OAD
ck
Our great sponsors
| OAD | ck | |
|---|---|---|
| 1 | 9 | |
| 118 | 579 | |
| 3.4% | 2.4% | |
| 6.1 | 10.0 | |
| 8 days ago | 3 days ago | |
| Jupyter Notebook | Python | |
| GNU General Public License v3.0 only | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
OAD
-
Anyone know of any microscope that has an API that I can buy. Or in other words, one that I can programmatically control via coding?
All motorised microscopes can be controlled by the software provided/sold by the manufacturer (eg ZEN for ZEISS, NIS for Nikon, LAS for Leica etc). ZEISS also offers interfaces to control the microscope using your own code (eg in Python or MATLAB): https://github.com/zeiss-microscopy/OAD
ck
-
Do you have an idle @Nvidia GPU? 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?
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 ?
- Sharing a tutorial to modularize ML Systems
-
[N] Tutorial to modularize ML Systems benchmarks from the Student Cluster Competition'22
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!
-
Asking for a favor to test modular ML benchmark for Student Cluster Competition
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!
- MLCommons is creating a new working group to modularize ML Systems
-
[N] Open working group to modularize ML Systems
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!
-
[N] Releasing the MLPerf automation framework to plug in real-world ML models, data sets and tools
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!
-
Research software code is likely to remain a tangled mess
– Their solution product https://cknowledge.io/ and source code https://github.com/ctuning/ck\
I guess it should be helpful to the researchers community.
What are some alternatives?
VevestaX - 2 Lines of code to track ML experiments + EDA + check into Github
osmnx - OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.
oemer - End-to-end Optical Music Recognition (OMR) system. Transcribe phone-taken music sheet image into MusicXML, which can be edited and converted to MIDI.
SmartSim - SmartSim Infrastructure Library.
PUMA - 3D Printed Microscope
budgetml - Deploy a ML inference service on a budget in less than 10 lines of code.
BioImager - A .NET microscopy imaging application based on Bio library. Supports various microscopes by using imported libraries & GUI automation. Supports XInput game controllers to move stage, take images, run ImageJ macros on images or Bio C# scripts.
dslinter - `dslinter` is a pylint plugin for linting data science and machine learning code. We plan to support the following Python libraries: TensorFlow, PyTorch, Scikit-Learn, Pandas and NumPy.
frontends-team-compass - A repository for team interaction, syncing, and handling meeting notes across the JupyterLab ecosystem.
aws-deployment-framework - The AWS Deployment Framework (ADF) is an extensive and flexible framework to manage and deploy resources across multiple AWS accounts and regions based on AWS Organizations.
terraform-tui - Terraform textual UI
inference - Reference implementations of MLPerf™ inference benchmarks