flag results from the CPAN

flag

AI-TensorFlow-Libtensorflow

view release on metacpan or search on metacpan

lib/AI/TensorFlow/Libtensorflow/Manual/CAPI.pod view on Meta::CPAN

  TF_CAPI_EXPORT extern const TF_SignatureDefParamList*
  TF_SignatureDefFunctionMetadataReturns(
      const TF_SignatureDefFunctionMetadata* list);

=head2 TF_EnableXLACompilation

=over 2

  When `enable` is true, set
  tensorflow.ConfigProto.OptimizerOptions.global_jit_level to ON_1, and also
  set XLA flag values to prepare for XLA compilation. Otherwise set
  global_jit_level to OFF.
  
  This and the next API are syntax sugar over TF_SetConfig(), and is used by
  clients that cannot read/write the tensorflow.ConfigProto proto.
  TODO: Migrate to TF_CreateConfig() below.

=back

  /* From <tensorflow/c/c_api_experimental.h> */
  TF_CAPI_EXPORT extern void TF_EnableXLACompilation(TF_SessionOptions* options,
                                                     unsigned char enable);

=head2 TF_SetXlaEnableLazyCompilation

=over 2

  Set XLA's internal BuildXlaOpsPassFlags.tf_xla_enable_lazy_compilation to the
  value of 'enabled'. Also returns the original value of that flag.
  
  Use in tests to allow XLA to fallback to TF classic. This has global effect.

=back

  /* From <tensorflow/c/c_api_experimental.h> */
  TF_CAPI_EXPORT unsigned char TF_SetXlaEnableLazyCompilation(
      unsigned char enable);

=head2 TF_SetTfXlaCpuGlobalJit

lib/AI/TensorFlow/Libtensorflow/Manual/CAPI.pod view on Meta::CPAN


=back

  /* From <tensorflow/c/c_api_experimental.h> */
  TF_CAPI_EXPORT void TF_SetXlaMinClusterSize(int size);

=head2 TF_GetXlaConstantFoldingDisabled

=over 2

  Gets/Sets TF/XLA flag for whether(true) or not(false) to disable constant
  folding. This is for testing to ensure that XLA is being tested rather than
  Tensorflow's CPU implementation through constant folding.

=back

  /* From <tensorflow/c/c_api_experimental.h> */
  TF_CAPI_EXPORT unsigned char TF_GetXlaConstantFoldingDisabled();

=head2 TF_SetXlaConstantFoldingDisabled

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN

    #by Martin49 from London, England, CC BY 2.0 <https://creativecommons.org/licenses/by/2.0>, via Wikimedia Commons
    "car" => "https://upload.wikimedia.org/wikipedia/commons/4/49/2013-2016_Toyota_Corolla_%28ZRE172R%29_SX_sedan_%282018-09-17%29_01.jpg",
    #by EurovisionNim, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons
    "cat" => "https://upload.wikimedia.org/wikipedia/commons/4/4d/Cat_November_2010-1a.jpg",
    #by Alvesgaspar, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
    "dog" => "https://upload.wikimedia.org/wikipedia/commons/archive/a/a9/20090914031557%21Saluki_dog_breed.jpg",
    #by Craig Pemberton, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
    "apple" => "https://upload.wikimedia.org/wikipedia/commons/1/15/Red_Apple.jpg",
    #by Abhijit Tembhekar from Mumbai, India, CC BY 2.0 <https://creativecommons.org/licenses/by/2.0>, via Wikimedia Commons
    "banana" => "https://upload.wikimedia.org/wikipedia/commons/1/1c/Bananas_white_background.jpg",
    #by fir0002  flagstaffotos [at] gmail.com		Canon 20D + Tamron 28-75mm f/2.8, GFDL 1.2 <http://www.gnu.org/licenses/old-licenses/fdl-1.2.html>, via Wikimedia Commons
    "turtle" => "https://upload.wikimedia.org/wikipedia/commons/8/80/Turtle_golfina_escobilla_oaxaca_mexico_claudio_giovenzana_2010.jpg",
    #by Claudio Giovenzana, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
    "flamingo" => "https://upload.wikimedia.org/wikipedia/commons/b/b8/James_Flamingos_MC.jpg",
    #by Christian MehlfÃ¼hrer, User:Chmehl, CC BY 3.0 <https://creativecommons.org/licenses/by/3.0>, via Wikimedia Commons
    "piano" => "https://upload.wikimedia.org/wikipedia/commons/d/da/Steinway_%26_Sons_upright_piano%2C_model_K-132%2C_manufactured_at_Steinway%27s_factory_in_Hamburg%2C_Germany.png",
    #by "Photo: Â© Copyright Steinway & Sons", CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
    "honeycomb" => "https://upload.wikimedia.org/wikipedia/commons/f/f7/Honey_comb.jpg",
    #by Merdal, CC BY-SA 3.0 <http://creativecommons.org/licenses/by-sa/3.0/>, via Wikimedia Commons
    "teapot" => "https://upload.wikimedia.org/wikipedia/commons/4/44/Black_tea_pot_cropped.jpg",
    #by Mendhak, CC BY-SA 2.0 <https://creativecommons.org/licenses/by-sa/2.0>, via Wikimedia Commons

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN

      #by Martin49 from London, England, CC BY 2.0 <https://creativecommons.org/licenses/by/2.0>, via Wikimedia Commons
      "car" => "https://upload.wikimedia.org/wikipedia/commons/4/49/2013-2016_Toyota_Corolla_%28ZRE172R%29_SX_sedan_%282018-09-17%29_01.jpg",
      #by EurovisionNim, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons
      "cat" => "https://upload.wikimedia.org/wikipedia/commons/4/4d/Cat_November_2010-1a.jpg",
      #by Alvesgaspar, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
      "dog" => "https://upload.wikimedia.org/wikipedia/commons/archive/a/a9/20090914031557%21Saluki_dog_breed.jpg",
      #by Craig Pemberton, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
      "apple" => "https://upload.wikimedia.org/wikipedia/commons/1/15/Red_Apple.jpg",
      #by Abhijit Tembhekar from Mumbai, India, CC BY 2.0 <https://creativecommons.org/licenses/by/2.0>, via Wikimedia Commons
      "banana" => "https://upload.wikimedia.org/wikipedia/commons/1/1c/Bananas_white_background.jpg",
      #by fir0002  flagstaffotos [at] gmail.com		Canon 20D + Tamron 28-75mm f/2.8, GFDL 1.2 <http://www.gnu.org/licenses/old-licenses/fdl-1.2.html>, via Wikimedia Commons
      "turtle" => "https://upload.wikimedia.org/wikipedia/commons/8/80/Turtle_golfina_escobilla_oaxaca_mexico_claudio_giovenzana_2010.jpg",
      #by Claudio Giovenzana, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
      "flamingo" => "https://upload.wikimedia.org/wikipedia/commons/b/b8/James_Flamingos_MC.jpg",
      #by Christian MehlfÃ¼hrer, User:Chmehl, CC BY 3.0 <https://creativecommons.org/licenses/by/3.0>, via Wikimedia Commons
      "piano" => "https://upload.wikimedia.org/wikipedia/commons/d/da/Steinway_%26_Sons_upright_piano%2C_model_K-132%2C_manufactured_at_Steinway%27s_factory_in_Hamburg%2C_Germany.png",
      #by "Photo: Â© Copyright Steinway & Sons", CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons
      "honeycomb" => "https://upload.wikimedia.org/wikipedia/commons/f/f7/Honey_comb.jpg",
      #by Merdal, CC BY-SA 3.0 <http://creativecommons.org/licenses/by-sa/3.0/>, via Wikimedia Commons
      "teapot" => "https://upload.wikimedia.org/wikipedia/commons/4/44/Black_tea_pot_cropped.jpg",
      #by Mendhak, CC BY-SA 2.0 <https://creativecommons.org/licenses/by-sa/2.0>, via Wikimedia Commons

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN


B<STREAM (STDERR)>:

=for html <span style="display:inline-block;margin-left:1em;"><pre style="display: block"><code><span style="color: #cc66cc;">PDL</span><span style="color: #33ccff;"> {</span><span style="">
    </span><span style="color: #6666cc;">Data    </span><span style=""> : </span><span style="color: #669933;">too long to print</span><span style="">
    </span><span style="color: #6666cc;">Type    </span><span style=""> : </span><span style="color: #cc66cc;">float</span><span style="">
    </span><span style="color: #6666cc;">Shape   </span><span style=""> : </span><span style="color: #33ccff;">[</span><span style="color: #9999cc;">3 224 224 12</span><span style="color: #33ccff;">]</span><span style="">
    </span><span style="color: #6666cc;">Nelem   </span><span style=""> : </span><span style="color: #dd6;">1806336</span><span style="">
    </span><span style="color: #6666cc;">Min     </span><span style=""> : </span><span style="color: #f66;">0</span><span style="">
    </span><span style="color: #6666cc;">Max     </span><span style=""> : </span><span style="color: #99f;">1</span><span style="">
    </span><span style="color: #6666cc;">Badflag </span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
    </span><span style="color: #6666cc;">Has Bads</span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Tensor</span><span style=""> </span><span style="color: #33ccff;">{</span><span style="">
    </span><span style="color: #6666cc;">Type           </span><span style=""> </span><span style="color: #cc66cc;">FLOAT</span><span style="">
    </span><span style="color: #6666cc;">Dims           </span><span style=""> </span><span style="color: #33ccff;">[</span><span style=""> </span><span style="color: #ff6633;">12</span><span style=""> </span><span style="color: #ff6633;">224</span><...
    </span><span style="color: #6666cc;">NumDims        </span><span style=""> </span><span style="color: #ff6633;">4</span><span style="">
    </span><span style="color: #6666cc;">ElementCount   </span><span style=""> </span><span style="color: #ff6633;">1806336</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span></code></pre></span>

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN


B<STREAM (STDERR)>:

=for html <span style="display:inline-block;margin-left:1em;"><pre style="display: block"><code><span style="color: #cc66cc;">PDL</span><span style="color: #33ccff;"> {</span><span style="">
    </span><span style="color: #6666cc;">Data    </span><span style=""> : </span><span style="color: #669933;">too long to print</span><span style="">
    </span><span style="color: #6666cc;">Type    </span><span style=""> : </span><span style="color: #cc66cc;">float</span><span style="">
    </span><span style="color: #6666cc;">Shape   </span><span style=""> : </span><span style="color: #33ccff;">[</span><span style="color: #9999cc;">1001 12</span><span style="color: #33ccff;">]</span><span style="">
    </span><span style="color: #6666cc;">Nelem   </span><span style=""> : </span><span style="color: #dd6;">12012</span><span style="">
    </span><span style="color: #6666cc;">Min     </span><span style=""> : </span><span style="color: #f66;">2.73727380317723e-07</span><span style="">
    </span><span style="color: #6666cc;">Max     </span><span style=""> : </span><span style="color: #99f;">0.980696022510529</span><span style="">
    </span><span style="color: #6666cc;">Badflag </span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
    </span><span style="color: #6666cc;">Has Bads</span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span></code></pre></span>

=head2 Results summary

Then select the top 5 of those and find their class labels.

  my $N = 5; # number to select

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN


B<STREAM (STDERR)>:

=for html <span style="display:inline-block;margin-left:1em;"><pre style="display: block"><code><span style="color: #cc66cc;">PDL</span><span style="color: #33ccff;"> {</span><span style="">
    </span><span style="color: #6666cc;">Data    </span><span style=""> : </span><span style="color: #33ccff;">[</span><span style="color: #ff6633;">1 1 1 1 1 1 1 1 1 1 1 1</span><span style="color: #33ccff;">]</span><span style="">
    </span><span style="color: #6666cc;">Type    </span><span style=""> : </span><span style="color: #cc66cc;">double</span><span style="">
    </span><span style="color: #6666cc;">Shape   </span><span style=""> : </span><span style="color: #33ccff;">[</span><span style="color: #9999cc;">12</span><span style="color: #33ccff;">]</span><span style="">
    </span><span style="color: #6666cc;">Nelem   </span><span style=""> : </span><span style="color: #dd6;">12</span><span style="">
    </span><span style="color: #6666cc;">Min     </span><span style=""> : </span><span style="color: #f66;">1</span><span style="">
    </span><span style="color: #6666cc;">Max     </span><span style=""> : </span><span style="color: #99f;">1</span><span style="">
    </span><span style="color: #6666cc;">Badflag </span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
    </span><span style="color: #6666cc;">Has Bads</span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span></code></pre></span>

B<RESULT>:

  1

=head1 RESOURCE USAGE

lib/AI/TensorFlow/Libtensorflow/Manual/Quickstart.pod view on Meta::CPAN


and when the links come up on the terminal, click the link to
C<http://127.0.0.1:8888/> in order to connect to the Jupyter Notebook interface
via the web browser. In the browser, click on the C<notebook> folder to access
the notebooks.

=head2 GPU Docker support

If using the GPU Docker image for NVIDIA support, make sure that the
L<TensorFlow Docker requirements|https://www.tensorflow.org/install/docker#tensorflow_docker_requirements>
are met and that the correct flags are passed to C<docker run>, for example

C<<
  docker run --rm --gpus all [...]
>>

More information about NVIDIA Docker containers can be found in the
NVIDIA Container Toolkit
L<Installation Guide|https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html>
(specifically L<Setting up NVIDIA Container Toolkit|https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#setting-up-nvidia-container-toolkit>)
and

( run in 1.931 second using v1.01-cache-2.11-cpan-94b05bcf43c )