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[rajesht2418]

load_dynamicreturn _load(spec)

[csat-bot[bot]]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

[csat-bot[bot]]

From the stack trace it looks like you are hitting windows path length limit.

• Try to disable path length limit on Windows 10.
  • Refer disable path length limit instructions guide.

Please let us know if this helps.

[csat-bot[bot]]

From the stack trace it looks like you are hitting windows path length limit.

• Try to disable path length limit on Windows 10.
  • Refer disable path length limit instructions guide.

Please let us know if this helps.

[csat-bot[bot]]

From the stack trace it looks like you are hitting windows path length limit.

• Try to disable path length limit on Windows 10.
  • Refer disable path length limit instructions guide.

Please let us know if this helps.

[csat-bot[bot]]

From the template it looks like you are installing TensorFlow (TF) prebuilt binaries:

• For TF-GPU - See point 1

• For TF-CPU - See point 2

  1. Installing TensorFlow-GPU (TF) prebuilt binaries

Make sure you are using compatible TF and CUDA versions. Please refer following TF version and CUDA version compatibility table.	TF	CUDA
2.1.0 - 2.2.0	10.1
1.13.1 - 2.0	10.0
1.5.0 - 1.12.0	9.0

• If you have above configuration and using Windows platform -
  • Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable.
  • Refer windows setup guide.
• If you have above configuration and using Ubuntu/Linux platform -
  • Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.
  • Refer linux setup guide.
• If error still persists then, apparently your CPU model does not support AVX instruction sets.
  • Refer hardware requirements.

---

2. Installing TensorFlow (TF) CPU prebuilt binaries

TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.

Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load. Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:

• Try Google Colab to use TensorFlow.
  • The easiest way to use TF will be to switch to google colab. You get pre-installed latest stable TF version. Also you can use pip install to install any other preferred TF version.
  • It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.
  • All you need is a good internet connection and you are all set.
• Try to build TF from sources by changing CPU optimization flags.

Please let us know if this helps.

[csat-bot[bot]]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.