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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?

#51

Post by oli5679 »

jointpdf wrote: post #46

Check out the CRAN task view on this topic: https://cran.r-project.org/web/views/ModelDeployment.html One dead simple way to do this (R model —> Java production) that I’ve done in the past is to use PMML (via pmml package), which converts models to an XML representation. ONNX is a similar/newer framework along these lines. You can also look at dbplyr for performing ( dplyr -like) data preprocessing in-database.

I didn't see you his comment when I posted but I would strongly recommend PMML-based approach.

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

#52

Post by [deleted] »

suyash wrote: post #18

For my pet projects, do training and testing locally on my machine either using Notebooks or on an IDE. Test and validate it further on my local machine before deploying it on a server as a micro service. This is for my pet projects only.

[deleted]

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

#53

Post by eggie5 »

Development of models in our data environment: notebooks, pyspark EMR clusters for analytical workloads and offline models, tensorflow/EC2 P2s for online models.

Jobs are scheduled (Azkaban) for reruns/re-training and pushed from data env to the feature/model-store in live env (Cassandra). Online models are exported to SaveModel format and can be loaded on any TF platform, eg java backends.

Online inference using TF Serving. Clients query models via grpc.

A lot of our models are NN embedding lookups, we use Annoy for indexing those.

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

#54

Post by superkitty »

FridgeSeal wrote: post #40

Currently: Models and feature engineering done in python, trained locally, weights uploaded to S3. Dockerfile with a tiny little web server gets deployed through or CI/CD pipeline for serving. Soon: Argo workflows + Polyaxon for data collection, feature engineering, training etc. Push best model tobS3, same CICD process with docker container deploys little web server onto our Kubernetes environment. Deep learning stu…

What about the A/B testing? What do you use for A/B strategy. How many predictions are being served by the model per second?

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

#55

Post by superkitty »

mmq wrote: post #45
FridgeSeal wrote: post #40

Currently: Models and feature engineering done in python, trained locally, weights uploaded to S3. Dockerfile with a tiny little web server gets deployed through or CI/CD pipeline for serving. Soon: Argo workflows + Polyaxon for data collection, feature engineering, training etc. Push best model tobS3, same CICD process with docker container deploys little web server onto our Kubernetes environment. Deep learning stu…

Glad to see that you are interested by using Polyaxon[0] for your MLOps. Although I was going to write a blog post about the upcoming v1.0 release of Polyaxon, I just wanted to point out that there will be a native support for different type of workflows, currently it supports parallelism and distributed learning, and in the next release there will be native support for DAGs as well. Here's a test fixture[1] of what…

Any inputs on Argo workflows vs Kubeflow vs MLFlow? Which is better suited?

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

#58

Post by superkitty »

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…

What about the A/B testing? What do you use for A/B strategy. How many predictions are being served by the model per second?

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

#59

Post by derision »

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…

Would love to hear your thoughts on this? cortex.dev

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

#60

Post by rozgo »

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 streams. Where each datum is related to a point in space and time. The goal is for tensors to be the streaming primitives, but the pipeline is still fragile, it’s a challenge to keep all this working with so many core technologies changing constantly (UE4 + Physx + Cuda + CuDNN + Tensorflow + …). We train robots in simulation.

Most of our tools are built in Rust. Several of those are for creating(cleaning) data streams out of datasets. They are converted into tensors or ROS messages.

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