make baselines run without mpi wip

Dockerfile

@@ -1,16 +1,7 @@
-FROM ubuntu:16.04
+FROM python:3.6
 
-RUN apt-get -y update && apt-get -y install git wget python-dev python3-dev libopenmpi-dev python-pip zlib1g-dev cmake python-opencv
+# RUN apt-get -y update && apt-get -y install git wget python-dev python3-dev libopenmpi-dev python-pip zlib1g-dev cmake python-opencv
 ENV CODE_DIR /root/code
-ENV VENV /root/venv
-
-RUN \
-    pip install virtualenv && \
-    virtualenv $VENV --python=python3 && \
-    . $VENV/bin/activate && \
-    pip install --upgrade pip
-
-ENV PATH=$VENV/bin:$PATH
 
 COPY . $CODE_DIR/baselines
 WORKDIR $CODE_DIR/baselines

baselines/common/mpi_running_mean_std.py

@@ -1,4 +1,8 @@
+try:
     from mpi4py import MPI
+except ImportError:
+    MPI = None
+
 import tensorflow as tf, baselines.common.tf_util as U, numpy as np
 
 class RunningMeanStd(object):
@@ -39,6 +43,7 @@ class RunningMeanStd(object):
         n = int(np.prod(self.shape))
         totalvec = np.zeros(n*2+1, 'float64')
         addvec = np.concatenate([x.sum(axis=0).ravel(), np.square(x).sum(axis=0).ravel(), np.array([len(x)],dtype='float64')])
+        if MPI is not None:
             MPI.COMM_WORLD.Allreduce(addvec, totalvec, op=MPI.SUM)
         self.incfiltparams(totalvec[0:n].reshape(self.shape), totalvec[n:2*n].reshape(self.shape), totalvec[2*n])
 

baselines/ddpg/ddpg_learner.py

@@ -9,7 +9,6 @@ from baselines import logger
 from baselines.common.mpi_adam import MpiAdam
 import baselines.common.tf_util as U
 from baselines.common.mpi_running_mean_std import RunningMeanStd
-from mpi4py import MPI
 
 def normalize(x, stats):
     if stats is None:
@@ -358,6 +357,11 @@ class DDPG(object):
         return stats
 
     def adapt_param_noise(self):
+        try:
+            from mpi4py import MPI
+        except ImportError:
+            MPI = None
+
         if self.param_noise is None:
             return 0.
 
@@ -371,7 +375,11 @@ class DDPG(object):
             self.param_noise_stddev: self.param_noise.current_stddev,
         })
 
+        if MPI is not None:
             mean_distance = MPI.COMM_WORLD.allreduce(distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
+        else:
+            mean_distance = distance
+
         self.param_noise.adapt(mean_distance)
         return mean_distance
 

baselines/ppo2/ppo2.py

@@ -12,7 +12,11 @@ from baselines.common.runners import AbstractEnvRunner
 from baselines.common.tf_util import get_session, save_variables, load_variables
 from baselines.common.mpi_adam_optimizer import MpiAdamOptimizer
 
+try:
     from mpi4py import MPI
+except ImportError:
+    MPI = None
+
 from baselines.common.tf_util import initialize
 from baselines.common.mpi_util import sync_from_root
 
@@ -93,7 +97,10 @@ class Model(object):
         # 1. Get the model parameters
         params = tf.trainable_variables('ppo2_model')
         # 2. Build our trainer
+        if MPI is not None:
             trainer = MpiAdamOptimizer(MPI.COMM_WORLD, learning_rate=LR, epsilon=1e-5)
+        else:
+            trainer = tf.train.AdamOptimizer(learning_rate=LR, epsilon=1e-5)
         # 3. Calculate the gradients
         grads_and_var = trainer.compute_gradients(loss, params)
         grads, var = zip(*grads_and_var)
@@ -136,7 +143,7 @@ class Model(object):
         self.save = functools.partial(save_variables, sess=sess)
         self.load = functools.partial(load_variables, sess=sess)
 
-        if MPI.COMM_WORLD.Get_rank() == 0:
+        if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
             initialize()
         global_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="")
         sync_from_root(sess, global_variables) #pylint: disable=E1101
@@ -392,9 +399,9 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
             logger.logkv('time_elapsed', tnow - tfirststart)
             for (lossval, lossname) in zip(lossvals, model.loss_names):
                 logger.logkv(lossname, lossval)
-            if MPI.COMM_WORLD.Get_rank() == 0:
+            if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
                 logger.dumpkvs()
-        if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir() and MPI.COMM_WORLD.Get_rank() == 0:
+        if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir() and (MPI is None or MPI.COMM_WORLD.Get_rank() == 0): 
             checkdir = osp.join(logger.get_dir(), 'checkpoints')
             os.makedirs(checkdir, exist_ok=True)
             savepath = osp.join(checkdir, '%.5i'%update)

baselines/trpo_mpi/trpo_mpi.py

@@ -4,7 +4,6 @@ import baselines.common.tf_util as U
 import tensorflow as tf, numpy as np
 import time
 from baselines.common import colorize
-from mpi4py import MPI
 from collections import deque
 from baselines.common import set_global_seeds
 from baselines.common.mpi_adam import MpiAdam
@@ -13,6 +12,11 @@ from baselines.common.input import observation_placeholder
 from baselines.common.policies import build_policy
 from contextlib import contextmanager
 
+try:
+    from mpi4py import MPI
+except ImportError:
+    MPI = None
+
 def traj_segment_generator(pi, env, horizon, stochastic):
     # Initialize state variables
     t = 0
@@ -146,9 +150,12 @@ def learn(*,
 
     '''
 
-
+    if MPI is not None:
         nworkers = MPI.COMM_WORLD.Get_size()
         rank = MPI.COMM_WORLD.Get_rank()
+    else:
+        nworkers = 1
+        rank = 0
 
     cpus_per_worker = 1
     U.get_session(config=tf.ConfigProto(
@@ -238,6 +245,7 @@ def learn(*,
     def allmean(x):
         assert isinstance(x, np.ndarray)
         out = np.empty_like(x)
+        if MPI is not None:
             MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
             out /= nworkers
         return out
@@ -247,7 +255,9 @@ def learn(*,
         pi.load(load_path)
 
     th_init = get_flat()
+    if MPI is not None:
         MPI.COMM_WORLD.Bcast(th_init, root=0)
+
     set_from_flat(th_init)
     vfadam.sync()
     print("Init param sum", th_init.sum(), flush=True)
@@ -353,7 +363,11 @@ def learn(*,
         logger.record_tabular("ev_tdlam_before", explained_variance(vpredbefore, tdlamret))
 
         lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
+        if MPI is not None:
             listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
+        else
+            listoflrpairs = [lrlocal]
+
         lens, rews = map(flatten_lists, zip(*listoflrpairs))
         lenbuffer.extend(lens)
         rewbuffer.extend(rews)

setup.py

@@ -15,6 +15,9 @@ extras = {
     ],
     'bullet': [
         'pybullet',
+    ],
+    'mpi': [
+        'mpi4py'
     ]
 }
 
@@ -34,7 +37,6 @@ setup(name='baselines',
           'joblib',
           'dill',
           'progressbar2',
-          'mpi4py',
           'cloudpickle',
           'click',
           'opencv-python'