Understanding where and when to use synchronous model versus asynchronous model is a foundational decision to designing

This one is aimed to let you run flows using RAPIDS. You can leverage GPUs and install any additional libraries you need. The interface is Jupyter Hub. Honestly, I somehow liked Colab more than BlazingSQL. It’s a good alternative to do the same GPU accelerated computation as well. You can mount the same google drive if you want, but if you’d upload your files here, it would stay there even if your server is restarted, not like in Colab.

The goal of the microservices is to sufficiently decompose/decouple the application into loosely coupled services organized around business capabilities. The distributed micro-units collectively serve application purposes.

In synchronous communication, a predefined source service address required, where exactly to send the request, and BOTH the service (caller and callee) should be up and running at the moment. Though Protocol may be synchronous, I/O operation can be asynchronous where the client need not necessarily wait for the response. This is a difference in I/O and Protocol. The common request-response approach common to web API includes REST, GraphQL, and gRPC.

Beware of some limitations. Collaboratory was designed for people to test some things, get familiar and share at ease. So probably, you won’t be able to run long pipelines on the free version. This version gives 12h of being connected (after that, you can’t use GPU). Also, it timeouts after 90mins of nothing being pressed, basically disrupting complex, long-running neural nets or computations; you have to click some things here and there to avoid it).

Like I said, I did this out of curiosity; my submission to the task was not the best of what I could have achieved (only 0.78 AUC). If you wouldn’t make the same mistakes as I did, it will benefit you as well as it did for me.

Like I said, I did this out of curiosity; my submission to the task was not the best of what I could have achieved (only 0.78 AUC). If you wouldn’t make the same mistakes as I did, it will benefit you as well as it did for me.

I was not too fond of that H2OFrame doesn’t quite work like pandas/spark/dask data frames sometimes. It was a bit hard to figure out how to do certain things, i.e., get a list of distinct column values. Here I had to use flatten, though in pandas would be list(). Nonetheless pretty cool library which can help anyone!

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to know that they were miserable, they felt screwed over, and they felt demonized. They wanted me to know that the only option available to address white male patriarchy was either to maintain the status quo that was making us all miserable, or death. They wanted me to know that they were not capable of growth or change and that any attempts to bring about that growth or change would end them.

Their framework is easy to work with, specifying layers similar to MLPClassifier in sklearn — pass a list with neuron count in each layer. Though running your training on CPU will drive you mad (it takes A LOT of time, like literally A LOT). In my case, when I used the CPU, it showed 150 hours. None of my owned machines had an Nvidia GPU inside, so it wasn’t on my list to try first. In later parts, I’ll tell how to use free GPU on some platforms and speed up any model training that can leverage GPU.

and access your data in the usual way, just from /content/drive. Though I guess you agree with google seeing your data, which might not work for sensitive information. Though for Kaggle and playing around, it works quite well.

This snippet would fix imbalances, remove outliers and fit multiple models with a search on hyperparameters. My data was around 1GB CSV file, so even 5 folds take some time on one model. Keeping in mind that there is hyperparameter tunning multiple models 5 folds = too long waiting time. If I’d have smaller datasets, I’d definitely give it a try again; it seems easy to work with, and it does all the work for you!

In the case of asynchronous communication, callers need not have the specific destination of the callee. Handling multiple consumers at a time becomes relatively easy (as services may add up consumers). Moreover, the message queues up if the receiving service is down