/
params_template.py
124 lines (110 loc) · 6.29 KB
/
params_template.py
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# experiment use
EXPERIMENT_NAME = None
# random seed
RANDOMNESS = False
np_seed = 9973 # seed for numpy
tf_seed = 53 # seed for tf
trace_seed = 103 # seed for trace, not used
# configuration
LOG_MODE = "INFO"
NUM_AGENTS = 1 # at most 28 for tesla p100 and 40 for gtx 1080ti
TRAINING_MODE = "RL" # or "RL"
HEURISTIC = "DRF" # the heuristic algorithm used for supervised learning
VALUE_NET = True # disable/enable critic network
POLICY_NN_MODEL = "Models/policy_sl_ps_worker_100.ckpt" # path of the checkpointed model, or None
VALUE_NN_MODEL = None # "Models/value_rl_ps_worker_1000.ckpt" # path of value network model
SAVE_VALUE_MODEL = False
SUMMARY_DIR = "TensorBoard/" # tensorboard logging dir
MODEL_DIR = "Models/" # checkpoint dir
MAX_NUM_CHECKPOINTS = 10 # max number of saved checkpoints
CHECKPOINT_INTERVAL = 1000
DISP_INTERVAL = 5 # display frequency
VISUAL_GW_INTERVAL = 100 # tf log gradients/weights frequency
NUM_RECORD_AGENTS = 2 # log details of 2 agents in tensorboard and ignore others for saved space
SKIP_FIRST_VAL = False # if False, the central agent will test the initialized model at first before training
SELECT_ACTION_MAX_PROB = False # whether to select the action with the highest probability or select based on distribution, default based on distribution
MASK_PROB = 1. # whether to mask actions mapped None jobs, set it to be lower seems to be worse
ASSIGN_BUNDLE = True # assign 1 ps and 1 worker for each in the beginning of each timeslot to avoid starvation
# hyperparameters
SL_LOSS_FUNCTION = "Cross_Entropy" # "Mean_Square", "Cross_Entropy", "Absolute_Difference"
OPTIMIZER = "Adam" # RMSProp
FIX_LEARNING_RATE = True # keep constant learning rate
ADJUST_LR_STEPS = [5000] # halving learning rate once reaching a certain step, not functional
LEARNING_RATE = 0.0001
MINI_BATCH_SIZE = 256
EPSILON_GREEDY = False # whether to enable epsilon greedy policy for exploration
VARYING_EPSILON = True # different values of epsilon for agents
EPSILON = 0.1 # not used
ENTROPY_WEIGHT = 0.1
ENTROPY_EPS = 1e-6
MAX_ENTROPY_WEIGHT = 10.0
ANNEALING_TEMPERATURE = 500.0
FIX_ENTROPY_WEIGHT = True # if true, the entropy weight is ENTROPY_WEIGHT; else, it is calculated based on ANNEALING_TEMPERATURE and MAX_ENTROPY_WEIGHT
RAND_RANGE = 100000
TOT_NUM_STEPS = 6000
TOT_TRAIN_EPOCHS = 2000 # number of training epochs
VAL_INTERVAL = 50 # validation interval
VAL_ON_MASTER = True # validation on agent uses CPU instead of GPU, and may cause use up all memory, do not know why, so far it must be set true
REPLAY_MEMORY_SIZE = 8192 # or 65536
RANDOM_FILL_MEMORY = False
PRIORITY_REPLAY = True
PRIORITY_MEMORY_SORT_REWARD = True # use reward as priority
PRIORITY_MEMORY_EVICT_PRIORITY = False # remove samples from experience buffer based on priority instead of age
PRIORITY_MEMORY_SORT_WEIGHTED_REWARD_GRADIENTS = False # not used
LT_REWARD_IN_TS = False # use long term reward within a timeslot
LT_REWARD_NUM_TS = 1 # not implemented
DISCOUNT_FACTOR = 0.99
TS_REWARD_PLUS_JOB_REWARD = False # another way to assign reward
NUM_UNCOMPLETED_JOB_REWARD = False
MEAN_REWARD_BASELINE = True # whether to use reward mean as baseline
INJECT_SAMPLES = True # inject samples to experience buffer to get samples with high reward
SAMPLE_INJECTION_PROB = 0.1 # probabilistically inject samples with high reward
VARYING_SKIP_NUM_WORKERS = True
MIN_ACTION_PROB_FOR_SKIP = 10**(-20) # 10**(-12)
NUM_TS_PER_UPDATE = 1 # update once after passing x timeslot(s), default 1, i.e., update weights per timeslot
VARYING_PS_WORKER_RATIO = True # explore different ratio of ps over worker
STEP_TRAIN_CRITIC_NET = 0 # number of steps for pretraining critic network, default 0, not functional
CHANGING_JOB_TYPES = False
JOB_RESR_BALANCE = True
FINE_GRAIN_JCT = True
# cluster
TESTBED = False
LARGE_SCALE = False
CLUSTER_NUM_NODES = 48 # should be at least 3 times of maximal number of uncompleted jobs at each ts, default 160
NUM_RESR_TYPES = 2 # number of resource types, e.g., cpu,gpu
NUM_RESR_SLOTS = 8 # number of available resource slots on each machine
# dataset
TRAIN_SPEED_ERROR = 0
JOB_EPOCH_EST_ERROR = 0
REAL_SPEED_TRACE = True # whether to use real traces collected from experiment testbed
JOB_LEN_PATTERN = "Ali_Trace" # Ali_Trace, Normal
JOB_ARRIVAL_PATTERN = "Ali_Trace" # Ali_Trace, Uniform, Google_Trace, Poisson
FIX_JOB_LEN = True
TRAIN_EPOCH_SIZE = 100 # number of traces for training dataset
TOT_NUM_JOBS = 60 # number of jobs in one trace
MAX_ARRVS_PER_TS = 3 # max number of jobs arrived in one time slot
MAX_NUM_EPOCHS = 80000 # maximum duration of jobs, epochs. default 200
MAX_NUM_WORKERS = 32
TS_DURATION = 1200
VAL_DATASET = 10 # number of traces for validation in each agent
MAX_TS_LEN = 1000 # maximal timeslot length for one trace
# neural network
JOB_ORDER_SHUFFLE = False # whether to shuffle the order of the jobs in the scheduling window, can also be used for data augmentation
JOB_SORT_PRIORITY = "Arrival" # or Arrival, Resource, Progress, sort job based on resource or arrival
SCHED_WINDOW_SIZE = 20 # maximum allowed number of jobs for NN input
PS_WORKER = True # whether consider ps and worker tasks separately or not
INPUTS_GATE= [("TYPE",True), ("STAY",True), ("PROGRESS",True), ("DOM_RESR",True), ("WORKERS",True), ("PS", True)]
BUNDLE_ACTION = True # add a 'bundle' action to each job, i.e., selecting a ps and a worker by one action
TYPE_BINARY = False # 4 bits
STATE_DIM = (3*TYPE_BINARY + sum([enable for (_,enable) in INPUTS_GATE]), SCHED_WINDOW_SIZE) # type, # of time slots in the system so far, normalized remaining epoch, dom resource, # of workers
SKIP_TS = True # whether we skip the timeslot
ACTION_DIM = 3 * SCHED_WINDOW_SIZE + 1
INPUT_RESCALE = False # not implemented on heuristic algorithms yet
JOB_CENTRAL_REPRESENTATION = False # treat each job as an input instead of treating each type of information of all jobs as an input
ATTRIBUTE_CENTRAL_REPRESENTATION = False # treat each property of all jobs as an input, default fully connected to input
ZERO_PADDING = True # how to represent None job as input
FIRST_LAYER_TANH = False
NN_SHORTCUT_CONN = False # connect the output of first layer to the NN layer before softmax output
NUM_FCN_LAYERS = 1 # number of fully connected layers, must be > 0
NUM_NEURONS_PER_FCN = STATE_DIM[0] * STATE_DIM[1] # default same number as input size
BATCH_NORMALIZATION = True