PyTorch Profiler Version 1.9: Unveiling New Features to Optimize Model Performance

The latest release of PyTorch Profiler, version 1.9, has finally arrived, and it's packed with exciting new features designed to help data scientists identify and visualize the most costly execution steps in their models. The primary goal of this release is to assist in pinpointing performance bottlenecks that may be causing issues such as out-of-memory errors or slow model training times.

One of the key additions to PyTorch Profiler is the Memory View feature. This new tool allows users to gain insights into the time and memory consumption of specific operators within their models, enabling them to tackle performance bottlenecks more effectively. By utilizing this feature, data scientists can quickly identify which exact operator or operation is contributing to high memory usage or slow execution times, facilitating targeted optimizations in their code.

The Memory View feature is particularly useful for identifying issues with distributed training, where debugging can be complex and challenging without a clear understanding of the performance bottlenecks. With PyTorch Profiler, users can observe issues within each individual node, providing a detailed view of the problems that need to be addressed. This level of granularity enables data scientists to diagnose the root cause of their performance issues more efficiently.

In addition to the Memory View feature, PyTorch Profiler also introduces the GPU Utilization View. This new tool allows users to observe GPU-level metrics at every step of their model's execution, providing an in-depth understanding of potential bottlenecks and areas for optimization. The GPU Utilization View is particularly useful when dealing with complex models or those that require significant GPU resources.

For instance, consider a ResNet-50 model trained on a batch size of four, where the overall GPU utilization is low, suggesting potential performance issues. By increasing the batch size to 32, users may find that the GPU utilization increases, but not quite reaching the desired level of 100%. In this scenario, the Trace View feature comes into play, enabling users to examine the overall utilization view in 10-millisecond buckets and identify unusual dips or anomalies.

Upon zooming in on specific kernels within the model, users can gain further insights into the GPU utilization patterns. By examining the SM efficiency, they can uncover finer details about each kernel's performance, including idle times between kernel executions. This level of granularity allows data scientists to pinpoint the exact causes of their performance bottlenecks and implement targeted optimizations in their code.

The new release of PyTorch Profiler marks a significant step forward in the development of tools designed to help data scientists optimize their model performance. By providing a comprehensive suite of features, including Memory View, GPU Utilization View, and Trace View, PyTorch Profiler empowers users to tackle complex performance issues with greater ease and accuracy.

As data scientists continue to push the boundaries of what is possible with deep learning models, the importance of accurate performance diagnosis and optimization cannot be overstated. With PyTorch Profiler version 1.9, users now have access to a suite of powerful tools designed to help them identify and address performance bottlenecks in their models. By leveraging these features, data scientists can unlock significant improvements in model performance and efficiency, ultimately driving breakthroughs in AI research and development.

To learn more about PyTorch Profiler version 1.9 and explore its capabilities, be sure to check out the latest release on GitHub at Kineto. With its comprehensive set of features and user-friendly interface, this tool is poised to become an essential part of any data scientist's toolkit.

"WEBVTTKind: captionsLanguage: enpytorch profiler version 1.9 has finally been released the goal of this release is to help data scientists target and visualize their execution steps that are the most costly in time and memory now let's take a look at a few new features memory view is an added feature that allows you to understand the time and memory consumption that may have caused performance bottlenecks like out of memory issues or your model taking a really long time to execute this memory view allows you to see which exact operator by the name is contributing to these high consumptions of time and memory so now perhaps speeding your model training is your goal so you conduct distributed training however debugging can be very complex and hard to diagnose without a distributed view like this you can actually observe issues within each individual node so each of these views as you can see over here can give you different information that can help you diagnose the reason for your bottleneck for example in this view over here if one of the computation and overlapping time of one worker is larger than the other this to a data scientist can suggest an issue in the workload not being balanced or a worker being a straggler which is an issue that needs to be optimized in the code now sometimes performance issues are beyond memory and node level issues in your model and perhaps you need to observe issues on the gpu level at every step and this is where the gpu utilization view comes in imagine you have a resin fifty model with a batch size of four and all of these important gpu metrics are low there's clearly a bottleneck since your goal is to get 100 gpu utilization down here it suggests us that perhaps increasing our batch size will reduce our bottleneck the next run will show 32 batch size and the utilization actually increased which is great but it's still not 100 so to further diagnose the trace view will allow you to see the overall utilization view in 10 millisecond buckets and you can see that there's an unusual dip in this area so let's investigate by zooming in the utilization in this kernel is 11 and beside it is around 50 but in order for us to get more finer detail we need to look at the sm efficiency which gives us finer details on each kernel and you can see that the reason why the utilization is not 100 is because of how sparse it is and the idle time between the eye the kernels as you can see performance issues are normally a black box and having the new release of pytorch profiler will allow you to diagnose and optimize your code better don't forget to check us out on github at kineto for more information and samplespytorch profiler version 1.9 has finally been released the goal of this release is to help data scientists target and visualize their execution steps that are the most costly in time and memory now let's take a look at a few new features memory view is an added feature that allows you to understand the time and memory consumption that may have caused performance bottlenecks like out of memory issues or your model taking a really long time to execute this memory view allows you to see which exact operator by the name is contributing to these high consumptions of time and memory so now perhaps speeding your model training is your goal so you conduct distributed training however debugging can be very complex and hard to diagnose without a distributed view like this you can actually observe issues within each individual node so each of these views as you can see over here can give you different information that can help you diagnose the reason for your bottleneck for example in this view over here if one of the computation and overlapping time of one worker is larger than the other this to a data scientist can suggest an issue in the workload not being balanced or a worker being a straggler which is an issue that needs to be optimized in the code now sometimes performance issues are beyond memory and node level issues in your model and perhaps you need to observe issues on the gpu level at every step and this is where the gpu utilization view comes in imagine you have a resin fifty model with a batch size of four and all of these important gpu metrics are low there's clearly a bottleneck since your goal is to get 100 gpu utilization down here it suggests us that perhaps increasing our batch size will reduce our bottleneck the next run will show 32 batch size and the utilization actually increased which is great but it's still not 100 so to further diagnose the trace view will allow you to see the overall utilization view in 10 millisecond buckets and you can see that there's an unusual dip in this area so let's investigate by zooming in the utilization in this kernel is 11 and beside it is around 50 but in order for us to get more finer detail we need to look at the sm efficiency which gives us finer details on each kernel and you can see that the reason why the utilization is not 100 is because of how sparse it is and the idle time between the eye the kernels as you can see performance issues are normally a black box and having the new release of pytorch profiler will allow you to diagnose and optimize your code better don't forget to check us out on github at kineto for more information and samples\n"