def run_launcher():
import scluster
if args.aws:
scluster.set_backend('aws')
script = os.path.basename(__file__)
assert script in os.listdir('.')
job = scluster.make_job(install_script='pip install ray',
image_name=args.image,
instance_type='c5.large',
num_tasks=2)
job.upload(script)
job.run('export RAY_USE_XRAY=1')
job.run('ray stop')
# https://ray.readthedocs.io/en/latest/resources.html?highlight=resources
ps_resource = """--resources='{"ps": 1}'"""
worker_resource = """--resources='{"worker": 1}'"""
ps, worker = job.tasks
ps.run(f"ray start --head {ps_resource} --redis-port=6379")
worker.run(f"ray start --redis-address={ps.ip}:6379 {worker_resource}")
worker.run(
f'./{script} --role=driver --ip={ps.ip}:6379 --size-mb={args.size_mb} --iters={args.iters}'
)
def run_launcher():
import scluster
if args.aws:
scluster.set_backend('aws')
job = scluster.make_job(args.name, num_tasks=2, image_name=args.image)
job.upload(__file__)
job.upload('util.py')
sender, receiver = job.tasks
# kill python just for when tmux session reuse is on
if not scluster.running_locally():
sender._run_raw('killall python', ignore_errors=True)
receiver._run_raw('killall python', ignore_errors=True)
if scluster.get_backend() == 'aws':
# on AWS probably running in conda DLAMI, switch into TF-enabled env
job.run('source activate tensorflow_p36')
ip_config = f'--sender-ip={sender.ip} --receiver-ip={receiver.ip}'
receiver.run(f'python {__file__} --role=receiver {ip_config}',
non_blocking=True)
sender.run(
f'python {__file__} --role=sender {ip_config} --iters={args.iters} --size-mb={args.size_mb} --shards={args.shards}')
print(sender.read('out'))
def run_launcher():
import scluster
scluster.util.assert_script_in_current_directory()
if args.aws:
scluster.set_backend('aws')
# use 4GB instance, 0.5GB not enough
worker = scluster.make_task(args.name,
image_name=args.image,
instance_type='t3.medium')
worker.upload(__file__)
worker.upload('util.py')
# kill python just for when tmux session reuse is on
if not scluster.running_locally():
# on AWS probably running in conda DLAMI, switch into TF-enabled env
worker._run_raw('killall python', ignore_errors=True)
worker.run('source activate tensorflow_p36')
ip_config = f'--sender-ip={worker.ip} --receiver-ip={worker.ip}'
worker.run(f'python {__file__} --role=receiver {ip_config}',
non_blocking=True)
worker.switch_window(1) # run in new tmux window
if not scluster.running_locally():
worker.run('source activate tensorflow_p36')
worker.run(
f'python {__file__} --role=sender {ip_config} --iters={args.iters} --size-mb={args.size_mb} --shards={args.shards}'
)
print(worker.read('out'))
def run_launcher():
import scluster
if args.aws:
scluster.set_backend('aws')
if args.nightly:
# running locally MacOS
if 'Darwin' in util.ossystem('uname') and not args.aws:
install_script = 'pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.5.2-cp36-cp36m-macosx_10_6_intel.whl'
else:
install_script = 'pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.5.2-cp36-cp36m-manylinux1_x86_64.whl'
else:
install_script = 'pip install ray'
worker = scluster.make_task(name=args.name,
install_script=install_script,
image_name=args.image)
if not scluster.running_locally():
worker._run_raw('killall python', ignore_errors=True)
worker.upload(__file__)
worker.upload('util.py')
if args.xray:
worker.run('export RAY_USE_XRAY=1')
worker.run('ray stop')
resources = """--resources='{"ps": 1, "worker": 1}'"""
worker.run(f"ray start --head {resources} --redis-port=6379")
# worker.run(f"ray start --redis-address={worker.ip}:6379 {resources}")
worker.run(
f'./{__file__} --role=driver --ip={worker.ip}:6379 --size-mb={args.size_mb} --iters={args.iters}')
print(worker.read('out'))
def launcher():
import scluster
if args.aws:
scluster.set_backend('aws')
job = scluster.make_job(args.name, num_tasks=2, image_name=args.image)
job.upload(__file__)
job.upload('util.py')
if args.aws:
job.run('source activate pytorch_p36')
else:
job.run('source deactivate')
job.run('source activate ncluster-test3')
script_name = os.path.basename(__file__)
common_args = f'--size=2 --master-addr={job.tasks[0].ip} --iters={args.iters} --size-mb={args.size_mb}'
job.tasks[0].run(f'python {script_name} --role=worker --rank=0 '+common_args,
non_blocking=True)
job.tasks[1].run(f'python {script_name} --role=worker --rank=1 '+common_args,
non_blocking=True)
job.tasks[0].join()
print(job.tasks[0].read('out'))
def main():
scluster.set_backend('local')
job = scluster.make_job(num_tasks=2)
start_time = time.time()
job.run('sleep 1')
print(f"waited for {time.time()-start_time} seconds")
def main():
scluster.set_backend('aws')
start_time = time.time()
job = scluster.make_job(num_tasks=16)
print(f"waited for startup for {time.time()-start_time} seconds")
start_time = time.time()
job.run('sleep 10')
print(f"waited for exec for {time.time()-start_time} seconds")
def run_launcher():
import scluster
if args.aws:
scluster.set_backend('aws')
job = scluster.make_job('tf_adder', num_tasks=2, image_name=args.image)
job.upload(__file__)
sender, receiver = job.tasks
if scluster.get_backend() == 'aws':
# on AWS probably running in conda DLAMI, switch into TF-enabled env
job.run('source activate tensorflow_p36')
ip_config = f'--sender-ip={sender.ip} --receiver-ip={receiver.ip}'
receiver.run(f'python tf_adder.py --role=receiver {ip_config}',
non_blocking=True)
sender.run(
f'python tf_adder.py --role=sender {ip_config} --iters={args.iters}')
def run_launcher():
import scluster
if args.aws:
scluster.set_backend('aws')
if args.nightly:
# running locally MacOS
print(f"asdfasdf {util.ossystem('uname')}")
if 'Darwin' in util.ossystem('uname') and not args.aws:
install_script = 'pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.5.2-cp36-cp36m-macosx_10_6_intel.whl'
print(f"asdfasdf got install script {install_script}")
else:
install_script = 'pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.5.2-cp36-cp36m-manylinux1_x86_64.whl'
else:
install_script = 'pip install ray'
job = scluster.make_job(name=args.name,
install_script=install_script,
image_name=args.image,
num_tasks=2)
ps, worker = job.tasks
if not scluster.running_locally():
ps._run_raw('killall python', ignore_errors=True)
worker._run_raw('killall python', ignore_errors=True)
job.upload(__file__)
job.upload('util.py')
if args.xray:
job.run('export RAY_USE_XRAY=1')
job.run('ray stop')
# https://ray.readthedocs.io/en/latest/resources.html?highlight=resources
ps_resource = """--resources='{"ps": 1}'"""
worker_resource = """--resources='{"worker": 1}'"""
ps.run(f"ray start --head {ps_resource} --redis-port=6379")
worker.run(f"ray start --redis-address={ps.ip}:6379 {worker_resource}")
worker.run(
f'./{__file__} --role=driver --ip={ps.ip}:6379 --size-mb={args.size_mb} --iters={args.iters}'
)
print(worker.read('out'))
def run_launcher():
import scluster
if args.aws:
scluster.set_backend('aws')
job = scluster.make_job(args.name, num_tasks=2, image_name=args.image)
job.upload(__file__)
job.upload('util.py')
# kill python just for when tmux session reuse is on
if not scluster.running_locally():
job._run_raw('killall python', ignore_errors=True)
if scluster.get_backend() == 'aws':
# on AWS probably running in conda DLAMI, switch into TF-enabled env
job.run('source activate tensorflow_p36')
hosts = [task.public_ip for task in job.tasks]
host_str = ','.join(hosts)
os.system(
f'mpirun -np 2 --host {host_str} python {__file__} --role=worker')
print(job.tasks[0].read('/tmp/out'))
def run_launcher():
import scluster
if args.aws:
scluster.set_backend('aws')
job = scluster.make_job('tf_adder_tb', num_tasks=2, image_name=args.image)
job.upload(__file__)
this_file = os.path.basename(__file__)
sender, receiver = job.tasks
if scluster.get_backend() == 'aws':
# on AWS probably are running in DLAMI, switch into TF-enabled env
job.run('source activate tensorflow_p36')
ip_config = f'--sender-ip={sender.ip} --receiver-ip={receiver.ip}'
job.tasks[1].run(f'python {this_file} --role=receiver {ip_config}',
non_blocking=True)
job.tasks[0].run(
f'python {this_file} --role=sender --logdir={job.logdir} {ip_config}')
job.tasks[0].run(f'tensorboard --logdir={job.logdir}/..',
non_blocking=True)
print(
f"Benchmark done, tensorboard at http://{job.tasks[0].public_ip}:6006")
help="instance name")
parser.add_argument('--image-name', type=str,
default='Deep Learning AMI (Ubuntu) Version 15.0',
help="name of AMI to use ")
parser.add_argument('--instance-type', type=str, default='p3.2xlarge',
help="type of instance")
parser.add_argument('--password',
default='DefaultNotebookPasswordPleaseChange',
help='password to use for jupyter notebook')
parser.add_argument("--aws", action="store_true", help="enable to run on AWS")
args = parser.parse_args()
module_path = os.path.dirname(os.path.abspath(__file__))
if args.aws:
scluster.set_backend('aws')
def main():
task = scluster.make_task(name=args.name,
instance_type=args.instance_type,
image_name=args.image_name)
# upload notebook config with provided password
jupyter_config_fn = _create_jupyter_config(args.password)
remote_config_fn = '~/.jupyter/jupyter_notebook_config.py'
task.upload(jupyter_config_fn, remote_config_fn)
# upload sample notebook and start Jupyter server
task.run('mkdir -p /ncluster/notebooks')
task.upload(f'{module_path}/gpubox_sample.ipynb',
'/ncluster/notebooks/gpubox_sample.ipynb',
