Tensorflow GPU causing fatal error
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Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 25/100
- Issue type
- Bug
- Clarity
- Needs clarification
- Activity status
- Stale
- Domain
- machine-learning, operating-systems
Research direction
Start by reproducing tf <- import('tensorflow') in the reported Python 3.7 conda environment on Windows 10, then inspect the TensorFlow stream executor log mentioning tensorflow/stream_executor/platform/default/dso_loader.cc. Done means identifying the dependency or environment condition that causes the R session to abort, with enough diagnostic output to confirm the cause.
Written by the indexing model from the issue text.
Description
Hello I am new to posting here so I will do my best to explain my situation if I need to add any details please let me know what the standard procedure is!
I have created a local conda environment with python 3.7 and have successfully installed tensorflow-gpu to it. Yet when I run: tf <- import('tensorflow') in R I always get the following output:
2020-02-29 08:24:54.101489: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
Then I immediately get an error saying "R session aborted R encountered a fatal error..." I'm certain we are all familiar with the screen. I was wondering if anyone might have some tips for diagnosing the problem.
I have a nvidia 2080ti, windows 10, and all of the up to date software supporting tensorflow (I.E. Py 3.7, R 3.6.2)
Again let me know if there is any additional information I can provide.
- Dominant language
- R
- Stars
- 1.3k
- Forks
- 316
- PR merge metrics
- No merged PRs in 30d
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