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<div class="section" id="cuda-environment-variables">
<span id="id1"></span><h1>CUDA Environment Variables<a class="headerlink" href="#cuda-environment-variables" title="Permalink to this heading">¶</a></h1>
<p>For more information on CUDA runtime environment variables, see <a class="reference external" href="https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars">CUDA Environment Variables</a>.</p>
<p><strong>PyTorch Environment Variables</strong></p>
<table class="docutils colwidths-auto align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Variable</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">PYTORCH_NO_CUDA_MEMORY_CACHING</span></code></p></td>
<td><p>If set to <code class="docutils literal notranslate"><span class="pre">1</span></code>, disables caching of memory allocations in CUDA. This can be useful for debugging.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">PYTORCH_CUDA_ALLOC_CONF</span></code></p></td>
<td><p>For a more in depth explanation of this environment variable, see <a class="reference internal" href="notes/cuda.html#cuda-memory-management"><span class="std std-ref">Memory management</span></a>.</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">PYTORCH_NVML_BASED_CUDA_CHECK</span></code></p></td>
<td><p>If set to <code class="docutils literal notranslate"><span class="pre">1</span></code>, before importing PyTorch modules that check if CUDA is available, PyTorch will use NVML to check if the CUDA driver is functional instead of using the CUDA runtime. This can be helpful if forked processes fail with a CUDA initialization error.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">TORCH_CUDNN_V8_API_LRU_CACHE_LIMIT</span></code></p></td>
<td><p>The cache limit for the cuDNN v8 API. This is used to limit the memory used by the cuDNN v8 API. The default value is 10000, which roughly corresponds to 2GiB assuming 200KiB per ExecutionPlan. Set to <code class="docutils literal notranslate"><span class="pre">0</span></code> for no limit or a negative value for no caching.</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">TORCH_CUDNN_V8_API_DISABLED</span></code></p></td>
<td><p>If set to <code class="docutils literal notranslate"><span class="pre">1</span></code>, disables the cuDNN v8 API. And will fall back to the cuDNN v7 API.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">TORCH_ALLOW_TF32_CUBLAS_OVERRIDE</span></code></p></td>
<td><p>If set to <code class="docutils literal notranslate"><span class="pre">1</span></code>, forces TF32 enablement, overrides <code class="docutils literal notranslate"><span class="pre">set_float32_matmul_precision</span></code> setting.</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">TORCH_NCCL_USE_COMM_NONBLOCKING</span></code></p></td>
<td><p>If set to <code class="docutils literal notranslate"><span class="pre">1</span></code>, enables non-blocking error handling in NCCL.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">TORCH_NCCL_AVOID_RECORD_STREAMS</span></code></p></td>
<td><p>If set to <code class="docutils literal notranslate"><span class="pre">0</span></code>, enables fallback to record streams-based synchronization behavior in NCCL.</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">TORCH_CUDNN_V8_API_DEBUG</span></code></p></td>
<td><p>If set to <code class="docutils literal notranslate"><span class="pre">1</span></code>, sanity check whether cuDNN V8 is being used.</p></td>
</tr>
</tbody>
</table>
<p><strong>CUDA Runtime and Libraries Environment Variables</strong></p>
<table class="docutils colwidths-auto align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Variable</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">CUDA_VISIBLE_DEVICES</span></code></p></td>
<td><p>Comma-separated list of GPU device IDs that should be made available to CUDA runtime. If set to <code class="docutils literal notranslate"><span class="pre">-1</span></code>, no GPUs are made available.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">CUDA_LAUNCH_BLOCKING</span></code></p></td>
<td><p>If set to <code class="docutils literal notranslate"><span class="pre">1</span></code>, makes CUDA calls synchronous. This can be useful for debugging.</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">CUBLAS_WORKSPACE_CONFIG</span></code></p></td>
<td><p>This environment variable is used to set the workspace configuration for cuBLAS per allocation. The format is <code class="docutils literal notranslate"><span class="pre">:[SIZE]:[COUNT]</span></code>.
As an example, the default workspace size per allocation is <code class="docutils literal notranslate"><span class="pre">CUBLAS_WORKSPACE_CONFIG=:4096:2:16:8</span></code> which specifies a total size of <code class="docutils literal notranslate"><span class="pre">2</span> <span class="pre">*</span> <span class="pre">4096</span> <span class="pre">+</span> <span class="pre">8</span> <span class="pre">*</span> <span class="pre">16</span> <span class="pre">KiB</span></code>.
To force cuBLAS to avoid using workspaces, set <code class="docutils literal notranslate"><span class="pre">CUBLAS_WORKSPACE_CONFIG=:0:0</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">CUDNN_CONV_WSCAP_DBG</span></code></p></td>
<td><p>Similar to <code class="docutils literal notranslate"><span class="pre">CUBLAS_WORKSPACE_CONFIG</span></code>, this environment variable is used to set the workspace configuration for cuDNN per allocation.</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">CUBLASLT_WORKSPACE_SIZE</span></code></p></td>
<td><p>Similar to <code class="docutils literal notranslate"><span class="pre">CUBLAS_WORKSPACE_CONFIG</span></code>, this environment variable is used to set the workspace size for cuBLASLT.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">CUDNN_ERRATA_JSON_FILE</span></code></p></td>
<td><p>Can be set to a file path for an errata filter that can be passed to cuDNN to avoid specific engine configs, used primarily for debugging or to hardcode autotuning.</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">NVIDIA_TF32_OVERRIDE</span></code></p></td>
<td><p>If set to <code class="docutils literal notranslate"><span class="pre">0</span></code>, disables TF32 globally across all kernels, overriding all PyTorch settings.</p></td>
</tr>
</tbody>
</table>
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