[sgd] Replaced class Resources in sgd with use_gpu (#5252)

python/ray/experimental/sgd/examples/train_example.py

@@ -3,7 +3,7 @@ from __future__ import division
 from __future__ import print_function
 
 import argparse
-from ray.experimental.sgd.pytorch import PyTorchTrainer, Resources
+from ray.experimental.sgd.pytorch import PyTorchTrainer
 
 from ray.experimental.sgd.tests.pytorch_utils import (
     model_creator, optimizer_creator, data_creator)
@@ -15,10 +15,11 @@ def train_example(num_replicas=1, use_gpu=False):
         data_creator,
         optimizer_creator,
         num_replicas=num_replicas,
-        resources_per_replica=Resources(
-            num_cpus=1, num_gpus=int(use_gpu), resources={}))
+        use_gpu=use_gpu,
+        backend="gloo")
     trainer1.train()
     trainer1.shutdown()
+    print("success!")
 
 
 if __name__ == "__main__":
@@ -28,6 +29,12 @@ if __name__ == "__main__":
         required=False,
         type=str,
         help="the address to use for Redis")
+    parser.add_argument(
+        "--num-replicas",
+        "-n",
+        type=int,
+        default=1,
+        help="Sets number of replicas for training.")
     parser.add_argument(
         "--use-gpu",
         action="store_true",
@@ -36,5 +43,6 @@ if __name__ == "__main__":
     args, _ = parser.parse_known_args()
 
     import ray
+
     ray.init(redis_address=args.redis_address)
-    train_example(num_replicas=2, use_gpu=args.use_gpu)
+    train_example(num_replicas=args.num_replicas, use_gpu=args.use_gpu)

python/ray/experimental/sgd/pytorch/__init__.py

@@ -3,6 +3,5 @@ from __future__ import division
 from __future__ import print_function
 
 from ray.experimental.sgd.pytorch.pytorch_trainer import PyTorchTrainer
-from ray.experimental.sgd.pytorch.utils import Resources
 
-__all__ = ["PyTorchTrainer", "Resources"]
+__all__ = ["PyTorchTrainer"]

python/ray/experimental/sgd/pytorch/pytorch_trainer.py

@@ -30,7 +30,7 @@ class PyTorchTrainer(object):
                  optimizer_creator=utils.sgd_mse_optimizer,
                  config=None,
                  num_replicas=1,
-                 resources_per_replica=None,
+                 use_gpu=False,
                  batch_size=16,
                  backend="auto"):
         """Sets up the PyTorch trainer.
@@ -46,8 +46,8 @@ class PyTorchTrainer(object):
                 'data_creator', and 'optimizer_creator'.
             num_replicas (int): the number of workers used in distributed
                 training.
-            resources_per_replica (Resources): resources used by each worker.
-                Defaults to Resources(num_cpus=1).
+            use_gpu (bool): Sets resource allocation for workers to 1 GPU
+                if true.
             batch_size (int): batch size for an update.
             backend (string): backend used by distributed PyTorch.
         """
@@ -64,19 +64,15 @@ class PyTorchTrainer(object):
         self.config = {} if config is None else config
         self.optimizer_timer = utils.TimerStat(window_size=1)
 
-        if resources_per_replica is None:
-            resources_per_replica = utils.Resources(
-                num_cpus=1, num_gpus=0, resources={})
-
         if backend == "auto":
-            backend = "nccl" if resources_per_replica.num_gpus > 0 else "gloo"
+            backend = "nccl" if use_gpu else "gloo"
+
+        logger.info("Using {} as backend.".format(backend))
 
         if num_replicas == 1:
             # Generate actor class
             Runner = ray.remote(
-                num_cpus=resources_per_replica.num_cpus,
-                num_gpus=resources_per_replica.num_gpus,
-                resources=resources_per_replica.resources)(PyTorchRunner)
+                num_cpus=1, num_gpus=int(use_gpu))(PyTorchRunner)
             # Start workers
             self.workers = [
                 Runner.remote(model_creator, data_creator, optimizer_creator,
@@ -87,10 +83,7 @@ class PyTorchTrainer(object):
         else:
             # Geneate actor class
             Runner = ray.remote(
-                num_cpus=resources_per_replica.num_cpus,
-                num_gpus=resources_per_replica.num_gpus,
-                resources=resources_per_replica.resources)(
-                    DistributedPyTorchRunner)
+                num_cpus=1, num_gpus=int(use_gpu))(DistributedPyTorchRunner)
             # Compute batch size per replica
             batch_size_per_replica = batch_size // num_replicas
             if batch_size % num_replicas > 0:

python/ray/experimental/sgd/pytorch/utils.py

@@ -2,7 +2,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from collections import namedtuple
 from contextlib import closing
 import numpy as np
 import socket
@@ -214,18 +213,6 @@ class AverageMeter(object):
         self.avg = self.sum / self.count
 
 
-class Resources(
-        namedtuple("Resources", ["num_cpus", "num_gpus", "resources"])):
-    __slots__ = ()
-
-    def __new__(cls, num_cpus=1, num_gpus=0, resources=None):
-        if resources is None:
-            resources = {}
-
-        return super(Resources, cls).__new__(cls, num_cpus, num_gpus,
-                                             resources)
-
-
 def sgd_mse_optimizer(model, config):
     """Returns the mean squared error criterion and SGD optimizer.
 

python/ray/experimental/sgd/tests/test_pytorch.py

@@ -9,7 +9,7 @@ import torch
 import torch.distributed as dist
 
 from ray.tests.conftest import ray_start_2_cpus  # noqa: F401
-from ray.experimental.sgd.pytorch import PyTorchTrainer, Resources
+from ray.experimental.sgd.pytorch import PyTorchTrainer
 
 from ray.experimental.sgd.tests.pytorch_utils import (
     model_creator, optimizer_creator, data_creator)
@@ -22,8 +22,7 @@ def test_train(ray_start_2_cpus, num_replicas):  # noqa: F811
         model_creator,
         data_creator,
         optimizer_creator,
-        num_replicas=num_replicas,
-        resources_per_replica=Resources(num_cpus=1))
+        num_replicas=num_replicas)
     train_loss1 = trainer.train()["train_loss"]
     validation_loss1 = trainer.validate()["validation_loss"]
 
@@ -44,8 +43,7 @@ def test_save_and_restore(ray_start_2_cpus, num_replicas):  # noqa: F811
         model_creator,
         data_creator,
         optimizer_creator,
-        num_replicas=num_replicas,
-        resources_per_replica=Resources(num_cpus=1))
+        num_replicas=num_replicas)
     trainer1.train()
 
     filename = os.path.join(tempfile.mkdtemp(), "checkpoint")
@@ -59,8 +57,7 @@ def test_save_and_restore(ray_start_2_cpus, num_replicas):  # noqa: F811
         model_creator,
         data_creator,
         optimizer_creator,
-        num_replicas=num_replicas,
-        resources_per_replica=Resources(num_cpus=1))
+        num_replicas=num_replicas)
     trainer2.restore(filename)
 
     os.remove(filename)

python/ray/rllib/agents/impala/impala.py

@@ -10,7 +10,7 @@ from ray.rllib.optimizers import AsyncSamplesOptimizer
 from ray.rllib.optimizers.aso_tree_aggregator import TreeAggregator
 from ray.rllib.utils.annotations import override
 from ray.tune.trainable import Trainable
-from ray.tune.trial import Resources
+from ray.tune.resources import Resources
 
 # yapf: disable
 # __sphinx_doc_begin__

python/ray/rllib/agents/trainer.py

@@ -24,7 +24,8 @@ from ray.rllib.utils.memory import ray_get_and_free
 from ray.rllib.utils import try_import_tf
 from ray.tune.registry import ENV_CREATOR, register_env, _global_registry
 from ray.tune.trainable import Trainable
-from ray.tune.trial import Resources, ExportFormat
+from ray.tune.trial import ExportFormat
+from ray.tune.resources import Resources
 from ray.tune.logger import UnifiedLogger
 from ray.tune.result import DEFAULT_RESULTS_DIR
 

python/ray/rllib/train.py

@@ -11,7 +11,7 @@ import ray
 from ray.tests.cluster_utils import Cluster
 from ray.tune.config_parser import make_parser
 from ray.tune.result import DEFAULT_RESULTS_DIR
-from ray.tune.trial import resources_to_json
+from ray.tune.resources import resources_to_json
 from ray.tune.tune import _make_scheduler, run_experiments
 
 EXAMPLE_USAGE = """

python/ray/tune/config_parser.py

@@ -10,7 +10,8 @@ import os
 from six import string_types
 
 from ray.tune import TuneError
-from ray.tune.trial import Trial, json_to_resources
+from ray.tune.trial import Trial
+from ray.tune.resources import json_to_resources
 from ray.tune.logger import _SafeFallbackEncoder
 
 

python/ray/tune/ray_trial_executor.py

@@ -13,7 +13,8 @@ import traceback
 import ray
 from ray.tune.error import AbortTrialExecution
 from ray.tune.logger import NoopLogger
-from ray.tune.trial import Trial, Resources, Checkpoint
+from ray.tune.trial import Trial, Checkpoint
+from ray.tune.resources import Resources
 from ray.tune.trial_executor import TrialExecutor
 from ray.tune.util import warn_if_slow
 

python/ray/tune/resources.py

@@ -0,0 +1,152 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from collections import namedtuple
+import logging
+import json
+# For compatibility under py2 to consider unicode as str
+from six import string_types
+
+from numbers import Number
+
+from ray.tune import TuneError
+
+logger = logging.getLogger(__name__)
+
+
+class Resources(
+        namedtuple("Resources", [
+            "cpu", "gpu", "extra_cpu", "extra_gpu", "custom_resources",
+            "extra_custom_resources"
+        ])):
+    """Ray resources required to schedule a trial.
+
+    Attributes:
+        cpu (float): Number of CPUs to allocate to the trial.
+        gpu (float): Number of GPUs to allocate to the trial.
+        extra_cpu (float): Extra CPUs to reserve in case the trial needs to
+            launch additional Ray actors that use CPUs.
+        extra_gpu (float): Extra GPUs to reserve in case the trial needs to
+            launch additional Ray actors that use GPUs.
+        custom_resources (dict): Mapping of resource to quantity to allocate
+            to the trial.
+        extra_custom_resources (dict): Extra custom resources to reserve in
+            case the trial needs to launch additional Ray actors that use
+            any of these custom resources.
+
+    """
+
+    __slots__ = ()
+
+    def __new__(cls,
+                cpu,
+                gpu,
+                extra_cpu=0,
+                extra_gpu=0,
+                custom_resources=None,
+                extra_custom_resources=None):
+        custom_resources = custom_resources or {}
+        extra_custom_resources = extra_custom_resources or {}
+        leftovers = set(custom_resources) ^ set(extra_custom_resources)
+
+        for value in leftovers:
+            custom_resources.setdefault(value, 0)
+            extra_custom_resources.setdefault(value, 0)
+
+        all_values = [cpu, gpu, extra_cpu, extra_gpu]
+        all_values += list(custom_resources.values())
+        all_values += list(extra_custom_resources.values())
+        assert len(custom_resources) == len(extra_custom_resources)
+        for entry in all_values:
+            assert isinstance(entry, Number), "Improper resource value."
+        return super(Resources,
+                     cls).__new__(cls, cpu, gpu, extra_cpu, extra_gpu,
+                                  custom_resources, extra_custom_resources)
+
+    def summary_string(self):
+        summary = "{} CPUs, {} GPUs".format(self.cpu + self.extra_cpu,
+                                            self.gpu + self.extra_gpu)
+        custom_summary = ", ".join([
+            "{} {}".format(self.get_res_total(res), res)
+            for res in self.custom_resources
+        ])
+        if custom_summary:
+            summary += " ({})".format(custom_summary)
+        return summary
+
+    def cpu_total(self):
+        return self.cpu + self.extra_cpu
+
+    def gpu_total(self):
+        return self.gpu + self.extra_gpu
+
+    def get_res_total(self, key):
+        return self.custom_resources.get(
+            key, 0) + self.extra_custom_resources.get(key, 0)
+
+    def get(self, key):
+        return self.custom_resources.get(key, 0)
+
+    def is_nonnegative(self):
+        all_values = [self.cpu, self.gpu, self.extra_cpu, self.extra_gpu]
+        all_values += list(self.custom_resources.values())
+        all_values += list(self.extra_custom_resources.values())
+        return all(v >= 0 for v in all_values)
+
+    @classmethod
+    def subtract(cls, original, to_remove):
+        cpu = original.cpu - to_remove.cpu
+        gpu = original.gpu - to_remove.gpu
+        extra_cpu = original.extra_cpu - to_remove.extra_cpu
+        extra_gpu = original.extra_gpu - to_remove.extra_gpu
+        all_resources = set(original.custom_resources).union(
+            set(to_remove.custom_resources))
+        new_custom_res = {
+            k: original.custom_resources.get(k, 0) -
+            to_remove.custom_resources.get(k, 0)
+            for k in all_resources
+        }
+        extra_custom_res = {
+            k: original.extra_custom_resources.get(k, 0) -
+            to_remove.extra_custom_resources.get(k, 0)
+            for k in all_resources
+        }
+        return Resources(cpu, gpu, extra_cpu, extra_gpu, new_custom_res,
+                         extra_custom_res)
+
+    def to_json(self):
+        return resources_to_json(self)
+
+
+def json_to_resources(data):
+    if data is None or data == "null":
+        return None
+    if isinstance(data, string_types):
+        data = json.loads(data)
+    for k in data:
+        if k in ["driver_cpu_limit", "driver_gpu_limit"]:
+            raise TuneError(
+                "The field `{}` is no longer supported. Use `extra_cpu` "
+                "or `extra_gpu` instead.".format(k))
+        if k not in Resources._fields:
+            raise ValueError(
+                "Unknown resource field {}, must be one of {}".format(
+                    k, Resources._fields))
+    return Resources(
+        data.get("cpu", 1), data.get("gpu", 0), data.get("extra_cpu", 0),
+        data.get("extra_gpu", 0), data.get("custom_resources"),
+        data.get("extra_custom_resources"))
+
+
+def resources_to_json(resources):
+    if resources is None:
+        return None
+    return {
+        "cpu": resources.cpu,
+        "gpu": resources.gpu,
+        "extra_cpu": resources.extra_cpu,
+        "extra_gpu": resources.extra_gpu,
+        "custom_resources": resources.custom_resources.copy(),
+        "extra_custom_resources": resources.extra_custom_resources.copy()
+    }

python/ray/tune/tests/test_ray_trial_executor.py

@@ -11,7 +11,8 @@ from ray.tune import Trainable
 from ray.tune.ray_trial_executor import RayTrialExecutor
 from ray.tune.registry import _global_registry, TRAINABLE_CLASS
 from ray.tune.suggest import BasicVariantGenerator
-from ray.tune.trial import Trial, Checkpoint, Resources
+from ray.tune.trial import Trial, Checkpoint
+from ray.tune.resources import Resources
 
 
 class RayTrialExecutorTest(unittest.TestCase):

python/ray/tune/tests/test_trial_runner.py

@@ -27,9 +27,9 @@ from ray.tune.result import (DEFAULT_RESULTS_DIR, TIMESTEPS_TOTAL, DONE,
 from ray.tune.logger import Logger
 from ray.tune.util import pin_in_object_store, get_pinned_object
 from ray.tune.experiment import Experiment
-from ray.tune.trial import (Trial, ExportFormat, Resources, resources_to_json,
-                            json_to_resources)
+from ray.tune.trial import Trial, ExportFormat
 from ray.tune.trial_runner import TrialRunner
+from ray.tune.resources import Resources, json_to_resources, resources_to_json
 from ray.tune.suggest import grid_search, BasicVariantGenerator
 from ray.tune.suggest.suggestion import (_MockSuggestionAlgorithm,
                                          SuggestionAlgorithm)

python/ray/tune/tests/test_trial_scheduler.py

@@ -18,8 +18,9 @@ from ray.tune.schedulers import (HyperBandScheduler, AsyncHyperBandScheduler,
                                  TrialScheduler)
 
 from ray.tune.schedulers.pbt import explore
-from ray.tune.trial import Trial, Resources, Checkpoint
+from ray.tune.trial import Trial, Checkpoint
 from ray.tune.trial_executor import TrialExecutor
+from ray.tune.resources import Resources
 
 from ray.rllib import _register_all
 _register_all()

python/ray/tune/trial.py

@@ -2,21 +2,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from collections import namedtuple
 import ray.cloudpickle as cloudpickle
 import copy
 from datetime import datetime
 import logging
-import json
 import uuid
 import time
 import tempfile
 import os
-from numbers import Number
-
-# For compatibility under py2 to consider unicode as str
-from six import string_types
-
 import ray
 from ray.tune import TuneError
 from ray.tune.logger import pretty_print, UnifiedLogger
@@ -28,6 +21,7 @@ from ray.tune.result import (DEFAULT_RESULTS_DIR, DONE, HOSTNAME, PID,
                              TIME_TOTAL_S, TRAINING_ITERATION, TIMESTEPS_TOTAL,
                              EPISODE_REWARD_MEAN, MEAN_LOSS, MEAN_ACCURACY)
 from ray.utils import binary_to_hex, hex_to_binary
+from ray.tune.resources import Resources, json_to_resources, resources_to_json
 
 DEBUG_PRINT_INTERVAL = 5
 MAX_LEN_IDENTIFIER = int(os.environ.get("MAX_LEN_IDENTIFIER", 130))
@@ -38,143 +32,6 @@ def date_str():
     return datetime.today().strftime("%Y-%m-%d_%H-%M-%S")
 
 
-class Resources(
-        namedtuple("Resources", [
-            "cpu", "gpu", "extra_cpu", "extra_gpu", "custom_resources",
-            "extra_custom_resources"
-        ])):
-    """Ray resources required to schedule a trial.
-
-    Attributes:
-        cpu (float): Number of CPUs to allocate to the trial.
-        gpu (float): Number of GPUs to allocate to the trial.
-        extra_cpu (float): Extra CPUs to reserve in case the trial needs to
-            launch additional Ray actors that use CPUs.
-        extra_gpu (float): Extra GPUs to reserve in case the trial needs to
-            launch additional Ray actors that use GPUs.
-        custom_resources (dict): Mapping of resource to quantity to allocate
-            to the trial.
-        extra_custom_resources (dict): Extra custom resources to reserve in
-            case the trial needs to launch additional Ray actors that use
-            any of these custom resources.
-
-    """
-
-    __slots__ = ()
-
-    def __new__(cls,
-                cpu,
-                gpu,
-                extra_cpu=0,
-                extra_gpu=0,
-                custom_resources=None,
-                extra_custom_resources=None):
-        custom_resources = custom_resources or {}
-        extra_custom_resources = extra_custom_resources or {}
-        leftovers = set(custom_resources) ^ set(extra_custom_resources)
-
-        for value in leftovers:
-            custom_resources.setdefault(value, 0)
-            extra_custom_resources.setdefault(value, 0)
-
-        all_values = [cpu, gpu, extra_cpu, extra_gpu]
-        all_values += list(custom_resources.values())
-        all_values += list(extra_custom_resources.values())
-        assert len(custom_resources) == len(extra_custom_resources)
-        for entry in all_values:
-            assert isinstance(entry, Number), "Improper resource value."
-        return super(Resources,
-                     cls).__new__(cls, cpu, gpu, extra_cpu, extra_gpu,
-                                  custom_resources, extra_custom_resources)
-
-    def summary_string(self):
-        summary = "{} CPUs, {} GPUs".format(self.cpu + self.extra_cpu,
-                                            self.gpu + self.extra_gpu)
-        custom_summary = ", ".join([
-            "{} {}".format(self.get_res_total(res), res)
-            for res in self.custom_resources
-        ])
-        if custom_summary:
-            summary += " ({})".format(custom_summary)
-        return summary
-
-    def cpu_total(self):
-        return self.cpu + self.extra_cpu
-
-    def gpu_total(self):
-        return self.gpu + self.extra_gpu
-
-    def get_res_total(self, key):
-        return self.custom_resources.get(
-            key, 0) + self.extra_custom_resources.get(key, 0)
-
-    def get(self, key):
-        return self.custom_resources.get(key, 0)
-
-    def is_nonnegative(self):
-        all_values = [self.cpu, self.gpu, self.extra_cpu, self.extra_gpu]
-        all_values += list(self.custom_resources.values())
-        all_values += list(self.extra_custom_resources.values())
-        return all(v >= 0 for v in all_values)
-
-    @classmethod
-    def subtract(cls, original, to_remove):
-        cpu = original.cpu - to_remove.cpu
-        gpu = original.gpu - to_remove.gpu
-        extra_cpu = original.extra_cpu - to_remove.extra_cpu
-        extra_gpu = original.extra_gpu - to_remove.extra_gpu
-        all_resources = set(original.custom_resources).union(
-            set(to_remove.custom_resources))
-        new_custom_res = {
-            k: original.custom_resources.get(k, 0) -
-            to_remove.custom_resources.get(k, 0)
-            for k in all_resources
-        }
-        extra_custom_res = {
-            k: original.extra_custom_resources.get(k, 0) -
-            to_remove.extra_custom_resources.get(k, 0)
-            for k in all_resources
-        }
-        return Resources(cpu, gpu, extra_cpu, extra_gpu, new_custom_res,
-                         extra_custom_res)
-
-    def to_json(self):
-        return resources_to_json(self)
-
-
-def json_to_resources(data):
-    if data is None or data == "null":
-        return None
-    if isinstance(data, string_types):
-        data = json.loads(data)
-    for k in data:
-        if k in ["driver_cpu_limit", "driver_gpu_limit"]:
-            raise TuneError(
-                "The field `{}` is no longer supported. Use `extra_cpu` "
-                "or `extra_gpu` instead.".format(k))
-        if k not in Resources._fields:
-            raise ValueError(
-                "Unknown resource field {}, must be one of {}".format(
-                    k, Resources._fields))
-    return Resources(
-        data.get("cpu", 1), data.get("gpu", 0), data.get("extra_cpu", 0),
-        data.get("extra_gpu", 0), data.get("custom_resources"),
-        data.get("extra_custom_resources"))
-
-
-def resources_to_json(resources):
-    if resources is None:
-        return None
-    return {
-        "cpu": resources.cpu,
-        "gpu": resources.gpu,
-        "extra_cpu": resources.extra_cpu,
-        "extra_gpu": resources.extra_gpu,
-        "custom_resources": resources.custom_resources.copy(),
-        "extra_custom_resources": resources.extra_custom_resources.copy()
-    }
-
-
 def has_trainable(trainable_name):
     return ray.tune.registry._global_registry.contains(
         ray.tune.registry.TRAINABLE_CLASS, trainable_name)