Thanks Pascal.
I tried using gpu preallocate 0.01 and 0.1. The run with 0.1, for instance,
starts like this:
$ python scripts/run_rl_mj.py --env_name CartPole-v0 --log trpo_logs/
CartPole-v0
Using cuDNN version 5105 on context None
Preallocating 1218/12186 Mb (0.100000) on cuda
Mapped name None to device cuda: TITAN X (Pascal) (0000:01:00.0)
But the same error message results:
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 283, in step
cfg=self.sim_cfg)
File "/home/daniel/imitation_noise/policyopt/__init__.py", line 425, 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: 1
Inputs types: [TensorType(float32, matrix)]
Inputs shapes: [(1, 4)]
Inputs strides: [(16, 4)]
Inputs values: [array([[ 0.02563, -0.03082, 0.01663, -0.00558]], dtype=
float32)]
Outputs clients: [[GpuElemwise{Composite{((i0 - i1) / i2)}}[(0, 0)]<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.
With -1 as the preallocate, I get this to start:
$ python scripts/run_rl_mj.py --env_name CartPole-v0 --log trpo_logs/
CartPole-v0
Using cuDNN version 5105 on context None
Disabling allocation cache on cuda
I get a similar error message except it's slightly different, with an
initialization error, but the same part of the code is running into
problems:
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 283, in step
cfg=self.sim_cfg)
File "/home/daniel/imitation_noise/policyopt/__init__.py", line 425, 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
Apply node that caused the error: GpuFromHost<None>(obsfeat_B_Df)
Toposort index: 1
Inputs types: [TensorType(float32, matrix)]
Inputs shapes: [(1, 4)]
Inputs strides: [(16, 4)]
Inputs values: [array([[ 0.01357, -0.02611, 0.0341 , 0.0162 ]], dtype=
float32)]
Outputs clients: [[GpuElemwise{Composite{((i0 - i1) / i2)}}[(0, 0)]<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.
Yes, the code seems to be using multiprocessing. I will try to see if I can
find out how to deal with the multiprocessing, or perhaps just disable it.
On Monday, July 3, 2017 at 3:08:44 PM UTC-7, Pascal Lamblin wrote:
>
> What happens if you set gpuarray.preallocate to something much smaller, or
> even to -1?
>
> Also, I see the script uses multiprocessing. Weird things happen if new
> Python processes are spawned after the GPU has been initialized. This is a
> limitation of how cuda handles GPU contexts I believe.
> The solution would be not to use `device=cuda`, but `device=cpu`, and call
> `theano.gpuarray.use('cuda')` manually in the subprocess, or after all
> processes have been launched.
>
> On Sunday, July 2, 2017 at 3:59:31 PM UTC-4, Daniel Seita wrote:
>>
>> 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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