Commit dc485350 authored by Brewer, Wes's avatar Brewer, Wes
Browse files

Fix to frontier.py to remove globals()

parent 80db3825

raps/dataloaders/frontier.py

+9 −10
Original line number Diff line number Diff line
@@ -47,7 +47,6 @@ def load_data_from_df(jobs_df: pd.DataFrame, jobprofile_df: pd.DataFrame, **kwar 
        The list of parsed jobs.

    """

    config = kwargs.get('config')

    globals().update(config)

    encrypt_bool = kwargs.get('encrypt')

    fastforward = kwargs.get('fastforward')

    reschedule = kwargs.get('reschedule')
@@ -101,25 +100,25 @@ def load_data_from_df(jobs_df: pd.DataFrame, jobprofile_df: pd.DataFrame, **kwar 
            cpu_power = jobprofile_df[jobprofile_df['allocation_id']

                                      == allocation_id]['sum_cpu0_power']

            cpu_power_array = cpu_power.values

            cpu_min_power = nodes_required * POWER_CPU_IDLE * CPUS_PER_NODE

            cpu_max_power = nodes_required * POWER_CPU_MAX * CPUS_PER_NODE

            cpu_min_power = nodes_required * config['POWER_CPU_IDLE'] * config['CPUS_PER_NODE']

            cpu_max_power = nodes_required * config['POWER_CPU_MAX'] * config['CPUS_PER_NODE']

            cpu_util = power_to_utilization(cpu_power_array, cpu_min_power, cpu_max_power)

            cpu_trace = cpu_util * CPUS_PER_NODE

            cpu_trace = cpu_util * config['CPUS_PER_NODE']



            gpu_power = jobprofile_df[jobprofile_df['allocation_id']

                                      == allocation_id]['sum_gpu_power']

            gpu_power_array = gpu_power.values



            gpu_min_power = nodes_required * POWER_GPU_IDLE * GPUS_PER_NODE

            gpu_max_power = nodes_required * POWER_GPU_MAX * GPUS_PER_NODE

            gpu_min_power = nodes_required * config['POWER_GPU_IDLE'] * config['GPUS_PER_NODE']

            gpu_max_power = nodes_required * config['POWER_GPU_MAX'] * config['GPUS_PER_NODE']

            gpu_util = power_to_utilization(gpu_power_array, gpu_min_power, gpu_max_power)

            gpu_trace = gpu_util * GPUS_PER_NODE

            gpu_trace = gpu_util * config['GPUS_PER_NODE']



        # Set any NaN values in cpu_trace and/or gpu_trace to zero

        cpu_trace[np.isnan(cpu_trace)] = 0

        gpu_trace[np.isnan(gpu_trace)] = 0



        wall_time = gpu_trace.size * TRACE_QUANTA # seconds

        wall_time = gpu_trace.size * config['TRACE_QUANTA'] # seconds



        time_start = jobs_df.loc[jidx+1, 'time_start']

        diff = time_start - time_zero
@@ -133,11 +132,11 @@ def load_data_from_df(jobs_df: pd.DataFrame, jobprofile_df: pd.DataFrame, **kwar 


        if reschedule: # Let the scheduler reschedule the jobs

            scheduled_nodes = None

            time_offset = next_arrival(1/JOB_ARRIVAL_TIME)

            time_offset = next_arrival(1/config['JOB_ARRIVAL_TIME'])

        else: # Prescribed replay

            scheduled_nodes = []

            for xname in xnames:

                indices = xname_to_index(xname)

                indices = xname_to_index(xname, config)

                scheduled_nodes.append(indices)



        if gpu_trace.size > 0 and (jid == job_id or jid == '*') and time_offset > 0: