def execute(self, new_basin_list):
"""Executing the Controler Algorithm. Load pre-analyzed lower dimensional
subspaces, process user query and identify the sampling space with
corresponding distribution function for each user query. Calculate
convergence rates, run sampler, and then execute fairness policy to
distribute resources among users' sampled values.
"""
logging.debug('CTL MT')
# PRE-PROCESSING ---------------------------------------------------------------------------------
logging.debug(
"============================ <PRE-PROCESS> ============================="
)
np.set_printoptions(precision=4, linewidth=150)
self.data['timestep'] += 1
logging.info('TIMESTEP: %d', self.data['timestep'])
settings = systemsettings()
bench = microbench('ctl_%s' % settings.name, self.seqNumFromID())
stat = StatCollector('ctl_%s' % settings.name, self.seqNumFromID())
if self.force_decision:
new_basin_list = []
# Connect to the cache
self.cacheclient = CacheClient(settings.APPL_LABEL)
# create the "binlist":
numresources = self.data['numresources']
explore_factor = float(self.data['sampler:explore'])
topo = self.protein.top
# Load new RMS Labels -- load all for now
bench.start()
logging.debug('Loading RMS Labels')
start_index = max(0, self.data['ctl_index_head'])
logging.debug(" Start_index=%d, batch_size=%d", start_index,
len(new_basin_list))
# Calculate variable PDF estimations for each subspace via bootstrapping:
logging.debug(
"======================= <SUBSPACE CONVERGENCE> (skip) ==================="
)
# IMPLEMENT USER QUERY with REWEIGHTING:
logging.debug(
"======================= <QUERY PROCESSING> ========================="
)
stat.collect('new_basin', len(new_basin_list))
##### BARRIER
self.wait_catalog()
selected_index_list = []
# QUERY PROCESSING & SAMPLING BELOW to select indices.
EXPERIMENT_NUMBER = self.experiment_number
logging.info("RUNNING EXPER CONFIGURATION #%d", EXPERIMENT_NUMBER)
# TODO: ABSTRACT FEATURE DIMENSIONALITY
n_features = 1653
# Basin List will be the list of basin representing the new Job Candidates
selected_basin_list = []
all_basins = self.data['basin:list']
num_prev_basins = int(self.data['basin:processed'])
# Load Previous Distance Space (historical data)
prev_ds_raw = self.catalog.lrange('dspace', 0, num_prev_basins)
local_basins = {}
if len(prev_ds_raw) > 0:
ds_prev = np.zeros(shape=(len(prev_ds_raw), n_features))
logging.info("Unpickling distance space to array: %s",
ds_prev.shape)
for i, elm in enumerate(prev_ds_raw):
ds_prev[i] = pickle.loads(elm)
logging.info('Prev DS loaded. Size = %d', len(ds_prev))
else:
logging.info('NO Prev DS')
ds_prev = []
# FOR Supervised Classification (included for all for post-partem processing)
bin_labels_10 = [
'T0', 'T1', 'T2', 'T3', 'T4', 'W0', 'W1', 'W2', 'W3', 'W4'
]
bin_labels_25 = [(a, b) for a in range(5) for b in range(5)]
bin_list_10 = {
k: [int(i) for i in self.catalog.lrange('bin:10:%s' % k, 0, -1)]
for k in bin_labels_10
}
bin_list_25 = {
k:
[int(i) for i in self.catalog.lrange('bin:25:%d_%d' % k, 0, -1)]
for k in bin_labels_25
}
# Merge locally analyzed distance space
delta_ds = np.zeros(shape=(len(new_basin_list), n_features))
logging.info('Collecting new data from basins: %s', new_basin_list)
for i, bid in enumerate(new_basin_list):
basin = self.data['basin:' + bid]
global_basin_index = int(basin['dsidx'])
local_basins[global_basin_index] = basin
# Update Distance Space
dmu_ = self.catalog.lindex('dspace', global_basin_index)
if dmu_ is None:
print("ERROR!!!!")
print(global_basin_index, i)
for k, v in basin.items():
print(k, v)
sys.exit(0)
delta_ds[i] = pickle.loads(dmu_)
# Update Supervise Classification
label_seq = [
int(i)
for i in self.catalog.lrange('basin:labelseq:' + bid, 0, -1)
]
local_basins[global_basin_index][
'label:10'] = label10 = bin_label_10(label_seq)
local_basins[global_basin_index][
'label:25'] = label25 = bin_label_25(label_seq)
bin_list_10[label10].append(global_basin_index)
bin_list_25[label25].append(global_basin_index)
self.data['basin:processed'] += 1
if len(new_basin_list) > 0 and len(prev_ds_raw) > 0:
dist_space = np.vstack((ds_prev, delta_ds))
elif len(delta_ds) == 0:
dist_space = np.array(ds_prev)
elif len(ds_prev) == 0:
logging.info('First Set of Distance Coord laoded')
dist_space = delta_ds
else:
logging.error("ERROR! NO DISTANCE SPACE IN THE CATALOG")
# UNIFORM SAMPLER (BASIC)
if EXPERIMENT_NUMBER == 12:
basin_idx_list = []
candidate_list = [[] for i in range(5)]
for k, v in bin_list_10.items():
candidate_list[int(k[1])].extend(v)
# UNIFORM SAMPLING
for sel_num in range(numresources):
# Select Random start state
start_state = np.random.randint(5)
# Select random index
rand_index = np.random.choice(len(candidate_list[start_state]))
basin_idx = candidate_list[start_state][rand_index]
basin_idx_list.append(basin_idx)
for i in basin_idx_list:
if i < num_prev_basins:
logging.info("Select index: %s (Retrieve from Catalog)",
i)
bid = self.data['basin:list'][i]
basin = self.catalog.hgetall('basin:%s' % bid)
else:
logging.info(
"Select index: %s (New locally built basin in mem)",
i)
basin = local_basins[i]
logging.debug(' BASIN: %s', basin['id'])
selected_basin_list.append(basin)
# BIASED SAMPLER (UPDATED)
if EXPERIMENT_NUMBER == 13:
# USING 10-BIN LABELS
distro = [len(bin_list_10[i]) for i in bin_labels_10]
# Create and invoke the sampler
logging.info('Running the biased (umbrella) samplers')
sampler = BiasSampler(distro)
samplecount = np.zeros(len(bin_labels_10), dtype=np.int16)
# Find the first index for each bin:
explore_direction = 1 if explore_factor < .5 else -1
for i, b in enumerate(bin_list_10):
if len(b) == 0:
idx = 0
else:
idx = np.floor(explore_factor * (len(b) - 1))
samplecount[i] = idx
sel_bins = sampler.execute(numresources)
logging.info(
'Processing selected bins to find starting candidates')
candidate_list = {}
basin_idx_list = []
for b in sel_bins:
target_bin = bin_labels_10[b]
if target_bin not in candidate_list:
candidate_list[target_bin] = bin_list_10[target_bin]
# TODO: FULLY IMPLEMENT EXPLORE/EXPLOIT BUT INCL HISTORY/PROVONANCE
# Lazy Update to centroid -- push to catalog immediately
# vals = dist_space[bin_list_10[target_bin]]
# logging.info('Updating Centroid for bin %s, bindata: %s', target_bin, vals.shape)
# centroid = np.mean(vals, axis=0)
# self.catalog.set('bin:10:centroid:%s' % target_bin, pickle.dumps(centroid))
# dist_center = [LA.norm(centroid - dist_space[i]) for i in bin_list_10[target_bin]]
# candidate_list[target_bin] = sorted(zip(bin_list_10[target_bin], dist_center), key=lambda x: x[1])
# basin_idx, basin_diff = candidate_list[target_bin][samplecount[b]]
# samplecount[b] += explore_direction
# # Wrap
# if samplecount[b] == 0:
# samplecount = len(candidate_list[target_bin]) - 1
# if samplecount[b] == len(candidate_list[target_bin]):
# samplecount = 0
# FOR NOW PICK A RANDOM CANDIDATE
rand_index = np.random.choice(len(candidate_list[target_bin]))
basin_idx = candidate_list[target_bin][rand_index]
logging.info('BIAS SAMPLER:\n Bin: %s\n basin: %d Delta from Center: %6.3f (note: dist not factored in)', \
target_bin, basin_idx, 0.)
basin_idx_list.append(basin_idx)
for i in basin_idx_list:
if i < num_prev_basins:
logging.info("Select index: %s (Retrieve from Catalog)",
i)
bid = self.data['basin:list'][i]
basin = self.catalog.hgetall('basin:%s' % bid)
else:
logging.info(
"Select index: %s (New locally built basin in mem)",
i)
basin = local_basins[i]
logging.debug(' BASIN: %s', basin['id'])
selected_basin_list.append(basin)
# LATTICE SAMPLER (WITH HISTORICAL DATA)
if EXPERIMENT_NUMBER == 14:
# Merge Existing delta with DEShaw Pre-Processed data:
logging.info('Merging DEShaw with existing generated data')
# Set parameters for lattice
Kr = [
int(i) for i in self.catalog.lrange('lattice:features', 0, -1)
]
support = int(self.data['lattice:support'])
dspt = self.catalog.get('lattice:delta_support')
delta_support = 5 if dspt is None else int(dspt)
cutoff = float(self.data['lattice:cutoff'])
logging.info('PARAMS Kr:%s\n support:%d dspt:%d cutoff:%f', Kr,
support, delta_support, cutoff)
# Load existing (base) lattice data
logging.info(
"Unpickling max/low FIS and derived lattice EMD values")
max_fis = pickle.loads(self.catalog.get('lattice:max_fis'))
low_fis = pickle.loads(self.catalog.get('lattice:low_fis'))
dlat = pickle.loads(self.catalog.get('lattice:dlat'))
# TODO: MOVE iset completely into catalog
# Ik = pickle.loads(self.catalog.get('lattice:iset'))
# FOR NOW: Full DEShaw Index is saved on disk (TODO: MOVE TO CATALOG)
logging.info(
"Loading full Itemset from disk (TODO: Det optimization on mem/time)"
)
Ik = pickle.load(open(settings.datadir + '/iset.p', 'rb'))
# Item_set Keys (Ik) are only saved as a delta for space conservation
if os.path.exists(settings.datadir + '/iset_delta.p'):
Ik_delta = pickle.load(
open(settings.datadir + '/iset_delta.p', 'rb'))
else:
Ik_delta = {}
# Merge previous item set delta with DEShaw index
logging.info("Merging DEShaw Ik with Delta IK")
for k, v in Ik_delta.items():
Ik[k] = np.concatenate((Ik[k], v)) if k in Ik else v
# Build Base Lattice Object
base_lattice = lat.Lattice(ds_prev, Kr, cutoff, support)
base_lattice.set_fis(max_fis, low_fis)
base_lattice.set_dlat(dlat, Ik)
if not self.force_decision and len(delta_ds) > 0:
# Build Delta Lattice Object
logging.info('Building Delta lattice. Num new items: %d',
len(delta_ds))
delta_lattice = lat.Lattice(delta_ds,
Kr,
cutoff,
delta_support,
invert=invert_vals)
delta_lattice.maxminer()
delta_lattice.derive_lattice()
# Update non-DEShaw delta itemset key index
logging.info("Updating Itemsets and Distance Space Matrix")
for k, v in delta_lattice.Ik.items():
Ik_delta[k] = np.concatenate(
(Ik_delta[k], v)) if k in Ik_delta else v
# Save Ik delta to disk
logging.info("Saving Delta Itemset (to disk)")
pickle.dump(Ik_delta,
open(settings.datadir + '/iset_delta.p', 'wb'))
# Perform incremental maintenance
logging.info('Merging Delta lattice with Base Lattice')
base_lattice.merge(delta_lattice)
# Create the Sampler object (also make clusters)
logging.info('Invoking the Lattice Sampler')
sampler = LatticeSampler(base_lattice)
basin_id_list = sampler.execute(numresources)
for index in basin_id_list:
bid = all_basins[index]
selected_basin_list.append(
self.catalog.hgetall('basin:%s' % bid))
# LATTICE SAMPLER (DE NOVO)
if EXPERIMENT_NUMBER == 15:
# PREPROCESS
N_features_src = topo.n_residues
N_features_corr = (N_features_src**2 - N_features_src) // 2
upt = np.triu_indices(N_features_src, 1)
old_basin_ids = all_basind[:num_prev_basins]
# DENOVO Exploratory Bootstrapping (RMSD)
explore_factor = float(self.data['sampler:explore'])
# TODO: Better transtion plan from explore to exploit
self.data['sampler:explore'] *= .75
executed_basins = self.catalog.lrange('executed', 0, -1)
if explore_factor > 0:
logging.info("EXPLORING Most active basins....")
basindata = [
self.catalog.hgetall(bid) for bid in old_basin_ids
]
for bid in new_basin_list:
basindata.append(self.data['basin:' + bid])
basins_with_rms = [b for b in basindata if 'resrms_delta' in b]
basin_by_rmsd = sorted(basins_with_rms,
key=lambda x: float(x['resrms_delta']),
reverse=True)
explore_samples = int(np.floor(numresources * explore_factor))
logging.info('Num to explore: %d out of %d', explore_samples,
len(basin_by_rmsd))
idx, num_sampled = 0, 0
while idx < len(
basin_by_rmsd) and num_sampled < explore_samples:
selb = basin_by_rmsd[idx]
if selb['id'] in executed_basins:
logging.info(
'Selected %s, but it has been executed. Selecting next option',
selb['id'])
else:
selected_basin_list.append(selb)
logging.info(' (%d) EXPLORE BASIN: %s %f',
selb['id'], selb['id'],
float(selb['resrms_delta']))
numresources -= 1
num_sampled += 1
idx += 1
# TODO: Reduced Feature Sets
# Using Reduced Feature Set Alg #2 HERE
support = int(.01 * len(dist_space))
cutoff = 8
# RE-Calc the whole lattice:
logging.info("Building the new lattice")
BUILD_NEW = not self.catalog.exists('lattice:bootstrapped')
# TODO: Decision to go from build new to incr maint
if BUILD_NEW:
tval = .05
Kr = lat.reduced_feature_set2(dist_space,
cutoff,
theta=tval,
maxk=25)
retry = 5
while len(Kr) < 12 and retry > 0:
tval /= 2
retry -= 1
Kr = lat.reduced_feature_set2(dist_space,
cutoff,
theta=tval,
maxk=25)
base_lattice = lat.Lattice(dist_space, Kr, cutoff, support)
base_lattice.maxminer()
base_lattice.derive_lattice()
with self.catalog.pipeline() as pipe:
pipe.delete('lattice:kr')
for i in sorted(Kr):
pipe.rpush('lattice:kr', i)
pipe.execute()
else:
# Load existing (base) lattice data
max_fis = pickle.loads(self.catalog.get('lattice:max_fis'))
low_fis = pickle.loads(self.catalog.get('lattice:low_fis'))
dlat = pickle.loads(self.catalog.get('lattice:dlat'))
Ik = pickle.loads(self.catalog.get('lattice:iset'))
num_k = self.catalog.get('lattice:num_k')
Kr = [int(i) for i in self.catalog.lrange('lattice:kr', 0, -1)]
if num_k is None:
num_k = max(8, min(15, numresources * 2))
# Build Lattice Object
logging.info('Building Existing lattice object')
base_lattice = lat.Lattice(ds_prev, Kr, cutoff, support)
base_lattice.set_fis(max_fis, low_fis)
base_lattice.set_dlat(dlat, Ik)
# Build Delta Lattice Object
logging.info('Building Delta lattice. Num new items: %d',
len(delta_ds))
delta_lattice = lat.Lattice(delta_ds, Kr, cutoff, 1)
delta_lattice.maxminer()
delta_lattice.derive_lattice()
# Perform incremental maintenance
logging.info('Merging Delta lattice with Base Lattice')
base_lattice.merge(delta_lattice)
if numresources > 0:
logging.info('Invoking the Lattice Sampler')
sampler = LatticeSampler(base_lattice)
basin_id_list = sampler.execute(numresources)
# For now retrieve immediately from catalog
self.wait_catalog()
for index in basin_id_list:
bid = all_basins[index]
key = 'basin:%s' % bid
# Check to ensure its not a new basin and that it exists in the DB
if self.catalog.exists(key):
logging.debug('KEY EXISTS: %s', key)
selbasin = self.catalog.hgetall(key)
else:
logging.debug('NO KEY: %s\n%s', key, self.data[key])
selbasin = self.data[key]
selected_basin_list.append(selbasin)
# CORRELATION SAMPLER (KMEANS)
if EXPERIMENT_NUMBER == 18:
sampler = CorrelationSampler(cm_all, mu=dist_space)
basin_id_list = sampler.execute(numresources)
for bid in basin_id_list:
selected_basin_list.append(
self.catalog.hgetall('basin:%s' % bid))
bench.mark('GlobalAnalysis')
# Generate new starting positions
jcqueue = OrderedDict()
src_traj_list = []
for basin in selected_basin_list:
src_traj_list.append(basin['traj'])
if basin['traj'].startswith('desh'):
global_params = getSimParameters(self.data, 'deshaw')
# fileno = int(basin['traj'][-4:])
# frame = int(basin['mindex'])
# jcID, config = generateDEShawJC(fileno, frame)
else:
global_params = getSimParameters(self.data, 'gen')
src_psf = self.catalog.hget('jc_' + basin['traj'], 'psf')
global_params.update({'psf': src_psf})
# jcID, config = generateExplJC(basin, jcid=None)
jcID, config = generateFromBasin(basin)
config.update(global_params)
config['name'] = jcID
logging.info("New Simulation Job Created: %s", jcID)
for k, v in config.items():
logging.debug(" %s: %s", k, str(v))
# Add to the output queue & save config info
jcqueue[jcID] = config
logging.info("New Job Candidate Completed: %s #%d on the Queue",
jcID, len(jcqueue))
stat.collect('src_traj_list', src_traj_list)
bench.mark('GenInputParams')
# POST-PROCESSING -------------------------------------
logging.debug(
"============================ <POST-PROCESSING & OUTPUT> ============================="
)
self.wait_catalog()
with self.catalog.pipeline() as pipe:
for basin in selected_basin_list:
pipe.rpush('executed', basin['id'])
pipe.execute()
# Append new distance values
if EXPERIMENT_NUMBER == 14:
# Save Ik delta to disk
logging.info("Saving Delta Itemset (to disk)")
pickle.dump(Ik_delta, open(settings.datadir + '/iset_delta.p',
'wb'))
with self.catalog.pipeline() as pipe:
pipe.set('lattice:max_fis', pickle.dumps(base_lattice.max_fis))
pipe.set('lattice:low_fis', pickle.dumps(base_lattice.low_fis))
pipe.set('lattice:dlat', pickle.dumps(base_lattice.dlat))
pipe.execute()
if EXPERIMENT_NUMBER == 15:
with self.catalog.pipeline() as pipe:
pipe.set('lattice:max_fis', pickle.dumps(base_lattice.max_fis))
pipe.set('lattice:low_fis', pickle.dumps(base_lattice.low_fis))
pipe.set('lattice:dlat', pickle.dumps(base_lattice.dlat))
pipe.execute()
self.catalog.set('lattice:iset', pickle.dumps(base_lattice.Ik))
for basin_idx, basin in local_basins.items():
key = 'basin:' + basin['id']
with self.catalog.pipeline() as pipe:
pipe.hset(key, 'label:10', basin['label:10'])
pipe.hset(key, 'label:25', basin['label:25'])
pipe.rpush('bin:10:%s' % basin['label:10'], basin_idx)
pipe.rpush('bin:25:%d_%d' % basin['label:25'], basin_idx)
pipe.execute()
self.catalog.set('basin:processed',
num_prev_basins + len(local_basins))
# Clear current queue, mark previously queues jobs for GC, push new queue
qlen = self.catalog.llen('jcqueue')
logging.debug('Current queue len; %s', str(qlen))
if qlen > 0:
curqueue = self.catalog.lrange('jcqueue', 0, -1)
logging.info("Marking %d obsolete jobs for garbage collection",
len(curqueue))
for jc_key in curqueue:
key = wrapKey('jc', jc_key)
config = self.catalog.hgetall(key)
config['gc'] = 0
# Add gc jobs it to the state to write back to catalog (flags it for gc)
self.addMut(key, config)
self.catalog.delete('jcqueue')
# Update cache hit/miss
# hit = self.cache_hit
# miss = self.cache_miss
# logging.info('##CACHE_HIT_MISS %d %d %.3f', hit, miss, (hit)/(hit+miss))
# self.catalog.rpush('cache:hit', self.cache_hit)
# self.catalog.rpush('cache:miss', self.cache_miss)
self.data['jcqueue'] = list(jcqueue.keys())
logging.debug(" JCQUEUE: %s", str(self.data['jcqueue']))
# Update Each new job with latest convergence score and save to catalog(TODO: save may not be nec'y)
logging.debug("Updated Job Queue length: %d",
len(self.data['jcqueue']))
for jcid, config in jcqueue.items():
# config['converge'] = self.data['converge']
self.addMut(wrapKey('jc', jcid), config)
self.notify('sim')
bench.mark('PostProcessing')
print('## TS=%d' % self.data['timestep'])
bench.show()
stat.show()
return list(jcqueue.keys())
def __init__(self, name, port, **kwargs):
settings = systemsettings()
settings.applyConfig(name + '.json')
# For Stat Collection
self.stat = StatCollector('Overlay')
self.stat.collect('name', name)
# DEBUG
self._host = socket.gethostname()
self._port = port
svc_name = kwargs.get('svc_name', type(self).__name__)
self._name_svc = svc_name
self._name_app = name
self.lockfile = '%s_%s.lock' % (self._name_app, self._name_svc)
logging.info('MY Lock file is: %s', self.lockfile)
self.numslaves = kwargs.get('numslaves', 0)
self.launchcmd = None
self.shutdowncmd = None
self._pid = None
self._state = 'INIT'
self._role = kwargs.get('role', 'NONE')
self.aggressive = kwargs.get('isaggressive', False)
# Check if this is a Slurm job and get the settings
self.slurm_id = os.getenv('SLURM_JOBID')
if self.slurm_id is None:
# Not running as a Slurm Job (set default TTL)
self.ttl = dt.now().timestamp() + DEFAULT_TTL
self.jobinfo = None
self.stat.collect("slurmid", "None")
else:
logging.info("SLURM JOB: %s", str(self.slurm_id))
self.jobinfo = slurm.jobinfo(int(self.slurm_id))
endtime = dtparser.parse(self.jobinfo['EndTime'])
self.ttl = endtime.timestamp()
self.stat.collect("slurmid", self.slurm_id)
self.stat.collect("ttl", self.ttl)
# TODO: All services' nodes assume to run on same port (different hosts)
# This the master hold only the host name
self.master = kwargs.get('master', None)
self.slavelist = {}
self.terminationFlag = Event()
self.handoverFlag = Event()
self.shuttingdown = Event()
self.state_running = Event()
# TODO: Should we require basic setting via systemsetting object???
# and thus, enforce this object on the abstract class?
self.SERVICE_STARTUP_DELAY = 60
self.SERVICE_HEARTBEAT_DELAY = 60
# Register tty signaling to gracefully shutdown when halted externall7
# (e.g. from scancel)
signal.signal(signal.SIGTERM, self.halt)
signal.signal(signal.SIGINT, self.halt)
signal.signal(signal.SIGQUIT, self.halt)
signal.signal(signal.SIGHUP, self.halt)
def execute(self, thru_index):
"""Executing the Controler Algorithm. Load pre-analyzed lower dimensional
subspaces, process user query and identify the sampling space with
corresponding distribution function for each user query. Calculate
convergence rates, run sampler, and then execute fairness policy to
distribute resources among users' sampled values.
"""
logging.debug('CTL MT')
# PRE-PROCESSING ---------------------------------------------------------------------------------
logging.debug("============================ <PRE-PROCESS> =============================")
np.set_printoptions(precision=4, linewidth=150)
self.data['timestep'] += 1
logging.info('TIMESTEP: %d', self.data['timestep'])
settings = systemsettings()
bench = microbench('ctl_%s' % settings.name, self.seqNumFromID())
stat = StatCollector('ctl_%s' % settings.name, self.seqNumFromID())
# Connect to the cache
self.cacheclient = CacheClient(settings.APPL_LABEL)
# create the "binlist":
numLabels = self.data['numLabels']
numresources = self.data['numresources']
# LOAD all new subspaces (?) and values
##### BARRIER
self.wait_catalog()
# Load new RMS Labels -- load all for now
bench.start()
logging.debug('Loading RMS Labels')
start_index = max(0, self.data['ctlIndexHead'])
# labeled_pts_rms = self.catalog.lrange('label:rms', self.data['ctlIndexHead'], thru_index)
logging.debug(" Start_index=%d, thru_index=%d, ctlIndexHead=%d", start_index, thru_index, self.data['ctlIndexHead'])
feallist = [np.fromstring(i) for i in self.catalog.lrange('subspace:feal', 0, -1)]
num_pts = len(feallist)
self.data['ctlIndexHead'] = thru_index
thru_count = self.data['observe:count']
logging.debug('##NUM_RMS_THIS_ROUND: %d', num_pts)
stat.collect('numpts', len(feallist))
# Calculate variable PDF estimations for each subspace via bootstrapping:
logging.debug("======================= <SUBSPACE CONVERGENCE> =========================")
# Bootstrap current sample for RMS
logging.info("Feature Landscapes for %d points loaded. Calculating PDF.....", len(feallist))
# Static for now
blocksize = 5000
mv_convergence = op.bootstrap_block(feallist, blocksize)
global_landscape = np.mean(feallist, axis=0)
stat.collect('convergence', mv_convergence)
stat.collect('globalfeal', global_landscape)
# logging.info('MV Convergence values:\nCONV,%s', ','.join(['%5.3f'%i for i in mv_convergence]))
# logging.info('Global Feature Landscape:\nFEAL,%s', ','.join(['%5.3f'%i for i in global_landscape]))
logging.info('MV Convergence values:\nCONV,%s', str(mv_convergence[-1]))
logging.info('Global Feature Landscape:\n%s', feal.tostring(global_landscape))
# IMPLEMENT USER QUERY with REWEIGHTING:
logging.debug("======================= <QUERY PROCESSING> =========================")
##### BARRIER
self.wait_catalog()
selected_index_list = []
# QUERY PROCESSING & SAMPLING BELOW to select indices.
EXPERIMENT_NUMBER = self.experiment_number
logging.info("RUNNING EXPER CONFIGURATION #%d", EXPERIMENT_NUMBER)
###### EXPERIMENT #5: BIASED (Umbrella) SAMPLER
if EXPERIMENT_NUMBER == 5:
if self.catalog.exists('label:deshaw'):
logging.info("Loading DEShaw historical points.... From Catalog")
rmslabel = [eval(x) for x in self.catalog.lrange('label:deshaw', 0, -1)]
else:
logging.info("Loading DEShaw historical points.... From File (and recalculating)")
rmslabel = deshaw.labelDEShaw_rmsd()
deshaw_samples = {b:[] for b in binlist}
for i, b in enumerate(rmslabel):
deshaw_samples[b].append(i)
coord_origin = []
conv_vals = np.array([v for k, v in sorted(convergence_rms.items())])
norm_pdf_conv = conv_vals / sum(conv_vals)
logging.info("Umbrella Samping PDF (Bootstrapping):")
sampled_distro_perbin = {b: 0 for b in binlist}
while numresources > 0:
# First sampling is BIASED
selected_bin = np.random.choice(len(binlist), p=norm_pdf_conv)
A, B = binlist[selected_bin]
sampled_distro_perbin[binlist[selected_bin]] += 1
if bincounts[selected_bin] is not None and bincounts[selected_bin] > 0:
# Secondary Sampling is Uniform
sample_num = np.random.randint(bincounts[selected_bin])
logging.debug('SAMPLER: selecting sample #%d from bin %s',
sample_num, str(binlist[selected_bin]))
index = self.catalog.lindex('varbin:rms:%d_%d' % binlist[selected_bin],
sample_num)
selected_index_list.append(index)
coord_origin.append(('sim', index, binlist[selected_bin], '%d-D'%A))
numresources -= 1
elif len(deshaw_samples[binlist[selected_bin]]) > 0:
index = np.random.choice(deshaw_samples[binlist[selected_bin]])
logging.debug('SAMPLER: selecting DEShaw frame #%d from bin %s',
index, str(binlist[selected_bin]))
# Negation indicates an historical index number
selected_index_list.append(-index)
coord_origin.append(('deshaw', index, binlist[selected_bin], '%d-D'%A))
numresources -= 1
else:
logging.info("NO Candidates for bin: %s", binlist[selected_bin])
###### EXPERIMENT #10: MutiVariate Nearest Neighbor (MVNN) SAMPLER
if EXPERIMENT_NUMBER == 10:
# Create the KD Tree from all feature landscapes (ignore first 5 features)
kd = KDTree(100, 15, np.array(feallist), 'median')
# Collect hypercubes
hc = kd.getleaves()
logging.info('KD Tree Stats')
logging.info(' # HCubes : %5d', len(hc))
logging.info(' Largest HC: %5d', max([v['count'] for k,v in hc.items()]))
logging.info(' Smallest HC: %5d', min([v['count'] for k,v in hc.items()]))
for key, hcube in hc.items():
hc_feal = [feallist[i] for i in hcube['elm']]
hc[key]['feal'] = np.mean(hc_feal, axis=0)
# Det scale and/or sep scales for each feature set
desired = 10 - global_landscape
logging.info('Desired Landscape:\n%s', feal.tostring(desired))
# Calc euclidean dist to each mean HC's feal
nn = {k: LA.norm(desired[5:] - v['feal'][5:]) for k,v in hc.items()}
# Grab top N Neighbors (10 for now)
neighbors = sorted(nn.items(), key=lambda x: x[1])[:10]
logging.info('BestFit Landscape:\n%s', feal.tostring(hc[neighbors[0][0]]['feal']))
## DATA SAMPLER
nn_keys = [i for i,w in neighbors]
nn_wgts = np.array([w for i,w in neighbors])
nn_wgts /= np.sum(nn_wgts) # normalize
coord_origin = []
while numresources > 0:
# First sampling is BIASED
selected_hc = np.random.choice(nn_keys, p=nn_wgts)
# Second is UNIFORM (within the HCube)
index = np.random.choice(hc[selected_hc]['elm'])
selected_index_list.append(index)
src_state = np.argmax(feallist[index][:5])
coord_origin.append(('sim', index, src_state, selected_hc))
logging.info('Sampled Landscape [hc=%s]:\n%s', selected_hc, feal.tostring(feallist[index]))
numresources -= 1
elif EXPERIMENT_NUMBER == 11:
# Use only right most 10 features (non-normalized ones)
inventory = np.array([f[10:] for f in feallist])
desired = 10 - global_landscape
logging.info('Desired Landscape (NOTE Only Including A-B values:\n%s', feal.tostring(desired))
selected_index_list = mvkp.knapsack(desired[10:], inventory, numresources, 2000000)
coord_origin = [('sim', index, np.argmax(feallist[index][:5]), 'D') for index in selected_index_list]
logging.info("KNAPSACK Completed:")
logging.info('Target Distribution:\n%s', str(desired[10:]))
logging.info('Target Distribution:\n%s', '\n'.join(['%s'%feallist[i] for i in selected_index_list]))
# Back Project to get new starting Coords for each sample
logging.debug("======================= <INPUT PARAM GENERATION> =================")
logging.info('All Indices sampled. Back projecting to high dim coords')
sampled_set = []
for i in selected_index_list:
traj = self.backProjection([i])
sampled_set.append(traj)
bench.mark('Sampler')
# Generate new starting positions
runtime = self.data['runtime']
jcqueue = OrderedDict()
for i, start_traj in enumerate(sampled_set):
jcID, params = generateNewJC(start_traj)
# TODO: Update/check adaptive runtime, starting state
jcConfig = dict(params,
name = jcID,
runtime = runtime, # In timesteps
dcdfreq = self.data['dcdfreq'], # Frame save rate
interval = self.data['dcdfreq'] * self.data['sim_step_size'],
temp = 310,
timestep = self.data['timestep'],
gc = 1,
origin = coord_origin[i][0],
src_index = coord_origin[i][1],
src_bin = coord_origin[i][2],
src_hcube = coord_origin[i][3],
application = settings.APPL_LABEL)
logging.info("New Simulation Job Created: %s", jcID)
for k, v in jcConfig.items():
logging.debug(" %s: %s", k, str(v))
# Add to the output queue & save config info
jcqueue[jcID] = jcConfig
logging.info("New Job Candidate Completed: %s #%d on the Queue", jcID, len(jcqueue))
bench.mark('GenInputParams')
# POST-PROCESSING -------------------------------------
logging.debug("============================ <POST-PROCESSING & OUTPUT> =============================")
self.wait_catalog()
# Clear current queue, mark previously queues jobs for GC, push new queue
qlen = self.catalog.llen('jcqueue')
logging.debug('Current queue len; %s', str(qlen))
if qlen > 0:
curqueue = self.catalog.lrange('jcqueue', 0, -1)
logging.info("Marking %d obsolete jobs for garbage collection", len(curqueue))
for jc_key in curqueue:
key = wrapKey('jc', jc_key)
config = self.catalog.hgetall(key)
config['gc'] = 0
# Add gc jobs it to the state to write back to catalog (flags it for gc)
self.addMut(key, config)
self.catalog.delete('jcqueue')
# CATALOG UPDATES
self.catalog.rpush('datacount', len(feallist))
# EXPR 7 Update:
if EXPERIMENT_NUMBER > 5 and EXPERIMENT_NUMBER < 10:
# self.catalog.storeNPArray(np.array(centroid), 'subspace:covar:centroid:%d' % cov_iteration)
self.catalog.rpush('subspace:covar:thruindex', len(covar_pts))
# Update cache hit/miss
hit = self.cache_hit
miss = self.cache_miss
logging.info('##CACHE_HIT_MISS %d %d %.3f', hit, miss, (hit)/(hit+miss))
self.catalog.rpush('cache:hit', self.cache_hit)
self.catalog.rpush('cache:miss', self.cache_miss)
self.data['jcqueue'] = list(jcqueue.keys())
logging.debug(" JCQUEUE: %s", str(self.data['jcqueue']))
# Update Each new job with latest convergence score and save to catalog(TODO: save may not be nec'y)
logging.debug("Updated Job Queue length: %d", len(self.data['jcqueue']))
for jcid, config in jcqueue.items():
# config['converge'] = self.data['converge']
self.addMut(wrapKey('jc', jcid), config)
bench.mark('PostProcessing')
print ('## TS=%d' % self.data['timestep'])
bench.show()
stat.show()
return list(jcqueue.keys())
def execute(self, job):
# PRE-PREOCESS ----------------------------------------------------------
settings = systemsettings()
bench = microbench('sim_%s' % settings.name, self.seqNumFromID())
bench.start()
stat = StatCollector('sim_%s' % settings.name, self.seqNumFromID())
mylogical_seqnum = str(self.seqNumFromID())
# Prepare working directory, input/output files
conFile = os.path.join(job['workdir'], job['name'] + '.conf')
logFile = conFile.replace('conf', 'log') # log in same place as config file
dcdFile = conFile.replace('conf', 'dcd') # dcd in same place as config file
USE_SHM = True
SIMULATE_RATIO = settings.SIMULATE_RATIO
if SIMULATE_RATIO > 1:
logging.warning(" USING SIMULATION RATIO OF %d -- THis is ONLY for debugging", SIMULATE_RATIO)
frame_size = (SIMULATE_RATIO * int(job['interval'])) / (1000)
logging.info('Frame Size is %f Using Sim Ratio of 1:%d', \
frame_size, SIMULATE_RATIO)
EXPERIMENT_NUMBER = settings.EXPERIMENT_NUMBER
logging.info('Running Experiment Configuration #%d', EXPERIMENT_NUMBER)
# # Grab historical basin data's relative time to start (for lineage)
traj = None
# EXECUTE SIMULATION ---------------------------------------------------------
if self.skip_simulation:
logging.info('1. SKIPPING SIMULATION.....')
USE_SHM = False
job['dcd'] = dcdFile
key = wrapKey('jc', job['name'])
self.data[key]['dcd'] = dcdFile
else:
logging.info('1. Run Simulation')
# Prepare & source to config file
with open(self.data['sim_conf_template'], 'r') as template:
source = template.read()
# >>>>Storing DCD into shared memory on this node
if USE_SHM:
ramdisk = gettempdir()
job['outputloc'] = ramdisk
dcd_ramfile = os.path.join(ramdisk, job['name'] + '.dcd')
else:
job['outputloc'] = ''
with open(conFile, 'w') as sysconfig:
sysconfig.write(source % job)
logging.info("Config written to: " + conFile)
# # Run simulation in parallel
# if 'parallel' in job:
# numnodes = job['parallel']
# total_tasks = numnodes * 24
# cmd = 'mpiexec -n %d namd2 %s > %s' % (total_tasks, conFile, logFile)
# # Run simulation single threaded
# else:
# cmd = 'namd2 %s > %s' % (conFile, logFile)
# cmd = 'mpirun -n %d namd2 %s > %s' % (PARALLELISM, conFile, logFile)
check = executecmd('module list')
logging.debug('%s', check)
cmd = 'namd2 +p%d %s > %s' % (PARALLELISM, conFile, logFile)
# MICROBENCH #1 (file to Lustre)
# logging.debug("Executing Simulation:\n %s\n", cmd)
# bench = microbench()
# bench.start()
# stdout = executecmd(cmd)
# logging.info("SIMULATION Complete! STDOUT/ERR Follows:")
# bench.mark('SimExec:%s' % job['name'])
# shm_contents = os.listdir('/dev/shm/out')
# logging.debug('Ramdisk contents (should have files) : %s', str(shm_contents))
# shutil.copy(ramdisk + job['name'] + '.dcd', job['workdir'])
# logging.info("Copy Complete to Lustre.")
# bench.mark('CopyLustre:%s' % job['name'])
# shutil.rmtree(ramdisk)
# shm_contents = os.listdir('/dev/shm')
# logging.debug('Ramdisk contents (should be empty) : %s', str(shm_contents))
# bench.show()
max_expected_obs = int(job['runtime']) // int(job['dcdfreq'])
# Retry upto 3 attempts if the sim fails
MAX_TRY = 3
for i in range(MAX_TRY, 0, -1):
min_required_obs = int(max_expected_obs * ((i-1)/(MAX_TRY)))
logging.debug("Executing Simulation:\n %s\n", cmd)
logging.debug('# Obs Expected to see: %d', max_expected_obs)
stdout = executecmd(cmd)
logging.info("SIMULATION Complete! STDOUT/ERR Follows:")
# Check file for expected data
if USE_SHM:
traj = md.load(dcd_ramfile, top=job['pdb'])
else:
traj = md.load(dcdFile, top=job['pdb'])
logging.info("Obs Threshold = %4d", min_required_obs)
logging.info("#Obs This Traj = %4d", traj.n_frames)
if traj.n_frames >= min_required_obs:
logging.info('Full (enough) Sim Completed')
break
logging.info('Detected a failed Simulation. Retrying the same sim.')
break
bench.mark('simulation')
# bench.mark('SimExec:%s' % job['name'])
# Internal stats
sim_length = self.data['sim_step_size'] * int(job['runtime'])
sim_realtime = bench.delta_last()
sim_run_ratio = (sim_realtime/60) / (sim_length/1000000)
logging.info('##SIM_RATIO %6.3f min-per-ns-sim', sim_run_ratio)
stat.collect('sim_ratio', sim_run_ratio)
if USE_SHM:
shm_contents = os.listdir(ramdisk)
logging.debug('Ramdisk contents (should have files) : %s', str(shm_contents))
if not os.path.exists(dcd_ramfile):
logging.warning("DCD FILE NOT FOUND!!!! Wait 10 seconds for sim to close it (???)")
time.sleep(10)
if not os.path.exists(dcd_ramfile):
logging.warning("DCD STIILL FILE NOT FOUND!!!!")
else:
logging.info("DCD File was found")
# # MICROBENCH #2 (file to Alluxio)
# allux = AlluxioClient()
# # copy to Aluxio FS
# allux.put(ramdisk + job['name'] + '.dcd', '/')
# logging.info("Copy Complete to Alluxio.")
# bench.mark('CopyAllux:%s' % job['name'])
# And copy to Lustre
# shutil.copy(ramdisk + job['name'] + '.dcd', job['workdir'])
# And copy to Lustre (usng zero-copy):
if USE_SHM:
# ALT: X-Mit to Cache Service and let cache write to disk lazily
src = open(dcd_ramfile, 'rb')
dest = open(dcdFile, 'w+b')
offset = 0
dcdfilesize = os.path.getsize(dcd_ramfile)
while True:
sent = sendfile(dest.fileno(), src.fileno(), offset, dcdfilesize)
if sent == 0:
break
offset += sent
logging.info("Copy Complete to Lustre.")
bench.mark('CopyLustre:%s' % job['name'])
# TODO: Update job's metadata
key = wrapKey('jc', job['name'])
self.data[key]['dcd'] = dcdFile
# ANALYSIS ------- ---------------------------------------------------------
# ANALYSIS ALGORITHM
# 1. With combined Sim-analysis: file is loaded locally from shared mem
logging.debug("2. Load DCD")
# Load topology and define filters
topo = self.protein.pdb
pdb = md.load(job['pdb'])
hfilt = pdb.top.select_atom_indices('heavy')
pfilt = pdb.top.select('protein')
afilt = pdb.top.select_atom_indices('alpha')
# Load full higher dim trajectory
if traj is None:
if USE_SHM:
traj = md.load(dcd_ramfile, top=pdb)
else:
traj = md.load(dcdFile, top=pdb)
logging.debug('Trajectory Loaded: %s (%s)', job['name'], str(traj))
traj_prot = traj.atom_slice(pfilt)
traj_heavy = traj.atom_slice(hfilt)
traj_alpha = traj.atom_slice(afilt)
# Superpose Coordinates to Common Reference
traj_prot.superpose(topo)
# Calculate output Distance Space
# Use of the second side chain atom is discussed in the ref paper on Timescape
# The atom pairs and selected atoms are in the timescape module
sc_pairs = side_chain_pairs(traj_prot)
n_features = len(sc_pairs)
# Use the CA atoms to calculate distance space
# NOTE THE CONVERSION FROM NM TO ANG!!!!!!
dist_space = 10 * DR.distance_space(traj_alpha)
# Get the frame rate to for conversion between source and timescape
# NOTE: Timescae is run at a more coarse level
logging.debug('Preprocessing output for TimeScapes: terrain')
traj_frame_per_ps = SIMULATE_RATIO * int(job['interval']) / 1000. # jc interval is in fs
ts_frame_per_ps = int(self.data['timescape:rate']) # this value is in ps
frame_rate = int(ts_frame_per_ps / traj_frame_per_ps)
logging.debug('%5.2f fr/ps (Traj) %5.2f fr/ps (TS) FrameRate= %4.1f', traj_frame_per_ps, ts_frame_per_ps, frame_rate)
# Execute Timescapes agility program to detect spatial-temporal basins
output_prefix = os.path.join(job['workdir'], job['name'])
if not self.skip_timescape:
# Prep file and save locally in shm
tmploc = gettempdir()
ts_out = tmploc + 'traj_ts'
ts_dcd = ts_out + '.dcd'
ts_pdb = ts_out + '.pdb'
heavy = traj_heavy.slice(range(0, traj.n_frames, frame_rate))
heavy.slice(0).save_pdb(ts_pdb)
heavy.save_dcd(ts_dcd)
# Gaussuan Full Width at Half-Max value affects sliding window size
# ref: http://timescapes.biomachina.org/guide.pdf
gmd_cut1 = int(self.data['timescape:gmd:low'])
gmd_cut2 = int(self.data['timescape:gmd:hi'])
gauss_wght_delta = int(self.data['timescape:delta'])
# Execute timescapes' terrain.py on the pre-processed trajectory
cmd = 'terrain.py %s %s %d %d %d GMD %s' %\
(ts_pdb, ts_dcd, gmd_cut1, gmd_cut2, gauss_wght_delta, output_prefix)
logging.info('Running Timescapes:\n %s', cmd)
stdout = executecmd(cmd)
logging.info('TimeScapes COMPLETE:\n%s', stdout)
# Collect and parse Timescape output
logging.debug('Parsing Timescapes output')
ts_parse = TimeScapeParser(job['pdb'], output_prefix, job['name'],
dcd=dcdFile, traj=traj, uniqueid=False)
basin_list = ts_parse.load_basins(frame_ratio=frame_rate)
n_basins = len(basin_list)
minima_coords = {}
basin_rms = {}
basins = {}
new_dmu={}
new_dsig={}
resid_rms_delta = {}
stat.collect('num_basin', n_basins)
downstream_list = []
# FOR BOOTSTRAPPING USING RMSD
ref_file = os.path.join(settings.workdir, self.data['pdb:ref:0'])
logging.info('Loading RMSD reference frame from %s', self.data['pdb:ref:0'])
refframe = md.load(ref_file)
ref_alpha = refframe.atom_slice(refframe.top.select_atom_indices('alpha'))
rmsd = 10*md.rmsd(traj_alpha, ref_alpha)
# FOR RESIDUE RMSD
res_rms_Kr = FEATURE_SET_RESID
resrmsd = 10*np.array([LA.norm(i-ref_alpha.xyz[0], axis=1) for i in traj_alpha.xyz])
basin_res_rms = np.zeros(shape=(len(basin_list), traj_alpha.n_atoms))
# LOAD CENROIDS -- todo move to immut
centroid = pickle.loads(self.catalog.get('centroid:ds'))
basin_label_list = {}
# Process each basin
for i, basin in enumerate(basin_list):
logging.info(' Processing basin #%2d', i)
bid = basin.id
downstream_list.append(bid)
# Slice out minima coord & save to disk (for now)
# TODO: Store in memory in cache
minima_coords[bid] = traj.slice(basin.mindex)
jc_filename = os.path.join(settings.datadir, 'basin_%s.pdb' % bid)
minima_coords[bid].save_pdb(jc_filename)
# METRIC CALCULATION
a, b = basin.start, basin.end
new_dmu[bid] = np.mean(dist_space[a:b], axis=0)
new_dsig[bid] = np.std(dist_space[a:b], axis=0)
# Collect Basin metadata
basin_hash = basin.kv()
basin_hash['pdbfile'] = jc_filename
basin_hash['rmsd'] = np.median(rmsd[a:b])
basin_res_rms[i] = np.median(resrmsd[a:b], axis=0)
# FOR EXP #16 -- Residue RMSD
# Set relative time (in ns)
# basin_hash['time'] = reltime_start + (a * frame_rate) / 1000
basins[bid] = basin_hash
# LABEL ATEMPORAL DATA
# Take every 4th frame
stride = [dist_space[i] for i in range(a,b,4)]
rms = [LA.norm(centroid - i, axis=1) for i in stride]
label_seq = [np.argmin(i) for i in rms]
basin_label_list[bid] = label_seq
logging.info(' Basin Processed: #%s, %d - %d', basin_hash['traj'],
basin_hash['start'], basin_hash['end'])
# RMSD DELTA (RESIDUE)
basin_res_rms_delta = np.array([rms_delta(i) for i in basin_res_rms.T]).T
basin_rms_delta_bykey = {basin.id: basin_res_rms_delta[i] for i, basin in enumerate(basin_list)}
for k in basins.keys():
basins[k]['resrms_delta'] = np.sum(basin_rms_delta_bykey[k][res_rms_Kr])
# TODO: Use Min Index as snapshot, median (or mean) DistSpace vals for each basin?????
bench.mark('analysis')
# BARRIER: WRITE TO CATALOG HERE -- Ensure Catalog is available
# try:
self.wait_catalog()
# except OverlayNotAvailable as e:
# logging.warning("Catalog Overlay Service is not available. Scheduling ASYNC Analysis")
# FOR EXPERIMENT METRICS
src_basin = self.catalog.hgetall("basin:" + job['src_basin'])
with open('/home-1/[email protected]/ddc/results/{0}_prov.log'.format(settings.name), 'a') as metric_out:
for i, basin in enumerate(basin_list):
bid = basin.id
label_seq = basin_label_list[bid]
basin_metric_label = LABEL10(label_seq)
metric_out.write('BASIN,%s,%s,%s,%s\n'% \
(bid, src_basin['label:10'], basin_metric_label, ''.join([str(i) for i in label_seq])))
# Update Index Synchronized data lists
basin_list_sorted =sorted(basins.keys(), key=lambda x: (x.split('_')[0], int(x.split('_')[1])))
for bid in basin_list_sorted:
with self.catalog.pipeline() as pipe:
while True:
try:
logging.debug('Updating %s basin indeces and distance space', len(basins))
pipe.rpush('basin:list', bid)
pipe.rpush('dspace', pickle.dumps(new_dmu[bid]))
basin_index,_ = pipe.execute()
break
except redis.WatchError as e:
logging.debug('WATCH ERROR. Someone else is writing to the catalog. Retrying...')
continue
basins[bid]['dsidx'] = basin_index - 1
# Update Catalog with 1 Long Atomic Transaction
with self.catalog.pipeline() as pipe:
while True:
try:
logging.debug('Update Filelist')
pipe.watch(wrapKey('jc', job['name']))
file_idx = pipe.rpush('xid:filelist', job['dcd']) - 1
# HD Points
logging.debug('Update HD Points')
start_index = pipe.llen('xid:reference')
pipe.multi()
pipe.rpush('xid:reference', *[(file_idx, x) for x in range(traj.n_frames)])
pipe.set('resrms:' + job['name'], resrmsd)
# Store all basin data
logging.debug('Updating %s basins', len(basins))
for bid in sorted(basins.keys(), key=lambda x: (x.split('_')[0], int(x.split('_')[1]))):
pipe.hmset('basin:'+bid, basins[bid])
pipe.set('minima:%s'%bid, pickle.dumps(minima_coords[bid]))
for i in basin_label_list[bid]:
pipe.rpush('basin:labelseq:'+bid, i)
pipe.hset('anl_sequence', job['name'], mylogical_seqnum)
logging.debug('Executing')
pipe.execute()
break
except redis.WatchError as e:
logging.debug('WATCH ERROR. Someone else is writing to the catalog. Retrying...')
continue
self.data[key]['xid:start'] = start_index
self.data[key]['xid:end'] = start_index + traj.n_frames
bench.mark('catalog')
# ---- POST PROCESSING
# Check for total data in downstream queue & Notify control manager to run
# 1. Det. what's still running
job_queue = slurm.currentqueue()
n_simjobs = -1 # -1 to exclude self
for j in job_queue:
if j['name'].startswith('sw'):
n_simjobs += 1
logging.info('SIM WORKER has detected %d other simulations running/pending.', len(job_queue))
remain_percent = n_simjobs / self.data['numresources']
# Fault Tolerance: ensure the pipeline persists
if n_simjobs == 0:
logging.info('I am the last simulation. Ensuring the controler executes.')
self.catalog.set('ctl:force', 1)
# Send downstream notification when less than 10% of jobs are still running
if not self.skip_notify and remain_percent < .1:
# Notify the control manager 'CM'
self.notify('ctl')
if USE_SHM:
shutil.rmtree(ramdisk)
shm_contents = os.listdir('/dev/shm')
logging.debug('Ramdisk contents (should be empty of DDC) : %s', str(shm_contents))
# For benchmarching:
bench.show()
stat.show()
# Return # of observations (frames) processed
return downstream_list
