dopt

How to use

We'll be running distributed_optimizer.py to start the optimization.

distributed_optimizer.py can be run in 2 modes: host mode and client mode. The host mode will call 2 processes: One that starts the Optimizer of choice and One that connect with other machines (the clients) to run distributed_optimizer.py on client mode. The client mode will run the user-specified MyWhateverTrainer. Host mode: python3 distributed_optimizer.py as host mode is the default. Client mode: python3 distributed_optimizer.py --run_as=client as client mode. You do not need to manually run client mode on each client machine as the host mode will do this. However, you can use it if you want to add client machines during optimization.

Essential parts of the script:

• The COMMANDS: A constant dictionary. The keys are machine categories, and values are the necessary commands to run distributed_optimizer.py on client mode on those different categories.

• A MyWhateverTrainer that inherits the Trainer abstract class from src.trainer, and implement the abstract method get_observation, in which the set of hyperparameters (candidate) given will be plugged into the objective function.

• A start_host() function that will be used to call 2 processes: one that start the Optimizer and one that runs appropriate sequence of commands to run distributed_optimizer.py on client mode, i.e. startTrainers on respective machines using the following: python3 distributed_optimizer.py --run_as=client

• A start_client() function that will run MyWhateverTrainer, i.e. run objective function.

• A main() that parse command line input and switch between host and client mode, and specify further information needed to run objective function on target machines.

Check list:

• Step 1: Create your specified MyWhatever Trainer that implements the get_observation method.
• Step 2: Make sure the appropriate environment can be chosen through ssh tunneling. Try: ssh [name]@[hostmachine] [YOUR COMMANDS]
• Step 3: Add the commands to the COMMANDS dictionary.
• Step 4: Make sure you have a copy of the distributed_optimizer.py and related files on all of the machines you intend to use