# enable cluster training
cluster = SlurmCluster(hyperparam_optimizer=hyperparams,
log_path=hyperparams.log_path,
python_cmd='python3',
test_tube_exp_name=hyperparams.test_tube_exp_name)
# email results if your hpc supports it
cluster.notify_job_status(email='*****@*****.**', on_done=True, on_fail=True)
# any modules for code to run in env
cluster.load_modules(['python-3', 'anaconda3'])
# add commands to the non slurm portion
cluster.add_command('source activate myCondaEnv')
# can also add custom slurm commands which show up as:
# #comment
# #SBATCH --cmd=value
# ############
# cluster.add_slurm_cmd(cmd='cpus-per-task', value='1', comment='nb cpus per task')
# set job compute details (this will apply PER set of hyperparameters)
cluster.per_experiment_nb_cpus = 20
cluster.per_experiment_nb_nodes = 10
# each job (24 in total here) will use 200 cpus for each set of hyperparams
# if job_display_name is set, it's what will display in the slurm queue
cluster.optimize_parallel_cluster_cpu(train,
nb_trials=24,
job_name='first_tt_job',
job_display_name='short_name')
cluster.notify_job_status(email='*****@*****.**',
on_done=True,
on_fail=True)
# SLURM Module to load.
cluster.load_modules(['python-3', 'anaconda3'])
# Add commands to the non-SLURM portion.
cluster.add_command('source activate myCondaEnv')
# Add custom SLURM commands which show up as:
# #comment
# #SBATCH --cmd=value
# ############
# cluster.add_slurm_cmd(
# cmd='cpus-per-task', value='1', comment='CPUS per task.')
# Set job compute details (this will apply PER set of hyperparameters.)
cluster.per_experiment_nb_cpus = 20
cluster.per_experiment_nb_nodes = 10
# Each hyperparameter combination will use 200 cpus.
cluster.optimize_parallel_cluster_cpu(
# Function to execute:
train,
# Number of hyperparameter combinations to search:
nb_trials=24,
job_name='first_tt_job',
# This is what will display in the slurm queue:
job_display_name='short_name')