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[ASK] Azure ML Compute based on algo and users / item size

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#896 opened on Aug 7, 2019

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Description

Description

I'm trying to run RBM on a problem with about 20K users and 40K items. Is there a way to quickly figure out what type of Azure Machine Learning Compute I would need to handle this size of problem, especially for the GPU. I can run this on an Standard NC12 on the CPU, but when I try to run it with the GPU I get an OOM error or the run fails with the last log entry somewhere deep in Tensorflow.

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Contributor guide

Research direction
Investigate Azure ML compute sizing for GPU based on dataset dimensions (20K users, 40K items) and RBM algorithm. Check TensorFlow memory usage and Azure VM GPU memory limits.
Tech stack
pythontensorflow
Domain
machine learningcloudbackend
Issue type
Research
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
Half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
PythonAzure MLTensorFlow
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
60