Amir-Ramezani
commented
7 years ago I changed the python version to 2.7 but still receiving the following error:
DDPG-master$ python ddpg_main.py
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
[2017-04-06 21:45:52,907] Making new env: Pendulum-v0
[2017-04-06 21:45:52,911] DEPRECATION WARNING: env.spec.timestep_limit has been deprecated. Replace your call to env.spec.timestep_limit with env.spec.tags.get('wrapper_config.TimeLimit.max_episode_steps'). This change was made 12/28/2016 and is included in version 0.7.0
[2017-04-06 21:45:52,911] Creating monitor directory ./results/Pendulum-v0/20170406214552/gym_ddpg
Continuous
0%| | 0/100000 [00:00<?, ?it/s][2017-04-06 21:45:53,228] Starting new video recorder writing to /home/amir-ai/DDPG-Codes/slowbull/DDPG-master/results/Pendulum-v0/20170406214552/gym_ddpg/openaigym.video.0.22739.video000000.mp4
Traceback (most recent call last):
File "ddpg_main.py", line 133, in
slowbull
Hi,
I tried to set the render flag to True but I received the following error:
DDPG-master$ python3 ddpg_main.py I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate (GHz) 1.898 pciBusID 0000:01:00.0 Total memory: 7.92GiB Free memory: 332.19MiB I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0) [2017-04-06 14:05:08,630] Making new env: Pendulum-v0 [2017-04-06 14:05:08,636] Creating monitor directory ./results/Pendulum-v0/20170406140508/gym_ddpg Continuous 0%| | 0/100000 [00:00<?, ?it/s][2017-04-06 14:05:10,029] Starting new video recorder writing to /home/amir-ai/DDPG-Codes/slowbull/DDPG-master/results/Pendulum-v0/20170406140508/gym_ddpg/openaigym.video.0.3584.video000000.mp4
Traceback (most recent call last): File "ddpg_main.py", line 133, in
tf.app.run()
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/platform/app.py", line 44, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "ddpg_main.py", line 129, in main
agent.train()
File "/home/amir-ai/DDPG-Codes/slowbull/DDPG-master/src/agent/ddpg_agent.py", line 35, in train
self.evaluate(cur_episode)
File "/home/amir-ai/DDPG-Codes/slowbull/DDPG-master/src/agent/ddpg_agent.py", line 117, in evaluate
state = self.env.reset()
File "/home/amir-ai/gym/gym/core.py", line 123, in reset
observation = self._reset()
File "/home/amir-ai/gym/gym/wrappers/monitoring.py", line 41, in _reset
self._after_reset(observation)
File "/home/amir-ai/gym/gym/wrappers/monitoring.py", line 198, in _after_reset
self._reset_video_recorder()
File "/home/amir-ai/gym/gym/wrappers/monitoring.py", line 219, in _reset_video_recorder
self.video_recorder.capture_frame()
File "/home/amir-ai/gym/gym/monitoring/video_recorder.py", line 106, in capture_frame
frame = self.env.render(mode=render_mode)
File "/home/amir-ai/gym/gym/core.py", line 174, in render
return self._render(mode=mode, close=close)
File "/home/amir-ai/gym/gym/core.py", line 341, in _render
return self.env.render(mode, close)
File "/home/amir-ai/gym/gym/core.py", line 174, in render
return self._render(mode=mode, close=close)
File "/home/amir-ai/gym/gym/envs/classic_control/pendulum.py", line 66, in _render
from gym.envs.classic_control import rendering
File "/home/amir-ai/gym/gym/envs/classic_control/rendering.py", line 23, in
from pyglet.gl import
File "/usr/local/lib/python3.4/dist-packages/pyglet/gl/init.py", line 236, in
import pyglet.window
File "/usr/local/lib/python3.4/dist-packages/pyglet/window/init.py", line 1816, in
gl._create_shadow_window()
File "/usr/local/lib/python3.4/dist-packages/pyglet/gl/init.py", line 205, in _create_shadow_window
_shadow_window = Window(width=1, height=1, visible=False)
File "/usr/local/lib/python3.4/dist-packages/pyglet/window/xlib/init.py", line 163, in init
super(XlibWindow, self).init( args, **kwargs)
File "/usr/local/lib/python3.4/dist-packages/pyglet/window/init.py", line 504, in init
config = screen.get_best_config(template_config)
File "/usr/local/lib/python3.4/dist-packages/pyglet/canvas/base.py", line 161, in get_best_config
configs = self.get_matching_configs(template)
File "/usr/local/lib/python3.4/dist-packages/pyglet/canvas/xlib.py", line 179, in get_matching_configs
configs = template.match(canvas)
File "/usr/local/lib/python3.4/dist-packages/pyglet/gl/xlib.py", line 29, in match
have_13 = info.have_version(1, 3)
File "/usr/local/lib/python3.4/dist-packages/pyglet/gl/glx_info.py", line 86, in have_version
client_version = self.get_client_version().split()[0]
File "/usr/local/lib/python3.4/dist-packages/pyglet/gl/glx_info.py", line 118, in get_client_version
return asstr(glXGetClientString(self.display, GLX_VERSION))
File "/usr/local/lib/python3.4/dist-packages/pyglet/compat.py", line 88, in asstr
return s.decode("utf-8")
AttributeError: 'NoneType' object has no attribute 'decode'
[2017-04-06 14:05:10,204] Finished writing results. You can upload them to the scoreboard via gym.upload('/home/amir-ai/DDPG-Codes/slowbull/DDPG-master/results/Pendulum-v0/20170406140508/gym_ddpg')
Could you tell me what do you think is the problem?
The code runs without problem when the flag is set to False.
I am using python3.4 and I tested gym separately and it rendered the env with no issue.
Thanks,