file_path stringlengths 3 280 | file_language stringclasses 66
values | content stringlengths 1 1.04M | repo_name stringlengths 5 92 | repo_stars int64 0 154k | repo_description stringlengths 0 402 | repo_primary_language stringclasses 108
values | developer_username stringlengths 1 25 | developer_name stringlengths 0 30 | developer_company stringlengths 0 82 |
|---|---|---|---|---|---|---|---|---|---|
rllib/offline/json_writer.py | Python | from datetime import datetime
import json
import logging
import numpy as np
import os
from six.moves.urllib.parse import urlparse
import time
try:
from smart_open import smart_open
except ImportError:
smart_open = None
from ray.rllib.policy.sample_batch import MultiAgentBatch
from ray.rllib.offline.io_context... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/offline/mixed_input.py | Python | import numpy as np
from ray.rllib.offline.input_reader import InputReader
from ray.rllib.offline.json_reader import JsonReader
from ray.rllib.utils.annotations import override, DeveloperAPI
@DeveloperAPI
class MixedInput(InputReader):
"""Mixes input from a number of other input sources.
Examples:
>>... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/offline/off_policy_estimator.py | Python | from collections import namedtuple
import logging
from ray.rllib.policy.sample_batch import MultiAgentBatch
from ray.rllib.utils.annotations import DeveloperAPI
logger = logging.getLogger(__name__)
OffPolicyEstimate = namedtuple("OffPolicyEstimate",
["estimator_name", "metrics"])
@De... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/offline/output_writer.py | Python | from ray.rllib.utils.annotations import override
from ray.rllib.utils.annotations import PublicAPI
@PublicAPI
class OutputWriter:
"""Writer object for saving experiences from policy evaluation."""
@PublicAPI
def write(self, sample_batch):
"""Save a batch of experiences.
Arguments:
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/offline/shuffled_input.py | Python | import logging
import random
from ray.rllib.offline.input_reader import InputReader
from ray.rllib.utils.annotations import override, DeveloperAPI
logger = logging.getLogger(__name__)
@DeveloperAPI
class ShuffledInput(InputReader):
"""Randomizes data over a sliding window buffer of N batches.
This increase... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/offline/wis_estimator.py | Python | from ray.rllib.offline.off_policy_estimator import OffPolicyEstimator, \
OffPolicyEstimate
from ray.rllib.utils.annotations import override
class WeightedImportanceSamplingEstimator(OffPolicyEstimator):
"""The weighted step-wise IS estimator.
Step-wise WIS estimator in https://arxiv.org/pdf/1511.03722.pd... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/__init__.py | Python | from ray.rllib.optimizers.policy_optimizer import PolicyOptimizer
from ray.rllib.optimizers.async_replay_optimizer import AsyncReplayOptimizer
from ray.rllib.optimizers.async_samples_optimizer import AsyncSamplesOptimizer
from ray.rllib.optimizers.async_gradients_optimizer import \
AsyncGradientsOptimizer
from ray.... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/aso_aggregator.py | Python | """Helper class for AsyncSamplesOptimizer."""
import numpy as np
import random
import ray
from ray.rllib.utils.actors import TaskPool
from ray.rllib.utils.annotations import override
from ray.rllib.utils.memory import ray_get_and_free
class Aggregator:
"""An aggregator collects and processes samples from worker... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/aso_learner.py | Python | """Helper class for AsyncSamplesOptimizer."""
import threading
from six.moves import queue
from ray.rllib.evaluation.metrics import get_learner_stats
from ray.rllib.optimizers.aso_minibatch_buffer import MinibatchBuffer
from ray.rllib.utils.timer import TimerStat
from ray.rllib.utils.window_stat import WindowStat
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/aso_minibatch_buffer.py | Python | """Helper class for AsyncSamplesOptimizer."""
class MinibatchBuffer:
"""Ring buffer of recent data batches for minibatch SGD.
This is for use with AsyncSamplesOptimizer.
"""
def __init__(self, inqueue, size, timeout, num_passes, init_num_passes=1):
"""Initialize a minibatch buffer.
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/aso_multi_gpu_learner.py | Python | """Helper class for AsyncSamplesOptimizer."""
import logging
import threading
import math
from six.moves import queue
from ray.rllib.evaluation.metrics import get_learner_stats
from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
from ray.rllib.optimizers.aso_learner import LearnerThread
from ray.rllib.optimi... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/aso_tree_aggregator.py | Python | """Helper class for AsyncSamplesOptimizer."""
import collections
import logging
import os
import time
import ray
from ray.rllib.utils.actors import TaskPool, create_colocated
from ray.rllib.utils.annotations import override
from ray.rllib.optimizers.aso_aggregator import Aggregator, \
AggregationWorkerBase
from r... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/async_gradients_optimizer.py | Python | import ray
from ray.rllib.evaluation.metrics import get_learner_stats
from ray.rllib.optimizers.policy_optimizer import PolicyOptimizer
from ray.rllib.utils.annotations import override
from ray.rllib.utils.timer import TimerStat
from ray.rllib.utils.memory import ray_get_and_free
class AsyncGradientsOptimizer(PolicyO... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/async_replay_optimizer.py | Python | """Implements Distributed Prioritized Experience Replay.
https://arxiv.org/abs/1803.00933"""
import collections
import os
import random
import time
import threading
import numpy as np
from six.moves import queue
import ray
from ray.rllib.evaluation.metrics import get_learner_stats
from ray.rllib.policy.sample_batch... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/async_samples_optimizer.py | Python | """Implements the IMPALA asynchronous sampling architecture.
https://arxiv.org/abs/1802.01561"""
import logging
import time
from ray.rllib.optimizers.aso_aggregator import SimpleAggregator
from ray.rllib.optimizers.aso_tree_aggregator import TreeAggregator
from ray.rllib.optimizers.aso_learner import LearnerThread
f... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/microbatch_optimizer.py | Python | import logging
import ray
from ray.rllib.optimizers.policy_optimizer import PolicyOptimizer
from ray.rllib.policy.sample_batch import SampleBatch, DEFAULT_POLICY_ID, \
MultiAgentBatch
from ray.rllib.utils.annotations import override
from ray.rllib.utils.filter import RunningStat
from ray.rllib.utils.timer import T... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/multi_gpu_impl.py | Python | from collections import namedtuple
import logging
from ray.rllib.utils.debug import log_once, summarize
from ray.rllib.utils import try_import_tf
tf = try_import_tf()
# Variable scope in which created variables will be placed under
TOWER_SCOPE_NAME = "tower"
logger = logging.getLogger(__name__)
class LocalSyncPar... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/multi_gpu_optimizer.py | Python | import logging
import math
import numpy as np
from collections import defaultdict
import ray
from ray.rllib.evaluation.metrics import LEARNER_STATS_KEY
from ray.rllib.policy.tf_policy import TFPolicy
from ray.rllib.optimizers.policy_optimizer import PolicyOptimizer
from ray.rllib.optimizers.multi_gpu_impl import Local... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/policy_optimizer.py | Python | import logging
from ray.rllib.utils.annotations import DeveloperAPI
from ray.rllib.evaluation.metrics import collect_episodes, summarize_episodes
logger = logging.getLogger(__name__)
@DeveloperAPI
class PolicyOptimizer:
"""Policy optimizers encapsulate distributed RL optimization strategies.
Policy optimiz... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/replay_buffer.py | Python | import numpy as np
import random
import sys
from ray.rllib.optimizers.segment_tree import SumSegmentTree, MinSegmentTree
from ray.rllib.utils.annotations import DeveloperAPI
from ray.rllib.utils.compression import unpack_if_needed
from ray.rllib.utils.window_stat import WindowStat
@DeveloperAPI
class ReplayBuffer:
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/rollout.py | Python | import logging
import ray
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.memory import ray_get_and_free
logger = logging.getLogger(__name__)
def collect_samples(agents, sample_batch_size, num_envs_per_worker,
train_batch_size):
"""Collects at least train_batch_siz... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/segment_tree.py | Python | import operator
class SegmentTree:
def __init__(self, capacity, operation, neutral_element):
"""Build a Segment Tree data structure.
https://en.wikipedia.org/wiki/Segment_tree
Can be used as regular array, but with two
important differences:
a) setting item's value is ... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/sync_batch_replay_optimizer.py | Python | import random
import ray
from ray.rllib.evaluation.metrics import get_learner_stats
from ray.rllib.optimizers.policy_optimizer import PolicyOptimizer
from ray.rllib.policy.sample_batch import SampleBatch, DEFAULT_POLICY_ID, \
MultiAgentBatch
from ray.rllib.utils.annotations import override
from ray.rllib.utils.tim... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/sync_replay_optimizer.py | Python | import logging
import collections
import numpy as np
import ray
from ray.rllib.optimizers.replay_buffer import ReplayBuffer, \
PrioritizedReplayBuffer
from ray.rllib.optimizers.policy_optimizer import PolicyOptimizer
from ray.rllib.evaluation.metrics import get_learner_stats
from ray.rllib.policy.sample_batch impo... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/sync_samples_optimizer.py | Python | import logging
import random
from collections import defaultdict
import ray
from ray.rllib.evaluation.metrics import LEARNER_STATS_KEY
from ray.rllib.optimizers.multi_gpu_optimizer import _averaged
from ray.rllib.optimizers.policy_optimizer import PolicyOptimizer
from ray.rllib.policy.sample_batch import SampleBatch, ... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/optimizers/tests/test_segment_tree.py | Python | import numpy as np
from ray.rllib.optimizers.segment_tree import SumSegmentTree, MinSegmentTree
def test_tree_set():
tree = SumSegmentTree(4)
tree[2] = 1.0
tree[3] = 3.0
assert np.isclose(tree.sum(), 4.0)
assert np.isclose(tree.sum(0, 2), 0.0)
assert np.isclose(tree.sum(0, 3), 1.0)
asse... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/policy/__init__.py | Python | from ray.rllib.policy.policy import Policy
from ray.rllib.policy.torch_policy import TorchPolicy
from ray.rllib.policy.tf_policy import TFPolicy
from ray.rllib.policy.torch_policy_template import build_torch_policy
from ray.rllib.policy.tf_policy_template import build_tf_policy
__all__ = [
"Policy",
"TFPolicy"... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/policy/dynamic_tf_policy.py | Python | """Graph mode TF policy built using build_tf_policy()."""
from collections import OrderedDict
import logging
import numpy as np
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.tf_policy import TFPolicy
from ray.rllib.models.catalog import ModelCat... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/policy/eager_tf_policy.py | Python | """Eager mode TF policy built using build_tf_policy().
It supports both traced and non-traced eager execution modes."""
import logging
import functools
import numpy as np
from ray.rllib.evaluation.episode import _flatten_action
from ray.rllib.models.catalog import ModelCatalog
from ray.rllib.policy.policy import Pol... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/policy/policy.py | Python | from abc import ABCMeta, abstractmethod
from collections import namedtuple
import gym
import numpy as np
from ray.rllib.utils.annotations import DeveloperAPI
# By convention, metrics from optimizing the loss can be reported in the
# `grad_info` dict returned by learn_on_batch() / compute_grads() via this key.
LEARNER... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/policy/rnn_sequencing.py | Python | """RNN utils for RLlib.
The main trick here is that we add the time dimension at the last moment.
The non-LSTM layers of the model see their inputs as one flat batch. Before
the LSTM cell, we reshape the input to add the expected time dimension. During
postprocessing, we dynamically pad the experience batches so that ... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/policy/sample_batch.py | Python | import six
import collections
import numpy as np
from ray.rllib.utils.annotations import PublicAPI, DeveloperAPI
from ray.rllib.utils.compression import pack, unpack, is_compressed
from ray.rllib.utils.memory import concat_aligned
# Default policy id for single agent environments
DEFAULT_POLICY_ID = "default_policy"
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/policy/tests/test_policy.py | Python | import random
from ray.rllib.policy.policy import Policy
class TestPolicy(Policy):
"""
A dummy Policy that returns a random (batched) int for compute_actions
and implements all other abstract methods of Policy with "pass".
"""
def compute_actions(self,
obs_batch,
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/policy/tf_policy.py | Python | import errno
import logging
import os
import numpy as np
import ray
import ray.experimental.tf_utils
from ray.rllib.policy.policy import Policy, LEARNER_STATS_KEY, \
ACTION_PROB, ACTION_LOGP
from ray.rllib.policy.rnn_sequencing import chop_into_sequences
from ray.rllib.policy.sample_batch import SampleBatch
from r... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/policy/tf_policy_template.py | Python | from ray.rllib.policy.dynamic_tf_policy import DynamicTFPolicy
from ray.rllib.policy import eager_tf_policy
from ray.rllib.policy.policy import Policy, LEARNER_STATS_KEY
from ray.rllib.policy.tf_policy import TFPolicy
from ray.rllib.utils import add_mixins
from ray.rllib.utils.annotations import override, DeveloperAPI
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/policy/torch_policy.py | Python | import numpy as np
from ray.rllib.policy.policy import Policy, LEARNER_STATS_KEY
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils import try_import_torch
from ray.rllib.utils.annotations import override, DeveloperAPI
from ray.rllib.utils.tracking_dict import UsageTrackingDict
from ray.rllib.u... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/policy/torch_policy_template.py | Python | from ray.rllib.policy.policy import Policy
from ray.rllib.policy.torch_policy import TorchPolicy
from ray.rllib.models.catalog import ModelCatalog
from ray.rllib.utils import add_mixins
from ray.rllib.utils.annotations import override, DeveloperAPI
@DeveloperAPI
def build_torch_policy(name,
los... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/rollout.py | Python | #!/usr/bin/env python
import argparse
import collections
import json
import os
import pickle
import shelve
from pathlib import Path
import gym
import ray
from ray.rllib.agents.registry import get_agent_class
from ray.rllib.env import MultiAgentEnv
from ray.rllib.env.base_env import _DUMMY_AGENT_ID
from ray.rllib.eval... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/scripts.py | Python | #!/usr/bin/env python
import argparse
from ray.rllib import train
from ray.rllib import rollout
EXAMPLE_USAGE = """
Example usage for training:
rllib train --run DQN --env CartPole-v0
Example usage for rollout:
rllib rollout /trial_dir/checkpoint_1/checkpoint-1 --run DQN
"""
def cli():
parser = argpar... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/mock_worker.py | Python | import numpy as np
from ray.rllib.evaluation import SampleBatch
from ray.rllib.utils.filter import MeanStdFilter
class _MockWorker:
def __init__(self, sample_count=10):
self._weights = np.array([-10, -10, -10, -10])
self._grad = np.array([1, 1, 1, 1])
self._sample_count = sample_count
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/multiagent_pendulum.py | Python | """Integration test: (1) pendulum works, (2) single-agent multi-agent works."""
import ray
from ray.rllib.tests.test_multi_agent_env import make_multiagent
from ray.tune import run_experiments
from ray.tune.registry import register_env
if __name__ == "__main__":
ray.init()
MultiPendulum = make_multiagent("Pen... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/run_regression_tests.py | Python | #!/usr/bin/env python
# Runs one or more regression tests. Retries tests up to 3 times.
#
# Example usage:
# ./run_regression_tests.sh regression-tests/cartpole-es.yaml
import yaml
import sys
import ray
from ray.tune import run_experiments
if __name__ == "__main__":
ray.init()
for test in sys.argv[1:]:
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_avail_actions_qmix.py | Python | import numpy as np
from gym.spaces import Tuple, Discrete, Dict, Box
import ray
from ray.tune import register_env
from ray.rllib.env.multi_agent_env import MultiAgentEnv
from ray.rllib.agents.qmix import QMixTrainer
class AvailActionsTestEnv(MultiAgentEnv):
action_space = Discrete(10)
observation_space = Dic... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_catalog.py | Python | import gym
import numpy as np
import unittest
from gym.spaces import Box, Discrete, Tuple
import ray
from ray.rllib.models import ModelCatalog, MODEL_DEFAULTS
from ray.rllib.models.model import Model
from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
from ray.rllib.models.preprocessors import (NoPrep... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_checkpoint_restore.py | Python | #!/usr/bin/env python
import os
import shutil
import gym
import numpy as np
import ray
from ray.rllib.agents.registry import get_agent_class
from ray.tune.trial import ExportFormat
def get_mean_action(alg, obs):
out = []
for _ in range(2000):
out.append(float(alg.compute_action(obs)))
return np.... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_dependency.py | Python | #!/usr/bin/env python
import os
import sys
os.environ["RLLIB_TEST_NO_TF_IMPORT"] = "1"
if __name__ == "__main__":
from ray.rllib.agents.a3c import A2CTrainer
assert "tensorflow" not in sys.modules, "TF initially present"
# note: no ray.init(), to test it works without Ray
trainer = A2CTrainer(
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_eager_support.py | Python | import unittest
import ray
from ray import tune
from ray.rllib.agents.registry import get_agent_class
def check_support(alg, config, test_trace=True):
config["eager"] = True
if alg in ["APEX_DDPG", "TD3", "DDPG", "SAC"]:
config["env"] = "Pendulum-v0"
else:
config["env"] = "CartPole-v0"
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_env_with_subprocess.py | Python | """Tests that envs clean up after themselves on agent exit."""
from gym.spaces import Discrete
import atexit
import gym
import os
import subprocess
import tempfile
import time
import ray
from ray.tune import run_experiments
from ray.tune.registry import register_env
# Dummy command to run as a subprocess with a uniq... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_evaluators.py | Python | import unittest
import ray
from ray.rllib.agents.dqn import DQNTrainer
from ray.rllib.agents.a3c import A3CTrainer
from ray.rllib.agents.dqn.dqn_policy import _adjust_nstep
from ray.tune.registry import register_env
import gym
class EvalTest(unittest.TestCase):
def testDqnNStep(self):
obs = [1, 2, 3, 4, ... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_external_env.py | Python | import gym
import numpy as np
import random
import unittest
import uuid
import ray
from ray.rllib.agents.dqn import DQNTrainer
from ray.rllib.agents.pg import PGTrainer
from ray.rllib.evaluation.rollout_worker import RolloutWorker
from ray.rllib.env.external_env import ExternalEnv
from ray.rllib.tests.test_rollout_wor... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_external_multi_agent_env.py | Python | import gym
import numpy as np
import random
import unittest
import ray
from ray.rllib.agents.pg.pg_tf_policy import PGTFPolicy
from ray.rllib.optimizers import SyncSamplesOptimizer
from ray.rllib.evaluation.rollout_worker import RolloutWorker
from ray.rllib.evaluation.worker_set import WorkerSet
from ray.rllib.env.ext... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_filters.py | Python | import unittest
import numpy as np
import ray
from ray.rllib.utils.filter import RunningStat, MeanStdFilter
from ray.rllib.utils import FilterManager
from ray.rllib.tests.mock_worker import _MockWorker
class RunningStatTest(unittest.TestCase):
def testRunningStat(self):
for shp in ((), (3, ), (3, 4)):
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_ignore_worker_failure.py | Python | import gym
import unittest
import ray
from ray.rllib import _register_all
from ray.rllib.agents.registry import get_agent_class
from ray.tune.registry import register_env
class FaultInjectEnv(gym.Env):
def __init__(self, config):
self.env = gym.make("CartPole-v0")
self.action_space = self.env.act... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_io.py | Python | import glob
import gym
import json
import numpy as np
import os
import random
import shutil
import tempfile
import time
import unittest
import ray
from ray.rllib.agents.pg import PGTrainer
from ray.rllib.agents.pg.pg_tf_policy import PGTFPolicy
from ray.rllib.evaluation import SampleBatch
from ray.rllib.offline import... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_legacy.py | Python | from ray.rllib.agents.ppo import PPOAgent
from ray import tune
import ray
if __name__ == "__main__":
ray.init()
# Test legacy *Agent classes work (renamed to Trainer)
tune.run(
PPOAgent,
config={"env": "CartPole-v0"},
stop={"training_iteration": 2})
| zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_local.py | Python | import unittest
from ray.rllib.agents.ppo import PPOTrainer, DEFAULT_CONFIG
import ray
class LocalModeTest(unittest.TestCase):
def testLocal(self):
ray.init(local_mode=True)
cf = DEFAULT_CONFIG.copy()
agent = PPOTrainer(cf, "CartPole-v0")
print(agent.train())
if __name__ == "__m... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_lstm.py | Python | import gym
import numpy as np
import pickle
import unittest
import ray
from ray.rllib.agents.ppo import PPOTrainer
from ray.rllib.policy.rnn_sequencing import chop_into_sequences, \
add_time_dimension
from ray.rllib.models import ModelCatalog
from ray.rllib.models.tf.misc import linear, normc_initializer
from ray.... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_multi_agent_env.py | Python | import gym
import random
import unittest
import ray
from ray.rllib.agents.pg import PGTrainer
from ray.rllib.agents.pg.pg_tf_policy import PGTFPolicy
from ray.rllib.agents.dqn.dqn_policy import DQNTFPolicy
from ray.rllib.optimizers import (SyncSamplesOptimizer, SyncReplayOptimizer,
As... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_nested_spaces.py | Python | import pickle
from gym import spaces
from gym.envs.registration import EnvSpec
import gym
import unittest
import ray
from ray.rllib.agents.a3c import A2CTrainer
from ray.rllib.agents.pg import PGTrainer
from ray.rllib.agents.pg.pg_tf_policy import PGTFPolicy
from ray.rllib.env import MultiAgentEnv
from ray.rllib.env.... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_optimizers.py | Python | import gym
import numpy as np
import time
import unittest
import ray
from ray.rllib.agents.ppo import PPOTrainer
from ray.rllib.agents.ppo.ppo_policy import PPOTFPolicy
from ray.rllib.evaluation import SampleBatch
from ray.rllib.evaluation.rollout_worker import RolloutWorker
from ray.rllib.evaluation.worker_set import... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_perf.py | Python | import gym
import time
import unittest
import ray
from ray.rllib.evaluation.rollout_worker import RolloutWorker
from ray.rllib.tests.test_rollout_worker import MockPolicy
class TestPerf(unittest.TestCase):
# Tested on Intel(R) Core(TM) i7-4600U CPU @ 2.10GHz
# 11/23/18: Samples per second 8501.125113727468
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_reproducibility.py | Python | import unittest
import ray
from ray.rllib.agents.dqn import DQNTrainer
from ray.tune.registry import register_env
import numpy as np
import gym
class TestReproducibility(unittest.TestCase):
def testReproducingTrajectory(self):
class PickLargest(gym.Env):
def __init__(self):
se... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_rollout.sh | Shell | #!/bin/bash -e
TRAIN=/ray/rllib/train.py
if [ ! -e "$TRAIN" ]; then
TRAIN=../train.py
fi
ROLLOUT=/ray/rllib/rollout.py
if [ ! -e "$ROLLOUT" ]; then
ROLLOUT=../rollout.py
fi
TMP=`mktemp -d`
echo "Saving results to $TMP"
$TRAIN --local-dir=$TMP --run=IMPALA --checkpoint-freq=1 \
--config='{"num_workers": 1... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_rollout_worker.py | Python | import gym
import numpy as np
import random
import time
import unittest
from collections import Counter
import ray
from ray.rllib.agents.pg import PGTrainer
from ray.rllib.agents.a3c import A2CTrainer
from ray.rllib.evaluation.rollout_worker import RolloutWorker
from ray.rllib.evaluation.metrics import collect_metrics... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/tests/test_supported_spaces.py | Python | import unittest
import traceback
import gym
from gym.spaces import Box, Discrete, Tuple, Dict, MultiDiscrete
from gym.envs.registration import EnvSpec
import numpy as np
import sys
import ray
from ray.rllib.agents.registry import get_agent_class
from ray.rllib.models.tf.fcnet_v2 import FullyConnectedNetwork as FCNetV... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/train.py | Python | #!/usr/bin/env python
import argparse
import yaml
import ray
from ray.cluster_utils import Cluster
from ray.tune.config_parser import make_parser
from ray.tune.result import DEFAULT_RESULTS_DIR
from ray.tune.resources import resources_to_json
from ray.tune.tune import _make_scheduler, run_experiments
from ray.rllib.u... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/__init__.py | Python | from ray.rllib.utils.annotations import override, PublicAPI, DeveloperAPI
from ray.rllib.utils.framework import try_import_tf, try_import_tfp, \
try_import_torch
from ray.rllib.utils.deprecation import deprecation_warning, renamed_agent, \
renamed_class, renamed_function
from ray.rllib.utils.filter_manager impo... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/actors.py | Python | import logging
import os
import ray
logger = logging.getLogger(__name__)
class TaskPool:
"""Helper class for tracking the status of many in-flight actor tasks."""
def __init__(self):
self._tasks = {}
self._objects = {}
self._fetching = []
def add(self, worker, all_obj_ids):
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/annotations.py | Python | def override(cls):
"""Annotation for documenting method overrides.
Arguments:
cls (type): The superclass that provides the overriden method. If this
cls does not actually have the method, an error is raised.
"""
def check_override(method):
if method.__name__ not in dir(cls)... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/compression.py | Python | from ray.rllib.utils.annotations import DeveloperAPI
import logging
import time
import base64
import numpy as np
import pyarrow
from six import string_types
logger = logging.getLogger(__name__)
try:
import lz4.frame
LZ4_ENABLED = True
except ImportError:
logger.warning("lz4 not available, disabling sampl... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/debug.py | Python | import numpy as np
import pprint
import time
from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch
_logged = set()
_disabled = False
_periodic_log = False
_last_logged = 0.0
_printer = pprint.PrettyPrinter(indent=2, width=60)
def log_once(key):
"""Returns True if this is the "first" call for a ... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/deprecation.py | Python | import logging
logger = logging.getLogger(__name__)
def deprecation_warning(old, new=None):
logger.warning(
"DeprecationWarning: `{}` has been deprecated.".format(old) +
(" Use `{}` instead." if new else "") +
" This will raise an error in the future!"
)
def renamed_class(cls, old_... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/error.py | Python | from ray.rllib.utils.annotations import PublicAPI
@PublicAPI
class UnsupportedSpaceException(Exception):
"""Error for an unsupported action or observation space."""
pass
| zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/explained_variance.py | Python | from ray.rllib.utils import try_import_tf, try_import_torch
tf = try_import_tf()
torch, nn = try_import_torch()
def explained_variance(y, pred, framework="tf"):
if framework == "tf":
_, y_var = tf.nn.moments(y, axes=[0])
_, diff_var = tf.nn.moments(y - pred, axes=[0])
return tf.maximum(-1... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/filter.py | Python | import logging
import numpy as np
import threading
logger = logging.getLogger(__name__)
class Filter:
"""Processes input, possibly statefully."""
def apply_changes(self, other, *args, **kwargs):
"""Updates self with "new state" from other filter."""
raise NotImplementedError
def copy(se... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/filter_manager.py | Python | import ray
from ray.rllib.utils.annotations import DeveloperAPI
from ray.rllib.utils.memory import ray_get_and_free
@DeveloperAPI
class FilterManager:
"""Manages filters and coordination across remote evaluators that expose
`get_filters` and `sync_filters`.
"""
@staticmethod
@DeveloperAPI
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/framework.py | Python | import logging
import os
logger = logging.getLogger(__name__)
def try_import_tf():
"""
Returns:
The tf module (either from tf2.0.compat.v1 OR as tf1.x.
"""
if "RLLIB_TEST_NO_TF_IMPORT" in os.environ:
logger.warning("Not importing TensorFlow for test purposes")
return None
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/memory.py | Python | import numpy as np
import time
import ray
FREE_DELAY_S = 10.0
MAX_FREE_QUEUE_SIZE = 100
_last_free_time = 0.0
_to_free = []
def ray_get_and_free(object_ids):
"""Call ray.get and then queue the object ids for deletion.
This function should be used whenever possible in RLlib, to optimize
memory usage. Th... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/numpy.py | Python | import numpy as np
SMALL_NUMBER = 1e-6
# Some large int number. May be increased here, if needed.
LARGE_INTEGER = 100000000
# Min and Max outputs (clipped) from an NN-output layer interpreted as the
# log(x) of some x (e.g. a stddev of a normal
# distribution).
MIN_LOG_NN_OUTPUT = -20
MAX_LOG_NN_OUTPUT = 2
def sigm... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/policy_client.py | Python | import logging
import pickle
from ray.rllib.utils.annotations import PublicAPI
logger = logging.getLogger(__name__)
try:
import requests # `requests` is not part of stdlib.
except ImportError:
requests = None
logger.warning(
"Couldn't import `requests` library. Be sure to install it on"
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/policy_server.py | Python | import pickle
import traceback
from http.server import SimpleHTTPRequestHandler, HTTPServer
from socketserver import ThreadingMixIn
from ray.rllib.utils.annotations import PublicAPI
from ray.rllib.utils.policy_client import PolicyClient
@PublicAPI
class PolicyServer(ThreadingMixIn, HTTPServer):
"""REST server t... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/schedules.py | Python | """This file is used for specifying various schedules that evolve over
time throughout the execution of the algorithm, such as:
- learning rate for the optimizer
- exploration epsilon for the epsilon greedy exploration strategy
- beta parameter for beta parameter in prioritized replay
Each schedule has a function `... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/seed.py | Python | import numpy as np
import random
from ray.rllib.utils import try_import_tf
tf = try_import_tf()
def seed(np_seed=0, random_seed=0, tf_seed=0):
np.random.seed(np_seed)
random.seed(random_seed)
tf.set_random_seed(tf_seed)
| zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/test_utils.py | Python | import numpy as np
from ray.rllib.utils.framework import try_import_tf
tf = try_import_tf()
def check(x, y, decimals=5, atol=None, rtol=None, false=False):
"""
Checks two structures (dict, tuple, list,
np.array, float, int, etc..) for (almost) numeric identity.
All numbers in the two structures have... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/tf_ops.py | Python | from ray.rllib.utils import try_import_tf
tf = try_import_tf()
def huber_loss(x, delta=1.0):
"""Reference: https://en.wikipedia.org/wiki/Huber_loss"""
return tf.where(
tf.abs(x) < delta,
tf.square(x) * 0.5, delta * (tf.abs(x) - 0.5 * delta))
def reduce_mean_ignore_inf(x, axis):
"""Same ... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/tf_run_builder.py | Python | import logging
import os
import time
from ray.rllib.utils.debug import log_once
from ray.rllib.utils import try_import_tf
tf = try_import_tf()
logger = logging.getLogger(__name__)
class TFRunBuilder:
"""Used to incrementally build up a TensorFlow run.
This is particularly useful for batching ops from multi... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/timer.py | Python | import numpy as np
import time
class TimerStat:
"""A running stat for conveniently logging the duration of a code block.
Example:
wait_timer = TimerStat()
with wait_timer:
ray.wait(...)
Note that this class is *not* thread-safe.
"""
def __init__(self, window_size=10)... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/torch_ops.py | Python | from ray.rllib.utils.framework import try_import_torch
torch, _ = try_import_torch()
def sequence_mask(lengths, maxlen, dtype=torch.bool):
"""
Exact same behavior as tf.sequence_mask.
Thanks to Dimitris Papatheodorou
(https://discuss.pytorch.org/t/pytorch-equivalent-for-tf-sequence-mask/39036).
"... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/tracking_dict.py | Python | class UsageTrackingDict(dict):
"""Dict that tracks which keys have been accessed.
It can also intercept gets and allow an arbitrary callback to be applied
(i.e., to lazily convert numpy arrays to Tensors).
We make the simplifying assumption only __getitem__ is used to access
values.
"""
d... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
rllib/utils/window_stat.py | Python | import numpy as np
class WindowStat:
def __init__(self, name, n):
self.name = name
self.items = [None] * n
self.idx = 0
self.count = 0
def push(self, obj):
self.items[self.idx] = obj
self.idx += 1
self.count += 1
self.idx %= len(self.items)
... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
setup_hooks.sh | Shell | #!/bin/bash
ln -s $PWD/scripts/pre-push $PWD/.git/hooks/pre-push
| zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
src/ray/common/buffer.h | C/C++ Header | #ifndef RAY_COMMON_BUFFER_H
#define RAY_COMMON_BUFFER_H
#include <cstdint>
#include <cstdio>
#include "plasma/client.h"
#include "ray/common/status.h"
namespace arrow {
class Buffer;
}
namespace ray {
/// The interface that represents a buffer of bytes.
class Buffer {
public:
/// Pointer to the data.
virtual u... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
src/ray/common/client_connection.cc | C++ | #include "client_connection.h"
#include <stdio.h>
#include <boost/bind.hpp>
#include <sstream>
#include "ray/common/ray_config.h"
#include "ray/util/util.h"
namespace ray {
ray::Status TcpConnect(boost::asio::ip::tcp::socket &socket,
const std::string &ip_address_string, int port) {
// Disa... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
src/ray/common/client_connection.h | C/C++ Header | #ifndef RAY_COMMON_CLIENT_CONNECTION_H
#define RAY_COMMON_CLIENT_CONNECTION_H
#include <deque>
#include <memory>
#include <boost/asio.hpp>
#include <boost/asio/error.hpp>
#include <boost/enable_shared_from_this.hpp>
#include "ray/common/id.h"
#include "ray/common/status.h"
namespace ray {
/// Connect a TCP socket.... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
src/ray/common/common_protocol.cc | C++ | #include "common_protocol.h"
#include "ray/util/logging.h"
std::string string_from_flatbuf(const flatbuffers::String &string) {
return std::string(string.data(), string.size());
}
std::vector<std::string> string_vec_from_flatbuf(
const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> &flatbuf_vec)... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
src/ray/common/common_protocol.h | C/C++ Header | #ifndef COMMON_PROTOCOL_H
#define COMMON_PROTOCOL_H
#include <flatbuffers/flatbuffers.h>
#include <unordered_set>
#include "ray/common/id.h"
#include "ray/util/logging.h"
/// Convert an unique ID to a flatbuffer string.
///
/// @param fbb Reference to the flatbuffer builder.
/// @param id The ID to be converted.
///... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
src/ray/common/constants.h | C/C++ Header | #ifndef RAY_CONSTANTS_H_
#define RAY_CONSTANTS_H_
#include <limits.h>
#include <stdint.h>
/// Length of Ray full-length IDs in bytes.
constexpr size_t kUniqueIDSize = 20;
/// Length of plasma ID in bytes.
constexpr size_t kPlasmaIdSize = 20;
/// An ObjectID's bytes are split into the task ID itself and the index of... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
src/ray/common/grpc_util.h | C/C++ Header | #ifndef RAY_COMMON_GRPC_UTIL_H
#define RAY_COMMON_GRPC_UTIL_H
#include <google/protobuf/map.h>
#include <google/protobuf/repeated_field.h>
#include <grpcpp/grpcpp.h>
#include <sstream>
#include "status.h"
namespace ray {
/// Wrap a protobuf message.
template <class Message>
class MessageWrapper {
public:
/// Co... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
src/ray/common/id.cc | C++ | #include "ray/common/id.h"
#include <limits.h>
#include <algorithm>
#include <chrono>
#include <mutex>
#include <random>
#include "ray/common/constants.h"
#include "ray/common/status.h"
#include "ray/util/util.h"
extern "C" {
#include "ray/thirdparty/sha256.h"
}
// Definitions for computing hash digests.
#define D... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta | |
src/ray/common/id.h | C/C++ Header | #ifndef RAY_ID_H_
#define RAY_ID_H_
#include <inttypes.h>
#include <limits.h>
#include <chrono>
#include <cstring>
#include <mutex>
#include <random>
#include <string>
#include "plasma/common.h"
#include "ray/common/constants.h"
#include "ray/util/logging.h"
#include "ray/util/util.h"
#include "ray/util/visibility.h... | zhuohan123/hoplite-rllib | 3 | Python | zhuohan123 | Zhuohan Li | vLLM / Meta |
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