Challenge-kdn July 2020

challenge-kdn@knowledgedefinednetworking.org

2 participants
2 discussions

Update the code: datanetAPI bug
by Albert López 20 Jul '20

20 Jul '20

Dear participants, We have found a small bug in the API we provide to read the datasets (datanetAPI.py). This particularly affects the functions "get_maxAvgLambda()" and "get_global_delay()" of the API. If you are using RouteNet (https://github.com/knowledgedefinednetworking/RouteNet-challenge), please update the code with the latest version of the repository. You can do it with "git pull" from the terminal or downloading the code directly from the GitHub website. Alternatively, if you have already made major modifications to the code, you can replace only the file "datanetAPI.py" under the directory "RouteNet-challenge/code/". The same applies if you are using the API code provided at https://github.com/knowledgedefinednetworking/datanetAPI. Sorry for the inconvenience. Enjoy the challenge! And remember, if you have any questions/comments don't hesitate to use this mailing list to share them. Best Regards, Albert López Barcelona Neural Networking center Universitat Politècnica de Catalunya

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Re: [Challenge-kdn] Question about RouteNet code on GitHub
by José Suárez-Varela 16 Jul '20

16 Jul '20

Dear Minh, Please, find my answers inline: > Dear GNNet challenge 2020 organizing team, > > My name is Minh, one of the participants in the challenge. I have > questions about the RouteNet code on GitHub that I want to ask as follows: > > 1) Can you please provide a requirements.txt file of your Python > environment? I'm aware that you wrote the codes depend on > tensorflow=2.1.0, networks>=2.4, and pandas >=0.24, but I often get > different errors when running on different machines, so I think a > requirements.txt file will make it easier not only for me but also for > all other participants. We are aware that the process to install all the libraries may be a bit confusing. However, we decided not to put a 'requirements.txt' file because we think it can be more error-prone. The main problem comes when you install TensorFlow in a machine with a CUDA-enabled GPU card, since you need to have the right CUDA and CuDNN versions pre-installed. These libraries depend on the operating system and the version you use. Thus, the most laborious part is to install correctly CUDA and CuDNN, but you can do it in a pretty systematic way following this TensorFlow tutorial: https://www.tensorflow.org/install/gpu > 2) What is the terminating point of RouteNet as a baseline for the > challenge? From what I see, it only stops after 5 million steps, which > takes an incredibly long time, should this be the terminating point of > the algorithm? And the results after 5 million steps are the baseline > results for the competition? This is just an arbitrary upper bound limit we set in the implementation. The idea is that you can stop the execution whenever you want based on the training progress, and the last models (checkpoints) are automatically saved in the "../logs/model_log" directory. Note that after 400k steps the model has iterated over all the training dataset. Further iterations can only help slightly refine the model. You can take the baseline as a reference implementation to develop your model. However, with this baseline you can expect a MAPE (Mean Absolute Percentage Error) above 100%. One main reason is that it does not encode information about queue scheduling, and this has a great impact on network delay. * * > I look forward to hearing from you soon. Thank you. > > Best regards, > > Minh Nguyen. > > King Abdullah University of Science and Technology (KAUST) > Al-Khawarizmi Applied Math. Building (Bldg. #1) | Level 3, Table > 3139-WS01 > Thuwal 23955-6900 | Makkah Province > Kingdom of Saudi Arabia I hope I answered all your questions. Please, let me know if you have any more questions/comments. Regards, José Suárez-varela Barcelona Neural Networking center - UPC

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Challenge-kdn July 2020