Dear Minh, Please, find my answers inline: 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 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 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