for pid, vals in unit_info.iteritems(): if "Terminated" in vals.keys(): line = "{0}, {1}, {2:0.5f}, {3:0.5f}, None\n".format(pid, vals["Interrupt"], vals["Started"],vals["Terminated"]) f1.write(line) elif "Done" in vals.keys(): line = "{0}, None, {1:0.5f}, None, {2:0.5f}\n".format(pid, vals["Started"],vals["Done"]) f1.write(line) f1.close() import radical.pilot.utils as rpu # we have a session sid = session.uid profiles = rpu.fetch_profiles(sid=sid, tgt='/tmp/') profile = rpu.combine_profiles (profiles) frame = rpu.prof2frame(profile) sf, pf, uf = rpu.split_frame(frame) rpu.add_info(uf) rpu.add_states(uf) adv = uf[uf['event'].isin(['advance'])] rpu.add_frequency(adv, 'f_exe', 0.5, {'state' : 'Executing', 'event' : 'advance'}) s_frame, p_frame, u_frame = rpu.get_session_frames(sid) print str(u_frame)
def profile_analysis(sid): import radical.pilot.utils as rpu report.header('profile analysis') # fetch profiles for all pilots profiles = rpu.fetch_profiles(sid=sid, tgt='/tmp/') print(profiles) # combine into a single profile profile = rpu.combine_profiles(profiles) # derive a global data frame frame = rpu.prof2frame(profile) # split into session / pilot / unit frames sf, pf, uf = rpu.split_frame(frame) print(len(sf)) print(len(pf)) print(len(uf)) print(sf[0:10]) print(pf[0:10]) print(uf[0:10]) # derive some additional 'info' columns, which contains some commonly used # tags rpu.add_info(uf) for index, row in uf.iterrows(): if str(row['info']) != 'nan': print("%-20s : %-10s : %-25s : %-20s" % (row['time'], row['uid'], row['state'], row['info'])) # add a 'state_from' columns which signals a state transition rpu.add_states(uf) adv = uf[uf['event'].isin(['advance'])] print('---------------') print(len(adv)) print(uf[uf['uid'] == 'unit.000001']) print(list(pf['event'])) tmp = uf[uf['uid'] == 'unit.000001'].dropna() print(tmp[['time', 'uid', 'state', 'state_from']]) # add a columns 'rate_out' which contains the rate (1/s) of the event # 'advance to state STAGING_OUTPUT' print('---------------') rpu.add_frequency(adv, 'rate_out', 0.5, { 'state': 'StagingOutput', 'event': 'advance' }) print(adv[['time', 'rate_out']].dropna(subset=['rate_out'])) print('---------------') fig, plot = rpu.create_plot() plot.set_title('rate of ouput staging transitions', y=1.05, fontsize=18) plot.set_xlabel('time (s)', fontsize=14) plot.set_ylabel('rate (1/s)', fontsize=14) plot.set_frame_on(True) adv[['time', 'rate_out']].dropna().plot(ax=plot, logx=False, logy=False, x='time', y='rate_out', drawstyle='steps', label='output rate', legend=False) plot.legend(labels=['output rate'], loc='best', fontsize=14, frameon=True) fig.savefig('profile.png', bbox_inches='tight')
import radical.pilot.utils as rpu # we have a session sid = session.uid profiles = rpu.fetch_profiles(sid=sid, tgt='/tmp/') profile = rpu.combine_profiles(profiles) frame = rpu.prof2frame(profile) sf, pf, uf = rpu.split_frame(frame) rpu.add_info(uf) rpu.add_states(uf) adv = uf[uf['event'].isin(['advance'])] rpu.add_frequency(adv, 'f_exe', 0.5, { 'state': 'Executing', 'event': 'advance' }) s_frame, p_frame, u_frame = rpu.get_session_frames(sid) #print str(u_frame) info = [ "uid", "Unscheduled", "StagingInput", "AgentStagingInputPending", "AgentStagingInput", "AllocatingPending", "Allocating", "ExecutingPending", "Executing", "AgentStagingOutputPending", "AgentStagingOutput", "StagingOutput", "Canceled", "Done" ] u_frame.to_csv( "execution_profile_nsims_{0}_simdur_{3}_anamin_{1}_anamax{5}_anatotdur_{2}_max_{4}.csv" .format(tot_sim_tasks, interrupt_gap_min, interrupt_total_duration, sim_arg, interrupt_max_tasks, interrupt_gap_max),
def profile_analysis(sid): import radical.pilot.utils as rpu report.header('profile analysis') # fetch profiles for all pilots profiles = rpu.fetch_profiles(sid=sid, tgt='/tmp/') print profiles # combine into a single profile profile = rpu.combine_profiles(profiles) # derive a global data frame frame = rpu.prof2frame(profile) # split into session / pilot / unit frames sf, pf, uf = rpu.split_frame(frame) print len(sf) print len(pf) print len(uf) print sf[0:10] print pf[0:10] print uf[0:10] # derive some additional 'info' columns, which contains some commonly used # tags rpu.add_info(uf) for index, row in uf.iterrows(): if str(row['info']) != 'nan': print "%-20s : %-10s : %-25s : %-20s" % \ (row['time'], row['uid'], row['state'], row['info']) # add a 'state_from' columns which signals a state transition rpu.add_states(uf) adv = uf[uf['event'].isin(['advance'])] print '---------------' print len(adv) print uf[uf['uid'] == 'unit.000001'] print list(pf['event']) tmp = uf[uf['uid'] == 'unit.000001'].dropna() print tmp[['time', 'uid', 'state', 'state_from']] # add a columns 'rate_out' which contains the rate (1/s) of the event # 'advance to state STAGING_OUTPUT' print '---------------' rpu.add_frequency(adv, 'rate_out', 0.5, {'state' : 'StagingOutput', 'event' : 'advance'}) print adv[['time', 'rate_out']].dropna(subset=['rate_out']) print '---------------' fig, plot = rpu.create_plot() plot.set_title('rate of ouput staging transitions', y=1.05, fontsize=18) plot.set_xlabel('time (s)', fontsize=14) plot.set_ylabel('rate (1/s)', fontsize=14) plot.set_frame_on(True) adv[['time', 'rate_out']].dropna().plot(ax=plot, logx=False, logy=False, x='time', y='rate_out', drawstyle='steps', label='output rate', legend=False) plot.legend(labels=['output rate'], loc='best', fontsize=14, frameon=True) fig.savefig('profile.png', bbox_inches='tight')