New Features
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The Channel Inspector can display summary and per-channel statistics about a layer's output tensor: mean, minimum/maximum, standard deviation, and sparsity (percentage of tensor elements close to zero).
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Output tensor shapes are now visible during editing.
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ConverenceNG can now save network outputs as .npy files.
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Users can now expand or collapse the parameters list of a layer glyph in the editor view.
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In Channel Inspector, users can now toggle a checkbox to perform auto scale and shift of the channels.
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Users can now save a template as a file that can be imported into another model.
We have added more data to network profiling reports:
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Per-layer device memory footprints
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Whole-network device memory footprint
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Percentage view for inference timings
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Layers' distance from the nearest input, for sorting by network depth
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Template-level inference timings
NvNeural Changes
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Plugin initialization has been refactored to reduce its reliance on translation-unit-scoped static initialization. The ExportPlugin framework now expects user plugin code to provide an implementation of the function void nvneural::plugin::InitializePluginTypes(). This function should call static ClassRegistry methods to make its export types visible to the client application.
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The SkipConcatenation optimization has been rewritten. Custom concatenation layers should implement nvneural::IConcatenationLayer2 to participate in this optimization.
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We added two new analysis layers: Saliency Generator and Saliency Mix. The Saliency Generator layer converts their input tensors into a single-channel tensor with the same H and W as their inputs. The Saliency Mix layer simply overlays saliency information (output of a Saliency Generator) onto another input tensor.
Known Issues
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When you have a high resolution monitor and you set the DPI scaling at larger than 100%, you may see rendering corruptions inside the editor.
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The DirectML exporter does not currently honor the apply_bias layer parameter during code generation.
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Command lines containing spaces are not quoted correctly for copy/paste in dialog boxes.
Platform Support
Windows 10: 20H1 or newer
Linux: Ubuntu 18.04 LTS or newer
Recommended Display Driver
You must have a recent NVIDIA display driver installed on your system to run NVIDIA Nsight Deep Learning Designer. The following display drivers are recommended:
Windows: Release 472 or newer
Linux: Release 470 or newer
Notices
Notice
ALL NVIDIA DESIGN SPECIFICATIONS, REFERENCE BOARDS, FILES, DRAWINGS, DIAGNOSTICS, LISTS, AND OTHER DOCUMENTS (TOGETHER AND SEPARATELY, "MATERIALS") ARE BEING PROVIDED "AS IS." NVIDIA MAKES NO WARRANTIES, EXPRESSED, IMPLIED, STATUTORY, OR OTHERWISE WITH RESPECT TO THE MATERIALS, AND EXPRESSLY DISCLAIMS ALL IMPLIED WARRANTIES OF NONINFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE.
Information furnished is believed to be accurate and reliable. However, NVIDIA Corporation assumes no responsibility for the consequences of use of such information or for any infringement of patents or other rights of third parties that may result from its use. No license is granted by implication of otherwise under any patent rights of NVIDIA Corporation. Specifications mentioned in this publication are subject to change without notice. This publication supersedes and replaces all other information previously supplied. NVIDIA Corporation products are not authorized as critical components in life support devices or systems without express written approval of NVIDIA Corporation.