In fact, every component in this workstation has been chosen around the concept of multi-GPU computing. If you’re looking to crunch some GPU workloads, this is where to start. Titan W599 Octane - Dual Intel Xeon Scalable CPUs - Quad GPU CUDA Render Workstation PC - Six Channel Memory & up to 56 Cores.
Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_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):
Linux 5.0.0-arch1-1-ARCH #1 SMP PREEMPT Mon Mar 4 14:11:43 UTC 2019 x86_64 GNU/Linux
- Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:None
- TensorFlow installed from (source or binary):
community python-tensorflow-opt-cuda
- TensorFlow version (use command below):
1.13.1
- Python version:
3.7.2
- Bazel version (if compiling from source):None
- GCC/Compiler version (if compiling from source):None
- CUDA/cuDNN version:
V10.0.130
/7.5.0
- GPU model and memory: 2 x
Geforce GTX 1080 Ti 11GB
; Driver Version:418.43
Describe the current behavior
The workstation completely crashes if a
The workstation completely crashes if a
tf.Session()
is created when multiple GPUs are present.I will roll back the last driver updates and post any updates.
Not sure if this is an error tensorflow can fix, maybe it is just a faulty driver.
Not sure if this is an error tensorflow can fix, maybe it is just a faulty driver.
Describe the expected behavior
Workstation should not crash.
Workstation should not crash.
Code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate the problem.
Provide a reproducible test case that is the bare minimum necessary to generate the problem.
or, in short
the following line crashes as well
python -c 'import tensorflow as tf; s = tf.Session()'
the following line crashes as well
CUDA_VISIBLE_DEVICES='0,1' python -c 'import tensorflow as tf; s = tf.Session()'
Other info / logs
The Problem exists only if multiple GPUs are present, so the following code works as expected:
The Problem exists only if multiple GPUs are present, so the following code works as expected:
CUDA_VISIBLE_DEVICES=' python -c 'import tensorflow as tf; s = tf.Session()'
CUDA_VISIBLE_DEVICES='0' python -c 'import tensorflow as tf; s = tf.Session()'
CUDA_VISIBLE_DEVICES='1' python -c 'import tensorflow as tf; s = tf.Session()'
I ran the code on a different machine with only one GPU and it worked just fine.