add pip wheel TF 2.1.0 for Python 3.8 and CUDA 10.2

README.md

@@ -11,14 +11,17 @@ The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are
 
 ## Introduction
 
-The builds enable CPU optimizations such as `SSE4`, `AVX2`, and `FMA`. If you have a CPU released after ~2013 then you'll benefit from them. Note that you will benefit from these even if you do all your training on GPU due to i/o pipeline optimizations. I think I've gained about 10-15% performance boost even on most straightforward supervised learning tasks. And of course in CPU only setting they give significant improvement, sometimes matching GPU speeds on smaller neural networks (especially true for laptops where even in higher end models GPUs tend to lag behind).
+The builds enable various performance flags targeting modern CPUs, including SIMD support (AVX2, SSE4, FMA).
+If you have a CPU released after ~2013 then you'll likely benefit from these on e.g. data pre-processing.
 
-Additionally, build enables [XLA](https://www.tensorflow.org/xla/) - an Accelerated Linear Algebra domain-specific just-in-time compiler, and [MPI](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/mpi) - a faster way to run distributed TensorFlow than what is offered built-in.
+Build also enables [XLA](https://www.tensorflow.org/xla/) - an Accelerated Linear Algebra domain-specific just-in-time compiler.
+
+Finally, additional compute capabilities support (5.0, 6.1, 7.0) is enabled, which means these wheels should also work on older GPUs (7xx - 9xx families).
 
 ## Available Wheels
 |TensorFlow|Python|CUDA|CuDNN|TensorRT|NCCL|Compute Capability|OS|Link|
 |---:|---:|---:|---:|---:|---:|---:|:---:|:---:|
-|2.1.0rc2|3.8|10.2|7.6|N/A|2.5|5.0,6.1,7.0|Linux|[tensorflow-2.1.0rc2-cp38-cp38-linux_x86_64.whl](https://github.com/inoryy/tensorflow-optimized-wheels/releases/download/v2.1.0rc2/tensorflow-2.1.0rc2-cp38-cp38-linux_x86_64.whl)|
+|2.1.0|3.8|10.2|7.6|N/A|2.5|5.0,6.1,7.0|Linux|[tensorflow-2.1.0-cp38-cp38-linux_x86_64.whl](https://github.com/inoryy/tensorflow-optimized-wheels/releases/download/v2.1.0/tensorflow-2.1.0-cp38-cp38-linux_x86_64.whl)|
 |2.0.0|3.8|10.2|7.6|N/A|2.5|5.0,6.1,7.0|Linux|[tensorflow-2.0.0-cp38-cp38-linux_x86_64.whl](https://github.com/inoryy/tensorflow-optimized-wheels/releases/download/v2.0.0-py3.8/tensorflow-2.0.0-cp38-cp38-linux_x86_64.whl)|
 |2.0.0|3.7|10.1|7.5|N/A|2.4|5.0,6.1,7.0|Linux|[tensorflow-2.0.0-cp37-cp37m-linux_x86_64.whl](https://github.com/inoryy/tensorflow-optimized-wheels/releases/download/v2.0.0/tensorflow-2.0.0-cp37-cp37m-linux_x86_64.whl)|
 |1.13.1|3.7|10.0|7.5|5.0|2.4|5.0,6.1,7.0|Linux|[tensorflow-1.13.1-cp37-cp37m-linux_x86_64.whl](https://github.com/inoryy/tensorflow-optimized-wheels/releases/download/v1.13.1-py37/tensorflow-1.13.1-cp37-cp37m-linux_x86_64.whl)|
@@ -38,15 +41,21 @@ pip install https://github.com/inoryy/tensorflow-optimized-wheels/releases/downl
 And verify the installation (notice no warning messages):
 
 ```
+$ python
+Python 3.8.0 | packaged by conda-forge | (default, Nov 22 2019, 19:11:38)
+[GCC 7.3.0] :: Anaconda, Inc. on linux
 >>> import tensorflow as tf
+I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.2
 >>> tf.__version__
-'2.0.0'
+'2.1.0'
 >>> tf.executing_eagerly()
 True
 >>> tf.constant([123]) + tf.constant([321])
-2019-12-30 17:47:56.055840: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
+I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
 ...
-<tf.Tensor: id=2, shape=(1,), dtype=int32, numpy=array([444], dtype=int32)>
+I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
+I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (...) -> physical GPU (...)
+<tf.Tensor: shape=(1,), dtype=int32, numpy=array([444], dtype=int32)>
 ```
 
 ## Requests