Please make sure that this is an issue related to performance of TensorFlow.
As per our
GitHub Policy,
we only address code/doc bugs, performance issues, feature requests and
build/installation issues on GitHub. tag:performance_template
System information
- Have I written custom code (as opposed to using a stock example script provided in TensorFlow): no
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): MacOS 11.6
- Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: no
- TensorFlow installed from (source or binary): pip
- TensorFlow version (use command below): 2.0
- Python version: 3.9
- Bazel version (if compiling from source): no
- GCC/Compiler version (if compiling from source): NIL
- CUDA/cuDNN version: NIL
- GPU model and memory: kaggle notebook
You can collect some of this information using our environment capture
script
You can also obtain the TensorFlow version with:
- TF 1.0:
python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)" - TF 2.0:
python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"
Describe the current behavior
this is the error i have been displayed:
Resource exhausted: OOM when allocating tensor with
shape[800000,32,30,62] and type float on
/job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node conv2d_1/convolution}}]]
Describe the expected behavior:
the expected behavior was that it runs the code and outputs the desired result.
Standalone code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to Colab/Jupyter/any notebook.
none
Other info / logs Include any logs or source code that would be helpful to
diagnose the problem. If including tracebacks, please include the full
traceback. Large logs and files should be attached.
none
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