Method not found: 'Void Module.train()'.

[drGarbinsky]

I'm currently trying to get the same running from this page: https://devblogs.microsoft.com/dotnet/introducing-the-ml-dotnet-text-classification-api-preview/
working with:
dotnet: 7.0.100-preview.6.22352.1
Microsoft.ML Version="2.0.0-preview.22313.1"
Microsoft.ML.TorchSharp Version="0.20.0-preview.22313.1"
TorchSharp-cpu Version="0.97.0"

WSL ubuntu: 20.04.3 LTS

code:

using Microsoft.ML;
using Microsoft.ML.TorchSharp;

// Initialize MLContext
var mlContext = new MLContext();

// Load your data
var reviews = new[]
{
new {Text = "This is a bad steak", Sentiment = "Negative"},
new {Text = "I really like this restaurant", Sentiment = "Positive"}
};

var reviewsDV = mlContext.Data.LoadFromEnumerable(reviews);

//Define your training pipeline
var pipeline =
mlContext.Transforms.Conversion.MapValueToKey("Label", "Sentiment")
.Append(mlContext.MulticlassClassification.Trainers.TextClassification(numberOfClasses: 2, sentence1ColumnName: "Text"))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));

// Train the model
var model = pipeline.Fit(reviewsDV);

[KernAlan]

I'm experiencing the same issue right now.

[JReed221]

I am experiencing this issue as well

[drGarbinsky]

I've noticed that this notebook references a newer version of the packages but I am unable to locate them

#i "nuget:https://pkgs.dev.azure.com/dnceng/public/_packaging/MachineLearning/nuget/v3/index.json"
#r "nuget:Microsoft.ML,2.0.0-preview.22324.1"
#r "nuget:Microsoft.ML.TorchSharp,0.20.0-preview.22324.1"
#r "nuget:TorchSharp-cpu,0.96.7"
#r "nuget:Microsoft.Data.Analysis,0.20.0-preview.22324.1"

I have also reproduced this issue within the nightly docker image so I don't think it is an environment issue

[drGarbinsky]

I was able to work around the issue by rolling back package versions:

<PackageReference Include="libtorch-cpu-linux-x64" Version="1.11.0.1" />
<PackageReference Include="Microsoft.Data.Analysis" Version="0.20.0-preview.22313.1" />
<PackageReference Include="Microsoft.ML" Version="2.0.0-preview.22313.1" />
<PackageReference Include="Microsoft.ML.TorchSharp" Version="0.20.0-preview.22313.1" />
<PackageReference Include="TorchSharp-cpu" Version="0.96.3" />

[JReed221]

that worked! thank you!

[NiklasGustafsson]

I believe that this is likely due to the fact that there was a breaking change made to Module.train() -- the previous version did not take an optional bool, which the Pytorch version does. That does not break source code dependencies, but breaks any binary dependency, since the signature has changed.

Adding @luisquintanilla and @michaelgsharp to consult on what to do about this.

[NiklasGustafsson]

A potential fix is to not make the bool optional, but to have two train() declarations -- one with, and one without. The latter would unbreak the binary dependency, I believe.

[luisquintanilla]

Hi all,

Sorry to hear you ran into issues.

I want to add clarity to the versions used in the notebook. The version in the notebook comes from the ML.NET daily feed. That version enables the use of the built-in Evaluate method in ML.NET and also makes it easier to work with the API. Previously you had to manually enter the number of classes you wanted to predict and couldn't use the evaluate method. This is the link to the daily feed if you want to get access to the latest versions of the ML.NET packages.

https://pkgs.dev.azure.com/dnceng/public/_packaging/MachineLearning/nuget/v3/index.json

That's a good suggestion @NiklasGustafsson. I also wonder if another alternative would be to instruct users to reference a specific version of TorchSharp (as @drGarbinsky has done to work around the issue). We've done something similar with TensorFlow.NET in the past. What would be the disadvantages of doing that (other than your code won't run using the latest version of TorchSharp)?

[NiklasGustafsson]

What would be the disadvantages of doing that (other than your code won't run using the latest version of TorchSharp)?

That would be the disadvantage, in addition to the inconvenience of having to keep track of which version numbers work and not.

[NiklasGustafsson]

@tarekgh -- what I suggested above should take care of it, right? The old train() was virtual, the new will not be -- that should be okay, right?

[luisquintanilla]

What would be the disadvantages of doing that (other than your code won't run using the latest version of TorchSharp)?

That would be the disadvantage, in addition to the inconvenience of having to keep track of which version numbers work and not.

Thanks for clarifying @NiklasGustafsson. Let's explore your initial suggestion - "not make the bool optional, but to have two train() declarations -- one with, and one without. "

[NiklasGustafsson]

Hmmm.

The change I was thinking of was introduced in 0.97.0 last week, so if you have to go all the way back to 0.96.3 to fix it, that's very odd. @luisquintanilla -- is there any way you could try it out with 0.96.8?

[tarekgh]

@NiklasGustafsson Changes that affect compatibility doc mentioning the following:

❌ DISALLOWED: Removing the virtual keyword from a member

I guess the compiler will not bind to the method if the virtual removed from the member.

@luisquintanilla should we just publish the latest of ML.NET to the NuGet instead of having it in the internal feed and recommend the users to use the latest?

[NiklasGustafsson]

Another approach is to rebuild the ML.NET preview with 0.97.0 and pick up the new method definition.

[NiklasGustafsson]

We should probably track this as an issue with ML.NET, too.