I am attempting to run some reinforcement learning code on the GPU. (The
code is https://github.com/openai/imitation if it matters, running
`scripts/run_rl_mj.py`.)
I converted the code to run on float32 by changing the way the data is
supplied via numpy. Unfortunately, with the new GPU backend, I am gettting
an out of memory error, despite having 12GB of memory on my Titan X Pascal
GPU. Here are my settings:
$ cat ~/.theanorc
[global]
device = cuda
floatX = float32
[gpuarray]
preallocate = 1
[cuda]
root = /usr/local/cuda-8.0
Theano seems to be importing correctly:
$ ipython
Python 2.7.13 |Anaconda custom (64-bit)| (default, Dec 20 2016, 23:09:15)
Type "copyright", "credits" or "license" for more information.
IPython 5.3.0 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: import theano
Using cuDNN version 5105 on context None
Preallocating 11576/12186 Mb (0.950000) on cuda
Mapped name None to device cuda: TITAN X (Pascal) (0000:01:00.0)
In [2]:
Unfortunately, running `python scripts/run_rl_mj.py --env_name CartPole-v0
--log trpo_logs/CartPole-v0` on the very low-dimensional CartPole setting
(state space is just four numbers, actions are just one number) gives me
(after a bit of a setup):
Traceback (most recent call last):
File "scripts/run_rl_mj.py", line 116, in <module>
main()
File "scripts/run_rl_mj.py", line 109, in main
iter_info = opt.step()
File "/home/daniel/imitation_noise/policyopt/rl.py", line 280, in step
cfg=self.sim_cfg)
File "/home/daniel/imitation_noise/policyopt/__init__.py", line 411, in
sim_mp
traj = job.get()
File "/home/daniel/anaconda2/lib/python2.7/multiprocessing/pool.py", line
567, in get
raise self._value
pygpu.gpuarray.GpuArrayException: Out of memory
Apply node that caused the error: GpuFromHost<None>(obsfeat_B_Df)
Toposort index: 4
Inputs types: [TensorType(float32, matrix)]
Inputs shapes: [(1, 4)]
Inputs strides: [(16, 4)]
Inputs values: [array([[ 0.04058, 0.00428, 0.03311, -0.02898]],
dtype=float32)]
Outputs clients: [[GpuElemwise{Composite{((i0 - i1) /
i2)}}[]<gpuarray>(GpuFromHost<None>.0,
/GibbsPolicy/obsnorm/Standardizer/mean_1_D, GpuElemwise{Composite{(i0 +
sqrt((i1 * (Composite{(i0 - sqr(i1))}(i2, i3) + Abs(Composite{(i0 -
sqr(i1))}(i2, i3))))))}}[]<gpuarray>.0)]]
HINT: Re-running with most Theano optimization disabled could give you a
back-trace of when this node was created. This can be done with by setting
the Theano flag 'optimizer=fast_compile'. If that does not work, Theano
optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and
storage map footprint of this apply node.
Closing remaining open files:trpo_logs/CartPole-v0...done
What I'm confused about is that
- This happens right at the beginning of the reinforcement learning, so
it's not as if the algorithm has been running a long time and then ran out
of memory.
- The input shapes are quite small, (1,4) and (16,4). In addition, the
output is supposed to do normalization and several other element-wise
operations. None of this suggests high memory usage.
I tried `optimizer = fast_compile` and re-ran this, but the error message
was actually less informative (it contains a subset of the above error
message). Running with `exception_verbosity = high` results in a different
error message:
Max traj len: 200
Traceback (most recent call last):
File "scripts/run_rl_mj.py", line 116, in <module>
main()
File "scripts/run_rl_mj.py", line 109, in main
iter_info = opt.step()
File "/home/daniel/imitation_noise/policyopt/rl.py", line 280, in step
cfg=self.sim_cfg)
File "/home/daniel/imitation_noise/policyopt/__init__.py", line 411, in
sim_mp
traj = job.get()
File "/home/daniel/anaconda2/lib/python2.7/multiprocessing/pool.py", line
567, in get
raise self._value
pygpu.gpuarray.GpuArrayException: initialization error
Closing remaining open files:trpo_logs/CartPole-v0...done
It somehow didn't even reach the correct point in the code??
I noticed a similar issue
here: https://github.com/costapt/vess2ret/issues/5 which seems to suggest
that the problem is not limited to just this script. What do you suggest I
do? Thanks.
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