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Ask HN: What is your ML stack like?

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Re: Ask HN: What is your ML stack like?

#121

Post by rozgo »

uoaei wrote: post #99
rozgo wrote: post #60

Custom Unreal Engine simulator, simulating agents with NVidia Physx and publishing sensors through GStreamer. GStreamer has sinks and sources for ROS, and tensorflow elements for inferencing. We package this all into NVidia Docker for scalable simulations. Setup is similar for training and inference. The core framework is a streaming engine with stream combinators that enable reasoning about spatio-temporal data stre…

I want to be where you are. How can I get in contact with you?

I'm building these labs/teams in Latin America, with a few remote people from around the world. Ping me at: alex.rozgo @ vertexstudio.co

Re: Ask HN: What is your ML stack like?

#122

Post by FridgeSeal »

streetcat1 wrote: post #110
FridgeSeal wrote: post #64

Earlier quoted context omitted.

For most of the stuff we’ve deployed, we’re not yet operating at a scale/level of interest where A/B rearing is worth it. Additionally, the purposes we’re using most of these models for don’t really necessitate A/B testing. When we do need A/B testing, we’ll probably use something like Seldon. As for predictions/second, not very much at the moment: 1 per 30 seconds maybe? It’s not deployed into a Kubernetes cluster b…

So how do you know if a new version of a model is better than the existing serving version?

As currently the only person doing data science things for the team, I’ll test to make sure changes I make to model/feature engineering/etc result in a better model. We’re not constantly, constantly retraining our models, because our incoming data and behaves the same. We’ve had the same model in prod for 4 months now; we don’t have any pressing issues with its predictions, and looking through the logs of what the input was the the output, it’s still performing as expected, so we’ll probably leave it longer.

Re: Ask HN: What is your ML stack like?

#123

Post by streetcat1 »

FridgeSeal wrote: post #122
streetcat1 wrote: post #110

Earlier quoted context omitted.

So how do you know if a new version of a model is better than the existing serving version?

As currently the only person doing data science things for the team, I’ll test to make sure changes I make to model/feature engineering/etc result in a better model. We’re not constantly, constantly retraining our models, because our incoming data and behaves the same. We’ve had the same model in prod for 4 months now; we don’t have any pressing issues with its predictions, and looking through the logs of what the in…

I see, so how do you measure the difference between the incoming data and your training data?

Also, it looks like you have a very low volume of predictions?

Re: Ask HN: What is your ML stack like?

#124

Post by hn2017 »

Datenstrom wrote: post #31

We are framework agnostic for model development, models get converted to ONNX[1] and served with the ONNX runtime[2]. They are deployed as microservices with docker. We are currently looking at MLflow[3] for the tracking server, it has some major pain points though. We use Tune[4] for hyperparameter search, and MLflow provides no way to delete artifacts from the parallel runs which will lead to massive amounts of was…

Any issues with the relatively new ONNX format? How do you handle model monitoring, verifying accuracy of model over time?

Re: Ask HN: What is your ML stack like?

#126

Post by PascLeRasc »

shantly wrote: post #84
PascLeRasc wrote: post #14

Earlier quoted context omitted.

If by ML noob you mean to say that you're like me and have zero formal CS training (as in, I don't know what a data structure is), pickling lets you write your Python workspace to a file just like Matlab's .mat file loading. It's excellent for writing scripts defining different parts of a data pipeline, or just for debugging/trying new things without waiting 20 minutes for something to filter.

> as in, I don't know what a data structure is Basically everything you work with in programming is a value (the number one-hundred-seventy-five, for example: "175") or the address—location in computer memory, say—of a value. You might record the address of that value above as the count of characters from the beginning of this post, for example, were this post the layout of data in some RAM, just as numbering houses…

I've read a lot about data structures and nothing's ever stuck, but this "did it" for me. Thanks so much, you're a really excellent writer.

Re: Ask HN: What is your ML stack like?

#127

Post by ospider »

9dl wrote: post #116
aaron-santos wrote: post #5

What didn't work: Shipping pickled models to other teams. Deploying Sagemaker endpoints (too costly). Requiring editing of config files to deploy endpoints. What did work: Shipping http endpoints. Deriving api documentation from model docstrings. Deploying lambdas (less costly than Sagemaker endpoints). Writing a ~150 line python script to pickle the model, save a requirements.txt, some api metadata, and test input/o…

>save a requirements.txt pipenv https://packaging.python.org/tutorials/managing-dependencies... https://dev.to/yukinagae/your-first-guide-to-getting-started...

Please do not use `pipenv`, use `poetry` or plain old `pip` instead.

1. https://news.ycombinator.com/item?id=18612590

Re: Ask HN: What is your ML stack like?

#130

Post by wing328hk »

ivalm wrote: post #114
wing328hk wrote: post #113

Earlier quoted context omitted.

FYI. One way to automatically generate API clients is to use OpenAPI Generator ( https://github.com/OpenAPITools/openapi-generator ), which is free, open-source and supports 30+ programming languages. Disclosure: I'm the top contributor to the project.

So we've been using swagger, is there some comparison between OpenAPITools and swagger code gen?

Here is the Q&A: https://github.com/OpenAPITools/openapi-generator/blob/maste...

Another way to compare is as follows:

https://www.openhub.net/p/openapi-generator vs https://www.openhub.net/p/swagger-codegen

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