligia', 'Prince'),
  'occupation': 'Remedial Therapist',
  'telephone': '+1-201-263-4777',
  'address': {'address': '809 Stanyan Expressway', 'city': 'Richland'},
  'credit-card': {'number': '3406 771128 95465', 'expiration-date': '03/18'}},
 {'age': 32,
  'name': ('Derrick', 'Herring'),
  'occupation': 'Breeder',
  'telephone': '+16811858944',
  'address': {'address': '745 Cedar Walk', 'city': 'Los Angeles'},
  'credit-card': {'number': '4157 4517 3612 6903',
   'expiration-date': '10/19'}},
 {'age': 118,
  'name': ('Georgann', 'Melendez'),
  'occupation': 'Kitchen Worker',
  'telephone': '+1-407-694-7390',
  'address': {'address': '438 Chenery Stravenue', 'city': 'Petaluma'},
  'credit-card': {'number': '5182 8467 8688 6296',
   'expiration-date': '04/25'}},
 {'age': 34,
  'name': ('Aldo', 'Burns'),
  'occupation': 'Instrument Engineer',
  'telephone': '+1-310-617-2315',
  'address': {'address': '1242 Davidson Station', 'city': 'Florissant'},
  'credit-card': {'number': '3437 635200 01396', 'expiration-date': '03/18'}},
 {'age': 44,
  'name': ('Soila', 'Cain'),
  'occupation': 'Shepherd',
  'telephone': '+19496281478',
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  'occupation': 'Ambulance Crew',
  'telephone': '+12488604379',
  'address': {'address': '289 Winthrop Alley', 'city': 'Webster Groves'},
  'credit-card': {'number': '2414 4786 2370 2770',
   'expiration-date': '05/16'}},
 {'age': 61,
  'name': ('Vince', 'Parks'),
  'occupation': 'Geologist',
  'telephone': '+14060181977',
  'address': {'address': '188 Cuba Field', 'city': 'Berkeley'},
  'credit-card': {'number': '4607 4221 0429 3944',
   'expiration-date': '09/24'}},
 {'age': 11,
  'name': ('Bryon', 'Bradford'),
  'occupation': 'Park Ranger',
  'telephone': '+13643070329',
  'address': {'address': '414 Dutch Windmill Access Path', 'city': 'Denver'},
  'credit-card': {'number': '3713 807752 68243', 'expiration-date': '01/21'}},
 {'age': 15,
  'name': ('Babette', 'Boyer'),
  'occupation': 'Lift Attendant',
  'telephone': '+15410211018',
  'address': {'address': '42 Belle Motorway', 'city': 'Idaho Falls'},
  'credit-card': {'number': '3752 028169 63986', 'expiration-date': '11/22'}},
 {'age': 102,
  'name': ('Francie', 'Leon'),
  'occupation': 'Plant Manager',
  'telephone': '+18478700016',
  'address': {'address': '952 Dwight Arcade', 'city': 'Vineland'},
  'credit-card': {'number': '5212 9907 2759 5923',
   'expiration-date': '02/19'}},
 {'age': 79,
  'name': ('Odis', 'Mckee'),
  'occupation': 'Post Sorter',
  'telephone': '+1-417-189-3026',
  'address': {'address': '663 Wallen Lane', 'city': 'Bend'},
  'credit-card': {'number': '3471 733253 17839', 'expiration-date': '01/23'}},
 {'age': 99,
  'name': ('Heriberto', 'Watson'),
  'occupation': 'Transcriber',
  'telephone': '+13370199298',
  'address': {'address': '802 Las Villas Row', 'city': 'Bethel Park'},
  'credit-card': {'number': '4736 6384 6008 0500',
   'expiration-date': '07/20'}},
 {'age': 58,
  'name': ('Dwayne', 'Robbins'),
  'occupation': 'Instrument Engineer',
  'telephone': '+1-614-786-2312',
  'address': {'address': '543 Waterville Avenue', 'city': 'Boston'},
  'credit-card': {'number': '2244 8222 7153 3902',
   'expiration-date': '11/20'}},
 {'age': 40,
  'name': ('Herbert', 'Delgado'),
  'occupation': 'Loss Adjustor',
  'telephone': '+17190889316',
  'address': {'address': '1144 Duboce Hills', 'city': 'Chico'},
  'credit-card': {'number': '3709 380484 06857', 'expiration-date': '11/17'}},
 {'age': 58,
  'name': ('Roman', 'Marquez'),
  'occupation': 'Employment',
  'telephone': '+1-274-739-1368',
  'address': {'address': '725 Martinez Alley', 'city': 'Indianapolis'},
  'credit-card': {'number': '3752 007525 64800', 'expiration-date': '02/19'}},
 {'age': 50,
  'name': ('Jacquiline', 'Burgess'),
  'occupation': 'Sales Manager',
  'telephone': '+1-859-987-7778',
  'address': {'address': '196 Augusta Plaza', 'city': 'Ceres'},
  'credit-card': {'number': '4541 6052 3622 5482',
   'expiration-date': '02/25'}},
 {'age': 89,
  'name': ('Claris', 'Cole'),
  'occupation': 'Travel Agent',
  'telephone': '+17131490298',
  'address': {'address': '304 Quickstep Lake', 'city': 'Miami Beach'},
  'credit-card': {'number': '2528 7247 6161 3094',
   'expiration-date': '04/17'}},
 {'age': 110,
  'name': ('Lucio', 'Rollins'),
  'occupation': 'Salesman',
  'telephone': '+13866964412',
  'address': {'address': '721 Vega Pass', 'city': 'Safety Harbor'},
  'credit-card': {'number': '5162 2107 6340 9792',
   'expiration-date': '02/25'}},
 {'age': 107,
  'name': ('Domingo', 'Everett'),
  'occupation': 'Pastry Chef',
  'telephone': '+1-719-510-6357',
  'address': {'address': '197 Adele Bend', 'city': 'Moreno Valley'},
  'credit-card': {'number': '3752 754522 17792', 'expiration-date': '01/22'}},
 {'age': 19,
  'name': ('Suanne', 'Sullivan'),
  'occupation': 'Telecommunication',
  'telephone': '+18452787106',
  'address': {'address': '6 Pleasant Junction', 'city': 'Steubenville'},
  'credit-card': {'number': '4628 9103 3370 2250',
   'expiration-date': '04/17'}},
 {'age': 57,
  'name': ('Walter', 'Crosby'),
  'occupation': 'School Inspector',
  'telephone': '+1-283-800-8761',
  'address': {'address': '727 Sergeant John V Young Lake',
   'city': 'Twinsburg'},
  'credit-card': {'number': '5429 9049 2417 9539',
   'expiration-date': '09/16'}},
 {'age': 90,
  'name': ('Babara', 'Cox'),
  'occupation': 'Planning Engineer',
  'telephone': '+12542648694',
  'address': {'address': '134 Wisser Run', 'city': 'Edwardsville'},
  'credit-card': {'number': '3415 631753 30823', 'expiration-date': '01/22'}},
 {'age': 85,
  'name': ('Roderick', 'Wiley'),
  'occupation': 'Stockbroker',
  'telephone': '+1-562-080-2403',
  'address': {'address': '104 Marin Ferry', 'city': 'Zion'},
  'credit-card': {'number': '3749 762783 52876', 'expiration-date': '09/18'}},
 {'age': 90,
  'name': ('Karlyn', 'Lester'),
  'occupation': 'Hearing Therapist',
  'telephone': '+16107292703',
  'address': {'address': '181 Hartford Plaza', 'city': 'Temple'},
  'credit-card': {'number': '2507 2909 4384 9928',
   'expiration-date': '12/17'}},
 {'age': 15,
  'name': ('Lourie', 'Le'),
  'occupation': 'Betting Shop',
  'telephone': '+14757295237',
  'address': {'address': '762 Dove Path', 'city': 'Baldwin Park'},
  'credit-card': {'number': '2255 3751 3092 7324',
   'expiration-date': '01/19'}},
 {'age': 15,
  'name': ('Wilber', 'Hampton'),
  'occupation': 'Messenger',
  'telephone': '+16230888707',
  'address': {'address': '872 Cerritos Trace', 'city': 'Warrensburg'},
  'credit-card': {'number': '4131 1592 4624 1887',
   'expiration-date': '06/21'}},
 {'age': 77,
  'name': ('Seth', 'Hays'),
  'occupation': 'Chartered Accountant',
  'telephone': '+1-218-872-2803',
  'address': {'address': '258 Tunnel Pike', 'city': 'New Bern'},
  'credit-card': {'number': '4177 7268 6778 3010',
   'expiration-date': '10/24'}},
 {'age': 58,
  'name': ('Ezequiel', 'Dodson'),
  'occupation': 'Wholesale Newspaper',
  'telephone': '+14133776762',
  'address': {'address': '270 Starview Bypass', 'city': 'Birmingham'},
  'credit-card': {'number': '2590 2420 7361 0248',
   'expiration-date': '10/21'}},
 {'age': 56,
  'name': ('Wiley', 'Mcgowan'),
  'occupation': 'Market Researcher',
  'telephone': '+14098456480',
  'address': {'address': '102 India Mall', 'city': 'East Point'},
  'credit-card': {'number': '2503 3457 7868 1004',
   'expiration-date': '05/19'}},
 {'age': 35,
  'name': ('Otha', 'Alvarez'),
  'occupation': 'Tree Surgeon',
  'telephone': '+1-385-395-0480',
  'address': {'address': '408 Hearst Arcade', 'city': 'Manteca'},
  'credit-card': {'number': '3794 240246 54175', 'expiration-date': '11/20'}},
 {'age': 23,
  'name': ('Porfirio', 'Huff'),
  'occupation': 'Catering Manager',
  'telephone': '+13696024144',
  'address': {'address': '489 Verna Avenue', 'city': 'Norfolk'},
  'credit-card': {'number': '5239 4615 9508 6394',
   'expiration-date': '03/23'}},
 {'age': 3,
  'name': ('Quintin', 'Moreno'),
  'occupation': 'Anthropologist',
  'telephone': '+13855970670',
  'address': {'address': '713 Iron Freeway', 'city': 'Ardmore'},
  'credit-card': {'number': '5307 0107 5392 7464',
   'expiration-date': '08/24'}},
 {'age': 22,
  'name': ('Cortez', 'Hull'),
  'occupation': 'Press Operator',
  'telephone': '+1-320-472-3269',
  'address': {'address': '180 Dewey Mall', 'city': 'Trenton'},
  'credit-card': {'number': '5256 6708 4918 3694',
   'expiration-date': '04/25'}},
 {'age': 34,
  'name': ('Michel', 'Weiss'),
  'occupation': 'Moneylender',
  'telephone': '+19102499594',
  'address': {'address': '875 Valencia Estate', 'city': 'Tuscaloosa'},
  'credit-card': {'number': '2435 3134 2062 9493',
   'expiration-date': '03/20'}},
 {'age': 27,
  'name': ('Wes', 'Shannon'),
  'occupation': 'Training Manager',
  'telephone': '+1-959-358-0074',
  'address': {'address': '517 Lester Pike', 'city': 'Ames'},
  'credit-card': {'number': '4600 0482 2098 9151',
   'expiration-date': '11/17'}},
 {'age': 59,
  'name': ('Oneida', 'Walters'),
  'occupation': 'Rector',
  'telephone': '+1-970-460-5457',
  'address': {'address': '777 Cayuga Path', 'city': 'Niles'},
  'credit-card': {'number': '4914 6707 9721 5479',
   'expiration-date': '01/21'}},
 {'age': 88,
  'name': ('Genaro', 'Reeves'),
  'occupation': 'Medical Supplier',
  'telephone': '+1-551-681-6326',
  'address': {'address': '535 Moulton Center', 'city': 'Ardmore'},
  'credit-card': {'number': '5210 6770 1554 4489',
   'expiration-date': '09/16'}},
 {'age': 79,
  'name': ('Taneka', 'Mueller'),
  'occupation': 'Treasurer',
  'telephone': '+1-713-941-4053',
  'address': {'address': '383 Borica Garden', 'city': 'West Chester'},
  'credit-card': {'number': '3722 818792 60608', 'expiration-date': '06/17'}},
 {'age': 20,
  'name': ('Chet', 'Banks'),
  'occupation': 'Market Gardener',
  'telephone': '+14242356054',
  'address': {'address': '343 Hoff Square', 'city': 'Niagara Falls'},
  'credit-card': {'number': '3458 340539 90608', 'expiration-date': '10/21'}},
 {'age': 53,
  'name': ('Meda', 'Carver'),
  'occupation': 'Receptionist',
  'telephone': '+1-557-068-9370',
  'address': {'address': '220 Brotherhood Garden', 'city': 'Elizabethtown'},
  'credit-card': {'number': '5178 1896 2687 4370',
   'expiration-date': '04/23'}},
 {'age': 42,
  'name': ('Monte', 'Powell'),
  'occupation': 'Reprographic Assistant',
  'telephone': '+14245632501',
  'address': {'address': '209 Fresnel Station', 'city': 'Crown Point'},
  'credit-card': {'number': '3482 300261 33094', 'expiration-date': '09/25'}},
 {'age': 43,
  'name': ('Marchelle', 'Rivas'),
  'occupation': 'Hot Foil Printer',
  'telephone': '+1-927-775-5704',
  'address': {'address': '882 Myra Path', 'city': 'Allen Park'},
  'credit-card': {'number': '3786 966938 88371', 'expiration-date': '08/21'}},
 {'age': 63,
  'name': ('Janella', 'Davidson'),
  'occupation': 'Hospital Orderly',
  'telephone': '+1-626-158-9097',
  'address': {'address': '106 Beale Alley', 'city': 'Ames'},
  'credit-card': {'number': '3757 461852 40046', 'expiration-date': '09/23'}},
 {'age': 15,
  'name': ('Stewart', 'Thomas'),
  'occupation': 'Merchant Banker',
  'telephone': '+1-907-053-4842',
  'address': {'address': '215 Red Rock Loop', 'city': 'Farmington Hills'},
  'credit-card': {'number': '2282 4799 7060 9328',
   'expiration-date': '08/24'}},
 {'age': 37,
  'name': ('Thanh', 'Cline'),
  'occupation': 'Gate Keeper',
  'telephone': '+17179621962',
  'address': {'address': '307 Filbert Run', 'city': 'North Ogden'},
  'credit-card': {'number': '2674 8382 6594 4508',
   'expiration-date': '03/16'}},
 {'age': 19,
  'name': ('Alease', 'Osborne'),
  'occupation': 'Lawyer',
  'telephone': '+1-509-509-5914',
  'address': {'address': '663 Giants Ranch', 'city': 'Stafford'},
  'credit-card': {'number': '3418 713331 49211', 'expiration-date': '07/23'}},
 {'age': 120,
  'name': ('Boyd', 'Ayala'),
  'occupation': 'Stone Sawyer',
  'telephone': '+1-769-325-0881',
  'address': {'address': '129 Visitacion Shore', 'city': 'Pittsburgh'},
  'credit-card': {'number': '3721 128584 89753', 'expiration-date': '06/21'}},
 {'age': 38,
  'name': ('Brittni', 'Wooten'),
  'occupation': 'Project Engineer',
  'telephone': '+1-775-418-3204',
  'address': {'address': '694 Schofield Pass', 'city': 'Port Hueneme'},
  'credit-card': {'number': '4186 6317 9041 7166',
   'expiration-date': '01/23'}},
 {'age': 40,
  'name': ('Trenton', 'Ochoa'),
  'occupation': 'Typist',
  'telephone': '+18081404090',
  'address': {'address': '140 Clarion Drive', 'city': 'Wausau'},
  'credit-card': {'number': '3464 035011 65509', 'expiration-date': '02/17'}},
 {'age': 115,
  'name': ('Cyrus', 'Kim'),
  'occupation': 'Nuclear Scientist',
  'telephone': '+15104909442',
  'address': {'address': '1096 Amity Walk', 'city': 'Round Lake Beach'},
  'credit-card': {'number': '4782 0411 8001 5075',
   'expiration-date': '11/19'}},
 {'age': 52,
  'name': ('Wilfredo', 'Roth'),
  'occupation': 'Shop Proprietor',
  'telephone': '+13185619393',
  'address': {'address': '150 Craig Glen', 'city': 'North Lauderdale'},
  'credit-card': {'number': '2706 7276 6134 5778',
   'expiration-date': '08/18'}},
 {'age': 5,
  'name': ('Denver', 'Berger'),
  'occupation': 'Stone Sawyer',
  'telephone': '+16190250826',
  'address': {'address': '344 Horace Estate', 'city': 'Blythe'},
  'credit-card': {'number': '2287 8492 0122 7626',
   'expiration-date': '07/16'}},
 {'age': 10,
  'name': ('Ronnie', 'Mosley'),
  'occupation': 'Assistant',
  'telephone': '+1-860-629-1205',
  'address': {'address': '1225 Sickles Manor', 'city': 'Santee'},
  'credit-card': {'number': '5172 1972 7150 6003',
   'expiration-date': '10/17'}},
 {'age': 93,
  'name': ('Horacio', 'Buchanan'),
  'occupation': 'Tax Inspector',
  'telephone': '+1-412-500-7880',
  'address': {'address': '419 Colby Viaduct', 'city': 'Mesa'},
  'credit-card': {'number': '3716 217596 48847', 'expiration-date': '08/18'}},
 {'age': 42,
  'name': ('Lucrecia', 'Daniel'),
  'occupation': 'Lighting Technician',
  'telephone': '+12709039924',
  'address': {'address': '696 Pueblo Loop', 'city': 'Camden'},
  'credit-card': {'number': '4804 4765 1246 3011',
   'expiration-date': '12/25'}},
 {'age': 112,
  'name': ('Robby', 'Lawson'),
  'occupation': 'Induction Moulder',
  'telephone': '+1-254-551-5525',
  'address': {'address': '1097 Sussex Run', 'city': 'Durant'},
  'credit-card': {'number': '3446 320592 70442', 'expiration-date': '10/23'}},
 {'age': 7,
  'name': ('Horacio', 'Craig'),
  'occupation': 'Temperature Time',
  'telephone': '+13016611909',
  'address': {'address': '515 Ada Lake', 'city': 'Council Bluffs'},
  'credit-card': {'number': '5148 8068 3805 7661',
   'expiration-date': '03/21'}},
 {'age': 105,
  'name': ('Franklin', 'Keller'),
  'occupation': 'Precision Engineer',
  'telephone': '+1-916-985-9109',
  'address': {'address': '6 Boalt Parkway', 'city': 'Corinth'},
  'credit-card': {'number': '4899 3542 7655 2517',
   'expiration-date': '10/19'}},
 {'age': 74,
  'name': ('Alan', 'Calderon'),
  'occupation': 'Fireman',
  'telephone': '+16611429593',
  'address': {'address': '656 Hollister Canyon', 'city': 'Wildwood'},
  'credit-card': {'number': '3722 498462 48725', 'expiration-date': '02/16'}},
 {'age': 84,
  'name': ('Edwin', 'Hunter'),
  'occupation': 'Health Nurse',
  'telephone': '+15036465005',
  'address': {'address': '284 Boalt Landing', 'city': 'Lynbrook'},
  'credit-card': {'number': '4607 8229 3095 8091',
   'expiration-date': '06/25'}},
 {'age': 96,
  'name': ('Theodore', 'Burns'),
  'occupation': 'Horticulturalist',
  'telephone': '+1-336-047-2660',
  'address': {'address': '1317 Velasco Path', 'city': 'Sugar Hill'},
  'credit-card': {'number': '2437 4027 1526 0102',
   'expiration-date': '10/24'}},
 {'age': 73,
  'name': ('Huey', 'Mcclure'),
  'occupation': 'Production Planner',
  'telephone': '+17756172495',
  'address': {'address': '1048 Springfield Heights', 'city': 'Clovis'},
  'credit-card': {'number': '4032 1306 2628 2064',
   'expiration-date': '04/19'}},
 {'age': 23,
  'name': ('Tyisha', 'Branch'),
  'occupation': 'Art Critic',
  'telephone': '+1-508-156-5975',
  'address': {'address': '666 Bucareli Lodge', 'city': 'Minot'},
  'credit-card': {'number': '3769 330155 55186', 'expiration-date': '07/23'}},
 {'age': 79,
  'name': ('Evelyne', 'Dunn'),
  'occupation': 'Book Binder',
  'telephone': '+1-662-363-6472',
  'address': {'address': '919 Thornburg Field', 'city': 'Weirton'},
  'credit-card': {'number': '3753 010531 00284', 'expiration-date': '04/24'}},
 {'age': 57,
  'name': ('Kristian', 'Farmer'),
  'occupation': 'Tattooist',
  'telephone': '+12836330927',
  'address': {'address': '153 Hayes Annex', 'city': 'Asbury Park'},
  'credit-card': {'number': '5412 3264 4448 2046',
   'expiration-date': '12/22'}},
 {'age': 91,
  'name': ('Reyes', 'Weber'),
  'occupation': 'Nursery Assistant',
  'telephone': '+19402003254',
  'address': {'address': '928 Stern Grove Shore', 'city': 'Bayonne'},
  'credit-card': {'number': '4629 3273 4556 6616',
   'expiration-date': '06/18'}},
 {'age': 20,
  'name': ('Scott', 'May'),
  'occupation': 'Project Manager',
  'telephone': '+1-561-539-8317',
  'address': {'address': '513 Plymouth Station', 'city': 'San Antonio'},
  'credit-card': {'number': '2423 1106 5857 7574',
   'expiration-date': '11/22'}},
 {'age': 40,
  'name': ('Oliver', 'Mcclure'),
  'occupation': 'Insurance Inspector',
  'telephone': '+16077999172',
  'address': {'address': '411 Judah Junction', 'city': 'Mount Juliet'},
  'credit-card': {'number': '4222 4007 9830 7936',
   'expiration-date': '01/23'}},
 {'age': 68,
  'name': ('Margarito', 'Newman'),
  'occupation': 'Fork Lift Truck Driver',
  'telephone': '+18131700988',
  'address': {'address': '154 Thor Turnpike', 'city': 'Hilton Head Island'},
  'credit-card': {'number': '5145 8259 0067 9577',
   'expiration-date': '09/17'}},
 {'age': 104,
  'name': ('Dion', 'Washington'),
  'occupation': 'Accounts Staff',
  'telephone': '+17346130458',
  'address': {'address': '250 Isis Run', 'city': 'Muskegon'},
  'credit-card': {'number': '4378 8744 1001 9503',
   'expiration-date': '08/24'}},
 {'age': 67,
  'name': ('Logan', 'Emerson'),
  'occupation': 'Tax Analyst',
  'telephone': '+19078912846',
  'address': {'address': '206 Lillian Lodge', 'city': 'San Diego'},
  'credit-card': {'number': '3767 097414 77956', 'expiration-date': '10/16'}},
 {'age': 11,
  'name': ('Lorenzo', 'Haley'),
  'occupation': 'Newsagent',
  'telephone': '+1-906-504-1591',
  'address': {'address': '558 Gaiser Ranch', 'city': 'Kirkland'},
  'credit-card': {'number': '4169 0379 8181 7431',
   'expiration-date': '12/21'}},
 {'age': 114,
  'name': ('Gianna', 'Hodges'),
  'occupation': 'Bill Poster',
  'telephone': '+1-631-379-3203',
  'address': {'address': '489 George Hills', 'city': 'Margate'},
  'credit-card': {'number': '4375 0493 1016 1878',
   'expiration-date': '02/17'}},
 {'age': 39,
  'name': ('Willis', 'Middleton'),
  'occupation': 'Book Seller',
  'telephone': '+1-229-631-8855',
  'address': {'address': '372 Sutro Heights Mall', 'city': 'Brownsville'},
  'credit-card': {'number': '2583 3379 8131 1878',
   'expiration-date': '03/25'}},
 {'age': 81,
  'name': ('Marvin', 'Massey'),
  'occupation': 'Sports Commentator',
  'telephone': '+1-973-447-2518',
  'address': {'address': '1297 Putnam Bypass', 'city': 'Murfreesboro'},
  'credit-card': {'number': '4121 9228 6490 7671',
   'expiration-date': '10/18'}},
 ...]

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.