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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)
2.06 μs ± 7 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
444 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
In [6]:
%%timeit
jit_f(data) # just run
3.66 μs ± 10.1 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)
87.3 ms ± 1.15 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
103 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
In [11]:
%%timeit
jit_f(data) # just run
39.3 ms ± 963 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
55 ms ± 122 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_15270/2672903129.py (10) File "../../../../../../tmp/ipykernel_15270/2672903129.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
501 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)
In [19]:
%%timeit
mandel_numpy(values) # just run
28.2 ms ± 119 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: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/3.12.9/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': 21,
'name': ('Chase', 'Everett'),
'occupation': 'Riding Instructor',
'telephone': '+18456241605',
'address': {'address': '346 Catalina Lake', 'city': 'Round Lake Beach'},
'credit-card': {'number': '4092 4647 9046 2229',
'expiration-date': '11/24'}},
{'age': 11,
'name': ('Ellyn', 'Harrison'),
'occupation': 'Ticket Agent',
'telephone': '+14255389732',
'address': {'address': '833 Hopkins Square', 'city': 'Prescott'},
'credit-card': {'number': '4480 8242 6825 8559',
'expiration-date': '09/19'}},
{'age': 34,
'name': ('Margart', 'Valentine'),
'occupation': 'Roofer',
'telephone': '+1-608-502-1025',
'address': {'address': '96 Johnstone Route', 'city': 'Colorado Springs'},
'credit-card': {'number': '3454 375612 80064', 'expiration-date': '05/20'}},
{'age': 113,
'name': ('Milford', 'Figueroa'),
'occupation': 'Coroner',
'telephone': '+14301957096',
'address': {'address': '365 Dawson Terrace', 'city': 'Baldwin'},
'credit-card': {'number': '4366 0540 9926 4175',
'expiration-date': '10/17'}},
{'age': 84,
'name': ('Karly', 'Sherman'),
'occupation': 'Laminator',
'telephone': '+1-971-719-7117',
'address': {'address': '962 Weldon Expressway', 'city': 'Florence'},
'credit-card': {'number': '4945 8879 0064 3917',
'expiration-date': '04/18'}},
{'age': 52,
'name': ('Ivey', 'Lamb'),
'occupation': 'Reflexologist',
'telephone': '+12693753356',
'address': {'address': '307 Olmstead Terrace', 'city': 'College Station'},
'credit-card': {'number': '2623 5188 7386 2852',
'expiration-date': '07/16'}},
{'age': 32,
'name': ('Azzie', 'Martin'),
'occupation': 'Civil Engineer',
'telephone': '+1-480-906-0195',
'address': {'address': '711 Walker Mews', 'city': 'Kettering'},
'credit-card': {'number': '3755 964081 63511', 'expiration-date': '04/23'}},
{'age': 60,
'name': ('Gerard', 'Leon'),
'occupation': 'Radio Operator',
'telephone': '+13105990440',
'address': {'address': '1062 Gilroy Expressway', 'city': 'Hialeah'},
'credit-card': {'number': '4598 2081 3386 4784',
'expiration-date': '05/17'}},
{'age': 79,
'name': ('Merlene', 'Vasquez'),
'occupation': 'Refrigeration Engineer',
'telephone': '+1-623-029-3052',
'address': {'address': '608 Harriet Row', 'city': 'West Bend'},
'credit-card': {'number': '4070 7770 8556 6305',
'expiration-date': '02/20'}},
{'age': 5,
'name': ('Jacqualine', 'Rivera'),
'occupation': 'Shop Assistant',
'telephone': '+12021594335',
'address': {'address': '699 Pink Creek', 'city': 'Streamwood'},
'credit-card': {'number': '5195 7038 9424 3974',
'expiration-date': '08/25'}},
{'age': 34,
'name': ('Nobuko', 'Harris'),
'occupation': 'Motor Dealer',
'telephone': '+1-309-956-6401',
'address': {'address': '379 Vandewater Alley', 'city': 'Azusa'},
'credit-card': {'number': '4302 8526 6685 6647',
'expiration-date': '03/19'}},
{'age': 55,
'name': ('Carlos', 'Stephenson'),
'occupation': 'Production Planner',
'telephone': '+19704153359',
'address': {'address': '802 Ignacio Lake', 'city': 'Carbondale'},
'credit-card': {'number': '3788 613201 15966', 'expiration-date': '08/23'}},
{'age': 19,
'name': ('Dorian', 'Fischer'),
'occupation': 'Trade Mark Agent',
'telephone': '+1-205-940-1566',
'address': {'address': '733 Bluestone Path', 'city': 'Findlay'},
'credit-card': {'number': '3478 468588 82242', 'expiration-date': '09/25'}},
{'age': 22,
'name': ('Lavonna', 'Daugherty'),
'occupation': 'Chef',
'telephone': '+18576721812',
'address': {'address': '42 Dorland Ranch', 'city': 'Garner'},
'credit-card': {'number': '4044 7671 2691 2546',
'expiration-date': '04/17'}},
{'age': 17,
'name': ('Thad', 'Parks'),
'occupation': 'Tax Assistant',
'telephone': '+19289982368',
'address': {'address': '229 Spreckels Lake Cove', 'city': 'Euclid'},
'credit-card': {'number': '2404 3205 2347 1878',
'expiration-date': '10/22'}},
{'age': 87,
'name': ('Shaunna', 'Barber'),
'occupation': 'Building Inspector',
'telephone': '+1-364-231-6130',
'address': {'address': '1058 Tulip Row', 'city': 'Springfield'},
'credit-card': {'number': '3483 240470 11346', 'expiration-date': '05/18'}},
{'age': 64,
'name': ('Arnulfo', 'Schroeder'),
'occupation': 'Plant Driver',
'telephone': '+17304152241',
'address': {'address': '1236 Arroyo Alley', 'city': 'Riverside'},
'credit-card': {'number': '4177 9929 4018 1057',
'expiration-date': '12/19'}},
{'age': 5,
'name': ('Stewart', 'Little'),
'occupation': 'Bill Poster',
'telephone': '+1-775-942-5993',
'address': {'address': '1345 Prospect Hills', 'city': 'West Lafayette'},
'credit-card': {'number': '3772 383426 12281', 'expiration-date': '09/16'}},
{'age': 60,
'name': ('Clarence', 'Hardy'),
'occupation': 'Gallery Owner',
'telephone': '+1-518-270-3467',
'address': {'address': '433 Yosemite Bypass', 'city': 'Valparaiso'},
'credit-card': {'number': '4985 3973 8964 7216',
'expiration-date': '07/18'}},
{'age': 70,
'name': ('Lashay', 'Walter'),
'occupation': 'Bursar',
'telephone': '+19784115503',
'address': {'address': '476 Delta Manor', 'city': 'University Park'},
'credit-card': {'number': '3404 004069 84444', 'expiration-date': '02/23'}},
{'age': 19,
'name': ('Veronika', 'Allen'),
'occupation': 'Agent',
'telephone': '+12151640832',
'address': {'address': '827 Flood Path', 'city': 'Carpentersville'},
'credit-card': {'number': '2460 9280 9315 2685',
'expiration-date': '01/16'}},
{'age': 32,
'name': ('Adrien', 'Spence'),
'occupation': 'Accounts Clerk',
'telephone': '+12314617082',
'address': {'address': '1091 Tocoloma Spur', 'city': 'Dyersburg'},
'credit-card': {'number': '3497 915636 50047', 'expiration-date': '08/23'}},
{'age': 19,
'name': ('Louetta', 'Sykes'),
'occupation': 'Marine Surveyor',
'telephone': '+15162556369',
'address': {'address': '1028 Owen Extension', 'city': 'Santa Cruz'},
'credit-card': {'number': '2538 4313 2495 5487',
'expiration-date': '01/23'}},
{'age': 49,
'name': ('Heriberto', 'Frazier'),
'occupation': 'Temperature Time',
'telephone': '+1-205-266-3236',
'address': {'address': '1085 Malvina Plaza', 'city': 'Susanville'},
'credit-card': {'number': '2389 9124 0448 0041',
'expiration-date': '10/19'}},
{'age': 64,
'name': ('Tobias', 'Nelson'),
'occupation': 'Progress Clerk',
'telephone': '+1-478-742-7756',
'address': {'address': '22 Vulcan Plaza', 'city': 'West Sacramento'},
'credit-card': {'number': '3703 064993 85626', 'expiration-date': '08/23'}},
{'age': 19,
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'occupation': 'Shipyard Worker',
'telephone': '+12510174071',
'address': {'address': '802 Colton Junction', 'city': 'Abilene'},
'credit-card': {'number': '5423 7408 3323 6804',
'expiration-date': '05/22'}},
{'age': 45,
'name': ('Rory', 'Pittman'),
'occupation': 'Attendant',
'telephone': '+1-857-932-0143',
'address': {'address': '207 Trenton Expressway', 'city': 'Sandusky'},
'credit-card': {'number': '3440 291360 51475', 'expiration-date': '01/23'}},
{'age': 48,
'name': ('Val', 'Henry'),
'occupation': 'Bus Company',
'telephone': '+16061905592',
'address': {'address': '274 Carter Lane', 'city': 'Hoboken'},
'credit-card': {'number': '5325 3726 7433 5526',
'expiration-date': '08/20'}},
{'age': 96,
'name': ('Quinton', 'Pittman'),
'occupation': 'Litigation Manager',
'telephone': '+19127558934',
'address': {'address': '481 Spooner Bend', 'city': 'Mountlake Terrace'},
'credit-card': {'number': '2308 5904 5470 0068',
'expiration-date': '07/19'}},
{'age': 106,
'name': ('Joaquin', 'Dudley'),
'occupation': 'History Teacher',
'telephone': '+14808878296',
'address': {'address': '1189 E Annex', 'city': 'Roswell'},
'credit-card': {'number': '2547 1454 4641 6983',
'expiration-date': '10/16'}},
{'age': 10,
'name': ('Penni', 'Wiggins'),
'occupation': 'Farmer',
'telephone': '+1-571-554-7125',
'address': {'address': '465 Leona Heights', 'city': 'St. Matthews'},
'credit-card': {'number': '3762 465365 82781', 'expiration-date': '04/24'}},
{'age': 107,
'name': ('Kathaleen', 'Kidd'),
'occupation': 'Metal Engineer',
'telephone': '+16606315256',
'address': {'address': '551 Arago Drive', 'city': 'Duncan'},
'credit-card': {'number': '4615 2833 6425 1935',
'expiration-date': '02/17'}},
{'age': 37,
'name': ('Inger', 'Roach'),
'occupation': 'Keep Fit Instructor',
'telephone': '+1-401-538-5672',
'address': {'address': '1300 Amatury Annex', 'city': 'Manhattan Beach'},
'credit-card': {'number': '4024 5156 3850 9795',
'expiration-date': '05/19'}},
{'age': 57,
'name': ('Edelmira', 'Calderon'),
'occupation': 'Preacher',
'telephone': '+14071216376',
'address': {'address': '1183 Rio Verde Highway', 'city': 'Cedar City'},
'credit-card': {'number': '5147 1473 9827 5026',
'expiration-date': '07/16'}},
{'age': 18,
'name': ('Magaly', 'Pruitt'),
'occupation': 'Operative',
'telephone': '+1-818-678-5623',
'address': {'address': '1355 Starview Trail', 'city': 'Brook Park'},
'credit-card': {'number': '4338 4319 6813 6921',
'expiration-date': '05/23'}},
{'age': 104,
'name': ('Tobias', 'Lindsey'),
'occupation': 'Charge Hand',
'telephone': '+1-215-664-5672',
'address': {'address': '380 Pierce Manor', 'city': 'Lomita'},
'credit-card': {'number': '2531 0510 0254 8938',
'expiration-date': '02/20'}},
{'age': 7,
'name': ('Lieselotte', 'Cole'),
'occupation': 'Systems Manager',
'telephone': '+1-251-859-1156',
'address': {'address': '789 Bluestone Green', 'city': 'Albany'},
'credit-card': {'number': '2567 5685 1512 6982',
'expiration-date': '01/25'}},
...]
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.