def main():
args = configuration()
base_name = args.name
base_id = args.id
for idx in range(1, 6):
args.id = base_id + idx - 1
seed_val = random.randrange(10000000)
seed_system(seed_val)
args.seed = seed_val
args.name = base_name + '_{}'.format(args.id)
time = str(datetime.now())
scores = exec_unit(args)
graph_path = create_graphs(sim_num=args.id, linear=True)
message = '{0}\nSimulation Number {1} Completed\n' \
'Min Error - {2:.3f}\n' \
'Mean Error - {3:.3f}\n' \
'Val Error - {4:.3f}'.format(time, args.id, scores[0], scores[1], scores[2])
send_notification(message, vars(args), graph_path, args)
def main():
# torch.distributed.init_process_group(backend='gloo', init_method='tcp://132.68.43.137:29500')
args = configuration()
base_name = args.name
base_id = args.id
seed_vals = [8678576, 4527389, 2113183, 518078, 7370063]
for idx in range(1, 2):
args.id = base_id + idx - 1
# seed_val = random.randrange(10000000)
seed_val = seed_vals[idx - 1]
seed_system(seed_val)
args.seed = seed_val
args.name = base_name + '_{}'.format(args.id)
time = str(datetime.now())
scores = exec_unit(args)
graph_path = create_graphs(sim_num=args.id, linear=True)
message = '{0}\nSimulation Number {1} Completed\n' \
'Min Error - {2:.3f}\n' \
'Mean Error - {3:.3f}\n' \
'Val Error - {4:.3f}'.format(time, args.id, scores[0], scores[1], scores[2])
send_notification(message, vars(args), graph_path, args)
def index(request):
# create graphs and get list of file names
file_paths = graphs.create_graphs()
# create a template that shows each graph image
# render the template
return render(request, 'history/index.html', {'graphs': file_paths})
def opsys_gr():
x = ft.unique_specifications(notebooks, "OpSys")
y = [ft.aver_prices(notebooks, "OpSys")[j] for j in x]
gr.create_graphs(x, y, "OpSys")
def ram_gr():
x = ft.unique_specifications(notebooks, "Ram")
y = [ft.aver_prices(notebooks, "Ram")[j] for j in x]
gr.create_graphs(x, y, "Ram")
def typename_gr():
x = ft.unique_specifications(notebooks, "TypeName")
y = [ft.aver_prices(notebooks, "TypeName")[j] for j in x]
gr.create_graphs(x, y, "TypeName")
def comp_gr():
x = ft.unique_specifications(notebooks, "Company")
y = [ft.aver_prices(notebooks, "Company")[j] for j in x]
gr.create_graphs(x, y, "Company")