def completeConfiguration(self, props): self.num_java_threads = props.getValue(self.p_num_java_threads) self.num_sim_GPU = props.getValue(self.p_num_sim_GPU) self.use_GPU_for_creation = props.getValue(self.p_use_GPU_for_creation) NodeThreadPool.setNumJavaThreads(self.num_java_threads) NEFGPUInterface.setRequestedNumDevices(self.num_sim_GPU) WeightedCostApproximator.setUseGPU(self.use_GPU_for_creation)
def completeConfiguration(self,props): self.num_java_threads=props.getValue(self.p_num_java_threads) self.num_sim_GPU=props.getValue(self.p_num_sim_GPU) self.use_GPU_for_creation=props.getValue(self.p_use_GPU_for_creation) NodeThreadPool.setNumJavaThreads(self.num_java_threads) NEFGPUInterface.setRequestedNumDevices(self.num_sim_GPU) WeightedCostApproximator.setUseGPU(self.use_GPU_for_creation)
def start(): (options, args) = parse_args() logging.basicConfig(filename=options.logfile, filemode='w', level=logging.INFO) logging.info("Parameters: " + str(options) + str(args)) command_line = 'cl' in args if options.dry_run: dry_run(options.results_file) NodeThreadPool.setNumJavaThreads(options.threads) if options.reduced_mode: reduced_run(options) else: normal_run(options)
data.record_sparsity(nav_agent.getNode("QNetwork").getNode("state_pop").getOrigin("AXON"), filter=filter) data.record_avg(ctrl_agent.getNode("QNetwork").getNode("valdiff").getOrigin("X"), filter=filter) data.record_avg(ctrl_agent.getNode("ErrorNetwork").getOrigin("error"), filter=filter) data.record_avg(ctrl_agent.getNode("BGNetwork").getNode("weight_actions").getNode("0").getOrigin("AXON"), filter=filter) data.record_avg(ctrl_agent.getNode("BGNetwork").getNode("weight_actions").getNode("1").getOrigin("AXON"), filter=filter) net.add_to_nengo() # net.view() net.run(2000) def run_workTermReport(): # run a bunch of configs config = {"time": {"linear": "please"}} # start at twice the normal scale and then let it degrade NodeThreadPool.setNumJavaThreads(8) #Set it equal to the number of cores? #run_deliveryenvironment({"learningrate": 9e-10, "discount": 0.1, "Qradius": 2.0, # "load_weights": os.path.join("weights", "contextgrid_decoder", "SMDPAgent")}, # {"learningrate": 9e-10, "discount": 0.1, "load_weights": None}, # seed=1) #run_contextenvironment({"learningrate":9e-10, "discount":0.1, "Qradius":2.0, # "load_weights":None}, # seed=0) #gen_evalpoints("contextbmp_evalpoints", seed=0) #run_flat_delivery({"learningrate":9e-10, "discount":0.1, "Qradius":2.0, # "load_weights":os.path.join("delivery", "flat", "NavAgent")}, # seed=2)