def test_subgraph_components(self):
return
# TODO fix when we have built selective subgraph fetching correctly.
# Create agent.
agent_config = config_from_path("configs/ray_apex_for_pong.json")
agent_config["execution_spec"].pop("ray_spec")
environment = OpenAIGymEnv("Pong-v0", frameskip=4)
# Do not build yet.
agent = ApexAgent.from_spec(agent_config,
state_space=environment.state_space,
action_space=environment.action_space,
auto_build=False)
# Prepare all steps until build device strategy so we can test subgraph fetching.
agent.graph_executor.init_execution()
agent.graph_executor.setup_graph()
# Meta graph must be built for sub-graph tracing.
agent.graph_builder.build_meta_graph(agent.input_spaces)
sub_graph = agent.graph_builder.get_subgraph(
"update_from_external_batch")
print("Sub graph components:")
print(sub_graph.sub_components)
print("Sub graph API: ")
print(sub_graph.api_methods)
def test_apex_compilation(self):
"""
Tests agent compilation without Ray to ease debugging on Windows.
"""
agent_config = config_from_path("configs/ray_apex_for_pong.json")
agent_config["execution_spec"].pop("ray_spec")
environment = OpenAIGymEnv("Pong-v0", frameskip=4)
agent = ApexAgent.from_spec(agent_config,
state_space=environment.state_space,
action_space=environment.action_space)
print("Compiled {}".format(agent))
def test_multi_gpu_apex_agent_compilation(self):
"""
Tests if the multi gpu strategy can compile successfully on a multi gpu system, but
also runs on a CPU-only system using fake-GPU logic for testing purposes.
"""
root_logger.setLevel(DEBUG)
agent_config = config_from_path("configs/multi_gpu_ray_apex_for_pong.json")
agent_config["execution_spec"].pop("ray_spec")
environment = OpenAIGymEnv("Pong-v0", frameskip=4)
agent = ApexAgent.from_spec(
agent_config, state_space=environment.state_space, action_space=environment.action_space
)
print("Compiled Apex agent")
def test_multi_gpu_apex_agent_compilation(self):
"""
Tests if the multi gpu strategy can compile successfully on a multi gpu system.
THIS TEST REQUIRES A MULTI GPU SYSTEM.
"""
root_logger.setLevel(DEBUG)
agent_config = config_from_path("configs/multi_gpu_ray_apex_for_pong.json")
agent_config["execution_spec"].pop("ray_spec")
environment = OpenAIGymEnv("Pong-v0", frameskip=4)
agent = ApexAgent.from_spec(
agent_config, state_space=environment.state_space, action_space=environment.action_space
)
print("Compiled Apex agent")
def test_apex_compilation(self):
"""
Tests agent compilation without Ray to ease debugging on Windows.
"""
agent_config = config_from_path("configs/ray_apex_for_pong.json")
agent_config["execution_spec"].pop("ray_spec")
# TODO remove after unified.
if get_backend() == "pytorch":
agent_config["memory_spec"]["type"] = "mem_prioritized_replay"
environment = OpenAIGymEnv("Pong-v0", frameskip=4)
agent = ApexAgent.from_spec(agent_config,
state_space=environment.state_space,
action_space=environment.action_space)
print('Compiled apex agent')
def test_post_processing(self):
env = OpenAIGymEnv("Pong-v0", frameskip=4, max_num_noops=30, episodic_life=True)
agent_config = config_from_path("configs/ray_apex_for_pong.json")
# Test cpu settings for batching here.
agent_config["memory_spec"]["type"] = "mem_prioritized_replay"
agent_config["execution_spec"]["torch_num_threads"] = 1
agent_config["execution_spec"]["OMP_NUM_THREADS"] = 1
agent = ApexAgent.from_spec(
# Uses 2015 DQN parameters as closely as possible.
agent_config,
state_space=env.state_space,
# Try with "reduced" action space (actually only 3 actions, up, down, no-op)
action_space=env.action_space
)
samples = 200
rewards = np.random.random(size=samples)
states = list(agent.preprocessed_state_space.sample(samples))
actions = agent.action_space.sample(samples)
terminals = np.zeros(samples, dtype=np.uint8)
next_states = states[1:]
next_states.extend([agent.preprocessed_state_space.sample(1)])
next_states = np.asarray(next_states)
states = np.asarray(states)
weights = np.ones_like(rewards)
for _ in range(1):
start = time.perf_counter()
_, loss_per_item = agent.post_process(
dict(
states=states,
actions=actions,
rewards=rewards,
terminals=terminals,
next_states=next_states,
importance_weights=weights
)
)
print("post process time = {}".format(time.perf_counter() - start))
profile = Component.call_times
print_call_chain(profile, False, 0.003)