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Make distributed coach work end-to-end.

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- With data store, memory backend and orchestrator interfaces.
This commit is contained in:
Balaji Subramaniam
2018-10-04 12:28:21 -07:00
committed by zach dwiel
parent 9f92064e67
commit 844a5af831
8 changed files with 300 additions and 169 deletions
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55
rl_coach/orchestrators/start_training.py
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@@ -2,19 +2,36 @@ import argparse
from rl_coach.orchestrators.kubernetes_orchestrator import KubernetesParameters, Kubernetes, RunTypeParameters
from rl_coach.memories.backend.redis import RedisPubSubMemoryBackendParameters
from rl_coach.data_stores.data_store import DataStoreParameters
from rl_coach.data_stores.s3_data_store import S3DataStoreParameters
from rl_coach.data_stores.nfs_data_store import NFSDataStoreParameters
def main(preset: str, image: str='ajaysudh/testing:coach', num_workers: int=1, nfs_server: str="", nfs_path: str="", memory_backend: str=""):
def main(preset: str, image: str='ajaysudh/testing:coach', num_workers: int=1, nfs_server: str=None, nfs_path: str=None,
memory_backend: str=None, data_store: str=None, s3_end_point: str=None, s3_bucket_name: str=None):
rollout_command = ['python3', 'rl_coach/rollout_worker.py', '-p', preset]
training_command = ['python3', 'rl_coach/training_worker.py', '-p', preset]
memory_backend_params = RedisPubSubMemoryBackendParameters()
memory_backend_params = None
if memory_backend == "redispubsub":
memory_backend_params = RedisPubSubMemoryBackendParameters()
ds_params_instance = None
if data_store == "s3":
ds_params = DataStoreParameters("s3", "", "")
ds_params_instance = S3DataStoreParameters(ds_params=ds_params, end_point=s3_end_point, bucket_name=s3_bucket_name,
checkpoint_dir="/checkpoint")
elif data_store == "nfs":
ds_params = DataStoreParameters("nfs", "kubernetes", {"namespace": "default"})
ds_params_instance = NFSDataStoreParameters(ds_params)
worker_run_type_params = RunTypeParameters(image, rollout_command, run_type="worker")
trainer_run_type_params = RunTypeParameters(image, training_command, run_type="trainer")
orchestration_params = KubernetesParameters([worker_run_type_params, trainer_run_type_params], kubeconfig='~/.kube/config', nfs_server=nfs_server,
nfs_path=nfs_path, memory_backend_parameters=memory_backend_params)
orchestration_params = KubernetesParameters([worker_run_type_params, trainer_run_type_params],
kubeconfig='~/.kube/config', nfs_server=nfs_server, nfs_path=nfs_path,
memory_backend_parameters=memory_backend_params,
data_store_params=ds_params_instance)
orchestrator = Kubernetes(orchestration_params)
if not orchestrator.setup():
print("Could not setup")
@@ -36,7 +53,7 @@ def main(preset: str, image: str='ajaysudh/testing:coach', num_workers: int=1, n
orchestrator.trainer_logs()
except KeyboardInterrupt:
pass
orchestrator.undeploy()
# orchestrator.undeploy()
if __name__ == '__main__':
@@ -46,21 +63,33 @@ if __name__ == '__main__':
type=str,
required=True)
parser.add_argument('-p', '--preset',
help="(string) Name of a preset to run (class name from the 'presets' directory.)",
help="(string) Name of a preset to run (class name from the 'presets' directory).",
type=str,
required=True)
parser.add_argument('--memory-backend',
help="(string) Memory backend to use.",
type=str,
default="redispubsub")
parser.add_argument('-ds', '--data-store',
help="(string) Data store to use.",
type=str,
default="s3")
parser.add_argument('-ns', '--nfs-server',
help="(string) Addresss of the nfs server.)",
help="(string) Addresss of the nfs server.",
type=str,
required=True)
parser.add_argument('-np', '--nfs-path',
help="(string) Exported path for the nfs server",
help="(string) Exported path for the nfs server.",
type=str,
required=True)
parser.add_argument('--memory_backend',
help="(string) Memory backend to use",
parser.add_argument('--s3-end-point',
help="(string) S3 endpoint to use when S3 data store is used.",
type=str,
default="redispubsub")
required=True)
parser.add_argument('--s3-bucket-name',
help="(string) S3 bucket name to use when S3 data store is used.",
type=str,
required=True)
# parser.add_argument('--checkpoint_dir',
# help='(string) Path to a folder containing a checkpoint to write the model to.',
@@ -68,4 +97,6 @@ if __name__ == '__main__':
# default='/checkpoint')
args = parser.parse_args()
main(preset=args.preset, image=args.image, nfs_server=args.nfs_server, nfs_path=args.nfs_path, memory_backend=args.memory_backend)
main(preset=args.preset, image=args.image, nfs_server=args.nfs_server, nfs_path=args.nfs_path,
memory_backend=args.memory_backend, data_store=args.data_store, s3_end_point=args.s3_end_point,
s3_bucket_name=args.s3_bucket_name)