And you're in this overview page, so you have different pages.
In the overview page, we have this performance summary.
We're given the average step time, all of the time combination, compilation time output, time input
time.
Notice how this input time is relatively larger than the others and you'll see that all you'll note
is that your the input time occupies the largest part of the step time.
So that's why you have a recommendation to first focus on reducing this input time.
And then we have 75 7.5% of total step time sample is spent in kernel launch.
It could be due to CPU contention with TF data.
In this case, you may try to set the environment variable to GPU trend mode to GPU private.
Then we have 6.6% of the total step time sample is spent on all of the time.
This could be due to Python execution overhead.
Only 0% of device computation is 16 bit, so you might want to replace more 32 bit operations by 16
bit operations to improve performance.
So this actually means we could make use of mix precision training.
Now we're going to look at expression training in subsequent sections.
We also have this audit tools we could use for reducing the input time, this input pipeline analyzer.
You could click here, you have this input pipeline analyzer.
Now notice that you have the different tools here, so we have the overview page.
Let's go back to overview page.
And then here we have this input pipeline analyzer.
Let's scroll down here we have the input pattern analyzer.
We have this TGF beta bottleneck analysis.
You could click on this.
You see we have this tab button and analysis and then you let's get back to the overview again.
Scroll up and then you have this trace viewer right here.
So you will also find this trace viewer in your scroll here and you have the trace view.
Now get into summary of the input pipeline analysis.
We get to see exactly the breakdown of the input processing time on the host, because we've seen already
from here that the input processing time is kind of ticking up close to 68.4% of the total step time.
So here you could see we have the data processing, which is like the main reason why this input processing
time is that large and we have this different steps we could take.
So what can be done to reduce both components of the host input time encoding data?
That is, you may want to combine small input data chunks into fewer larger chunks.
Data processing.
You may increase number of power calls in the dataset map or process the data offline reading data from
files in advance, reading data from files on demand or the data reading or processing.
And then here we have a more detailed input operation statistics.
So let's click on this scroll up and then you could have this statistics given to you right here.
So as you could see, we know exactly why our input processing is taking up much time.
And based on this different suggestions, we could reduce this time.
Now from here we have the channel stats.
Then we also have this memory profile and then we have the part viewer from the spot viewer.
We have the TensorFlow stats.
And then here we have this TensorFlow data bottleneck analysis.
You could also look at this from here.
You see we have the root pre fetch.
Look at the cell duration here.
Ten microseconds, 36 microseconds.
See, this one now is very large.
So here's our bottleneck.
The level of the mapping and batching shuffling just 118 37, three pre fetching and so on and so forth.
So we now know that the problem comes from the mapping as we are seeing it, as we've seen previously.
And now selecting this trace viewer we have this year and we will make use of this year.
So we could carry this around by clicking on this.
And then you have the arrow, you have this to like pull this from place to place.
You have the zoom.
So you could you have the pan, you have the zoom, you have this timing.
So let's click on the zoom and then you see, see click, you click and you drag to the top.
You could zoom in and zoom out.
Now you notice this strain.
We have values from 100.
Like, let's get back to what we had defined previously.
Here we have 100, 232.
And that's why you notice here we have this train from 100 to 182 to getting back here.
We can zoom.
See you have that.
And then you could click here on the pan and then you pull this to one side.
Now stop.
And right here you could zoom again and then you get to see all those different operations carried out
during a single process, all these different operations carried out during a single training step.
And with this time and tool, you click on this time and tool, you can be able to like, let's zoom
this again and zoom and then drag this here, zoom again.
As we're saying, measure the timing for a given operation.
So once you click here, you just click and then you see you drag and you can measure timing for different
operations.
You see you have the time, you're 166.5 microseconds and that's it.
We now go on the distributions here.
We have batch normalization.
Batch normalization.
Let's check out on this, come to the you see that different biases and weights that you see the kernel
years.
The weights have values which fall under this zone, the values fall under or the values are between
0.30 -0.3 0.34.
The biases between -1.2 about 0.4.
And then for this dance, we have this sort of dance layer.
Here we have this convert to the two, which you could see here instead of the range of values.
And then we have the dense one, we have the dense two, We also have the different value ranges for
both the canals and the biases.
So that's it for this distributions, we have the histograms too.
You could check this out time series and the way this Time series is kind of similar to information
we've seen already.
So here we have this all we could select justice killers.
So here we have the epoch loss, we have the different evaluation accuracy and so on and so forth.
Now click on images.
You select only images not to be shown.
You see, that's why you have no information here.
Click on histogram and then you have the histogram data we've just seen already.
That's it for this section on ten.
So bar ten supporters, other functionalities which we shall explore subsequently.
And thank you for getting right up to this point.