Mercurial > hg > orthanc-book
annotate Sphinx/source/plugins/python.rst @ 353:0122c668f4ec
python licensing
| author | Sebastien Jodogne <s.jodogne@gmail.com> |
|---|---|
| date | Sat, 28 Mar 2020 14:46:03 +0100 |
| parents | 6258b2c14e56 |
| children | 1ba75bac55fd |
| rev | line source |
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1 .. _python-plugin: |
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2 |
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4 Python plugin for Orthanc |
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5 ========================= |
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6 |
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7 .. contents:: |
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8 |
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9 Work-in-progress. |
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10 |
| 349 | 11 Being a plugin, the Python API has access to more features than |
| 350 | 12 :ref:`Lua scripts <lua>`. |
| 349 | 13 |
| 345 | 14 The Python API is automatically generated from the `Orthanc plugin SDK |
| 15 in C | |
| 16 <https://hg.orthanc-server.com/orthanc/file/Orthanc-1.5.7/Plugins/Include/orthanc/OrthancCPlugin.h>`__ | |
| 17 using the `Clang <https://en.wikipedia.org/wiki/Clang>`__ compiler | |
| 18 front-end. The coverage of the C SDK is about 75% (105 functions are | |
| 19 automatically wrapped in Python out of a total of 139 functions in C). | |
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20 |
| 353 | 21 Licensing |
| 22 --------- | |
| 23 | |
| 24 Pay attention to the fact that this plugin is licensed under the terms | |
| 25 of the `AGPL license | |
| 26 <https://en.wikipedia.org/wiki/GNU_Affero_General_Public_License>`__. | |
| 27 | |
| 28 This has an important consequence: If you distribute Orthanc to | |
| 29 clients together with one Python script, or if you make an Orthanc | |
| 30 server equiped with one Python script available on a Web portal, you | |
| 31 **must** disclose the source code of your Python script to the Orthanc | |
| 32 community under the terms of the AGPL license. We suggest you to put | |
| 33 your code on the `dedicated "OrthancContributed" repository on GitHub | |
| 34 <https://github.com/jodogne/OrthancContributed/tree/master/Plugins>`__. | |
| 35 | |
| 36 Check out the :ref:`FAQ about licensing <licensing>` for more context. | |
| 37 | |
| 38 | |
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39 |
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40 Samples |
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41 ------- |
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42 |
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43 Extending the REST API |
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44 ...................... |
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45 |
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46 .. highlight:: python |
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47 |
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48 Here is a basic Python script that registers two new routes in the |
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49 REST API:: |
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50 |
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51 import orthanc |
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52 import pprint |
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53 |
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54 def OnRest(output, uri, **request): |
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55 pprint.pprint(request) |
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56 print('Accessing uri: %s' % uri) |
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57 output.AnswerBuffer('ok\n', 'text/plain') |
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58 |
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59 orthanc.RegisterRestCallback('/(to)(t)o', OnRest) |
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60 orthanc.RegisterRestCallback('/tata', OnRest) |
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61 |
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62 .. highlight:: json |
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63 |
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64 Here is the associated minimal configuration file for Orthanc |
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65 (provided the Python script is saved as ``rest.py``):: |
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66 |
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67 { |
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68 "Plugins" : [ "." ], |
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69 "PythonScript" : "rest.py", |
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70 "PythonVerbose" : false |
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71 } |
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72 |
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73 .. highlight:: bash |
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74 |
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75 The route can then be accessed as:: |
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76 |
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77 $ curl http://localhost:8042/toto |
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78 ok |
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79 |
| 345 | 80 |
| 81 Listening to changes | |
| 82 .................... | |
| 83 | |
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84 .. highlight:: python |
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85 |
| 345 | 86 This sample uploads a DICOM file as soon as Orthanc is started:: |
| 87 | |
| 88 import orthanc | |
| 89 | |
| 90 def OnChange(changeType, level, resource): | |
| 91 if changeType == orthanc.ChangeType.ORTHANC_STARTED: | |
| 92 print('Started') | |
| 93 | |
| 94 with open('/tmp/sample.dcm', 'rb') as f: | |
| 95 orthanc.RestApiPost('/instances', f.read()) | |
| 96 | |
| 97 elif changeType == orthanc.ChangeType.ORTHANC_STOPPED: | |
| 98 print('Stopped') | |
| 99 | |
| 100 elif changeType == orthanc.ChangeType.NEW_INSTANCE: | |
| 101 print('A new instance was uploaded: %s' % resource) | |
| 102 | |
| 103 orthanc.RegisterOnChangeCallback(OnChange) | |
| 104 | |
| 105 | |
| 106 Accessing the content of a new instance | |
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107 ....................................... |
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108 |
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109 .. highlight:: python |
| 345 | 110 |
| 111 :: | |
| 112 | |
| 113 import orthanc | |
| 114 import json | |
| 115 import pprint | |
| 116 | |
| 117 def OnStoredInstance(dicom, instanceId): | |
| 118 print('Received instance %s of size %d (transfer syntax %s, SOP class UID %s)' % ( | |
| 119 instanceId, dicom.GetInstanceSize(), | |
| 120 dicom.GetInstanceMetadata('TransferSyntax'), | |
| 121 dicom.GetInstanceMetadata('SopClassUid'))) | |
| 122 | |
| 123 # Print the origin information | |
| 124 if dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.DICOM_PROTOCOL: | |
| 125 print('This instance was received through the DICOM protocol') | |
| 126 elif dicom.GetInstanceOrigin() == orthanc.InstanceOrigin.REST_API: | |
| 127 print('This instance was received through the REST API') | |
| 128 | |
| 129 # Print the DICOM tags | |
| 130 pprint.pprint(json.loads(dicom.GetInstanceSimplifiedJson())) | |
| 131 | |
| 132 orthanc.RegisterOnStoredInstanceCallback(OnStoredInstance) | |
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133 |
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134 |
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135 Calling pydicom |
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136 ............... |
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137 |
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138 .. highlight:: python |
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139 |
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140 Here is a sample Python plugin that registers a REST callback to dump |
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141 the content of the dataset of one given DICOM instance stored in |
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142 Orthanc, using `pydicom <https://pydicom.github.io/>`__:: |
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143 |
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144 import io |
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145 import orthanc |
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146 import pydicom |
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147 |
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148 def DecodeInstance(output, uri, **request): |
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149 if request['method'] == 'GET': |
| 347 | 150 # Retrieve the instance ID from the regular expression (*) |
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151 instanceId = request['groups'][0] |
| 347 | 152 # Get the content of the DICOM file |
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153 f = orthanc.GetDicomForInstance(instanceId) |
| 347 | 154 # Parse it using pydicom |
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155 dicom = pydicom.dcmread(io.BytesIO(f)) |
| 347 | 156 # Return a string representation the dataset to the caller |
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157 output.AnswerBuffer(str(dicom), 'text/plain') |
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158 else: |
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159 output.SendMethodNotAllowed('GET') |
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160 |
| 347 | 161 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*) |
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162 |
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163 .. highlight:: bash |
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164 |
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165 This can be called as follows:: |
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166 |
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167 $ curl http://localhost:8042/pydicom/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5 |
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168 |
| 347 | 169 |
| 170 Auto-routing studies | |
| 171 .................... | |
| 172 | |
| 173 .. highlight:: python | |
| 174 | |
| 175 Here is a sample Python plugin that routes any :ref:`stable study | |
| 176 <lua-callbacks>` to a modality named ``samples`` (as declared in the | |
| 177 ``DicomModalities`` configuration option):: | |
| 178 | |
| 179 import orthanc | |
| 180 | |
| 181 def OnChange(changeType, level, resourceId): | |
| 182 if changeType == orthanc.ChangeType.STABLE_STUDY: | |
| 183 print('Stable study: %s' % resourceId) | |
| 184 orthanc.RestApiPost('/modalities/sample/store', resourceId) | |
| 185 | |
| 186 orthanc.RegisterOnChangeCallback(OnChange) | |
| 348 | 187 |
| 188 | |
| 352 | 189 Rendering a thumbnail using PIL/Pillow |
| 190 ...................................... | |
| 348 | 191 |
| 192 .. highlight:: python | |
| 193 | |
| 194 :: | |
| 195 | |
| 196 from PIL import Image | |
| 197 import io | |
| 198 import orthanc | |
| 199 | |
| 200 def DecodeInstance(output, uri, **request): | |
| 201 if request['method'] == 'GET': | |
| 202 # Retrieve the instance ID from the regular expression (*) | |
| 203 instanceId = request['groups'][0] | |
| 204 | |
| 205 # Render the instance, then open it in Python using PIL/Pillow | |
| 206 png = orthanc.RestApiGet('/instances/%s/rendered' % instanceId) | |
| 207 image = Image.open(io.BytesIO(png)) | |
| 208 | |
| 209 # Downsize the image as a 64x64 thumbnail | |
| 210 image.thumbnail((64, 64), Image.ANTIALIAS) | |
| 211 | |
| 212 # Save the thumbnail as JPEG, then send the buffer to the caller | |
| 213 jpeg = io.BytesIO() | |
| 214 image.save(jpeg, format = "JPEG", quality = 80) | |
| 215 jpeg.seek(0) | |
| 216 output.AnswerBuffer(jpeg.read(), 'text/plain') | |
| 217 | |
| 218 else: | |
| 219 output.SendMethodNotAllowed('GET') | |
| 220 | |
| 221 orthanc.RegisterRestCallback('/pydicom/(.*)', DecodeInstance) # (*) | |
| 351 | 222 |
| 223 | |
| 224 Performance and concurrency | |
| 225 --------------------------- | |
| 226 | |
| 227 .. highlight:: python | |
| 228 | |
| 229 Let us consider the following sample Python script that makes a | |
| 230 CPU-intensive computation on a REST callback:: | |
| 231 | |
| 232 import math | |
| 233 import orthanc | |
| 234 import time | |
| 235 | |
| 236 # CPU-intensive computation taking about 4 seconds | |
| 237 def SlowComputation(): | |
| 238 start = time.time() | |
| 239 for i in range(1000): | |
| 240 for j in range(30000): | |
| 241 math.sqrt(float(j)) | |
| 242 end = time.time() | |
| 243 duration = (end - start) | |
| 244 return 'computation done in %.03f seconds\n' % duration | |
| 245 | |
| 246 def OnRest(output, uri, **request): | |
| 247 answer = SlowComputation() | |
| 248 output.AnswerBuffer(answer, 'text/plain') | |
| 249 | |
| 250 orthanc.RegisterRestCallback('/computation', OnRest) | |
| 251 | |
| 252 | |
| 253 .. highlight:: text | |
| 254 | |
| 255 Calling this REST route from the command-line returns the time that is | |
| 256 needed to compute 30 million times a squared root on your CPU:: | |
| 257 | |
| 258 $ curl http://localhost:8042/computation | |
| 259 computation done in 4.208 seconds | |
| 260 | |
| 261 Now, let us call this route three times concurrently (we use bash):: | |
| 262 | |
| 263 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation ) | |
| 264 computation done in 11.262 seconds | |
| 265 computation done in 12.457 seconds | |
| 266 computation done in 13.360 seconds | |
| 267 | |
| 268 As can be seen, the computation time has tripled. This means that the | |
| 269 computations were not distributed across the available CPU cores. | |
| 270 This might seem surprising, as Orthanc is a threaded server (in | |
| 271 Orthanc, a pool of C++ threads serves concurrent requests). | |
| 272 | |
| 273 The explanation is that the Python interpreter (`CPython | |
| 274 <https://en.wikipedia.org/wiki/CPython>`__ actually) is built on the | |
| 275 top of a so-called `Global Interpreter Lock (GIL) | |
| 276 <https://en.wikipedia.org/wiki/Global_interpreter_lock>`__. The GIL is | |
| 277 basically a mutex that protects all the calls to the Python | |
| 278 interpreter. If multiple C++ threads from Orthanc call a Python | |
| 353 | 279 callback, only one can proceed at any given time. Note however that |
| 280 the GIL only applies to the Python script: The baseline REST API of | |
| 281 Orthanc is not affected by the GIL. | |
| 351 | 282 |
| 283 .. highlight:: python | |
| 284 | |
| 285 The solution is to use the `multiprocessing primitives | |
| 286 <https://docs.python.org/3/library/multiprocessing.html>`__ of Python. | |
| 287 The "master" Python interpreter that is initially started by the | |
| 288 Orthanc plugin, can start several `children processes | |
| 289 <https://en.wikipedia.org/wiki/Process_(computing)>`__, each of these | |
| 290 processes running a separate Python interpreter. This allows to | |
| 291 offload intensive computations from the "master" Python interpreter of | |
| 292 Orthanc onto those "slave" interpreters. The ``multiprocessing`` | |
| 293 library is actually quite straightforward to use:: | |
| 294 | |
| 295 import math | |
| 296 import multiprocessing | |
| 297 import orthanc | |
| 298 import signal | |
| 299 import time | |
| 300 | |
| 301 # CPU-intensive computation taking about 4 seconds | |
| 302 # (same code as above) | |
| 303 def SlowComputation(): | |
| 304 start = time.time() | |
| 305 for i in range(1000): | |
| 306 for j in range(30000): | |
| 307 math.sqrt(float(j)) | |
| 308 end = time.time() | |
| 309 duration = (end - start) | |
| 310 return 'computation done in %.03f seconds\n' % duration | |
| 311 | |
| 312 # Ignore CTRL+C in the slave processes | |
| 313 def Initializer(): | |
| 314 signal.signal(signal.SIGINT, signal.SIG_IGN) | |
| 315 | |
| 316 # Create a pool of 4 slave Python interpreters | |
| 317 POOL = multiprocessing.Pool(4, initializer = Initializer) | |
| 318 | |
| 319 def OnRest(output, uri, **request): | |
| 320 # Offload the call to "SlowComputation" onto one slave process. | |
| 321 # The GIL is unlocked until the slave sends its answer back. | |
| 322 answer = POOL.apply(SlowComputation) | |
| 323 output.AnswerBuffer(answer, 'text/plain') | |
| 324 | |
| 325 orthanc.RegisterRestCallback('/computation', OnRest) | |
| 326 | |
| 327 .. highlight:: text | |
| 328 | |
| 329 Here is now the result of calling this route three times concurrently:: | |
| 330 | |
| 331 $ (curl http://localhost:8042/computation & curl http://localhost:8042/computation & curl http://localhost:8042/computation ) | |
| 332 computation done in 4.211 seconds | |
| 333 computation done in 4.215 seconds | |
| 334 computation done in 4.225 seconds | |
| 335 | |
| 336 As can be seen, the calls to the Python computation now fully run in | |
| 337 parallel (the time is cut down from 12 seconds to 4 seconds, the same | |
| 338 as for one isolated request). | |
| 339 | |
| 340 Note also how the ``multiprocessing`` library allows to make a fine | |
| 341 control over the computational resources that are available to the | |
| 342 Python script: The number of "slave" interpreters can be easily | |
| 343 changed in the constructor of the ``multiprocessing.Pool`` object, and | |
| 344 are fully independent of the threads used by the Orthanc server. | |
| 345 | |
| 352 | 346 .. highlight:: python |
| 347 | |
| 348 Very importantly, pay attention to the fact that only the "master" | |
| 349 Python interpreter has access to the Orthanc SDK. For instance, here | |
| 350 is how you would parse a DICOM file in a slave process:: | |
| 351 | |
| 352 import pydicom | |
| 353 import io | |
| 354 | |
| 355 def OffloadedDicomParsing(dicom): | |
| 356 # No access to the "orthanc" library here, as we are in the slave process | |
| 357 dataset = pydicom.dcmread(io.BytesIO(dicom)) | |
| 358 return str(dataset) | |
| 359 | |
| 360 def OnRest(output, uri, **request): | |
| 361 # The call to "orthanc.RestApiGet()" is only possible in the master process | |
| 362 dicom = orthanc.RestApiGet('/instances/19816330-cb02e1cf-df3a8fe8-bf510623-ccefe9f5/file') | |
| 363 answer = POOL.apply(OffloadedDicomParsing, args = (dicom, )) | |
| 364 output.AnswerBuffer(answer, 'text/plain') | |
| 365 | |
| 366 Communication primitives such as ``multiprocessing.Queue`` are | |
| 367 available to exchange messages from the "slave" Python interpreters to | |
| 368 the "master" Python interpreter if further calls to the Orthanc SDK | |
| 369 are required. | |
| 370 | |
| 351 | 371 Obviously, an in-depth discussion about the ``multiprocessing`` |
| 372 library is out of the scope of this document. There are many | |
| 373 references available on Internet. Also, note that ``multithreading`` | |
| 374 is not useful here, as Python multithreading is also limited by the | |
| 375 GIL, and is more targeted at dealing with costly I/O operations. |