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- #INSTALL CUDA TOOLKIT UBUNTU 14.04 HOW TO#
- #INSTALL CUDA TOOLKIT UBUNTU 14.04 INSTALL#
- #INSTALL CUDA TOOLKIT UBUNTU 14.04 UPDATE#
- #INSTALL CUDA TOOLKIT UBUNTU 14.04 ARCHIVE#
#INSTALL CUDA TOOLKIT UBUNTU 14.04 UPDATE#
Update CombinedNMS converter ( tensorflow#23) Don't try to verify dynamic shapes in CheckValidSize ( tensorflow#24) Don't limit to 1 input per engineĪllow expand dims to work on dynamic inputs by slicing shape. * Fix bugs with ConvertAxis, StridedSlice shrink_axis, Gather * Update StridedSlice shrink_axis for TRT6 * Update BatchMatMul for TRT6 - may need more changes * Use a pointer for final_shape arg in ConvertStridedSliceHelper. * Support shrink_axis_mask for StridedSlice Support const scalars and pack with constants in TRT6įixes/improvements for BERT ( tensorflow#22) Support const scalars + Pack on constants ( tensorflow#21) Support dynamic shapes for strided slice ( tensorflow#20) Update converter for reshape ( tensorflow#17)Īllow INT32 for elementwise ( tensorflow#18) * Update GetTrtBroadcastShapes for use_implicit_batch Update GetTrtBroadcastShapes for use_implicit_batch ( tensorflow#14) * use HasStaticShape API, add new line at EOFĪllow opt profiles to be set via env variables temporarily.įix segfault by properly returning the status from OverwriteStaticDims function * Changes to make sure activation test passes with TRT trunk
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Use TRT6 API's for dynamic shape ( tensorflow#11) * Update RN50 converters for use_implicit_batch: Conv2D, BiasAdd, Transpose, MaxPool, Squeeze, MatMul, Pad Update converters for ResNet50 (except Binary ops) ( tensorflow#9) * change order of parameters in ConvertAxis function
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Update ConvertAxis to account for use_implicit batchįix use of use_implicit_batch ( tensorflow#7) Rename use_explicit_batch/use_implicit_batchĪdd simple activtion test for testing dynamic input shapes. Propagate flag to ConversionParams and TRTEngineOp Revise validation checks to account for use_explicit_batch.
#INSTALL CUDA TOOLKIT UBUNTU 14.04 INSTALL#
(this is a known issue: docker driver version and system driver version must exactly match)Īdd use_explicit_batch parameter available in OpConverterParams and other placesįormatting and make const bool everywhere Ubuntu 15.04 install apt-get install nvidia-304 apt-get install nvidia-cuda-toolkit (should install cuda 6.5) Nvidia GTX 960 To enable CUDA computing without login-loop: Ubuntu 14.04 install apt-get install nvidia-cuda-toolkit (cuda 7.5) It may also be necessary to remove other (much) older nvidia gpu cards from PCI slots to prevent the login. Tried the docker install, but the docker image is configured for a particular NVIDIA driver version, and doesn't work with others.
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ImportError: libcudart.so.7.0: cannot open shared object file: No such file or directory _mod = imp.load_module( '_pywrap_tensorflow ', fp, pathname, description) _pywrap_tensorflow = swig_import_helper()įile "/home/awesomebox/anaconda/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py ", line 24, in swig_import_helper
#INSTALL CUDA TOOLKIT UBUNTU 14.04 HOW TO#
#INSTALL CUDA TOOLKIT UBUNTU 14.04 ARCHIVE#
Sudo dpkg -i cuda-repo-ubuntu-local-ga2_8.0.61-1_bĭpkg: error processing archive cuda-repo-ubuntu-local-ga2_8.0.61-1_b (–install):Ĭannot access archive: No such file or directoryĮrrors were encountered while processing:Ĭuda-repo-ubuntu-local-ga2_8.0.61-1_b Linux-headers-4.10.0-38-generic set to manually installed.Ġ upgraded, 0 newly installed, 0 to remove and 38 not upgraded. Sudo apt-get install linux-headers-$(uname -r) I’ve downloaded the kernel packages via this line: I’m following your steps but I’m running into an error when installing via debian package I’m new to Ubuntu so might be making some obvious mistakes.