[HN Gopher] Show HN: Graphsignal - Machine learning profiler for...
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       Show HN: Graphsignal - Machine learning profiler for training and
       inference
        
       Hi HN, I'm the founder of Graphsignal (https://graphsignal.com).
       Graphsignal is a machine learning profiler. We've created it to
       make ML profiling simple and usable. It provides performance
       summaries, ML operation and kernel level statistics as well as
       detailed resource usage information necessary for making training
       and inference faster and more efficient.  Profilers help fix
       performance issues, improve user experience and reduce computation
       costs. Such improvements benefit machine learning profoundly; model
       training jobs that run for hours or days could be made much shorter
       and inference latency could be reduced resulting in significantly
       lower costs and improved user experience.  I realized the benefits
       in one of my previous projects, where the model would have to be
       trained regularly and be used for inference on huge amount of data.
       Having spent last decade developing profiling and monitoring tools,
       it seemed logical for me to use a profiler for the task. But since
       the training and inference were running remotely, I had a hard time
       using existing ML profilers.  TensorFlow and PyTorch provide built-
       in ML profilers, which utilize NVIDIA's profiling interface (CUPTI)
       under the hood for GPU profiling. One way to use those profilers is
       via locally installed TensorBoard or by logging the profiles.  In
       turn, Graphsignal Profiler
       (https://github.com/graphsignal/graphsignal) uses the built-in
       profilers as well as other tools to enable automatic profiling in
       any environment, including notebooks, training pipelines, periodic
       batch jobs, model serving and so on, without installing additional
       servers/software. It also allows teams to share and collaborate
       online. Basically, the profiles along with environment and usage
       information are be automatically recorded and sent to Graphsignal
       where they are available for analysis.  Trying it out is easy: 1)
       sign up for a free account; 2) add the profiler to your ML code and
       run it; 3) see and analyze the profiles at graphsignal.com.
       Everything is described in the Quick Start Guide
       https://graphsignal.com/docs/profiler/quick-start/.  I'm very
       excited to show it to you here and will appreciate any thoughts,
       comments and feedback!
        
       Author : dmitrim
       Score  : 20 points
       Date   : 2022-03-10 16:18 UTC (1 days ago)
        
 (HTM) web link (graphsignal.com)
 (TXT) w3m dump (graphsignal.com)
        
       | [deleted]
        
       | civilized wrote:
       | Why a web service rather than fully client-side software? $$$
       | business model?
        
         | dmitrim wrote:
         | Many training and inference workloads run in the cloud or on
         | remote servers and profiling them is not straightforward.
         | Having a SaaS makes things much simpler, and also enables
         | additional features, such as team access and sharing. As far as
         | the data privacy is concerned, profiles do not contain any
         | model or raw data, just resource usage, execution statistics,
         | etc., which is acceptable for most of the users to send to a
         | third party. And for the business model, in my opinion, SaaS
         | allows to better monetize the offering and ensure better and
         | up-to-date end product in this case. But this is open, we may
         | consider a free client-side version as well at some point.
        
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       (page generated 2022-03-11 23:02 UTC)