def _run(args):
_env_setup(args.logfile, args.verbose)
if args.distributed:
try:
from dask.distributed import Client, LocalCluster
except ImportError as ie:
ie.msg += ('\n\nIt seems like `dask` is not installed.\n'
'Please install `dask` and `distributed` using:\n'
'\n pip install dask distributed')
raise
client = Client(
LocalCluster(n_workers=args.workers,
threads_per_worker=args.threads))
client.register_worker_callbacks(
lambda: _env_setup(args.logfile, args.verbose))
workers = 'dask'
else:
workers = args.workers
synthesizers = sdgym.get_all_synthesizers()
if args.models:
synthesizers = {model: synthesizers[model] for model in args.models}
lb = sdgym.run(synthesizers=synthesizers,
datasets=args.datasets,
iterations=args.iterations,
output_path=args.output_path,
cache_dir=args.cache_dir,
workers=workers,
show_progress=args.progress)
if lb is not None:
print(lb)
def _run(args):
_env_setup(args.logfile, args.verbose)
if args.distributed:
try:
from dask.distributed import Client, LocalCluster
except ImportError as ie:
ie.msg += ('\n\nIt seems like `dask` is not installed.\n'
'Please install `dask` and `distributed` using:\n'
'\n pip install dask distributed')
raise
processes = args.workers > 1
client = Client(
LocalCluster(
processes=processes,
n_workers=args.workers,
threads_per_worker=args.threads,
), )
client.register_worker_callbacks(
lambda: _env_setup(args.logfile, args.verbose))
workers = 'dask'
else:
workers = args.workers
if args.jobs:
args.jobs = json.loads(args.jobs)
scores = sdgym.run(
synthesizers=args.synthesizers,
datasets=args.datasets,
datasets_path=args.datasets_path,
modalities=args.modalities,
metrics=args.metrics,
bucket=args.bucket,
iterations=args.iterations,
cache_dir=args.cache_dir,
workers=workers,
show_progress=args.progress,
timeout=args.timeout,
output_path=args.output_path,
aws_key=args.aws_key,
aws_secret=args.aws_secret,
jobs=args.jobs,
max_rows=args.max_rows,
max_columns=args.max_columns,
)
if args.groupby:
scores = scores.groupby(args.groupby).mean().reset_index()
if scores is not None:
_print_table(scores)
def initialize_cluster(
hpc: bool = True,
jobs: int = 10,
temp: str = tf.gettempdir(),
scheduler: str = None,
verbose: bool = True,
**kwargs
) -> Client:
"""
Initialize a distributed dask cluster.
arguments
hpc: if true, initialize a HPC cluster running PBS/TORQUE
jobs: number of jobs to submit to an HPC cluster
temp: temp directory for intermediate/worker output
scheduler: scheduler address if using a custom cluster
verbose: logging verbosity
returns
a dask client
"""
## Idk if this is actually needed
dask.config.set({'temporary_directory': temp})
dask.config.set({'local_directory': temp})
if hpc:
cluster = _initialize_pbs_cluster(**kwargs)
cluster.scale(jobs=jobs)
client = Client(cluster)
elif kwargs['environment']['local']:
cluster = _initialize_local_cluster(**kwargs)
client = Client(cluster)
elif kwargs['environment']['custom']:
client = Client(address=scheduler)
else:
## Should never get here
log._logger.error('Something horrible happened while starting the cluster')
exit(1)
## Run the logging init function on each worker and register the callback so
## future workers also run the function
init_logging_partial = partial(log._initialize_logging, verbose=verbose)
client.register_worker_callbacks(setup=init_logging_partial)
return client
def _run(args):
_env_setup(args.logfile, args.verbose)
if args.distributed:
try:
from dask.distributed import Client, LocalCluster
except ImportError as ie:
ie.msg += (
'\n\nIt seems like `dask` is not installed.\n'
'Please install `dask` and `distributed` using:\n'
'\n pip install dask distributed'
)
raise
processes = args.workers > 1
client = Client(
LocalCluster(
processes=processes,
n_workers=args.workers,
threads_per_worker=args.threads,
),
)
client.register_worker_callbacks(lambda: _env_setup(args.logfile, args.verbose))
workers = 'dask'
else:
workers = args.workers
lb = sdgym.run(
synthesizers=args.synthesizers,
datasets=args.datasets,
datasets_path=args.datasets_path,
modalities=args.modalities,
metrics=args.metrics,
iterations=args.iterations,
cache_dir=args.cache_dir,
workers=workers,
show_progress=args.progress,
timeout=args.timeout,
output_path=args.output_path,
)
if lb is not None:
_print_table(lb)
cluster = KubeCluster.from_yaml('worker-cpu-spec.yml')
client = Client(cluster)
#cluster.adapt(minimum=0, maximum=10)
cluster.scale(10)
files_list = ['deap_ga.py', 'fillPool.py', 'mutations.py', 'utils.py']
for i in range(len(files_list)):
fname = files_list[i]
with open(fname, 'rb') as f:
data = f.read()
def _worker_upload(dask_worker, *, data, fname):
dask_worker.loop.add_callback(
callback=dask_worker.upload_file,
comm=None, # not used
filename=fname,
data=data,
load=True)
client.register_worker_callbacks(setup=functools.partial(
_worker_upload,
data=data,
fname=fname,
))
bi, final_cluster = cluster_GA(nPool, eleNames, eleNums, eleRadii,
generations, calc, filename, log_file, CXPB,
singleTypeCluster)
#view(final_cluster)
#view(bi[0])
def get_client(
address=None,
auto_spawn=True,
worker_log_level="ERROR",
**clustser_kwargs,
):
"""
Args:
address (str, optional): address of the cluster scheduler, or 'slurm' to launch
a dask cluster through SLURM
auto_spawn (bool, optional): automagically spawn cluster if not found
work_log_level (str, optional): worker log level
"""
cluster_klass, client = None, None
if address == "slurm":
# create SLURM jobs
cluster_klass = ManagedSLURMCluster
elif address is None:
# nothing specified, use:
# - already connected client
# - spawn new local cluster
try:
# we try to acquire current session first
client = Client.current()
address = client.scheduler_info()["address"]
logger.info(f"connect to existing cluster (scheduler: {address})")
yield client
# NOTE we do NOT close client when using this method, managed by others
except ValueError:
# nothing exists, continue to spawn managed cluster
if not auto_spawn:
raise RuntimeError("please spawn a dask cluster first")
cluster_klass = ManagedLocalCluster
# local cluster needs address info
clustser_kwargs.update({"address": address})
else:
# directly specify the scheduler to connect to
client = Client(address)
yield client
client.close()
# NOTE we open this client, therefore, we need to close it ourself
if not cluster_klass:
# no need to spawn a cluster
return
with cluster_klass(**clustser_kwargs) as cluster:
client = cluster.client
# register loggers
try:
import coloredlogs
except ImportError:
logger.install(
"install `coloredlogs` to configure loggers automatically")
else:
def install_logger(dask_worker):
# we know this is annoying, silence it
logging.getLogger("tifffile").setLevel(logging.ERROR)
coloredlogs.install(
level=worker_log_level,
fmt="%(asctime)s %(levelname)s %(message)s",
datefmt="%H:%M:%S",
)
logger.debug(
f'install logger for workers, level="{worker_log_level}"')
client.register_worker_callbacks(install_logger)
yield client
class Remote(object):
"""
Remote.
Args:
address (str): Remote scheduler address formed by `ip:port`.
tls_ca_file (str, optional): TLS CA certificate file path. Defaults to None.
tls_client_cert (str, optional): TLS certificate file path. Defaults to None.
tls_client_key (str, optional): TLS private key file path. Defaults to None.
require_encryption (bool, optional): Encrypt data exchange. Defaults to False.
Note:
TLS will be enabled only if all three TLS arguments are provided.
Remember to change network protocol to `tls://<address>`.
"""
def __init__(self, address: str,
tls_ca_file: str = None, tls_client_cert: str = None, tls_client_key: str = None,
require_encryption: bool = False):
# authentication
sec = None
if tls_ca_file and tls_client_cert and tls_client_key:
sec = Security(tls_ca_file=tls_ca_file,
tls_client_cert=tls_client_cert,
tls_client_key=tls_client_key,
require_encryption=require_encryption)
# init
self._client = Client(address=address, security=sec)
self._client.register_worker_callbacks(Remote._worker_startup)
@staticmethod
def _worker_startup(dask_worker: Worker):
os.chdir(dask_worker.local_dir)
def add_dependencies(self, files):
"""
Add list of dependencies, order matters.
Args:
files (list): List of dependent files.
"""
# TODO: automatically resolve module dependencies
if isinstance(files, str):
files = [files]
for f in files:
self._client.upload_file(f)
def scatter(self, *args, **kwargs):
"""
Scatter data.
"""
return self._client.scatter(*args, **kwargs)
def submit(self, func, *args, **kwargs):
"""
Submit function and data.
Args:
func (callable): User function.
"""
return self._client.submit(func, *args, **kwargs)
def fetch(self, futures_, **kwargs):
"""
Fetch data of future objects.
Args:
futures_ (list): Future objects.
"""
return self._client.gather(futures_, **kwargs)
def cancel(self, futures_, **kwargs):
"""
Cancel job of future objects.
Args:
futures_ (list): Future objects.
"""
return self._client.cancel(futures_, **kwargs)
def close(self, *args, **kwargs):
"""
Close connection.
"""
return self._client.close(*args, **kwargs)
def run_dask_function(config):
"""Start a Dask Cluster using dask-kubernetes and run a function.
Talks to kubernetes to create `n` amount of new `pods` with a dask worker inside of each
forming a `dask` cluster. Then, a function specified from `config` is being imported and
run with the given arguments. The tasks created by this `function` are being run on the
`dask` cluster for distributed computation.
The config dict must contain the following sections:
* run
* dask_cluster
* output
Args:
config (dict):
Config dictionary.
"""
output_conf = config.get('output')
if output_conf:
path = output_conf.get('path')
if not path:
raise ValueError('An output path must be provided when providing `output`.')
cluster_spec = _generate_cluster_spec(config, master=False)
# Importing here to avoid an aiohttp error if not used.
from dask_kubernetes import KubeCluster # pylint: disable=C0415
cluster = KubeCluster.from_dict(cluster_spec)
workers = config['dask_cluster'].get('workers')
if not workers:
cluster.adapt()
elif isinstance(workers, int):
cluster.scale(workers)
else:
cluster.adapt(**workers)
client = Client(cluster)
client.get_versions(check=True)
client.register_worker_callbacks(_logging_setup)
try:
run = _import_function(config['run'])
kwargs = config['run']['args']
results = run(**kwargs)
finally:
client.close()
cluster.close()
if output_conf:
bucket = output_conf.get('bucket')
try:
if bucket:
aws_key = output_conf.get('key')
aws_secret = output_conf.get('secret_key')
_upload_to_s3(bucket, path, results, aws_key, aws_secret)
else:
dirname = os.path.dirname(path)
if dirname:
os.makedirs(dirname, exist_ok=True)
results.to_csv(path)
except Exception: # pylint: disable=W0703
print('Error storing results. Falling back to console dump.')
print(_dataframe_to_csv_str(results))
return None
return results