sdfdfsd

[rajesht2418]

I am using

Python 3.76 TensorFlow 2.1 Installed using: pip install tensorflow Processor: Intel(R) Core(TM) i7-6500U CPU @ 2.50GHz, 2601 Mhz, 2 Core(s), 4 Logical Processor(s) Laptop System Model: HP Spectre x360 Convertible Reproduce: All I have to type is "import tensorflow as tf" and it fails.

Note: I also tried using tensorflow-cpu and still got the same issue.

Stack Trace:

        import tensorflow as tf
        Traceback (most recent call last):
        File "C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow.py", line 58, in
        from tensorflow.python.pywrap_tensorflow_internal import *
        File "C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow_internal.py", line 28, in
        _pywrap_tensorflow_internal = swig_import_helper()
        File "C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper
        _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
        File "C:\Development\Python\Python37\lib\imp.py", line 242, in load_module
        return load_dynamic(name, filename, file)
        File "C:\Development\Python\Python37\lib\imp.py", line 342, in load_dynamic
        return _load(spec)
        ImportError: DLL load failed: The specified module could not be found.

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "", line 1, in File "C:\Development\Python\Python37\lib\site-packages\tensorflowinit.py", line 101, in from tensorflow_core import File "C:\Development\Python\Python37\lib\site-packages\tensorflow_coreinit.py", line 40, in from tensorflow.python.tools import module_util as module_util File "C:\Development\Python\Python37\lib\site-packages\tensorflow_init.py", line 50, in getattr module = self.load() File "C:\Development\Python\Python37\lib\site-packages\tensorflow_init.py", line 44, in _load module = importlib.import_module(self.name) File "C:\Development\Python\Python37\lib\importlib_init.py", line 127, in import_module return _bootstrap.gcd_import(name[level:], package, level) File "C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python_init.py", line 49, in from tensorflow.python import pywrap_tensorflow File "C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow.py", line 74, in raise ImportError(msg) ImportError: Traceback (most recent call last): File "C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow.py", line 58, in from tensorflow.python.pywrap_tensorflow_internal import File "C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow_internal.py", line 28, in _pywrap_tensorflow_internal = swig_import_helper() File "C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description) File "C:\Development\Python\Python37\lib\imp.py", line 242, in load_module return load_dynamic(name, filename, file) File "C:\Development\Python\Python37\lib\imp.py", line 342, in load_dynamic return _load(spec) ImportError: DLL load failed: The specified module could not be found.

Failed to load the native TensorFlow runtime.

See https://www.tensorflow.org/install/errors

for some common reasons and solutions. Include the entire stack trace above this error message when asking for help.

<class 'ImportError'>, ImportError('Traceback (most recent call last):\n File "C:\Development\Python\Python37\lib\site-packages\tensorflow_core\python\pywrap_tensorflow.py", line 58, in \n from tensorflow.python.pywrap_tensorflow_internal import *\n File "C:\Development\Python\Python37\lib\site- packages\tensorflow_core\python\pywrap_tensorflow_internal.py", line 28, in \n _pywrap_tensorflow_internal = swig_import_helper()\n File "C:\Development\Python\Python37\lib\site- packages\tensorflow_core\python\pywrap_tensorflow_internal.py", line 24, in swig_import_helper\n _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)\n File "C:\Development\Python\Python37\lib\imp.py", line 242, in load_module\n return load_dynamic(name, filename, file)\n File "C:\Development\Python\Python37\lib\imp.py", line 342, in load_dynamic\n return _load(spec)\nImportError: DLL load failed: The specified module could not be found.\n\n\nFailed to load the native TensorFlow runtime.\n\nSee https://www.tensorflow.org/install/errors\n\nfor some common reasons and solutions. Include the entire stack trace\nabove this error message when asking for help.'), <traceback object at 0x000001E0E43DCA48>

Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: TensorFlow installed from (source or binary): TensorFlow version (use command below): Python version: Bazel version (if compiling from source): GCC/Compiler version (if compiling from source): CUDA/cuDNN version: GPU model and memory: You can collect some of this information using our environment capture script You can also obtain the TensorFlow version with: 1. TF 1.0: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)" 2. TF 2.0: python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"

Describe the current behavior

Describe the expected behavior

Code to reproduce the issue Provide a reproducible test case that is the bare minimum necessary to generate the problem.

Other info / logs Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.

[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

TF Version >= 1.13 requires CUDA 10.0 and TF Version < 1.13 (till TF 1.5) requires CUDA 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 usepip 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]]

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

TF Version >= 1.13 requires CUDA 10.0 and TF Version < 1.13 (till TF 1.5) requires CUDA 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 usepip 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]]

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

TF Version >= 1.13 requires CUDA 10.0 and TF Version < 1.13 (till TF 1.5) requires CUDA 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 usepip 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]]

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

TF Version >= 1.13 requires CUDA 10.0 and TF Version < 1.13 (till TF 1.5) requires CUDA 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 usepip 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]]

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

TF Version >= 1.13 requires CUDA 10.0 and TF Version < 1.13 (till TF 1.5) requires CUDA 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 usepip 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]]

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

TF Version >= 1.13 requires CUDA 10.0 and TF Version < 1.13 (till TF 1.5) requires CUDA 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 usepip 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.

[rajesht2418]

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