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Documentation updates #5635

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Documentation updates #5635

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d8f2082
documentation updates
michaelgsharp Mar 1, 2021
973a127
fixed spelling error
michaelgsharp Mar 1, 2021
f09fbb4
Update docs/building/unix-instructions.md
michaelgsharp Mar 1, 2021
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14 changes: 7 additions & 7 deletions docs/api-reference/tensorflow-usage.md
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@@ -1,6 +1,6 @@
## Using TensorFlow based APIs
In order to run any TensorFlow based ML.Net APIs you must first add a NuGet dependency
on the TensorFlow redist library. There are currently two versions you can use. One which is
In order to run any TensorFlow based ML.Net APIs you must first add a NuGet dependency
on the TensorFlow redist library. There are currently two versions you can use. One which is
compiled for GPU support, and one which has CPU support only.

### CPU only
Expand All @@ -22,18 +22,18 @@ As of now TensorFlow does not support running on GPUs for MacOS, so we cannot su
You must have at least one CUDA compatible GPU, for a list of compatible GPUs see
[Nvidia's Guide](https://developer.nvidia.com/cuda-gpus).

Install [CUDA v10.0](https://developer.nvidia.com/cuda-10.0-download-archive) and [CUDNN v7.6.4](https://developer.nvidia.com/rdp/cudnn-download).
Install [CUDA v10.1](https://developer.nvidia.com/cuda-10.1-download-archive-update2) and [CUDNN v7.6.4](https://developer.nvidia.com/rdp/cudnn-download).

Make sure you install CUDA v10.0, not any other newer version.
Make sure you install CUDA v10.1, not any other newer version.
After downloading CUDNN v7.6.4 .zip file and unpacking it, you need to do the following steps:

`copy <CUDNN_zip_files_path>\cuda\bin\cudnn64_7.dll to <YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin`
`copy <CUDNN_zip_files_path>\cuda\bin\cudnn64_7.dll to <YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin`

For C/C++ development:

`Copy <CUDNN_zip_files_path>\cuda\ include\cudnn.h to <YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\include`
`Copy <CUDNN_zip_files_path>\cuda\ include\cudnn.h to <YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\include`

`Copy <CUDNN_zip_files_path>\cuda\lib\x64\cudnn.lib to <YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\lib\x64`
`Copy <CUDNN_zip_files_path>\cuda\lib\x64\cudnn.lib to <YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\lib\x64`

For further details in cuDNN you can follow the [cuDNN Installation guide](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows).

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2 changes: 2 additions & 0 deletions docs/building/unix-instructions.md
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Expand Up @@ -54,3 +54,5 @@ One way of obtaining CMake and other required libraries is via [Homebrew](https:
```sh
$ brew update && brew install cmake https://raw.githubusercontent.com/dotnet/machinelearning/master/build/libomp.rb mono-libgdiplus gettext && brew link gettext --force && brew link libomp --force
```

Please note that newer versions of Homebrew [don't allow installing directly from a URL](https://github.com/Homebrew/brew/issues/8791). If you run into this issue, you may need to download libomp.rb first and install it with the local file instead.
8 changes: 4 additions & 4 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyONNXModelWithInMemoryImages.cs
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Expand Up @@ -13,7 +13,7 @@ public static class ApplyOnnxModelWithInMemoryImages
public static void Example()
{
// Download the squeeznet image model from ONNX model zoo, version 1.2
// https://github.com/onnx/models/tree/master/squeezenet or use
// https://github.com/onnx/models/tree/master/vision/classification/squeezenet or use
// Microsoft.ML.Onnx.TestModels nuget.
// It's a multiclass classifier. It consumes an input "data_0" and
// produces an output "softmaxout_1".
Expand Down Expand Up @@ -45,7 +45,7 @@ public static void Example()
// Map column "data_0" to column "softmaxout_1"
var pipeline = mlContext.Transforms.ExtractPixels("data_0", "Image")
.Append(mlContext.Transforms.ApplyOnnxModel("softmaxout_1",
"data_0", modelPath));
"data_0", modelPath));

var model = pipeline.Fit(dataView);
var onnx = model.Transform(dataView);
Expand All @@ -60,12 +60,12 @@ public static void Example()
ImageDataPoint>(onnx, false).ToList();

// The scores are probabilities of all possible classes, so they should
// all be positive.
// all be positive.
foreach (var dataPoint in transformedDataPoints)
{
var firstClassProb = dataPoint.Scores.First();
var lastClassProb = dataPoint.Scores.Last();
Console.WriteLine("The probability of being the first class is " +
Console.WriteLine("The probability of being the first class is " +
(firstClassProb * 100) + "%.");

Console.WriteLine($"The probability of being the last class is " +
Expand Down