This package provides debug information for package tensorflow-avx2_1_13_2-gnu-openmpi2-hpc. Debug information is useful when developing applications that use this package or when debugging this package.
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This package is not currently present in any repository
Latest updates
OpenSUSE Leap 15.2 debug/oss: Version 1.13.2-lp152.2.1 removed
2020-03-24
OpenSUSE Tumbleweed debug/oss: Version 1.13.2-8.1 removed
2020-03-20
OpenSUSE Leap 15.2 debug/oss: Updated from 1.13.2-lp152.1.1 to 1.13.2-lp152.2.1
2020-03-15
- Add 'Provides' only for hpc flavors, otherwise it matches the package name
OpenSUSE Tumbleweed debug/oss: Updated from 1.13.2-7.1 to 1.13.2-8.1
2020-03-03
- Add 'Provides' only for hpc flavors, otherwise it matches the package name
OpenSUSE Leap 15.2 debug/oss: Version 1.13.2-lp152.1.1 introduced
2020-02-24
- Increase RAM requirements and limit_build value to avoid OOM
OpenSUSE Tumbleweed debug/oss: Updated from 1.13.2-6.1 to 1.13.2-7.1
2020-02-07
- Increase RAM requirements and limit_build value to avoid OOM
OpenSUSE Tumbleweed debug/oss: Updated from 1.13.2-5.1 to 1.13.2-6.1
2020-01-18
- Build TensorFlow Lite (only) for %arm
OpenSUSE Tumbleweed debug/oss: Updated from 1.13.2-4.1 to 1.13.2-5.1
2020-01-16
- ExcludeArch %arm since build fails due to no support for aws
OpenSUSE Tumbleweed debug/oss: Updated from 1.13.2-3.2 to 1.13.2-4.1
2020-01-15
- Use pip install --no-compile (boo#1094323)
OpenSUSE Tumbleweed debug/oss: Updated from 1.13.2-3.1 to 1.13.2-3.2
2019-12-27
- Increase a bit %limit_build to avoid OOM errors seen on x86_64
OpenSUSE Tumbleweed debug/oss: Updated from 1.13.2-2.1 to 1.13.2-3.1
2019-12-16
- Increase a bit %limit_build to avoid OOM errors seen on x86_64
OpenSUSE Tumbleweed debug/oss: Updated from 1.13.2-1.1 to 1.13.2-2.1
2019-11-29
- Generate and package protobuf files (for armNN)
OpenSUSE Tumbleweed debug/oss: Version 1.13.2-1.1 introduced
2019-11-27
- Apply grpc-namespace-corrections.patch for Tumbleweed only
Related packages
tensorflow - Library for computation using data flow graphs for scalable machine learning
