I am trying to compile TensorflowLite for emscripten (I am aware of TensorflowJS) and pthreads are currently disabled. Is there a way to use it without pthreads?

Currently we didn't port all test cases from v1alpha1 to v1beta1. Part of the reasons is because we haven't implement functions that v1alpha1 has:

• SyncCache
• [GetSecret](https://github.com/kubeflow/kubeflow/blob/2f15d8dbe85f0081652c1e5

Support for storing large tensor values in external files was introduced in #678, but AFAICT is undocumented.

This is a pretty important feature, functionally, but it's also important for end users who may not realise that they need to move around more than just the *.onnx file.

I would suggest it should be documented in IR.md, and perhaps there are other locations from which it could be s

FieldAwareFactorizationMachine is good for large dataset like the Criteo 1TB dataset.

Currently FieldAwareFactorizationMachine is not swept over in AutoML.

Task:

• Add trainer to default list of binary learners to try
• Add sweep range
• Add to CLI's C# CodeGen

Should be easy to just replicate an existing trainer like SDCA:
https://github.com/dotnet/machinelearning/blob/d518b587b06a

System information

• Have I written custom code (as opposed to using a stock example script provided in MLflow): yes
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): macOS
• MLflow installed from (source or binary): pip install mlflow
• MLflow version (run mlflow --version): 0.9.0
• Python version: 3.7.3

Describe the problem

When trying to deploy

System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux
• TensorFlow Serving installed from (source or binary): Binary
• TensorFlow Serving version: Stable

Describe the problem

The documentation shows how you can save assets but not how you can load assets back into the model. I have a 14GB embedding that requires preprocessing that I would li

All tensorflow examples use ancient version of tensorflow. They should be updated to work without deprecation warnings with modern versions (1.14+). It is hard to get into polyaxon when examples are years behind (tf 1.4 was released in 2017) in such fast moving field.

The LPNorm here is extraneous as an LpNorm is already included in the FeaturizeText by default:
https://github.com/dotnet/machinelearning-samples/blob/c3b14f76b1fd88eb75015cb1198da52ec17c967d/samples/csharp/getting-started/BinaryClassification_SpamDetection/SpamDetectionConsoleApp/Program.cs#L41-L49

If you want it to be explicit, you can call out the LpNorm within the FeaturizeText options, b

Hi.

I wanna understand the embeddings of the USE model in detail; where should I get the info?

For example, ELMo's embeddings are described on https://tfhub.dev/google/elmo/2.
But, in the case of USE, there is only a description the output is a 512 dimensional vector on https://tfhub.dev/google/universal-sentence-encoder/2.
From where is the output coming?

I could find the output is

I am trying to explain the predictions made by my XGboost model using MMLSparks Lime package for scala. This is my first time using LIME library, I am able to perform a fit operation on the dataset and when I am trying to perform the transform operation, the program stops with an exception, "Caused by: java.lang.ClassCastException: org.apache.spark.ml.linalg.SparseVector cannot be cast to org.apac

Problem
Some of our transformers & estimators are not thoroughly tested or not tested at all.

Solution
Use OpTransformerSpec and OpEstimatorSpec base test specs to provide tests for all existing transformers & estimators.

Instructions

• Check all FutureWarning and DeprecationWarning raised by the test suite.
• Adapt the code to make sure the library won't be broken by imminent changes in dependencies API.

Describe the issue

The current test suite is raising a few FutureWarning and DeprecationWarning raised by sklearn. In order to avoid getting surprised by changes in sklearn and numpy APIs, it

In augmentation, elastic_transform, it only applies a random transform on one input image array. I would think to be used for training, the image and mask pair should be transform in the same way. However, this single-input-image, single-output-image method makes it very inconvenient. Could we deform a list of images (np.arrays) using the same transformation in this method ? Thanks!

I am not clear about what the iteration means. Could you explain more about it? Also, how is it related to epoch?

Thanks.