def read_spec_and_run(spec_file, spec_name, lab_mode):
'''Read a spec and run it in lab mode'''
logger.info(
f'Running lab spec_file:{spec_file} spec_name:{spec_name} in mode:{lab_mode}'
)
if lab_mode in TRAIN_MODES:
spec = spec_util.get(spec_file, spec_name)
else: # eval mode
if '@' in lab_mode:
lab_mode, prename = lab_mode.split('@')
spec = spec_util.get_eval_spec(spec_file, spec_name, prename)
else:
spec = spec_util.get(spec_file, spec_name)
if 'spec_params' not in spec:
run_spec(spec, lab_mode)
else: # spec is parametrized; run them in parallel
param_specs = spec_util.get_param_specs(spec)
num_pro = spec['meta']['param_spec_process']
# can't use Pool since it cannot spawn nested Process, which is needed for VecEnv and parallel sessions. So these will run and wait by chunks
workers = [
mp.Process(target=run_spec, args=(spec, lab_mode))
for spec in param_specs
]
for chunk_w in ps.chunk(workers, num_pro):
for w in chunk_w:
w.start()
for w in chunk_w:
w.join()
rgi_queue = PriorityQueue(maxsize=0)
rgo_queue = PriorityQueue(maxsize=0)
app = Flask(__name__)
os.environ['lab_mode'] = 'eval'
spec_file = sys.argv[1]
spec_name = sys.argv[2]
lab_mode = sys.argv[3]
if '@' in lab_mode:
lab_mode, prename = lab_mode.split('@')
spec = spec_util.get_eval_spec(spec_file, spec_name, prename)
else:
spec = spec_util.get(spec_file, spec_name)
# # lab_mode, prename = sys.argv[3].split('@')
# spec = spec_util.get_eval_spec(spec_file, prename)
spec = spec_util.override_eval_spec(spec)
agent_spec = spec['agent'][0]
env = make_env(spec)
body = Body(env, spec['agent'])
agent = DialogAgent(spec, body)
# last_obs = 'hi'
# agent.reset(last_obs)
# obs = 'hi can you find me a hotel in the west?'
# action = agent.act(obs)
# next_obs = 'we have six people'