hyperparameters | ES6 hyperparameters search for tfjs | Machine Learning library

I have created a class for word2vec vectorisation which is working fine. But when I create a model pickle file and use that pickle file in a Flask App, I am getting an error like:

AttributeError: module '__main__' has no attribute 'GensimWord2VecVectorizer'

I am creating the model on Google Colab.

Code in Jupyter Notebook:

Here is a high-level picture of what I am trying to achieve: I want to train a LightGBM model with spark as a compute backend, all in SageMaker using their Training Job api. To clarify:

1. I have to use LightGBM in general, there is no option here.
2. The reason I need to use spark compute backend is because the training with the current dataset does not fit in memory anymore.
3. I want to use SageMaker Training job setting so I could use SM Hyperparameter optimisation job to find the best hyperparameters for LightGBM. While LightGBM spark interface itself does offer some hyperparameter tuning capabilities, it does not offer Bayesian HP tuning.

Now, I know the general approach to running custom training in SM: build a container in a certain way, and then just pull it from ECR and kick-off a training job/hyperparameter tuning job through sagemaker.Estimator API. Now, in this case SM would handle resource provisioning for you, would create an instance and so on. What I am confused about is that essentially, to use spark compute backend, I would need to have an EMR cluster running, so the SDK would have to handle that as well. However, I do not see how this is possible with the API above.

Now, there is also that thing called Sagemaker Pyspark SDK. However, the provided SageMakerEstimator API from that package does not support on-the-fly cluster configuration either.

Does anyone know a way how to run a Sagemaker training job that would use an EMR cluster so that later the same job could be used for hyperparameter tuning activities?

One way I see is to run an EMR cluster in the background, and then just create a regular SM estimator job that would connect to the EMR cluster and do the training, essentially running a spark driver program in SM Estimator job.

Has anyone done anything similar in the past?

Thanks

I'm trying to use GridSearchCV to find the best hyperparameters for an LSTM model, including the best parameters for vocab size and the word embeddings dimension. First, I prepared my testing and training data.

I tried to construct a pipeline that has some optional steps. However, I would like to optimize hyperparameters for those steps as I want to get the best option between not using them and using them with different configurations (in my case SelectFromModel - sfm).

I'm looking to write a function that takes an audio signal (assuming it contains a single instrument playing), out of which I would like to extract the instrument-like features out of the audio and into a vector space. So in theory, if I had two signals with similar-sounding instruments (such as two pianos), their respective vectors should be fairly similar (by euclidian distance/cosine similarity/etc.). How would one go about doing this?

What I've tried: I'm currently extracting (and temporally averaging) the chroma energy, spectral contrast, MFCC (and their 1st and 2nd derivatives), as well as the Mel spectrogram and concatenating them into a single representation vector:

I'm trying to run a HyperparameterTuner on an Estimator for an LDA model in a SageMaker notebook using mxnet but am running into errors related to the feature_dim hyperparameter in my code. I believe this is related to the differing dimensions of the train and test datasets but I'm not 100% certain if this is the case or how to fix it.

[note that I'm setting the feature_dim to the training dataset's dimensions]

I'm trying to tune hyperparameters for KNN on a quite small datasets ( Kaggle Leaf which has around 990 lines ):

The variational autoencoder loss function is this: Loss = Loss_reconstruction + Beta * Loss_kld. I am trying to efficiently implement Kullback-Liebler Divergence Cyclic Annealing--that is changing the weight of beta dynamically during training. I subclass the tf.keras.callbacks.Callback class as a start, but I don't know how I can update a tf.keras.Model variable from a custom keras callback. Furthermore, I would like to track how the betas change at the end of each training step (on_train_batch_end), and right now I have a list in the callback class, but I know python lists don't play well with TensorFlow. When I fit the model, I get a warning that my on_train_batch_end function is slower than the processing of the batch itself. I think I should use a tf.TensorArray instead of python lists, but then the tf.TensorArray method write cannot use a tf.Variable for the index (i.e., as the number of steps changes, the index in the tf.TensorArray to which a new beta for that step should be written changes)... is there a better way to store value changes? It looks like this github shows a solution that doesn't involve a custom tf.keras.Model and that uses a different kind of KL annealing. Below is a callback function and dummy VAE.

I've run into an issue where R INLA isn't computing the fitted marginal values. I first had it with my own dataset, and have been able to reproduce it following an example from this book. I suspect there must be some configuration I need to change, or maybe INLA isn't working well with something under the hood? Anyways here is the code: