ck VS dslinter

If you have an idle Nvidia GPU and Linux, can you please help the community test the beta version of the open-source framework for composable benchmarking and design space exploration of ML systems: https://github.com/mlcommons/ck/blob/master/cm-mlops/project/mlperf-inference-v3.0-submissions/docs/crowd-benchmark-mlperf-bert-inference-cuda.md ?

Hi! Just sharing this tutorial from the Student Cluster Competition at SuperComputing'22 to learn how to modularize and run ML Systems benchmarks. 10 international teams had about 30 minutes to run it and most of them succeeded while sharing their results at the live dashboard . It is a part of the ongoing effort to modularize ML Systems and automate their benchmarking and optimization. Feedback is very welcome!

We would like to ask for a favor: we have prepared a tutorial to help students run the MLPerf inference benchmark across different platforms at the Student Cluster Competition at SuperComputing'22 in a few days: https://github.com/mlcommons/ck/blob/master/docs/tutorials/s... .

We would like to test it across different machines before students run it ;) . If you have time, please help us go through this tutorial and run this benchmark on any available system - it should not take more than 20..30 minutes.

If you encounter any issues, please report them at https://github.com/mlcommons/ck/issues so that we could fix them before the competition.

Thank you for supporting this community project!

Just to let you know that we are preparing a new working group at MLCommons to help the community modularize ML/AI Systems and automate their benchmarking, optimization and deployment. It will be based on the MLPerf methodology and MLCommons "Collective Knowledge" automation meta-framework that was already used to automate recent MLPerf inference benchmark submissions from Qualcomm, HPE, Lenovo, Krai, DELL and OctoML. Please join the group here to provide your feedback and help with this community effort! Thank you!

Hi! Just sharing our open-source project to automate MLPerf benchmarks and make it easier for everyone to plug in their real-world ML models, data sets, frameworks/SDKs and hardware. Feedback is very welcome!

– Their solution product https://cknowledge.io/ and source code https://github.com/ctuning/ck\

I guess it should be helpful to the researchers community.

Check out our repo for more information: https://github.com/SERG-Delft/dslinter

It would be a massive help if you could run `dslinter` on your machine learning project in the industry setting and share the text and the json output with us. You can simply use pip to install dslinter and run dslinter. The steps and commands can be found here: https://github.com/SERG-Delft/dslinter/blob/main/STEPS_TO_FOLLOW.md . The running time of the dslinter should be approximately 1 minute for a project with 10000 lines. The whole process should take no longer than 10 minutes. The process is anonymous and we will remove any sensitive information before the results are published. We can also arrange a meeting and go through the process together if you prefer. If you have time to check whether the output linting message can help you with the project development, we are more than happy to hear the feedback! Please don't hesitate to contact me if you have any questions : )