# Abstract

## Abstract

DLProf Viewer User Guide provides instructions on how to use the DLProf Viewer to analyze performance results gathered by the NVIDIA Deep Learning Profiler.

## 1. DLProf Viewer Overview

### 1.1. Overview

The DLProf Viewer makes it easy to visualize the performance of your models by showing Top 10 operations that took the most time, eligibility of Tensor Core operations and Tensor Core usage, as well as interactive iteration reports.

### 1.2. What's New in 1.5.0

• Removed left sidebar, and moved droplists to top navbar.

### 1.3. Features

This release includes these commands and features:

• Panelized Dashboard Summary View: A summary view comprising several panels that provide a quick overview of the performance results.
• Top-level Key Metrics: The Summary view displays several key metrics that are used to quickly gauge the quality of the performance, including Average Iteration Time and Tensor Core Utilization.
• Top 10 GPU Ops Node: A table in the Summary view lists the top 10 Op Nodes with the most time spent on the GPU.
• Expert Systems Panel: This panel displays any issues detected by the DLProf Expert Systems, along with suggestions on how to address the issues and improve the models performance.
• Iteration Summary Panel: This panel visually displays iterations. Users can quickly see how many iterations are in the model, the iterations that were aggregated/profiled, and the durations of tensor core kernels in each iteration.
• Interactive Tables: All tables in detailed views are completely interactive, allowing the use to sort, filter, and paginate the display.
• Interoperable Tables: Several views have the ability to drill down for more information. Selecting a row in one table will populate the next table with performance information pertaining to the selection.
• Client/server architecture:
• All of the data is now in a relational database.
• Load times have improved for all views.
• GPUs View: Displays GPU utilization and GPU properties of all GPUs used during profiling.

## 2. Quick Start

### 2.1. Installing Using Python Wheel

The DLPROF Viewer can be easily installed from the NVIDIA PY index.

Install nvidia-pyindex.

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$pip install nvidia-pyindex  Next install the DLProf Viewer using pip: Copy Copied!  $ pip install nvidia-dlprofviewer


### 2.2. Using the NGC Docker Container

The following steps are required to use pre-built NGC containers:

Use docker pull to get the TensorFlow container from NGC:

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$docker pull nvcr.io/nvidia/tensorflow:xx.yy>-tf1-py3  Assuming the training data for the model is available in /full/path/to/training/data, you can launch the container with the following command: Copy Copied!  $ docker run --rm --gpus=1 --shm-size=1g --ulimit memlock=-1 \
--ulimit stack=67108864 -it -p8000:8000 -v/full/path/to/training/data:/data \
nvcr.io/nvidia/tensorflow:<xx.yy>-tf1-py3


### 2.3. Generating DLProf Database

The DLProf database is created directly from the Deep Learning Profiler. See the Deep Learning Profiler User Guide for information on how to generate the database.

### 2.4. Starting DLProf Viewer

The DLProf Viewer is installed in all containers that DLProf is installed in the NVIDIA GPU Cloud (NGC). The container must be run with the -p8000:8000 option to open port 8000 for the DLProf Viewer. Use any port such as 8001, 8002, etc.

Usage:

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Usage: dlprofviewer [-h] [--version] [-b ADDRESS] [-p PORT] database

positional arguments:
database              Specify path to DLProf database.

optional arguments:
-h, --help            Show this help message and exit
--version             Show program's version number and exit
Specify alternate bind address. Use '0.0.0.0' to serve to the entire local network. [default: localhost]
-p PORT, --port PORT  Specify alternate port. [default: 8000]


The DLProf Viewer server is launched directly from the container:

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$dlprofviewer dlprof_dldb.sqlite  Where dlprof_dldb.sqlite is the path to the DLProf database. Note: Note: Using this example limits the visibility of this viewer on the network to just ‘localhost’. See the Bind section below for information on how to increase the visibility of this DLProf database on your network. ### 2.5. Bind If you want anyone on your local network to be able to access your dlprofviewer server in their browser, launch with -b 0.0.0.0: Copy Copied!  $ dlprofviewer -b 0.0.0.0 dlprof_dldb.sqlite


Once the server is running, the DLProf app can be viewed in a browser using the URL:

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http://<machine IP Address>:8000


### 2.6. Port

You can also specify a custom port with -p PORT, e.g. (port 8001):

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\$ dlprofviewer -b 0.0.0.0 -p 8001 dlprof_dldb.sqlite


Then use your browser to go to this URL:

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http://<machine IP Address>:8000


### 2.7. How to Launch DLProf Viewer

The DLProf Viewer is installed in all containers that DLProf is installed in the NVIDIA GPU Cloud (NGC). The container must be run with the -p8000:8000 option to open port 8000 for the DLProf Viewer. Use any port such as 8001, 8002, etc.

Server Location Browser and Server on the same machine Browser and Server on different machines
Server inside docker
• Run docker with -p=<port>:<port>. Substitute <port> for the port you want to use

• Run dlprofviewer with -b hostname -i

• Go to http://localhost:<port> in browser

• Run docker with -p=<port>:<port>. Substitute <port> for the port you want to use

• Run dlprofviewer with -b 0.0.0.0

• Go to http://<server machine’s ip>:<port> in browser
Server outside docker
• No -b option needed when running dlprofviewer

• Go to http://localhost:<port> in browser

• Run dlprofviewer with -b 0.0.0.0

• Go to http://<server machine’s ip>:<port> in browser

### 2.8. Backwards Compatibility

Some older versions of the DLProf database can be used as input to the latest dlprofviewer:

• A DLProf database’s initial version is the version of DLProf used to create it.
• DLProf databases v1.2+ can be used as input to DLProf CLI v1.2+
• The DLProf database version should still be less than or equal to the dlprofviewer version.
• DLProf databases v1.0 and v1.1 cannot be used as input to dlprofviewer v1.2+.
• When an older DLProf database is used as input, some minor changes will be made to it to allow it to be used correctly with the latest dlprofviewer.
• Backwards compatibility gets “reset” each major release.
• dlprofviewer v2.x will not support DLProf databases v1.y.

## 3. DLProf Viewer

This section describes each of the available views in the DLProf Viewer.

### 3.1. Terms and Definitions

Term Definition
Aggregation The rollup of metrics, given an iteration start, iteration stop, and key node.
Op Node A node in the graph where an operation is performed on the incoming tensor.
Model, Graph, Network <synonyms>

### 3.2. Pane Overview

The DLProf Viewer user interface is divided into two panes:

 Pane Definintion Content Pane Otherwise known as the VIEWS pane, this is where you will see all of the different pieces of the profiled neural network. Details Pane This pane is to display additional details about the information selected in the content pane. Let’s say you’re looking at the Kernel Summary panel in the content pane, and you want to see more information about the kernels. Click on the button (tooltip = ‘Show Kernel Details’). The detailed kernel information will appear in the Details Pane. Click the ‘X’ button to remove the details pane.

This standard navigation bar is a navigation header that is placed at the top of the viewer. It contains a few buttons like most standard web sites. It does not scroll off the screen so these buttons are always available.

UI Controls

### 3.4.1. Dashboard

The Dashboard view provides a high level summary of the performance results in a panelized view. This view serves as a starting point in analyzing the results and provides several key metrics.

### 3.4.1.1. GPU Utilization Panel

The GPU Idle panel visually indicates the percentage of GPU utilization time during execution of aggregated iterations. Hovering over a slice in the chart will show the numeric percentage.

Fields

 Legend Label Definition Using GPU The average GPU utilization percentage across all GPUs. Idle GPU The average GPU idle percentage across of all GPUs.

UI Controls

 Control Definition Legend Label Toggle between hiding and showing legend entry in chart. More... Show drop-down menu of more views (only visible when more than one GPU was used during profiling).

### 3.4.1.2. Resource Usage Breakdown Panel

The panel provides a Breakdown of profile activity into resource categories.

Fields

 Legend Label Definition Using Tensor Cores Accumulated duration of all kernels using tensor cores. Not Using Tensor Cores Accumulated duration of all kernels not using tensor cores. Memory Accumulated duration of all memory operations. CPU Accumulated duration of all CPU operations. Other All time not in any of the other categories.

UI Controls

 Control Definition Legend Label Toggle between hiding and showing legend entry in chart.

### 3.4.1.3. Total Kernel GPU Time Panel

The panel provides key metrics about the kernels in the network aggregated over the specific iteration range.

• Hovering over a slice in the chart will show the aggregated GPU time.
• Clicking a legend item will toggle its visualization in the chart.

Fields

 Legend Label Definition Using Tensor Cores Aggregates the total GPU time for all kernels using Tensor Cores. Memory Kernels Aggregates the total GPU time for all memory-related kernels. All Other Kernels Aggregates the total GPU time for all remaining kernel types.

UI Controls

 Control Definition Show Kernel Details panel in Details Pane. Legend Label Toggle between hiding and showing legend entry in chart. More... Show drop-down menu of more views.

### 3.4.1.4. Tensor Core Kernel Efficiency Panel

• Hovering over a slice in the chart will show the percentage.
• Clicking a legend item will toggle its visualization in the chart.

### Performance Summary Panel

The Performance Summary panel provides top level key metrics about the performance data aggregated over the specific iteration range. A helpful tooltip text will appear when hovering over the ‘i’ icon.

 Field Definition Wall Clock Time This is the total run time for the aggregation range, and is defined as the time between the start time of the first op in the starting iteration on the CPU and the end time of the last op in the final iteration on either the CPU or GPU, whichever timestamp is greatest. Tensor Core Kernel Efficiency % This high level metric represents the utilization of Tensor Core enabled kernels. Tensor Core operations can provide a performance improvement and should be used when possible. This metric is calculated by: [Total GPU Time for Tensor Core kernels] / [Total GPU Time for Tensor Core Eligible Ops] A 100% Tensor Core Utilization means that all eligible Ops are running only Tensor Core enabled kernels on the GPU. A 50% Tensor Core Utilization can mean anything from all eligible Ops are running Tensor Core kernels only half of the time to only half of all eligible Ops are running Tensor Core kernels only. This metric should be used with the Op Summary Panel to determine the quality of Tensor Core usage. Higher is better. GPU Utilization % Average GPU utilization across all GPUs. Higher is better. Total Iterations The total number of iterations found in the network. Profiled Iterations The total number of iterations used to aggregate the performance results. This number is calculated using ‘Start Iteration’ and ‘Stop Iteration’. Start Iteration The starting iteration number used to generate performance results. Stop Iteration The ending iteration number used to generate performance results. Average Iteration Time The average iteration time is the total Wall Time divided by the number of iterations.

### 3.4.1.6. Iteration Summary panel

This panel visually displays iterations. Users can quickly see how many iterations are in the model, the iterations that were aggregated/profiled, and the durations of tensor core kernels in each iteration. The colors on this panel match the colors on all the other dashboard panels.

### 3.4.1.7. Top 10 GPU Ops Panel

Top 10 GPU Ops table shows the top 10 operations with the largest execution times on the GPU. This table comes pre-sorted with the order of each row in descending GPU Time. The table is not sortable or searchable.

 Column Definition GPU Time Shows total GPU time of all kernels across all GPUs. Op Name The name of the op. Direction The fprop/bprop direction of the op. (only visible on PyTorch runs). Op Type The type of the op. Calls The number of times the op was called. TC Eligible A true/false field indicating whether or not the op is eligible to use Tensor Core kernels. Using TC A true/false field indicating whether or not one of the kernels launched in this op is using Tensor Cores.

### 3.4.1.8. System Configuration Panel

 Field Definition Profile Name (Optional) Helpful label to describe the profiled network. The value in this field corresponds to the value supplied in the --profile_name command line argument in DLProf. GPU Count The number of GPU devices found on the computer during training. GPU Name(s) A list of the GPU devices found on the computer during training. CPU Model The model of the CPU on the computer during training. GPU Driver Version The version of the driver used for NVIDIA Graphics GPU. Framework The framework used to generate profiling data (eg, TensorFlow, PyTorch). CUDA Version The version of the CUDA parallel computing platform. cuDNN Version The version of CUDA Deep Neural Network used during training. NSys Version The version of Nsight Systems used during training. DLProf Version The version of the Deep Learning Profiler used to generate the data visualized in the DLProf Viewer. DLProf DB Version The version of the DLProf database. DLProf Viewer Version The version of the DLProf Viewer.

### 3.4.1.9. Expert Systems panel

The Expert Systems panel displays common issues detected in the profiled network and provides potential solutions and suggestions to address the issues. The panel will only show issues that have been detected by DLProf. For a full list of potential issues that DLProf looks for, see the Expert Systems section in the Deep Learning Profiler User Guide.

Clicking on the double-green arrows shows additional information about the detected problem.

 Column Definition Problem The description of the scenario that DLProf detected when profiling the network. >> (Optional) When present, clicking on the double arrows will display a new view displaying the problem in detail. Recommendation A recommendation or actionable feedback, a tangible suggestion that the user can do to improve the network. Clicking on a hyperlink inside the recommendation will open a new tab in the browser.

### 3.4.1.10. Guidance Panel

This panel provides static guidance to the user to help the user learn more about Tensor Cores, Mixed Precision training. The panel has hyperlinks for further reading. Clicking on a hyperlink inside the Guidance Panel will open a new tab in the browser.

### 3.4.2. Op Type Summary

This table aggregates metrics over all op types and enables users to see the performance of all the ops in terms of its types, such as Convolutions, Matrix Multiplications, etc.

Op Type Data Table

Column name Description
Op Type The operation type.
No. Ops The number of ops that have the Op Type above.
No. Calls Number of instances that the operation was called / executed.
Total CPU Time (ns) The total CPU time of all instances of this op type.
Avg. CPU Time (ns) The average CPU time of all instances of this op type.
Min CPU Time (ns) The minimum CPU time found amongst all instances of this op type.
Max CPU Time (ns) The maximum CPU time found amongst all instances of this op type.
Total GPU Time (ns) The total GPU time of all instances of this op type.
Avg. GPU Time (ns) The average GPU time of all instances of this op type.
Min GPU Time (ns) The minimum GPU time found amongst all instances of this op type.
Max GPU Time (ns) The maximum GPU time found amongst all instances of this op type.
Total CPU Overhead Time (ns) The total CPU overhead of all instances of this op type.
Avg. CPU Overhead Time (ns) The average CPU overhead of all instances of this op type.
Min CPU Overhead Time (ns) The minimum CPU overhead found amongst all instances of this op type.
Max CPU Overhead Time (ns) The maximum CPU overhead found amongst all instances of this op type.
Total GPU Idle Time (ns) The total GPU idle time of all instances of this op type.
Avg. GPU Idle Time (ns) The average GPU idle time of all instances of this op type.
Min GPU Idle Time (ns) The minimum GPU idle time found amongst all instances of this op type.
Max GPU Idle Time (ns) The maximum GPU idle time found amongst all instances of this op type.

### 3.4.3. Ops and Kernels

This view enables users to view, search, sort all ops and their corresponding kernels in the entire network.

### 3.4.3.1. Ops Data Table

When a row is selected in the Ops table, a summary of each kernel for that op is displayed in the bottom table.

Entry Description
GPU Time (ns) Cumulative time executing all GPU kernels launched for the op.
CPU Time (ns) Cumulative time executing all op instances on the CPU.
Op Name The name of the op.
Direction

The fprop/bprop direction of the op. (only visible on PyTorch runs).

Op Type The operation of the op.
Calls The number of times the op was called.
TC Eligible A true/false field indicating whether or not the op is eligible to use Tensor Core kernels. To filter, type ‘1’ for true, and ‘0’ for false.
Using TC A true/false field indicating whether or not one of the kernels launched in this op is using Tensor Cores. To filter, type ‘1’ for true, and ‘0’ for false.
Kernel Calls The number of kernels launched in the op.
Data Type The data type of this op (eg, float16, int64, int32)
Stack Trace

The stack trace of the op. (only visible on PyTorch runs).

If the contents of this cell is more than 100 characters, a ‘See More’ hyperlink appears. When clicked, the full contents of the cell appears. When the cell is expanded, the hyperlink text is changed to ‘See Less’. When clicked, the cell collapses back to the first 100 characters.

### 3.4.3.2. Kernel Summaries Data Table

Kernels Summary table

Entry Description
Kernel name The full name of the kernel.
Using TC A true/false field indicating whether or not the kernel is actually using a Tensor Core. To filter, type ‘1’ for true, and ‘0’ for false.
Calls The number of times this kernel was launched.
GPU Time (ns) The aggregate duration of each time the kernel was launched.
Avg (ns) The average duration of each time this kernel was launched.
Min (ns) The minimum duration of all kernel launches.
Max (ns) The maximum duration of all kernel launches.

### 3.4.4. Kernels by Iteration

The Kernels by Iterations view shows operations and their execution time for every iteration. At a glance, you can compare iterations with respect to time as well as Kernels executed.

### 3.4.4.1. Iteration Summary Data Table

To see the kernels for a specific iteration, click a row in the top table. The ‘Kernels in Selected Iteration’ table will be filled with the kernels from the selection iteration.

Column Description
Iteration The iteration interval numbers.
Timestamp The exact time when this iteration started.
Duration The length of time it took for this iteration to execute. Units on this column are dynamic.
Total Kernels The number of GPU kernels called during this iteration.
TC Kernels The number of GPU Tensor Core kernels called during the iteration.
Average GPU Time (μs) Average execution time for all kernels across all GPUs during the iteration. The contents of this cell are broken into multiple pieces:
1. The value is the average execution time of this iteration across all GPUs.
2. The progress meter visually indicates how much of this iteration executed on the GPU.
3. The percentage is the average GPU time of all kernels across all GPUs over the total iteration time.
4. Hovering over this cell displays a tooltip text with helpful information.

Units on this column are dynamic.

Average TC GPU Time (μs) Average execution time for all Tensor Core kernels across all GPUs during the iteration. The contents of this cell are broken into multiple pieces:
1. The value is the average execution time of Tensor Core kernels during this iteration across all GPUs.
2. The progress meter visually indicates how much of this iteration executed Tensor Core kernels.
3. The percentage is the average GPU time of kernels using Tensor Core across all GPUs over the total iteration time.
4. Hovering over this cell displays a tooltip text with helpful information.

Units on this column are dynamic.

### 3.4.4.2. Kernels in Selected Iteration

Column Description
Op Name The name of the op that launched the kernel.
Kernel Name The name of the kernel.
Device ID the device ID of the kernel.
Kernel Timestamp (ns) The timestamp for when the CUDA API call was made for this kernel in the CPU thread. Useful to see the order in which the kernels were called.
GPU Time (ns) The time spent executing the kernel on the GPU.
Users TC A true/false field indicating whether or not the kernel uses Tensor Cores. To filter, type ‘1’ for true, and ‘0’ for false.
Grid The grid size of the kernel.
Block The block size of the kernel.

### 3.4.5. Kernels by Op

The Kernels by Op view is a variation of the Kernels by Iterations view. It has the capability to filter the list of kernels by iterations and op.

### 3.4.5.1. Iteration Summary Data Table

Selecting an iteration in the Iteration Summary table will populate the Ops in Selected Iteration table with all the profile data for the ops from the selected iteration. Selecting an op in the Ops in Selected Iteration table will populate the Kernels in Selected Op table with the list of kernels and timing data executed for the selected Op and Iteration.

Column Description
Iteration The iteration interval numbers
Timestamp The exact time when this iteration started.
Duration The length of time it took for this iteration to execute. Units on this column are dynamic.
Total Kernels The number of GPU kernels called during this iteration.
TC Kernels The number of GPU Tensor Core kernels called during the iteration.
Total GPU Time (μs) Total execution time for all kernels across all GPUs during the iteration. The contents of this cell are broken into multiple pieces:
1. The value is the average execution time of this iteration across all GPUs.
2. The progress meter visually indicates how much of this iteration executed on the GPU.
3. The percentage is the average GPU time of all kernels across all GPUs over the total iteration time.
4. Hovering over this cell displays a tooltip text with helpful information.

Units on this column are dynamic.

Total TC GPU Time (μs) Total execution time for all Tensor Core kernels across all GPUs during the iteration. The contents of this cell are broken into multiple pieces:
1. The value is the average execution time of Tensor Core kernels during this iteration across all GPUs.
2. The progress meter visually indicates how much of this iteration executed Tensor Core kernels.
3. The percentage is the average GPU time of kernels using Tensor Core across all GPUs over the total iteration time.
4. Hovering over this cell displays a tooltip text with helpful information.

Units on this column are dynamic.

### 3.4.5.2. Ops in Selected Iteration Table

Ops in Selected Iteration

Column Description
Op Name The name of the op that launched the kernel.
Direction

The fprop/bprop direction of the op. (only visible on PyTorch runs).

Op Type The type of the op.
Total Kernels The number of GPU kernels called during this iteration.
TC Kernels The number of GPU Tensor Core kernels called during the iteration.
Total GPU Time (ns) Cumulative execution time for all kernels on the GPU during the op.
TC GPU Time (ns) Cumulative execution time for all Tensor Core kernels on the GPU during the op.
Data Type The data type of this op (eg, float16, int64, int32).
Stack Trace

The stack trace of the op. (only visible on PyTorch runs)

If the contents of this cell is more than 100 characters, a ‘See More’ hyperlink appears. When clicked, the full contents of the cell appears. When the cell is expanded, the hyperlink text is changed to ‘See Less’. When clicked, the cell collapses back to the first 100 characters.

### 3.4.5.3. Kernels Selected Iteration / Op Combination Table

Kernels in Selected Iteration / Op combination

Column Description
Kernel Name The name of the kernel.
Device ID

The device ID of the kernel.

Kernel Timestamp (ns)

The timestamp for when the CUDA API call was made for this kernel in the CPU thread. Useful to see the order in which the kernels were called.

GPU Time (ns)

The time spent executing the kernel on the GPU.

Uses TC A true/false field indicating whether or not the kernel uses Tensor Cores. To filter, type ‘1’ for true, and ‘0’ for false.
Grid

The grid size of the kernel.

Block

The block size of the kernel.

### 3.4.6. Iterations view

This view displays iterations visually. Users can quickly see how many iterations are in the model, the iterations that were aggregated/profiled, and the accumulated durations of tensor core kernels in each iteration.

Fields

 Field Definition X-Axis The iterations of the model. Y-Axis Duration of iterations. The unit of this axis is dynamic (e.g., ms, ns, s, etc). Badges In the top right, four badges display iteration specifics of the profiled network. Total Profiled Start Stop The values inside the start and stop badges come directly from DLProf: see the --iter_start and --iter_stop command line options. Legend Dark Green: Accumulated duration of all kernels using tensor cores Light Green: Accumulated duration of all kernels not using tensor cores Blue: Accumulated duration of all memory operations Orange: Accumulated duration of all CPU operations Pink: All time not in any of the other categories Gray: Duration of iteration outside the profile. Black: User-positioned aggregation boundary. See ‘Select Iter Start’ and ‘Select Iter Stop’ below.

UI Controls

 Control Definition Toggle Buttons All Iterations: Show all the iterations in the entire network Profiled Iterations: Zooms in to only show the iterations that were profiled Hover When mouse hovers over an iteration, a popup window appears displaying the values of all the constituents of the iteration. Select Iter Start Specification of a new “iteration start” value. Used for re-aggregating the data inside the viewer. The value can be changed by typing into the field, by clicking the up/down arrow spinners, or by hovering the cursor over the field and spinning the mouse wheel. When this value changes, the left black bar will move accordingly. Select Iter Stop Specification of a new “iteration stop” value. Used for re-aggregating the data inside the viewer. The value can be changed by typing into the field, by clicking the up/down arrow spinners, or by hovering the cursor over the field and spinning the mouse wheel. When this value changes, the right black bar will move accordingly. Select Key Node The existing key node value is displayed in a read-only field. Sometimes key nodes can be very long. If a long key node is selected, the first few characters of the key node will appear in the field. The entire length of the key node field will appear when the cursor hovers over the field. Click the Key Node Picker button to view and pick a new key node. See the dialog below. Reaggregate Instructs the DLProf back-end server to reaggregate the network with the specified parameters.

Workflow

Sometimes the original aggregation parameters on the DLProf command-line specified an iteration start value, an iteration stop value, or even key node that yielded a suboptimal representation of the network’s profile (such as including warm-up nodes). This feature allows the user to change those values and re-aggregate. Here is an example:

1. Notice in the Iterations View screenshot above:
1. Iteration eight contains no work, and
2. Iteration nine through fifteen (among others) contain way too much work. The aggregation values throughout the viewer contain those ops and kernels and potentially skews the results.
2. You can change the iteration start and iteration stop values in a number of ways: typing and clicking the up and down spinners. The best way is to hover over the field and spin the mouse wheel.
3. Some platforms do not have a default key node for a neural network. Sometimes the predefined key node is suboptimal. By clicking on the ‘Select Key Node’ picker, a key node can be selected. This is a full-featured panel that allows for filtering, sorting, and pagination to find a Key Node. See the view called Ops and Kernels for more details.
Note:

Note: There are fewer columns here than in the view, but the usability is the same.

4. Once any of these three fields change, click the Reaggregate button. A confirmation dialog is displayed:
5. After confirmation, a message is sent to the back-end DLProf server to reaggregate the profile with these values. This reaggregation process could potentially take minutes, so the following panel appears above all panels:

The viewer is fully functional while the re-aggregation is taking place. The “Underway!” panel will remain in view above all other views and panels until reaggregation is complete.

Note:

Note: Do not click the browser’s BACK or REFRESH button. If either are accidentally, the “Underway!” panel will no longer appear. The re-aggregation will continue regardless.

6. When the reaggregation is complete, a “Success!” panel will appear like this:
7. At this point, clicking the REFRESH button will load all the newly reaggregated data into the viewer. If it’s not already there, the dropdown called Aggregation is displayed in the navigation bar:

### 3.4.7. GPUs View

This view shows the utilization of all GPUs during the profile run. It is broken down into two different but related elements:

• Bar Chart - Quick visualization where you can see the GPU utilization for every GPU used during the profile. This view appears only when more than one GPU is used in a profile.
• Table - This table shows a little more detail about each GPU device, including its name, Compute Capability, and SM count.

### 3.4.8. Details Pane

This pane shows more details about a particular area of the system.

### 3.4.8.1. Kernel Details Panel

This panel provides key metrics about the kernels in the network aggregated over the specific iteration range.

Fields

 Field Definition All Kernels Aggregates the total GPU time and count for all kernels in the network. Kernels Using TC Aggregates the total GPU time and count for all kernels using Tensor Cores. Memory Kernels Aggregates the total GPU time and count for all memory kernels. All Other Kernels Aggregates the total GPU time and count for all remaining kernel types.

UI Controls

 Control Definition X Closes the Details Pane.

### 3.5. Using Data Tables

DataTables are used in many views in the DLProf Viewer. The features in DataTables enable users to quickly find information. Below is a screenshot to see the location of the features, followed by a table describing the functionality of each feature.

 Data Table Feature Definition Showing label The label under the table (bottom left) will show a real-time count of rows in the table. Search text box Filter results by text search. Typing in this field will display only those rows that contain the text in the box. Adding or removing text in the Search box will update the “Showing…” label. Column search Typing into the text box under most column headers will display only those rows with the entered text. This is powerful since users can enter search criteria for multiple columns to zero-in on interesting rows. Columns with ‘x’ and ‘check mark’ are boolean fields. Users can enter "1" to show those rows with a ‘check mark’, and ‘0’ to show only those rows with ‘x’. Sort toggle button Clicking on a column header will sort the table. When clicked, all rows are sorted either ascending or descending. The initial sort on most numeral columns are descending. Show Entries dropdown This dropdown allows the user to display 10, 25, 50, or 100 rows. Changing the setting will update the “Showing…” label. Pagination buttons Previous, next, and page# navigation. Allows users to quickly page through large data sets. Uses ‘Show Entries’ setting and updates the “Showing…” label. Export to buttons Allows users to easily export the data in the datatable to well known formats. An optional profile name is displayed in the header of all exports when the--profile_name command-line argument is used in DLProf. Warning: A slight delay occurs when any of these buttons are clicked on large networks. Enable pop-ups in your browser to export to PDF.

## 4. Troubleshooting FAQ

The database does not contain a legal configuration.

The DLProf Database cannot be found.

An older database was found.

A new database was found.

## Notices

### Notice

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### HDMI

HDMI, the HDMI logo, and High-Definition Multimedia Interface are trademarks or registered trademarks of HDMI Licensing LLC.