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Traceback (most recent call last):
  File "/home/wwhitney/anaconda3/lib/python3.6/multiprocessing/process.py", line 249, in _bootstrap
    self.run()
  File "/home/wwhitney/anaconda3/lib/python3.6/multiprocessing/process.py", line 93, in run
    self._target(*self._args, **self._kwargs)
  File "/home/wwhitney/anaconda3/lib/python3.6/multiprocessing/pool.py", line 108, in worker
    task = get()
  File "/home/wwhitney/anaconda3/lib/python3.6/multiprocessing/queues.py", line 345, in get
    return _ForkingPickler.loads(res)
  File "/home/wwhitney/anaconda3/lib/python3.6/site-packages/torch/multiprocessing/reductions.py", line 86, in rebuild_storage_filename

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  File "parallel_main.py", line 56, in <module>
    parents = random.choices(population, scores + 1e-10, k=pop_size)
  File "/home/wwhitney/anaconda3/lib/python3.6/multiprocessing/pool.py", line 260, in map
    return self._map_async(func, iterable, mapstar, chunksize).get()
  File "/home/wwhitney/anaconda3/lib/python3.6/multiprocessing/pool.py", line 608, in get
    raise self._value
multiprocessing.pool.MaybeEncodingError: Error sending result:
	<my object>
Reason: 'RuntimeError('unable to open shared memory object </torch_29253_1584213566> in read-write mode at /py/conda-bld/pytorch_1493680494901/work/torch/lib/TH/THAllocator.c:226',)'

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#----------------------------------------------
# things to change:
# code_dir (the full path of the directory that contains your source dir)
# true_source_dir (change it from TD3 to whatever your source dir is called)
# job_source_dir (someplace to throw a duplicate of the source dir for this job)
#----------------------------------------------

import os
import sys
import itertools

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from tensorboard.backend.event_processing import event_accumulator
import tensorflow as tf
import glob
import pandas as pd
tf.logging.set_verbosity(tf.logging.ERROR)

basedir = "/path/to/log/directory/"

def load_tf(dirname):
    prefix = basedir + "tboard/VisibleSwimmer-v2/"

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import signal

# depends on requesting SIGUSR1 in runner file: https://gist.github.com/willwhitney/e1509c86522896c6930d2fe9ea49a522

def handle_signal(signal_value, _):
    signame = signal.Signals(signal_value).name
    if signal_value == signal.SIGUSR1:
        print('Process {} got signal {}. Saving and restarting.'.format(
                os.getpid(), signame), flush=True)
        save_dynamics(epoch)

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class DmMujocoModel(nn.Module):
    def __init__(self, embed_dim, env_name, traj_len, qpos_only=False, qpos_qvel=False):
        super().__init__()
        self.embed_dim = embed_dim
        self.dataset = DmData(env_name, traj_len, qpos_only, qpos_qvel)
        self.dataset.make_env()
        self.env = self.dataset.env
        self.dummy_parameter = nn.Parameter(torch.zeros(1))

    def forward(self, s, a):

willwhitney

[{"x":0,"y":0},{"x":1,"y":1},{"x":2,"y":2},{"x":3,"y":3},{"x":4,"y":4},{"x":5,"y":5},{"x":6,"y":6},{"x":7,"y":7},{"x":8,"y":8},{"x":9,"y":9}]

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import numpy as np

from jax import numpy as jnp
from jax.lib import pytree


def tree_stack(trees):
    """Takes a list of trees and stacks every corresponding leaf.
    For example, given two trees ((a, b), c) and ((a', b'), c'), returns
    ((stack(a, a'), stack(b, b')), stack(c, c')).