-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathcuda.html
792 lines (607 loc) · 52.1 KB
/
cuda.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
<meta name="robots" content="noindex">
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>CUDA semantics — PyTorch master documentation</title>
<link rel="canonical" href="https://pytorch.org/docs/stable/notes/cuda.html"/>
<link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
<!-- <link rel="stylesheet" href="../_static/pygments.css" type="text/css" /> -->
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.css" type="text/css" />
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/katex.min.css" type="text/css" />
<link rel="stylesheet" href="../_static/katex-math.css" type="text/css" />
<link rel="index" title="Index" href="../genindex.html" />
<link rel="search" title="Search" href="../search.html" />
<link rel="next" title="Extending PyTorch" href="extending.html" />
<link rel="prev" title="CPU threading and TorchScript inference" href="cpu_threading_torchscript_inference.html" />
<script src="../_static/js/modernizr.min.js"></script>
</head>
<div class="container-fluid header-holder tutorials-header" id="header-holder">
<div class="container">
<div class="header-container">
<a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>
<div class="main-menu">
<ul>
<li>
<a href="https://pytorch.org/get-started">Get Started</a>
</li>
<li>
<a href="https://pytorch.org/features">Features</a>
</li>
<li>
<a href="https://pytorch.org/ecosystem">Ecosystem</a>
</li>
<li>
<a href="https://pytorch.org/blog/">Blog</a>
</li>
<li>
<a href="https://pytorch.org/tutorials">Tutorials</a>
</li>
<li class="active">
<a href="https://pytorch.org/docs/stable/index.html">Docs</a>
</li>
<li>
<a href="https://pytorch.org/resources">Resources</a>
</li>
<li>
<a href="https://github.com/pytorch/pytorch">Github</a>
</li>
</ul>
</div>
<a class="main-menu-open-button" href="#" data-behavior="open-mobile-menu"></a>
</div>
</div>
</div>
<body class="pytorch-body">
<div class="table-of-contents-link-wrapper">
<span>Table of Contents</span>
<a href="#" class="toggle-table-of-contents" data-behavior="toggle-table-of-contents"></a>
</div>
<nav data-toggle="wy-nav-shift" class="pytorch-left-menu" id="pytorch-left-menu">
<div class="pytorch-side-scroll">
<div class="pytorch-menu pytorch-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
<div class="pytorch-left-menu-search">
<div class="version">
<a href='http://pytorch.org/docs/versions.html'>1.2.0 ▼</a>
</div>
<div role="search">
<form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
<input type="text" name="q" placeholder="Search Docs" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
</div>
</div>
<p class="caption"><span class="caption-text">Notes</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">CUDA semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="extending.html">Extending PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="faq.html">Frequently Asked Questions</a></li>
<li class="toctree-l1"><a class="reference internal" href="large_scale_deployments.html">Features for large-scale deployments</a></li>
<li class="toctree-l1"><a class="reference internal" href="multiprocessing.html">Multiprocessing best practices</a></li>
<li class="toctree-l1"><a class="reference internal" href="randomness.html">Reproducibility</a></li>
<li class="toctree-l1"><a class="reference internal" href="serialization.html">Serialization semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="windows.html">Windows FAQ</a></li>
</ul>
<p class="caption"><span class="caption-text">Community</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../community/contribution_guide.html">PyTorch Contribution Guide</a></li>
<li class="toctree-l1"><a class="reference internal" href="../community/governance.html">PyTorch Governance</a></li>
<li class="toctree-l1"><a class="reference internal" href="../community/persons_of_interest.html">PyTorch Governance | Persons of Interest</a></li>
</ul>
<p class="caption"><span class="caption-text">Package Reference</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../torch.html">torch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tensors.html">torch.Tensor</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tensor_attributes.html">Tensor Attributes</a></li>
<li class="toctree-l1"><a class="reference internal" href="../type_info.html">Type Info</a></li>
<li class="toctree-l1"><a class="reference internal" href="../sparse.html">torch.sparse</a></li>
<li class="toctree-l1"><a class="reference internal" href="../cuda.html">torch.cuda</a></li>
<li class="toctree-l1"><a class="reference internal" href="../storage.html">torch.Storage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../nn.html">torch.nn</a></li>
<li class="toctree-l1"><a class="reference internal" href="../nn.functional.html">torch.nn.functional</a></li>
<li class="toctree-l1"><a class="reference internal" href="../nn.init.html">torch.nn.init</a></li>
<li class="toctree-l1"><a class="reference internal" href="../optim.html">torch.optim</a></li>
<li class="toctree-l1"><a class="reference internal" href="../autograd.html">torch.autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="../distributed.html">torch.distributed</a></li>
<li class="toctree-l1"><a class="reference internal" href="../distributions.html">torch.distributions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../hub.html">torch.hub</a></li>
<li class="toctree-l1"><a class="reference internal" href="../jit.html">torch.jit</a></li>
<li class="toctree-l1"><a class="reference internal" href="../multiprocessing.html">torch.multiprocessing</a></li>
<li class="toctree-l1"><a class="reference internal" href="../random.html">torch.random</a></li>
<li class="toctree-l1"><a class="reference internal" href="../bottleneck.html">torch.utils.bottleneck</a></li>
<li class="toctree-l1"><a class="reference internal" href="../checkpoint.html">torch.utils.checkpoint</a></li>
<li class="toctree-l1"><a class="reference internal" href="../cpp_extension.html">torch.utils.cpp_extension</a></li>
<li class="toctree-l1"><a class="reference internal" href="../data.html">torch.utils.data</a></li>
<li class="toctree-l1"><a class="reference internal" href="../dlpack.html">torch.utils.dlpack</a></li>
<li class="toctree-l1"><a class="reference internal" href="../model_zoo.html">torch.utils.model_zoo</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tensorboard.html">torch.utils.tensorboard</a></li>
<li class="toctree-l1"><a class="reference internal" href="../onnx.html">torch.onnx</a></li>
<li class="toctree-l1"><a class="reference internal" href="../__config__.html">torch.__config__</a></li>
</ul>
<p class="caption"><span class="caption-text">torchvision Reference</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../torchvision/index.html">torchvision</a></li>
</ul>
</div>
</div>
</nav>
<div class="pytorch-container">
<div class="pytorch-page-level-bar" id="pytorch-page-level-bar">
<div class="pytorch-breadcrumbs-wrapper">
<div role="navigation" aria-label="breadcrumbs navigation">
<ul class="pytorch-breadcrumbs">
<li>
<a href="../index.html">
Docs
</a> >
</li>
<li>CUDA semantics</li>
<li class="pytorch-breadcrumbs-aside">
<a href="../_sources/notes/cuda.rst.txt" rel="nofollow"><img src="../_static/images/view-page-source-icon.svg"></a>
</li>
</ul>
</div>
</div>
<div class="pytorch-shortcuts-wrapper" id="pytorch-shortcuts-wrapper">
Shortcuts
</div>
</div>
<section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
<div class="pytorch-content-left">
<div class="rst-content">
<div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
<article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
<div class="section" id="cuda-semantics">
<span id="id1"></span><h1>CUDA semantics<a class="headerlink" href="#cuda-semantics" title="Permalink to this headline">¶</a></h1>
<p><a class="reference internal" href="../cuda.html#module-torch.cuda" title="torch.cuda"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.cuda</span></code></a> is used to set up and run CUDA operations. It keeps track of
the currently selected GPU, and all CUDA tensors you allocate will by default be
created on that device. The selected device can be changed with a
<a class="reference internal" href="../cuda.html#torch.cuda.device" title="torch.cuda.device"><code class="xref any py py-class docutils literal notranslate"><span class="pre">torch.cuda.device</span></code></a> context manager.</p>
<p>However, once a tensor is allocated, you can do operations on it irrespective
of the selected device, and the results will be always placed in on the same
device as the tensor.</p>
<p>Cross-GPU operations are not allowed by default, with the exception of
<a class="reference internal" href="../tensors.html#torch.Tensor.copy_" title="torch.Tensor.copy_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">copy_()</span></code></a> and other methods with copy-like functionality
such as <a class="reference internal" href="../tensors.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">to()</span></code></a> and <a class="reference internal" href="../tensors.html#torch.Tensor.cuda" title="torch.Tensor.cuda"><code class="xref py py-meth docutils literal notranslate"><span class="pre">cuda()</span></code></a>.
Unless you enable peer-to-peer memory access, any attempts to launch ops on
tensors spread across different devices will raise an error.</p>
<p>Below you can find a small example showcasing this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span> <span class="c1"># Default CUDA device</span>
<span class="n">cuda0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda:0'</span><span class="p">)</span>
<span class="n">cuda2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda:2'</span><span class="p">)</span> <span class="c1"># GPU 2 (these are 0-indexed)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda0</span><span class="p">)</span>
<span class="c1"># x.device is device(type='cuda', index=0)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">])</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="c1"># y.device is device(type='cuda', index=0)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="mi">1</span><span class="p">):</span>
<span class="c1"># allocates a tensor on GPU 1</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda</span><span class="p">)</span>
<span class="c1"># transfers a tensor from CPU to GPU 1</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">])</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="c1"># a.device and b.device are device(type='cuda', index=1)</span>
<span class="c1"># You can also use ``Tensor.to`` to transfer a tensor:</span>
<span class="n">b2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">cuda</span><span class="p">)</span>
<span class="c1"># b.device and b2.device are device(type='cuda', index=1)</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="n">b</span>
<span class="c1"># c.device is device(type='cuda', index=1)</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="c1"># z.device is device(type='cuda', index=0)</span>
<span class="c1"># even within a context, you can specify the device</span>
<span class="c1"># (or give a GPU index to the .cuda call)</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda2</span><span class="p">)</span>
<span class="n">e</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">cuda2</span><span class="p">)</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">(</span><span class="n">cuda2</span><span class="p">)</span>
<span class="c1"># d.device, e.device, and f.device are all device(type='cuda', index=2)</span>
</pre></div>
</div>
<div class="section" id="asynchronous-execution">
<h2>Asynchronous execution<a class="headerlink" href="#asynchronous-execution" title="Permalink to this headline">¶</a></h2>
<p>By default, GPU operations are asynchronous. When you call a function that
uses the GPU, the operations are <em>enqueued</em> to the particular device, but not
necessarily executed until later. This allows us to execute more computations
in parallel, including operations on CPU or other GPUs.</p>
<p>In general, the effect of asynchronous computation is invisible to the caller,
because (1) each device executes operations in the order they are queued, and
(2) PyTorch automatically performs necessary synchronization when copying data
between CPU and GPU or between two GPUs. Hence, computation will proceed as if
every operation was executed synchronously.</p>
<p>You can force synchronous computation by setting environment variable
<cite>CUDA_LAUNCH_BLOCKING=1</cite>. This can be handy when an error occurs on the GPU.
(With asynchronous execution, such an error isn’t reported until after the
operation is actually executed, so the stack trace does not show where it was
requested.)</p>
<p>As an exception, several functions such as <a class="reference internal" href="../tensors.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">to()</span></code></a> and
<a class="reference internal" href="../tensors.html#torch.Tensor.copy_" title="torch.Tensor.copy_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">copy_()</span></code></a> admit an explicit <code class="xref py py-attr docutils literal notranslate"><span class="pre">non_blocking</span></code> argument,
which lets the caller bypass synchronization when it is unnecessary.
Another exception is CUDA streams, explained below.</p>
<div class="section" id="cuda-streams">
<h3>CUDA streams<a class="headerlink" href="#cuda-streams" title="Permalink to this headline">¶</a></h3>
<p>A <a class="reference external" href="http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#streams">CUDA stream</a> is a linear sequence of execution that belongs to a specific
device. You normally do not need to create one explicitly: by default, each
device uses its own “default” stream.</p>
<p>Operations inside each stream are serialized in the order they are created,
but operations from different streams can execute concurrently in any
relative order, unless explicit synchronization functions (such as
<a class="reference internal" href="../cuda.html#torch.cuda.synchronize" title="torch.cuda.synchronize"><code class="xref py py-meth docutils literal notranslate"><span class="pre">synchronize()</span></code></a> or <a class="reference internal" href="../cuda.html#torch.cuda.Stream.wait_stream" title="torch.cuda.Stream.wait_stream"><code class="xref py py-meth docutils literal notranslate"><span class="pre">wait_stream()</span></code></a>) are
used. For example, the following code is incorrect:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Stream</span><span class="p">()</span> <span class="c1"># Create a new stream.</span>
<span class="n">A</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda</span><span class="p">)</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
<span class="c1"># sum() may start execution before normal_() finishes!</span>
<span class="n">B</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
</pre></div>
</div>
<p>When the “current stream” is the default stream, PyTorch automatically performs
necessary synchronization when data is moved around, as explained above.
However, when using non-default streams, it is the user’s responsibility to
ensure proper synchronization.</p>
</div>
</div>
<div class="section" id="memory-management">
<span id="cuda-memory-management"></span><h2>Memory management<a class="headerlink" href="#memory-management" title="Permalink to this headline">¶</a></h2>
<p>PyTorch uses a caching memory allocator to speed up memory allocations. This
allows fast memory deallocation without device synchronizations. However, the
unused memory managed by the allocator will still show as if used in
<code class="docutils literal notranslate"><span class="pre">nvidia-smi</span></code>. You can use <a class="reference internal" href="../cuda.html#torch.cuda.memory_allocated" title="torch.cuda.memory_allocated"><code class="xref py py-meth docutils literal notranslate"><span class="pre">memory_allocated()</span></code></a> and
<a class="reference internal" href="../cuda.html#torch.cuda.max_memory_allocated" title="torch.cuda.max_memory_allocated"><code class="xref py py-meth docutils literal notranslate"><span class="pre">max_memory_allocated()</span></code></a> to monitor memory occupied by
tensors, and use <a class="reference internal" href="../cuda.html#torch.cuda.memory_cached" title="torch.cuda.memory_cached"><code class="xref py py-meth docutils literal notranslate"><span class="pre">memory_cached()</span></code></a> and
<a class="reference internal" href="../cuda.html#torch.cuda.max_memory_cached" title="torch.cuda.max_memory_cached"><code class="xref py py-meth docutils literal notranslate"><span class="pre">max_memory_cached()</span></code></a> to monitor memory managed by the caching
allocator. Calling <a class="reference internal" href="../cuda.html#torch.cuda.empty_cache" title="torch.cuda.empty_cache"><code class="xref py py-meth docutils literal notranslate"><span class="pre">empty_cache()</span></code></a> releases all <strong>unused</strong>
cached memory from PyTorch so that those can be used by other GPU applications.
However, the occupied GPU memory by tensors will not be freed so it can not
increase the amount of GPU memory available for PyTorch.</p>
</div>
<div class="section" id="cufft-plan-cache">
<span id="id2"></span><h2>cuFFT plan cache<a class="headerlink" href="#cufft-plan-cache" title="Permalink to this headline">¶</a></h2>
<p>For each CUDA device, an LRU cache of cuFFT plans is used to speed up repeatedly
running FFT methods (e.g., <a class="reference internal" href="../torch.html#torch.fft" title="torch.fft"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.fft()</span></code></a>) on CUDA tensors of same geometry
with same configuration. Because some cuFFT plans may allocate GPU memory,
these caches have a maximum capacity.</p>
<p>You may control and query the properties of the cache of current device with
the following APIs:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache.max_size</span></code> gives the capacity of the
cache (default is 4096 on CUDA 10 and newer, and 1023 on older CUDA versions).
Setting this value directly modifies the capacity.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache.size</span></code> gives the number of plans
currently residing in the cache.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache.clear()</span></code> clears the cache.</p></li>
</ul>
<p>To control and query plan caches of a non-default device, you can index the
<code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache</span></code> object with either a <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code>
object or a device index, and access one of the above attributes. E.g., to set
the capacity of the cache for device <code class="docutils literal notranslate"><span class="pre">1</span></code>, one can write
<code class="docutils literal notranslate"><span class="pre">torch.backends.cuda.cufft_plan_cache[1].max_size</span> <span class="pre">=</span> <span class="pre">10</span></code>.</p>
</div>
<div class="section" id="best-practices">
<h2>Best practices<a class="headerlink" href="#best-practices" title="Permalink to this headline">¶</a></h2>
<div class="section" id="device-agnostic-code">
<h3>Device-agnostic code<a class="headerlink" href="#device-agnostic-code" title="Permalink to this headline">¶</a></h3>
<p>Due to the structure of PyTorch, you may need to explicitly write
device-agnostic (CPU or GPU) code; an example may be creating a new tensor as
the initial hidden state of a recurrent neural network.</p>
<p>The first step is to determine whether the GPU should be used or not. A common
pattern is to use Python’s <code class="docutils literal notranslate"><span class="pre">argparse</span></code> module to read in user arguments, and
have a flag that can be used to disable CUDA, in combination with
<a class="reference internal" href="../cuda.html#torch.cuda.is_available" title="torch.cuda.is_available"><code class="xref py py-meth docutils literal notranslate"><span class="pre">is_available()</span></code></a>. In the following, <code class="docutils literal notranslate"><span class="pre">args.device</span></code> results in a
<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code> object that can be used to move tensors to CPU or CUDA.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">argparse</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="n">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s1">'PyTorch Example'</span><span class="p">)</span>
<span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">'--disable-cuda'</span><span class="p">,</span> <span class="n">action</span><span class="o">=</span><span class="s1">'store_true'</span><span class="p">,</span>
<span class="n">help</span><span class="o">=</span><span class="s1">'Disable CUDA'</span><span class="p">)</span>
<span class="n">args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
<span class="n">args</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">args</span><span class="o">.</span><span class="n">disable_cuda</span> <span class="ow">and</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span>
<span class="n">args</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">args</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">)</span>
</pre></div>
</div>
<p>Now that we have <code class="docutils literal notranslate"><span class="pre">args.device</span></code>, we can use it to create a Tensor on the
desired device.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">42</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">Network</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
</pre></div>
</div>
<p>This can be used in a number of cases to produce device agnostic code. Below
is an example when using a dataloader:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cuda0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda:0'</span><span class="p">)</span> <span class="c1"># CUDA GPU 0</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_loader</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">cuda0</span><span class="p">)</span>
</pre></div>
</div>
<p>When working with multiple GPUs on a system, you can use the
<code class="docutils literal notranslate"><span class="pre">CUDA_VISIBLE_DEVICES</span></code> environment flag to manage which GPUs are available to
PyTorch. As mentioned above, to manually control which GPU a tensor is created
on, the best practice is to use a <a class="reference internal" href="../cuda.html#torch.cuda.device" title="torch.cuda.device"><code class="xref any py py-class docutils literal notranslate"><span class="pre">torch.cuda.device</span></code></a> context manager.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"Outside device is 0"</span><span class="p">)</span> <span class="c1"># On device 0 (default in most scenarios)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="mi">1</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Inside device is 1"</span><span class="p">)</span> <span class="c1"># On device 1</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Outside device is still 0"</span><span class="p">)</span> <span class="c1"># On device 0</span>
</pre></div>
</div>
<p>If you have a tensor and would like to create a new tensor of the same type on
the same device, then you can use a <code class="docutils literal notranslate"><span class="pre">torch.Tensor.new_*</span></code> method
(see <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>).
Whilst the previously mentioned <code class="docutils literal notranslate"><span class="pre">torch.*</span></code> factory functions
(<a class="reference internal" href="../torch.html#tensor-creation-ops"><span class="std std-ref">Creation Ops</span></a>) depend on the current GPU context and
the attributes arguments you pass in, <code class="docutils literal notranslate"><span class="pre">torch.Tensor.new_*</span></code> methods preserve
the device and other attributes of the tensor.</p>
<p>This is the recommended practice when creating modules in which new
tensors need to be created internally during the forward pass.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cuda</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda'</span><span class="p">)</span>
<span class="n">x_cpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">x_gpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda</span><span class="p">)</span>
<span class="n">x_cpu_long</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
<span class="n">y_cpu</span> <span class="o">=</span> <span class="n">x_cpu</span><span class="o">.</span><span class="n">new_full</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">fill_value</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">y_cpu</span><span class="p">)</span>
<span class="n">tensor</span><span class="p">([[</span> <span class="mf">0.3000</span><span class="p">,</span> <span class="mf">0.3000</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">0.3000</span><span class="p">,</span> <span class="mf">0.3000</span><span class="p">],</span>
<span class="p">[</span> <span class="mf">0.3000</span><span class="p">,</span> <span class="mf">0.3000</span><span class="p">]])</span>
<span class="n">y_gpu</span> <span class="o">=</span> <span class="n">x_gpu</span><span class="o">.</span><span class="n">new_full</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">fill_value</span><span class="o">=-</span><span class="mi">5</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">y_gpu</span><span class="p">)</span>
<span class="n">tensor</span><span class="p">([[</span><span class="o">-</span><span class="mf">5.0000</span><span class="p">,</span> <span class="o">-</span><span class="mf">5.0000</span><span class="p">],</span>
<span class="p">[</span><span class="o">-</span><span class="mf">5.0000</span><span class="p">,</span> <span class="o">-</span><span class="mf">5.0000</span><span class="p">],</span>
<span class="p">[</span><span class="o">-</span><span class="mf">5.0000</span><span class="p">,</span> <span class="o">-</span><span class="mf">5.0000</span><span class="p">]],</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda:0'</span><span class="p">)</span>
<span class="n">y_cpu_long</span> <span class="o">=</span> <span class="n">x_cpu_long</span><span class="o">.</span><span class="n">new_tensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">y_cpu_long</span><span class="p">)</span>
<span class="n">tensor</span><span class="p">([[</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
</pre></div>
</div>
<p>If you want to create a tensor of the same type and size of another tensor, and
fill it with either ones or zeros, <a class="reference internal" href="../torch.html#torch.ones_like" title="torch.ones_like"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ones_like()</span></code></a> or
<a class="reference internal" href="../torch.html#torch.zeros_like" title="torch.zeros_like"><code class="xref py py-meth docutils literal notranslate"><span class="pre">zeros_like()</span></code></a> are provided as convenient helper functions (which
also preserve <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code> of a Tensor).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x_cpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">x_gpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">y_cpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">x_cpu</span><span class="p">)</span>
<span class="n">y_gpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">x_gpu</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="use-pinned-memory-buffers">
<span id="cuda-memory-pinning"></span><h3>Use pinned memory buffers<a class="headerlink" href="#use-pinned-memory-buffers" title="Permalink to this headline">¶</a></h3>
<p>Host to GPU copies are much faster when they originate from pinned (page-locked)
memory. CPU tensors and storages expose a <a class="reference internal" href="../tensors.html#torch.Tensor.pin_memory" title="torch.Tensor.pin_memory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">pin_memory()</span></code></a>
method, that returns a copy of the object, with data put in a pinned region.</p>
<p>Also, once you pin a tensor or storage, you can use asynchronous GPU copies.
Just pass an additional <code class="docutils literal notranslate"><span class="pre">non_blocking=True</span></code> argument to a
<a class="reference internal" href="../tensors.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">to()</span></code></a> or a <a class="reference internal" href="../tensors.html#torch.Tensor.cuda" title="torch.Tensor.cuda"><code class="xref py py-meth docutils literal notranslate"><span class="pre">cuda()</span></code></a> call. This can be used
to overlap data transfers with computation.</p>
<p>You can make the <a class="reference internal" href="../data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> return batches placed in
pinned memory by passing <code class="docutils literal notranslate"><span class="pre">pin_memory=True</span></code> to its constructor.</p>
</div>
<div class="section" id="use-nn-dataparallel-instead-of-multiprocessing">
<span id="cuda-nn-dataparallel-instead"></span><h3>Use nn.DataParallel instead of multiprocessing<a class="headerlink" href="#use-nn-dataparallel-instead-of-multiprocessing" title="Permalink to this headline">¶</a></h3>
<p>Most use cases involving batched inputs and multiple GPUs should default to
using <a class="reference internal" href="../nn.html#torch.nn.DataParallel" title="torch.nn.DataParallel"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataParallel</span></code></a> to utilize more than one GPU. Even with
the GIL, a single Python process can saturate multiple GPUs.</p>
<p>As of version 0.1.9, large numbers of GPUs (8+) might not be fully utilized.
However, this is a known issue that is under active development. As always,
test your use case.</p>
<p>There are significant caveats to using CUDA models with
<a class="reference internal" href="../multiprocessing.html#module-torch.multiprocessing" title="torch.multiprocessing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">multiprocessing</span></code></a>; unless care is taken to meet the data handling
requirements exactly, it is likely that your program will have incorrect or
undefined behavior.</p>
</div>
</div>
</div>
</article>
</div>
<footer>
<div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
<a href="extending.html" class="btn btn-neutral float-right" title="Extending PyTorch" accesskey="n" rel="next">Next <img src="../_static/images/chevron-right-orange.svg" class="next-page"></a>
<a href="cpu_threading_torchscript_inference.html" class="btn btn-neutral" title="CPU threading and TorchScript inference" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
</div>
<hr>
<div role="contentinfo">
<p>
© Copyright 2019, Torch Contributors.
</p>
</div>
<div>
Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
</div>
</footer>
</div>
</div>
<div class="pytorch-content-right" id="pytorch-content-right">
<div class="pytorch-right-menu" id="pytorch-right-menu">
<div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
<ul>
<li><a class="reference internal" href="#">CUDA semantics</a><ul>
<li><a class="reference internal" href="#asynchronous-execution">Asynchronous execution</a><ul>
<li><a class="reference internal" href="#cuda-streams">CUDA streams</a></li>
</ul>
</li>
<li><a class="reference internal" href="#memory-management">Memory management</a></li>
<li><a class="reference internal" href="#cufft-plan-cache">cuFFT plan cache</a></li>
<li><a class="reference internal" href="#best-practices">Best practices</a><ul>
<li><a class="reference internal" href="#device-agnostic-code">Device-agnostic code</a></li>
<li><a class="reference internal" href="#use-pinned-memory-buffers">Use pinned memory buffers</a></li>
<li><a class="reference internal" href="#use-nn-dataparallel-instead-of-multiprocessing">Use nn.DataParallel instead of multiprocessing</a></li>
</ul>
</li>
</ul>
</li>
</ul>
</div>
</div>
</div>
</section>
</div>
<script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
<script type="text/javascript" src="../_static/jquery.js"></script>
<script type="text/javascript" src="../_static/underscore.js"></script>
<script type="text/javascript" src="../_static/doctools.js"></script>
<script type="text/javascript" src="../_static/language_data.js"></script>
<script type="text/javascript" src="../_static/js/vendor/popper.min.js"></script>
<script type="text/javascript" src="../_static/js/vendor/bootstrap.min.js"></script>
<script type="text/javascript" src="../_static/js/theme.js"></script>
<script type="text/javascript">
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
});
</script>
<!-- Begin Footer -->
<div class="container-fluid docs-tutorials-resources" id="docs-tutorials-resources">
<div class="container">
<div class="row">
<div class="col-md-4 text-center">
<h2>Docs</h2>
<p>Access comprehensive developer documentation for PyTorch</p>
<a class="with-right-arrow" href="https://pytorch.org/docs/stable/index.html">View Docs</a>
</div>
<div class="col-md-4 text-center">
<h2>Tutorials</h2>
<p>Get in-depth tutorials for beginners and advanced developers</p>
<a class="with-right-arrow" href="https://pytorch.org/tutorials">View Tutorials</a>
</div>
<div class="col-md-4 text-center">
<h2>Resources</h2>
<p>Find development resources and get your questions answered</p>
<a class="with-right-arrow" href="https://pytorch.org/resources">View Resources</a>
</div>
</div>
</div>
</div>
<footer class="site-footer">
<div class="container footer-container">
<div class="footer-logo-wrapper">
<a href="https://pytorch.org/" class="footer-logo"></a>
</div>
<div class="footer-links-wrapper">
<div class="footer-links-col">
<ul>
<li class="list-title"><a href="https://pytorch.org/">PyTorch</a></li>
<li><a href="https://pytorch.org/get-started">Get Started</a></li>
<li><a href="https://pytorch.org/features">Features</a></li>
<li><a href="https://pytorch.org/ecosystem">Ecosystem</a></li>
<li><a href="https://pytorch.org/blog/">Blog</a></li>
<li><a href="https://pytorch.org/resources">Resources</a></li>
</ul>
</div>
<div class="footer-links-col">
<ul>
<li class="list-title"><a href="https://pytorch.org/support">Support</a></li>
<li><a href="https://pytorch.org/tutorials">Tutorials</a></li>
<li><a href="https://pytorch.org/docs/stable/index.html">Docs</a></li>
<li><a href="https://discuss.pytorch.org" target="_blank">Discuss</a></li>
<li><a href="https://github.com/pytorch/pytorch/issues" target="_blank">Github Issues</a></li>
<li><a href="https://pytorch.slack.com" target="_blank">Slack</a></li>
<li><a href="https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md" target="_blank">Contributing</a></li>
</ul>
</div>
<div class="footer-links-col follow-us-col">
<ul>
<li class="list-title">Follow Us</li>
<li>
<div id="mc_embed_signup">
<form
action="https://twitter.us14.list-manage.com/subscribe/post?u=75419c71fe0a935e53dfa4a3f&id=91d0dccd39"
method="post"
id="mc-embedded-subscribe-form"
name="mc-embedded-subscribe-form"
class="email-subscribe-form validate"
target="_blank"
novalidate>
<div id="mc_embed_signup_scroll" class="email-subscribe-form-fields-wrapper">
<div class="mc-field-group">
<label for="mce-EMAIL" style="display:none;">Email Address</label>
<input type="email" value="" name="EMAIL" class="required email" id="mce-EMAIL" placeholder="Email Address">
</div>
<div id="mce-responses" class="clear">
<div class="response" id="mce-error-response" style="display:none"></div>
<div class="response" id="mce-success-response" style="display:none"></div>
</div> <!-- real people should not fill this in and expect good things - do not remove this or risk form bot signups-->
<div style="position: absolute; left: -5000px;" aria-hidden="true"><input type="text" name="b_75419c71fe0a935e53dfa4a3f_91d0dccd39" tabindex="-1" value=""></div>
<div class="clear">
<input type="submit" value="" name="subscribe" id="mc-embedded-subscribe" class="button email-subscribe-button">
</div>
</div>
</form>
</div>
</li>
</ul>
<div class="footer-social-icons">
<a href="https://www.facebook.com/pytorch" target="_blank" class="facebook"></a>
<a href="https://twitter.com/pytorch" target="_blank" class="twitter"></a>
</div>
</div>
</div>
</div>
</footer>
<div class="cookie-banner-wrapper">
<div class="container">
<p class="gdpr-notice">To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: <a href="https://www.facebook.com/policies/cookies/">Cookies Policy</a>.</p>
<img class="close-button" src="../_static/images/pytorch-x.svg">
</div>
</div>
<!-- End Footer -->
<!-- Begin Mobile Menu -->
<div class="mobile-main-menu">
<div class="container-fluid">
<div class="container">
<div class="mobile-main-menu-header-container">
<a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>
<a class="main-menu-close-button" href="#" data-behavior="close-mobile-menu"></a>
</div>
</div>
</div>
<div class="mobile-main-menu-links-container">
<div class="main-menu">
<ul>
<li>
<a href="#">Get Started</a>
</li>
<li>
<a href="#">Features</a>
</li>
<li>
<a href="#">Ecosystem</a>
</li>
<li>
<a href="https://pytorch.org/blog/">Blog</a>
</li>
<li>
<a href="https://pytorch.org/tutorials">Tutorials</a>
</li>
<li class="active">
<a href="https://pytorch.org/docs/stable/index.html">Docs</a>
</li>
<li>
<a href="https://pytorch.org/resources">Resources</a>
</li>
<li>
<a href="https://github.com/pytorch/pytorch">Github</a>
</li>
</ul>
</div>
</div>
</div>
<!-- End Mobile Menu -->
<script type="text/javascript" src="../_static/js/vendor/anchor.min.js"></script>
<script type="text/javascript">
$(document).ready(function() {
mobileMenu.bind();
mobileTOC.bind();
pytorchAnchors.bind();
sideMenus.bind();
scrollToAnchor.bind();
highlightNavigation.bind();
// Add class to links that have code blocks, since we cannot create links in code blocks
$("article.pytorch-article a span.pre").each(function(e) {
$(this).closest("a").addClass("has-code");
});
})
</script>
</body>
</html>