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<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"><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"><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"><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"><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"><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"><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"><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"><span class="pre">copy_()</span></code></a> admit an explicit <code class="xref py py-attr docutils literal"><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"><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"><span class="pre">wait_stream()</span></code></a>) are
used. For example, the following code is incorrect:</p>
<div class="highlight-default"><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"><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"><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"><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"><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"><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"><span class="pre">empty_cache()</span></code></a> can release 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="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"><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"><span class="pre">is_available()</span></code></a>. In the following, <code class="docutils literal"><span class="pre">args.device</span></code> results in a
<code class="xref py py-class docutils literal"><span class="pre">torch.device</span></code> object that can be used to move tensors to CPU or CUDA.</p>
<div class="highlight-default"><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"><span class="pre">args.device</span></code>, we can use it to create a Tensor on the
desired device.</p>
<div class="highlight-default"><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"><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"><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"><span class="pre">torch.cuda.device</span></code></a> context manager.</p>
<div class="highlight-default"><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"><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"><span class="pre">torch.Tensor</span></code></a>).
Whilst the previously mentioned <code class="docutils literal"><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"><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"><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"><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"><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"><span class="pre">torch.device</span></code> and <code class="xref py py-class docutils literal"><span class="pre">torch.dtype</span></code> of a Tensor).</p>
<div class="highlight-default"><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">
<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"><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"><span class="pre">non_blocking=True</span></code> argument to a <a class="reference internal" href="../tensors.html#torch.Tensor.cuda" title="torch.Tensor.cuda"><code class="xref py py-meth docutils literal"><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"><span class="pre">DataLoader</span></code></a> return batches placed in
pinned memory by passing <code class="docutils literal"><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"><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"><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>
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<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>
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<li><a class="reference internal" href="#memory-management">Memory management</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>
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