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docs: [Automated] Regenerating documenation for 7861ab3
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Signed-off-by: TRTorch Github Bot <[email protected]>
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TRTorch Github Bot committed Aug 6, 2021
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2 changes: 1 addition & 1 deletion docs/_notebooks/Resnet50-example.html
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</div>
</div>
<p>
<img alt="eac40fcdfb9d444fafdced53ee615533" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
<img alt="8806a948f32744bc9364d1d91489d05d" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
</p>
<section id="TRTorch-Getting-Started---ResNet-50">
<h1 id="notebooks-resnet50-example--page-root">
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2 changes: 1 addition & 1 deletion docs/_notebooks/lenet-getting-started.html
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</div>
</div>
<p>
<img alt="d612ebd4ae644e6f90790f015213aa9f" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
<img alt="5a196ac0b5e945e69ff2d98c8de96e9c" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
</p>
<section id="TRTorch-Getting-Started---LeNet">
<h1 id="notebooks-lenet-getting-started--page-root">
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2 changes: 1 addition & 1 deletion docs/_notebooks/ssd-object-detection-demo.html
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</div>
</div>
<p>
<img alt="5877b75a445b497ba8c7b47e376ed418" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
<img alt="d195cbfd5c804f06956a87f57ca4ef17" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
</p>
<section id="Object-Detection-with-TRTorch-(SSD)">
<h1 id="notebooks-ssd-object-detection-demo--page-root">
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10 changes: 9 additions & 1 deletion docs/_sources/tutorials/trtorchc.rst.txt
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Expand Up @@ -39,7 +39,14 @@ to standard TorchScript. Load with ``torch.jit.load()`` and run like you would r
--allow-gpu-fallback (Only used when targeting DLA
(device-type)) Lets engine run layers on
GPU if they are not supported on DLA
--disable-tf32 Prevent Float32 layers from using the
--allow-torch-fallback Enable layers to run in torch
if they are not supported in TensorRT
--ffo,
--forced-fallback-ops List of operators in the graph that
should be forced to fallback to Pytorch for execution
--disable-tf32 Prevent Float32 layers from using the
TF32 data format
-p[precision...],
--enabled-precison=[precision...] (Repeatable) Enabling an operating
Expand All @@ -48,6 +55,7 @@ to standard TorchScript. Load with ``torch.jit.load()`` and run like you would r
calibration-cache argument) [ float |
float32 | f32 | half | float16 | f16 |
int8 | i8 ] (default: float)
-d[type], --device-type=[type] The type of device the engine should be
built for [ gpu | dla ] (default: gpu)
--gpu-id=[gpu_id] GPU id if running on multi-GPU platform
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2 changes: 1 addition & 1 deletion docs/py_api/trtorch.html
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<span class="sig-paren">
)
</span>
→ &lt;torch._C.ScriptClass object at 0x7f4d69cff630&gt;
→ &lt;torch._C.ScriptClass object at 0x7f4c257c0cb0&gt;
<a class="headerlink" href="#trtorch.TensorRTCompileSpec" title="Permalink to this definition">
</a>
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2 changes: 1 addition & 1 deletion docs/searchindex.js

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10 changes: 9 additions & 1 deletion docs/tutorials/trtorchc.html
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--allow-gpu-fallback (Only used when targeting DLA
(device-type)) Lets engine run layers on
GPU if they are not supported on DLA
--disable-tf32 Prevent Float32 layers from using the

--allow-torch-fallback Enable layers to run in torch
if they are not supported in TensorRT
--ffo,
--forced-fallback-ops List of operators in the graph that
should be forced to fallback to Pytorch for execution

--disable-tf32 Prevent Float32 layers from using the
TF32 data format
-p[precision...],
--enabled-precison=[precision...] (Repeatable) Enabling an operating
Expand All @@ -456,6 +463,7 @@ <h1 id="tutorials-trtorchc--page-root">
calibration-cache argument) [ float |
float32 | f32 | half | float16 | f16 |
int8 | i8 ] (default: float)

-d[type], --device-type=[type] The type of device the engine should be
built for [ gpu | dla ] (default: gpu)
--gpu-id=[gpu_id] GPU id if running on multi-GPU platform
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