TensorFlow: Difference between revisions

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==Install==

* Install CUDA and CuDNN

* Create a conda environment with python 3.5+

** <code>conda create -n my_env python=3.8</code>

* Install with pip

===Install TF2===

~~Easiest way is to install using conda to get a compatible~~ tensorflow~~, cuda, and cudnn installed together~~.

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

Install tensorflow and [https://www.tensorflow.org/addons/overview tensorflow-addons]

<pre>

~~conda install tensorflow-gpu~~

pip install tensorflow-addons

</pre>

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===Install TF1===

The last official version of TensorFlow v1 is 1.15. This version does not work on RTX 3000+ (Ampere) GPUs. Your code will run but output bad results.<br>

If you need TensorFlow v1, see [https://github.com/NVIDIA/tensorflow nvidia-tensorflow].

<pre>

~~conda~~ install tensorflow~~-gpu=1.15~~

pip install nvidia-pyindex

pip install nvidia-tensorflow

</pre>

~~;Notes~~

* Conda will automatically install a compatible cuda and cudnn into the cuda environment. Your host OS only needs to have a sufficiently new version of nvidia drivers installed.

* Sometimes, I get <code>CUDNN_STATUS_INTERNAL_ERROR</code>. This is fixed by setting the environment variable <code>TF_FORCE_GPU_ALLOW_GROWTH=true</code> in my conda env. See [https://stackoverflow.com/questions/46826497/conda-set-ld-library-path-for-env-only Add env variables to conda env]

==Usage (TF2)==

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This is using the Keras API in tensorflow.keras.

===Keras Pipeline===

[https://www.tensorflow.org/api_docs/python/tf/keras/Model tf.keras.Model]

The general pipeline using Keras is:

* Define a model, typically using [https://www.tensorflow.org/api_docs/python/tf/keras/Sequential tf.keras.Sequential]

@@ Line 2: / Line 2: @@
 ==Install==
-* Install CUDA and CuDNN
-* Create a conda environment with python 3.5+
-** <code>conda create -n my_env python=3.8</code>
-* Install with pip
 ===Install TF2===
-Easiest way is to install using conda to get a compatible tensorflow, cuda, and cudnn installed together.
+See https://www.tensorflow.org/install/pip
 Install tensorflow and [https://www.tensorflow.org/addons/overview tensorflow-addons]
 <pre>
-conda install tensorflow-gpu
 pip install tensorflow-addons
 </pre>
@@ Line 23: / Line 18: @@
 ===Install TF1===
+The last official version of TensorFlow v1 is 1.15. This version does not work on RTX 3000+ (Ampere) GPUs. Your code will run but output bad results.<br>
+If you need TensorFlow v1, see [https://github.com/NVIDIA/tensorflow nvidia-tensorflow].
 <pre>
-conda install tensorflow-gpu=1.15
+pip install nvidia-pyindex
+pip install nvidia-tensorflow
 </pre>
-;Notes
-* Conda will automatically install a compatible cuda and cudnn into the cuda environment. Your host OS only needs to have a sufficiently new version of nvidia drivers installed.
-* Sometimes, I get <code>CUDNN_STATUS_INTERNAL_ERROR</code>. This is fixed by setting the environment variable <code>TF_FORCE_GPU_ALLOW_GROWTH=true</code> in my conda env. See [https://stackoverflow.com/questions/46826497/conda-set-ld-library-path-for-env-only Add env variables to conda env]
 ==Usage (TF2)==
@@ Line 35: / Line 29: @@
 This is using the Keras API in tensorflow.keras.
 ===Keras Pipeline===
+[https://www.tensorflow.org/api_docs/python/tf/keras/Model tf.keras.Model]
 The general pipeline using Keras is:
 * Define a model, typically using [https://www.tensorflow.org/api_docs/python/tf/keras/Sequential tf.keras.Sequential]