Comments (5)
PR #18 recently changed training on GPU. It is possible that it puts too many ops on the GPU. What tensors fails? What GPU do you use? Can you please set use_gpu = False
in the config and verify that CPU-only training works?
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@wwxFromTju Could you provide an update, please?
from batch-ppo.
@danijar what update?
from batch-ppo.
I asked a few questions in my first comment that could help debug your problem. Did you have a chance to try those?
from batch-ppo.
I disabled GPU usage by default and implemented splitting episodes (using the chunk_length
and batch_size
config options) to be able to train on episodes with many time steps or large observations.
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Related Issues (20)
- Enabling GPU compromises learning performance HOT 9
- Distributed training with Kubernetes HOT 28
- No initial states in update_steps HOT 1
- Potential integration of MonitoredTrainingSession and revision of tools/loop.py
- Support for discrete action spaces HOT 3
- Typo in Paper HOT 1
- GPU doesn't seem to work HOT 9
- Generalized advantage estimation HOT 4
- Errors about the regularizer term HOT 2
- OpenAI Retro environment support/example HOT 3
- Minimal Example For TPU? HOT 3
- Maximum number of clients reachedSegmentation fault (core dumped) HOT 4
- setup.py calls this module agents and refers to tensorflow/agents HOT 1
- How to draw the score curve of PPO? HOT 6
- With GPU enabled, tensorflow freezes unless I force "Discounted Monte-Carlo returns." to CPU HOT 3
- Performance issues in the program HOT 1
- Performance issue in agents/parts/iterate_sequences.py HOT 2
- Different frames error when adding another RNNCell HOT 2
- Computation time needed for HalfCheetah HOT 2
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