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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">CUDA Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
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<section id="module-torch.cuda">
<span id="torch-cuda"></span><h1>torch.cuda<a class="headerlink" href="#module-torch.cuda" title="Permalink to this heading">¶</a></h1>
<p>This package adds support for CUDA tensor types, that implement the same
function as CPU tensors, but they utilize GPUs for computation.</p>
<p>It is lazily initialized, so you can always import it, and use
<a class="reference internal" href="generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available"><code class="xref py py-func docutils literal notranslate"><span class="pre">is_available()</span></code></a> to determine if your system supports CUDA.</p>
<p><a class="reference internal" href="notes/cuda.html#cuda-semantics"><span class="std std-ref">CUDA semantics</span></a> has more details about working with CUDA.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.cuda.StreamContext"/><a class="reference internal" href="generated/torch.cuda.StreamContext.html#torch.cuda.StreamContext" title="torch.cuda.StreamContext"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StreamContext</span></code></a></p></td>
<td><p>Context-manager that selects a given stream.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.can_device_access_peer"/><a class="reference internal" href="generated/torch.cuda.can_device_access_peer.html#torch.cuda.can_device_access_peer" title="torch.cuda.can_device_access_peer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">can_device_access_peer</span></code></a></p></td>
<td><p>Checks if peer access between two devices is possible.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.current_blas_handle"/><a class="reference internal" href="generated/torch.cuda.current_blas_handle.html#torch.cuda.current_blas_handle" title="torch.cuda.current_blas_handle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">current_blas_handle</span></code></a></p></td>
<td><p>Returns cublasHandle_t pointer to current cuBLAS handle</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.current_device"/><a class="reference internal" href="generated/torch.cuda.current_device.html#torch.cuda.current_device" title="torch.cuda.current_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">current_device</span></code></a></p></td>
<td><p>Returns the index of a currently selected device.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.current_stream"/><a class="reference internal" href="generated/torch.cuda.current_stream.html#torch.cuda.current_stream" title="torch.cuda.current_stream"><code class="xref py py-obj docutils literal notranslate"><span class="pre">current_stream</span></code></a></p></td>
<td><p>Returns the currently selected <a class="reference internal" href="generated/torch.cuda.Stream.html#torch.cuda.Stream" title="torch.cuda.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> for a given device.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.default_stream"/><a class="reference internal" href="generated/torch.cuda.default_stream.html#torch.cuda.default_stream" title="torch.cuda.default_stream"><code class="xref py py-obj docutils literal notranslate"><span class="pre">default_stream</span></code></a></p></td>
<td><p>Returns the default <a class="reference internal" href="generated/torch.cuda.Stream.html#torch.cuda.Stream" title="torch.cuda.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> for a given device.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.device"/><a class="reference internal" href="generated/torch.cuda.device.html#torch.cuda.device" title="torch.cuda.device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></a></p></td>
<td><p>Context-manager that changes the selected device.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.device_count"/><a class="reference internal" href="generated/torch.cuda.device_count.html#torch.cuda.device_count" title="torch.cuda.device_count"><code class="xref py py-obj docutils literal notranslate"><span class="pre">device_count</span></code></a></p></td>
<td><p>Returns the number of GPUs available.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.device_of"/><a class="reference internal" href="generated/torch.cuda.device_of.html#torch.cuda.device_of" title="torch.cuda.device_of"><code class="xref py py-obj docutils literal notranslate"><span class="pre">device_of</span></code></a></p></td>
<td><p>Context-manager that changes the current device to that of given object.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.get_arch_list"/><a class="reference internal" href="generated/torch.cuda.get_arch_list.html#torch.cuda.get_arch_list" title="torch.cuda.get_arch_list"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_arch_list</span></code></a></p></td>
<td><p>Returns list CUDA architectures this library was compiled for.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.get_device_capability"/><a class="reference internal" href="generated/torch.cuda.get_device_capability.html#torch.cuda.get_device_capability" title="torch.cuda.get_device_capability"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_device_capability</span></code></a></p></td>
<td><p>Gets the cuda capability of a device.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.get_device_name"/><a class="reference internal" href="generated/torch.cuda.get_device_name.html#torch.cuda.get_device_name" title="torch.cuda.get_device_name"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_device_name</span></code></a></p></td>
<td><p>Gets the name of a device.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.get_device_properties"/><a class="reference internal" href="generated/torch.cuda.get_device_properties.html#torch.cuda.get_device_properties" title="torch.cuda.get_device_properties"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_device_properties</span></code></a></p></td>
<td><p>Gets the properties of a device.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.get_gencode_flags"/><a class="reference internal" href="generated/torch.cuda.get_gencode_flags.html#torch.cuda.get_gencode_flags" title="torch.cuda.get_gencode_flags"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_gencode_flags</span></code></a></p></td>
<td><p>Returns NVCC gencode flags this library was compiled with.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.get_sync_debug_mode"/><a class="reference internal" href="generated/torch.cuda.get_sync_debug_mode.html#torch.cuda.get_sync_debug_mode" title="torch.cuda.get_sync_debug_mode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_sync_debug_mode</span></code></a></p></td>
<td><p>Returns current value of debug mode for cuda synchronizing operations.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.init"/><a class="reference internal" href="generated/torch.cuda.init.html#torch.cuda.init" title="torch.cuda.init"><code class="xref py py-obj docutils literal notranslate"><span class="pre">init</span></code></a></p></td>
<td><p>Initialize PyTorch's CUDA state.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.ipc_collect"/><a class="reference internal" href="generated/torch.cuda.ipc_collect.html#torch.cuda.ipc_collect" title="torch.cuda.ipc_collect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ipc_collect</span></code></a></p></td>
<td><p>Force collects GPU memory after it has been released by CUDA IPC.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.is_available"/><a class="reference internal" href="generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_available</span></code></a></p></td>
<td><p>Returns a bool indicating if CUDA is currently available.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.is_initialized"/><a class="reference internal" href="generated/torch.cuda.is_initialized.html#torch.cuda.is_initialized" title="torch.cuda.is_initialized"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_initialized</span></code></a></p></td>
<td><p>Returns whether PyTorch's CUDA state has been initialized.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.memory_usage"/><a class="reference internal" href="generated/torch.cuda.memory_usage.html#torch.cuda.memory_usage" title="torch.cuda.memory_usage"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memory_usage</span></code></a></p></td>
<td><p>Returns the percent of time over the past sample period during which global (device) memory was being read or written.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.set_device"/><a class="reference internal" href="generated/torch.cuda.set_device.html#torch.cuda.set_device" title="torch.cuda.set_device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_device</span></code></a></p></td>
<td><p>Sets the current device.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.set_stream"/><a class="reference internal" href="generated/torch.cuda.set_stream.html#torch.cuda.set_stream" title="torch.cuda.set_stream"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_stream</span></code></a></p></td>
<td><p>Sets the current stream.This is a wrapper API to set the stream.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.set_sync_debug_mode"/><a class="reference internal" href="generated/torch.cuda.set_sync_debug_mode.html#torch.cuda.set_sync_debug_mode" title="torch.cuda.set_sync_debug_mode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_sync_debug_mode</span></code></a></p></td>
<td><p>Sets the debug mode for cuda synchronizing operations.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.stream"/><a class="reference internal" href="generated/torch.cuda.stream.html#torch.cuda.stream" title="torch.cuda.stream"><code class="xref py py-obj docutils literal notranslate"><span class="pre">stream</span></code></a></p></td>
<td><p>Wrapper around the Context-manager StreamContext that selects a given stream.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.synchronize"/><a class="reference internal" href="generated/torch.cuda.synchronize.html#torch.cuda.synchronize" title="torch.cuda.synchronize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">synchronize</span></code></a></p></td>
<td><p>Waits for all kernels in all streams on a CUDA device to complete.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.utilization"/><a class="reference internal" href="generated/torch.cuda.utilization.html#torch.cuda.utilization" title="torch.cuda.utilization"><code class="xref py py-obj docutils literal notranslate"><span class="pre">utilization</span></code></a></p></td>
<td><p>Returns the percent of time over the past sample period during which one or more kernels was executing on the GPU as given by <cite>nvidia-smi</cite>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.OutOfMemoryError"/><a class="reference internal" href="generated/torch.cuda.OutOfMemoryError.html#torch.cuda.OutOfMemoryError" title="torch.cuda.OutOfMemoryError"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OutOfMemoryError</span></code></a></p></td>
<td><p>Exception raised when CUDA is out of memory</p></td>
</tr>
</tbody>
</table>
<section id="random-number-generator">
<h2>Random Number Generator<a class="headerlink" href="#random-number-generator" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.cuda.get_rng_state"/><a class="reference internal" href="generated/torch.cuda.get_rng_state.html#torch.cuda.get_rng_state" title="torch.cuda.get_rng_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_rng_state</span></code></a></p></td>
<td><p>Returns the random number generator state of the specified GPU as a ByteTensor.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.get_rng_state_all"/><a class="reference internal" href="generated/torch.cuda.get_rng_state_all.html#torch.cuda.get_rng_state_all" title="torch.cuda.get_rng_state_all"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_rng_state_all</span></code></a></p></td>
<td><p>Returns a list of ByteTensor representing the random number states of all devices.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.set_rng_state"/><a class="reference internal" href="generated/torch.cuda.set_rng_state.html#torch.cuda.set_rng_state" title="torch.cuda.set_rng_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_rng_state</span></code></a></p></td>
<td><p>Sets the random number generator state of the specified GPU.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.set_rng_state_all"/><a class="reference internal" href="generated/torch.cuda.set_rng_state_all.html#torch.cuda.set_rng_state_all" title="torch.cuda.set_rng_state_all"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_rng_state_all</span></code></a></p></td>
<td><p>Sets the random number generator state of all devices.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.manual_seed"/><a class="reference internal" href="generated/torch.cuda.manual_seed.html#torch.cuda.manual_seed" title="torch.cuda.manual_seed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">manual_seed</span></code></a></p></td>
<td><p>Sets the seed for generating random numbers for the current GPU.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.manual_seed_all"/><a class="reference internal" href="generated/torch.cuda.manual_seed_all.html#torch.cuda.manual_seed_all" title="torch.cuda.manual_seed_all"><code class="xref py py-obj docutils literal notranslate"><span class="pre">manual_seed_all</span></code></a></p></td>
<td><p>Sets the seed for generating random numbers on all GPUs.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.seed"/><a class="reference internal" href="generated/torch.cuda.seed.html#torch.cuda.seed" title="torch.cuda.seed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">seed</span></code></a></p></td>
<td><p>Sets the seed for generating random numbers to a random number for the current GPU.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.seed_all"/><a class="reference internal" href="generated/torch.cuda.seed_all.html#torch.cuda.seed_all" title="torch.cuda.seed_all"><code class="xref py py-obj docutils literal notranslate"><span class="pre">seed_all</span></code></a></p></td>
<td><p>Sets the seed for generating random numbers to a random number on all GPUs.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.initial_seed"/><a class="reference internal" href="generated/torch.cuda.initial_seed.html#torch.cuda.initial_seed" title="torch.cuda.initial_seed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initial_seed</span></code></a></p></td>
<td><p>Returns the current random seed of the current GPU.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="communication-collectives">
<h2>Communication collectives<a class="headerlink" href="#communication-collectives" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.cuda.comm.broadcast.html#torch.cuda.comm.broadcast" title="torch.cuda.comm.broadcast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">comm.broadcast</span></code></a></p></td>
<td><p>Broadcasts a tensor to specified GPU devices.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.cuda.comm.broadcast_coalesced.html#torch.cuda.comm.broadcast_coalesced" title="torch.cuda.comm.broadcast_coalesced"><code class="xref py py-obj docutils literal notranslate"><span class="pre">comm.broadcast_coalesced</span></code></a></p></td>
<td><p>Broadcasts a sequence tensors to the specified GPUs.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.cuda.comm.reduce_add.html#torch.cuda.comm.reduce_add" title="torch.cuda.comm.reduce_add"><code class="xref py py-obj docutils literal notranslate"><span class="pre">comm.reduce_add</span></code></a></p></td>
<td><p>Sums tensors from multiple GPUs.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.cuda.comm.scatter.html#torch.cuda.comm.scatter" title="torch.cuda.comm.scatter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">comm.scatter</span></code></a></p></td>
<td><p>Scatters tensor across multiple GPUs.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.cuda.comm.gather.html#torch.cuda.comm.gather" title="torch.cuda.comm.gather"><code class="xref py py-obj docutils literal notranslate"><span class="pre">comm.gather</span></code></a></p></td>
<td><p>Gathers tensors from multiple GPU devices.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="streams-and-events">
<h2>Streams and events<a class="headerlink" href="#streams-and-events" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.cuda.Stream"/><a class="reference internal" href="generated/torch.cuda.Stream.html#torch.cuda.Stream" title="torch.cuda.Stream"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Stream</span></code></a></p></td>
<td><p>Wrapper around a CUDA stream.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.ExternalStream"/><a class="reference internal" href="generated/torch.cuda.ExternalStream.html#torch.cuda.ExternalStream" title="torch.cuda.ExternalStream"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ExternalStream</span></code></a></p></td>
<td><p>Wrapper around an externally allocated CUDA stream.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.Event"/><a class="reference internal" href="generated/torch.cuda.Event.html#torch.cuda.Event" title="torch.cuda.Event"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Event</span></code></a></p></td>
<td><p>Wrapper around a CUDA event.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="graphs-beta">
<h2>Graphs (beta)<a class="headerlink" href="#graphs-beta" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.cuda.is_current_stream_capturing"/><a class="reference internal" href="generated/torch.cuda.is_current_stream_capturing.html#torch.cuda.is_current_stream_capturing" title="torch.cuda.is_current_stream_capturing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_current_stream_capturing</span></code></a></p></td>
<td><p>Returns True if CUDA graph capture is underway on the current CUDA stream, False otherwise.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.graph_pool_handle"/><a class="reference internal" href="generated/torch.cuda.graph_pool_handle.html#torch.cuda.graph_pool_handle" title="torch.cuda.graph_pool_handle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">graph_pool_handle</span></code></a></p></td>
<td><p>Returns an opaque token representing the id of a graph memory pool.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.CUDAGraph"/><a class="reference internal" href="generated/torch.cuda.CUDAGraph.html#torch.cuda.CUDAGraph" title="torch.cuda.CUDAGraph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CUDAGraph</span></code></a></p></td>
<td><p>Wrapper around a CUDA graph.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.graph"/><a class="reference internal" href="generated/torch.cuda.graph.html#torch.cuda.graph" title="torch.cuda.graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">graph</span></code></a></p></td>
<td><p>Context-manager that captures CUDA work into a <a class="reference internal" href="generated/torch.cuda.CUDAGraph.html#torch.cuda.CUDAGraph" title="torch.cuda.CUDAGraph"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.CUDAGraph</span></code></a> object for later replay.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.make_graphed_callables"/><a class="reference internal" href="generated/torch.cuda.make_graphed_callables.html#torch.cuda.make_graphed_callables" title="torch.cuda.make_graphed_callables"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_graphed_callables</span></code></a></p></td>
<td><p>Accepts callables (functions or <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">nn.Module</span></code></a>s) and returns graphed versions.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="memory-management">
<h2>Memory management<a class="headerlink" href="#memory-management" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.cuda.empty_cache"/><a class="reference internal" href="generated/torch.cuda.empty_cache.html#torch.cuda.empty_cache" title="torch.cuda.empty_cache"><code class="xref py py-obj docutils literal notranslate"><span class="pre">empty_cache</span></code></a></p></td>
<td><p>Releases all unoccupied cached memory currently held by the caching allocator so that those can be used in other GPU application and visible in <cite>nvidia-smi</cite>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.list_gpu_processes"/><a class="reference internal" href="generated/torch.cuda.list_gpu_processes.html#torch.cuda.list_gpu_processes" title="torch.cuda.list_gpu_processes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">list_gpu_processes</span></code></a></p></td>
<td><p>Returns a human-readable printout of the running processes and their GPU memory use for a given device.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.mem_get_info"/><a class="reference internal" href="generated/torch.cuda.mem_get_info.html#torch.cuda.mem_get_info" title="torch.cuda.mem_get_info"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mem_get_info</span></code></a></p></td>
<td><p>Returns the global free and total GPU memory occupied for a given device using cudaMemGetInfo.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.memory_stats"/><a class="reference internal" href="generated/torch.cuda.memory_stats.html#torch.cuda.memory_stats" title="torch.cuda.memory_stats"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memory_stats</span></code></a></p></td>
<td><p>Returns a dictionary of CUDA memory allocator statistics for a given device.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.memory_summary"/><a class="reference internal" href="generated/torch.cuda.memory_summary.html#torch.cuda.memory_summary" title="torch.cuda.memory_summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memory_summary</span></code></a></p></td>
<td><p>Returns a human-readable printout of the current memory allocator statistics for a given device.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.memory_snapshot"/><a class="reference internal" href="generated/torch.cuda.memory_snapshot.html#torch.cuda.memory_snapshot" title="torch.cuda.memory_snapshot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memory_snapshot</span></code></a></p></td>
<td><p>Returns a snapshot of the CUDA memory allocator state across all devices.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.memory_allocated"/><a class="reference internal" href="generated/torch.cuda.memory_allocated.html#torch.cuda.memory_allocated" title="torch.cuda.memory_allocated"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memory_allocated</span></code></a></p></td>
<td><p>Returns the current GPU memory occupied by tensors in bytes for a given device.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.max_memory_allocated"/><a class="reference internal" href="generated/torch.cuda.max_memory_allocated.html#torch.cuda.max_memory_allocated" title="torch.cuda.max_memory_allocated"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_memory_allocated</span></code></a></p></td>
<td><p>Returns the maximum GPU memory occupied by tensors in bytes for a given device.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.reset_max_memory_allocated"/><a class="reference internal" href="generated/torch.cuda.reset_max_memory_allocated.html#torch.cuda.reset_max_memory_allocated" title="torch.cuda.reset_max_memory_allocated"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_max_memory_allocated</span></code></a></p></td>
<td><p>Resets the starting point in tracking maximum GPU memory occupied by tensors for a given device.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.memory_reserved"/><a class="reference internal" href="generated/torch.cuda.memory_reserved.html#torch.cuda.memory_reserved" title="torch.cuda.memory_reserved"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memory_reserved</span></code></a></p></td>
<td><p>Returns the current GPU memory managed by the caching allocator in bytes for a given device.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.max_memory_reserved"/><a class="reference internal" href="generated/torch.cuda.max_memory_reserved.html#torch.cuda.max_memory_reserved" title="torch.cuda.max_memory_reserved"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_memory_reserved</span></code></a></p></td>
<td><p>Returns the maximum GPU memory managed by the caching allocator in bytes for a given device.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.set_per_process_memory_fraction"/><a class="reference internal" href="generated/torch.cuda.set_per_process_memory_fraction.html#torch.cuda.set_per_process_memory_fraction" title="torch.cuda.set_per_process_memory_fraction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_per_process_memory_fraction</span></code></a></p></td>
<td><p>Set memory fraction for a process.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.memory_cached"/><a class="reference internal" href="generated/torch.cuda.memory_cached.html#torch.cuda.memory_cached" title="torch.cuda.memory_cached"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memory_cached</span></code></a></p></td>
<td><p>Deprecated; see <a class="reference internal" href="generated/torch.cuda.memory_reserved.html#torch.cuda.memory_reserved" title="torch.cuda.memory_reserved"><code class="xref py py-func docutils literal notranslate"><span class="pre">memory_reserved()</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.max_memory_cached"/><a class="reference internal" href="generated/torch.cuda.max_memory_cached.html#torch.cuda.max_memory_cached" title="torch.cuda.max_memory_cached"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_memory_cached</span></code></a></p></td>
<td><p>Deprecated; see <a class="reference internal" href="generated/torch.cuda.max_memory_reserved.html#torch.cuda.max_memory_reserved" title="torch.cuda.max_memory_reserved"><code class="xref py py-func docutils literal notranslate"><span class="pre">max_memory_reserved()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.reset_max_memory_cached"/><a class="reference internal" href="generated/torch.cuda.reset_max_memory_cached.html#torch.cuda.reset_max_memory_cached" title="torch.cuda.reset_max_memory_cached"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_max_memory_cached</span></code></a></p></td>
<td><p>Resets the starting point in tracking maximum GPU memory managed by the caching allocator for a given device.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.reset_peak_memory_stats"/><a class="reference internal" href="generated/torch.cuda.reset_peak_memory_stats.html#torch.cuda.reset_peak_memory_stats" title="torch.cuda.reset_peak_memory_stats"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_peak_memory_stats</span></code></a></p></td>
<td><p>Resets the "peak" stats tracked by the CUDA memory allocator.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cuda.caching_allocator_alloc"/><a class="reference internal" href="generated/torch.cuda.caching_allocator_alloc.html#torch.cuda.caching_allocator_alloc" title="torch.cuda.caching_allocator_alloc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">caching_allocator_alloc</span></code></a></p></td>
<td><p>Performs a memory allocation using the CUDA memory allocator.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.cuda.caching_allocator_delete"/><a class="reference internal" href="generated/torch.cuda.caching_allocator_delete.html#torch.cuda.caching_allocator_delete" title="torch.cuda.caching_allocator_delete"><code class="xref py py-obj docutils literal notranslate"><span class="pre">caching_allocator_delete</span></code></a></p></td>
<td><p>Deletes memory allocated using the CUDA memory allocator.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="nvidia-tools-extension-nvtx">
<h2>NVIDIA Tools Extension (NVTX)<a class="headerlink" href="#nvidia-tools-extension-nvtx" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.cuda.nvtx.mark.html#torch.cuda.nvtx.mark" title="torch.cuda.nvtx.mark"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nvtx.mark</span></code></a></p></td>
<td><p>Describe an instantaneous event that occurred at some point.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.cuda.nvtx.range_push.html#torch.cuda.nvtx.range_push" title="torch.cuda.nvtx.range_push"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nvtx.range_push</span></code></a></p></td>
<td><p>Pushes a range onto a stack of nested range span.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.cuda.nvtx.range_pop.html#torch.cuda.nvtx.range_pop" title="torch.cuda.nvtx.range_pop"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nvtx.range_pop</span></code></a></p></td>
<td><p>Pops a range off of a stack of nested range spans.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="jiterator-beta">
<h2>Jiterator (beta)<a class="headerlink" href="#jiterator-beta" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.cuda.jiterator._create_jit_fn.html#torch.cuda.jiterator._create_jit_fn" title="torch.cuda.jiterator._create_jit_fn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">jiterator._create_jit_fn</span></code></a></p></td>
<td><p>Create a jiterator-generated cuda kernel for an elementwise op.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.cuda.jiterator._create_multi_output_jit_fn.html#torch.cuda.jiterator._create_multi_output_jit_fn" title="torch.cuda.jiterator._create_multi_output_jit_fn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">jiterator._create_multi_output_jit_fn</span></code></a></p></td>
<td><p>Create a jiterator-generated cuda kernel for an elementwise op that supports returning one or more outputs.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="stream-sanitizer-prototype">
<h2>Stream Sanitizer (prototype)<a class="headerlink" href="#stream-sanitizer-prototype" title="Permalink to this heading">¶</a></h2>
<p>CUDA Sanitizer is a prototype tool for detecting synchronization errors between streams in PyTorch.
See the <a class="reference internal" href="cuda._sanitizer.html"><span class="doc">documentation</span></a> for information on how to use it.</p>
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