def get_config():
config = ConfigDict()
config.learner = ConfigDict()
config.learner.cpu = 1
config.learner.mem = 0
config.learner.gpu_compute = [10]
config.learner.gpu_mem = [14]
# directive only applies to slurm allocations
config.learner.slurm_exclusive_gpu = True
config.bundled_actor = ConfigDict()
config.bundled_actor.cpu = 1
config.bundled_actor.mem = 0
config.bundled_actor.gpu_compute = [.5]
config.bundled_actor.gpu_mem = [10]
config.actor = ConfigDict()
config.actor.cpu = 1
config.actor.mem = 0
config.actor.gpu_compute = []
config.actor.gpu_mem = []
config.evaluator = ConfigDict()
config.evaluator.cpu = 1
config.evaluator.mem = 0
config.evaluator.gpu_compute = [.1]
config.evaluator.gpu_mem = [0]
config.replay = ConfigDict()
config.replay.cpu = 0
config.replay.mem = 0 #50 * 1e3 # 50 GB
config.replay.gpu_compute = []
config.replay.gpu_mem = []
config.ps = ConfigDict(**config.replay)
config.irs = ConfigDict()
config.irs.cpu = 1
config.irs.mem = 0
config.irs.gpu_compute = []
config.irs.gpu_mem = []
config.visualizers = ConfigDict(**config.irs)
config.visualizers.cpu = 1
config.visualizers.mem = 0
config.tensorplex = ConfigDict(**config.irs)
config.tensorplex.cpu = 1
config.tensorplex.mem = 0
return config
def get_config(): config = ConfigDict() config.learner = ConfigDict() config.learner.publish_every = 100 config.learner.checkpoint_every = int(1e9) config.learner.n_train_steps = int(1e9) config.learner.use_gpu = True config.learner.batch_size = 8 # max number of samples (not batches) that could be waiting in the prefetch queue config.learner.max_prefetch_queue = 16 # prefetch workers are spawned in a seperate process. config.learner.prefetch_processes = 1 config.learner.prefetch_threads_per_process = 8 config.learner.inmem_tmp_dir = '/tmp/caraml/' # first sess.run is not profiled. # generates a profile after every this many steps. config.learner.profile_step = 5 config.learner.restore_from = '' config.learner.compress_before_send = True config.actor = ConfigDict() config.actor.class_path = 'liaison.distributed.actor' config.actor.class_name = 'Actor' config.actor.n_unrolls = None # loop forever. config.actor.use_parallel_envs = True config.actor.use_threaded_envs = False config.actor.discount_factor = 1.0 config.actor.compress_before_send = True config.bundled_actor = ConfigDict() config.bundled_actor.n_actors = 64 config.shell = ConfigDict() # shell class path is default to the distributed folder. config.shell.class_path = 'liaison.distributed.shell' config.shell.class_name = 'Shell' config.shell.agent_scope = 'shell' config.shell.ps_client_timeout = 2 config.shell.ps_client_not_ready_sleep = 2 config.shell.sync_period = 10 # in # steps. config.shell.use_gpu = True config.shell.restore_from = None config.replay = ConfigDict() config.replay.class_path = 'liaison.replay.uniform_replay' config.replay.class_name = 'Replay' config.replay.load_balanced = False # unknown bug config.replay.n_shards = 1 config.replay.evict_interval = 0 config.replay.memory_size = 100 config.replay.sampling_start_size = 0 config.replay.tensorboard_display = True config.replay.loggerplex = ConfigDict() config.replay.loggerplex.tensorboard_display = True config.replay.loggerplex.enable_local_logger = True config.replay.loggerplex.local_logger_level = 'info' config.replay.loggerplex.local_logger_time_format = 'hms' config.replay.compress_before_send = True config.replay.max_times_sampled = 5 config.loggerplex = ConfigDict() config.loggerplex.enable_local_logger = True config.loggerplex.time_format = 'hms' config.loggerplex.local_logger_level = 'info' config.loggerplex.local_logger_time_format = 'hms' config.loggerplex.overwrite = True config.loggerplex.level = 'info' config.loggerplex.show_level = True config.tensorplex = ConfigDict() config.tensorplex.tensorboard_preferred_ports = [6006 + i for i in range(100)] config.tensorplex.systemboard_preferred_ports = [7007 + i for i in range(100)] config.tensorplex.max_processes = 1 config.tensorplex.agent_bin_size = 64 config.tensorplex.serializer = 'pickle' config.tensorplex.deserializer = 'pickle' config.ps = ConfigDict() # config.ps.n_shards = 1 config.irs = ConfigDict() config.irs.n_shards = 1 config.irs.max_to_keep = 20 config.irs.keep_ckpt_every_n_hrs = 1 return config