ck VS seurat

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.

So there is an issue with the ModularityOptimizer? I looked it up and it comes from this script: https://github.com/satijalab/seurat/blob/master/src/RModularityOptimizer.cpp

With regards to using selected features for UMAP, my gut tells me it's possible, but it looks like the folks here were having some issues getting it work. Your mileage may vary.

The strangest thing is that I'm not even a follower of this repo where the discussion was started, which by the way is a R toolkit for single cell genomics.

There is a good wiki of the Seurat data object and information about the slots and objects can be found here: https://github.com/satijalab/seurat/wiki

Here is link number 1 - Previous text "1"

Wow, it's not easy to find, indeed. They should do a better job commenting the code, and documenting the code organisation. But it's all in the Github repo, for example NormalizeData is defined here: https://github.com/satijalab/seurat/blob/4e868fcde49dc0a3df47f94f5fb54a421bfdf7bc/R/generics.R#L337