[theano-users] Re: Getting "pygpu.gpuarray.GpuArrayException: Out of memory" for a small application
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 (0.10) on cuda
Mapped name None to device cuda: TITAN X (Pascal) (:01:00.0)
But the same error message results:
Traceback (most recent call last):
File "scripts/run_rl_mj.py", line 116, in
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(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)](GpuFromHost.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))}}[].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
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(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)](GpuFromHost.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))}}[].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 =
[theano-users] Re: Getting "pygpu.gpuarray.GpuArrayException: Out of memory" for a small application
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 (0.95) on cuda
> Mapped name None to device cuda: TITAN X (Pascal) (: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
>
> 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(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)}}[](GpuFromHost.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))}}[].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
>
> 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
