ahmad-hl / pensieve-py38

Pensieve on python 3.8 & tensorflow 2.7

Pensieve

Pensieve is a system that generates adaptive bitrate algorithms using reinforcement learning. http://web.mit.edu/pensieve/

Prerequisites

• Create a virtual envirnment with python 3.8
• Install prerequisites (tested with Ubuntu 18.04, python3.8, Tensorflow v2.7.0, TFLearn v0.5.0 but not tested on Selenium)

pip install tensorflow tflearn matplotlib selenium
python setup.py

Training

• To train a new model, put training data in sim/cooked_traces and testing data in sim/cooked_test_traces, then in sim/ run python get_video_sizes.py and then run

python multi_agent.py

The reward signal and meta-setting of video can be modified in multi_agent.py and env.py. Monitoring the testing curve of rewards, entropy and td loss can be done by launching tensorboard from the terminal as follows:

tensorboard --logdir=path/to/results

Where path/to/results is in dir sim. More details can be found in sim/README.md.

Testing

• To test the trained model in simulated environment, first copy over the model to test/models and modify the NN_MODEL field of test/rl_no_training.py , and then in test/ run python get_video_sizes.py and then run

python rl_no_training.py

Similar testing can be performed for buffer-based approach (bb.py), mpc (mpc.py) and offline-optimal (dp.cc) in simulations. More details can be found in test/README.md.

Running experiments over Mahimahi

• To run experiments over mahimahi emulated network, first copy over the trained model to rl_server/results and modify the NN_MODEL filed of rl_server/rl_server_no_training.py, and then in run_exp/ run

python run_all_traces.py

This script will run all schemes (buffer-based, rate-based, Festive, BOLA, fastMPC, robustMPC and Pensieve) over all network traces stored in cooked_traces/. The results will be saved to run_exp/results folder. More details can be found in run_exp/README.md.

Real-world experiments

• To run real-world experiments, first setup a server (setup.py automatically installs an apache server and put needed files in /var/www/html). Then, copy over the trained model to rl_server/results and modify the NN_MODEL filed of rl_server/rl_server_no_training.py. Next, modify the url field in real_exp/run_video.py to the server url. Finally, in real_exp/ run

python run_exp.py

The results will be saved to real_exp/results folder. More details can be found in real_exp/README.md.

Training and cross-validation (testing) Curve using tensorboard

The RL-model converges after 3 days of continuous training using training data in sim/cooked_traces and testing data in sim/cooked_test_traces.

TD Loss & Total Reward of sim/multi-agent.py: