f7a4663c34
Comment says the file is generated automatically, from Dockerfile components, but this is not the case. Probably a copy-paste artifact. PiperOrigin-RevId: 335897369 Change-Id: Id36ba3c9e3c42830ef0ec229ad263c323d263f13
111 lines
4.7 KiB
YAML
111 lines
4.7 KiB
YAML
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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# A list of assignees
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assignees:
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- amahendrakar
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- ravikyram
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- Saduf2019
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# A list of assignees for compiler folder
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compiler_assignees:
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- joker-eph
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# filesystem path
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filesystem_path:
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- tensorflow/c/experimental/filesystem
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# security path
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security_path:
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- tensorflow/security
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# words checklist
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segfault_memory:
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- segfault
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- memory leaks
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# assignees
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filesystem_security_assignee:
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- mihaimaruseac
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tflite_micro_path:
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- tensorflow/lite/micro
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tflite_micro_comment: >
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Thanks for contributing to TensorFlow Lite Micro.
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To keep this process moving along, we'd like to make sure that you have completed the items on this list:
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* Read the [contributing guidelines for TensorFlow Lite Micro](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/CONTRIBUTING.md)
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* Created a [TF Lite Micro Github issue](https://github.com/tensorflow/tensorflow/issues/new?labels=comp%3Amicro&template=70-tflite-micro-issue.md)
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* Linked to the issue from the PR description
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We would like to have a discussion on the Github issue first to determine the best path forward, and then proceed to the PR review.
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# Cuda Comment
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cuda_comment: >
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From the template it looks like you are installing **TensorFlow** (TF) prebuilt binaries:
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* For TF-GPU - See point 1
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* For TF-CPU - See point 2
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-----------------------------------------------------------------------------------------------
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**1. Installing **TensorFlow-GPU** (TF) prebuilt binaries**
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Make sure you are using compatible TF and CUDA versions.
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Please refer following TF version and CUDA version compatibility table.
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| TF | CUDA |
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| :-------------: | :-------------: |
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| 2.1.0 - 2.2.0 | 10.1 |
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| 1.13.1 - 2.0 | 10.0 |
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| 1.5.0 - 1.12.0 | 9.0 |
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* If you have above configuration and using _**Windows**_ platform -
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* Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable.
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* Refer [windows setup guide](https://www.tensorflow.org/install/gpu#windows_setup).
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* If you have above configuration and using _**Ubuntu/Linux**_ platform -
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* Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.
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* Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup).
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* If error still persists then, apparently your CPU model does not support AVX instruction sets.
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* Refer [hardware requirements](https://www.tensorflow.org/install/pip#hardware-requirements).
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-----------------------------------------------------------------------------------------------
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**2. Installing **TensorFlow** (TF) CPU prebuilt binaries**
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*TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
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Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
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Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
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* Try Google Colab to use TensorFlow.
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* The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install``` to install any other preferred TF version.
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* It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.
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* All you need is a good internet connection and you are all set.
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* Try to build TF from sources by changing CPU optimization flags.
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*Please let us know if this helps.*
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windows_comment: >
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From the stack trace it looks like you are hitting windows path length limit.
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* Try to disable path length limit on Windows 10.
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* Refer [disable path length limit instructions guide.](https://mspoweruser.com/ntfs-260-character-windows-10/)
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Please let us know if this helps.
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