Problems about starting training task

[MrWiffer]

When using the following command to start a training task, the error is TypeError: h5py objects cannot be pickled:

export CUDA_VISIBLE_DEVICES=0
export MUJOCO_EGL_DEVICE_ID=0
python libero/lifelong/main.py seed=0 benchmark_name=LIBERO_10 policy=bc_rnn_policy lifelong=base

according to issue #19, I comment part of code in libero/lifelong/main.py as following:

Then, start the training task again, error occurs as shown in below:

Traceback (most recent call last):
  File "libero/lifelong/main.py", line 272, in <module>
    main()
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/main.py", line 90, in decorated_main
    _run_hydra(
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/_internal/utils.py", line 389, in _run_hydra
    _run_app(
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/_internal/utils.py", line 452, in _run_app
    run_and_report(
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/_internal/utils.py", line 216, in run_and_report
    raise ex
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/_internal/utils.py", line 213, in run_and_report
    return func()
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/_internal/utils.py", line 453, in <lambda>
    lambda: hydra.run(
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 132, in run
    _ = ret.return_value
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/core/utils.py", line 260, in return_value
    raise self._return_value
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/core/utils.py", line 186, in run_job
    ret.return_value = task_function(task_cfg)
  File "libero/lifelong/main.py", line 219, in main
    s_fwd, l_fwd = algo.learn_one_task(
  File "/root/workspace/LIBERO-master/libero/lifelong/algos/base.py", line 200, in learn_one_task
    success_rate = evaluate_one_task_success(
  File "/root/workspace/LIBERO-master/libero/lifelong/metric.py", line 112, in evaluate_one_task_success
    env.reset()
  File "/root/workspace/LIBERO-master/libero/libero/envs/venv.py", line 709, in reset
    ret_list = [self.workers[i].recv() for i in id]
  File "/root/workspace/LIBERO-master/libero/libero/envs/venv.py", line 709, in <listcomp>
    ret_list = [self.workers[i].recv() for i in id]
  File "/root/workspace/LIBERO-master/libero/libero/envs/venv.py", line 437, in recv
    result = self.parent_remote.recv()
  File "/root/miniforge/envs/libero/lib/python3.8/multiprocessing/connection.py", line 250, in recv
    buf = self._recv_bytes()
  File "/root/miniforge/envs/libero/lib/python3.8/multiprocessing/connection.py", line 414, in _recv_bytes
    buf = self._recv(4)
  File "/root/miniforge/envs/libero/lib/python3.8/multiprocessing/connection.py", line 379, in _recv
    chunk = read(handle, remaining)
ConnectionResetError: [Errno 104] Connection reset by peer

Accoring to issue #3, add code in libero/lifelong/metric.py:

HOWEVER, error occurs again after start training, which is same as the error at the beginning.

Traceback (most recent call last):
  File "libero/lifelong/main.py", line 272, in <module>
    main()
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/main.py", line 90, in decorated_main
    _run_hydra(
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/_internal/utils.py", line 389, in _run_hydra
    _run_app(
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/_internal/utils.py", line 452, in _run_app
    run_and_report(
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/_internal/utils.py", line 216, in run_and_report
    raise ex
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/_internal/utils.py", line 213, in run_and_report
    return func()
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/_internal/utils.py", line 453, in <lambda>
    lambda: hydra.run(
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/_internal/hydra.py", line 132, in run
    _ = ret.return_value
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/core/utils.py", line 260, in return_value
    raise self._return_value
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/hydra/core/utils.py", line 186, in run_job
    ret.return_value = task_function(task_cfg)
  File "libero/lifelong/main.py", line 228, in main
    L = evaluate_loss(cfg, algo, benchmark, datasets[: i + 1])
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
  File "/root/workspace/LIBERO-master/libero/lifelong/metric.py", line 216, in evaluate_loss
    for data in dataloader:
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 440, in __iter__
    return self._get_iterator()
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 388, in _get_iterator
    return _MultiProcessingDataLoaderIter(self)
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1038, in __init__
    w.start()
  File "/root/miniforge/envs/libero/lib/python3.8/multiprocessing/process.py", line 121, in start
    self._popen = self._Popen(self)
  File "/root/miniforge/envs/libero/lib/python3.8/multiprocessing/context.py", line 224, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "/root/miniforge/envs/libero/lib/python3.8/multiprocessing/context.py", line 284, in _Popen
    return Popen(process_obj)
  File "/root/miniforge/envs/libero/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 32, in __init__
    super().__init__(process_obj)
  File "/root/miniforge/envs/libero/lib/python3.8/multiprocessing/popen_fork.py", line 19, in __init__
    self._launch(process_obj)
  File "/root/miniforge/envs/libero/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 47, in _launch
    reduction.dump(process_obj, fp)
  File "/root/miniforge/envs/libero/lib/python3.8/multiprocessing/reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
  File "/root/miniforge/envs/libero/lib/python3.8/site-packages/h5py/_hl/base.py", line 370, in __getnewargs__
    raise TypeError("h5py objects cannot be pickled")
TypeError: h5py objects cannot be pickled

I'm curious to know where the problem is and what needs to be done to resolve it so that I can complete this training task.