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>they didn't use it ... The workflow they described was really odd. SSHing into boxes to use a cluster, etc. no clue what you're talking about - most everyone

>they didn't use it ... The workflow they described was really odd. SSHing into boxes to use a cluster, etc. no clue what you're talking about - most everyone on a product team uses fblearner (the platform you're alluding to) which is a job queue type tool i.e. submit fblearner jobs and watch them run along with metrics tracking. >Someone went as far as saying that "experiment tracking" was "I told my manager which hyperparameters worked best" hyperparameters are rarely fiddled with because of how much data there is to train on but like i said fblearner has plenty of views to help with "experiment tracking" when it comes to hpo.

Jugurtha 6y ago
This is why I found it odd. I wondered why they didn't use FBLearner Flow and figured that not all teams were using it even though they did machine learning. We like these conversations where people share problems they may be having in order to get a bigger picture. We built our ML platform to solve our own problems that we faced over the years, but it's always nice to be exposed with problems we have not seen before to solve a slightly more general problem.
- anon_tor_12345 6y ago
  >I wondered why they didn't use FBLearner Flow were they in FAIR? conceivably FAIR might need more flexibility (because they're trying to "innovate") and so they fall back on lower level tools. but i know people at FAIR and they too use fblearner. regardless FAIR (or whatever other org you spoke to) is very small relative to the total number of people using/doing ML at FB so extrapolating from their needs is unwise (if you're trying to build a business around some typical process).
  - Jugurtha 6y ago
    It makes sense. As I said, I'm building for our own needs as we help organizations with machine learning and we needed to deliver faster, but I appreciate talking with people in the field to cluster families of problems and see a slightly bigger picture, and I generally like talking with this kind of people. They remind me of my colleagues, and I really like my colleagues.

Jugurtha 6y ago

This is why I found it odd. I wondered why they didn't use FBLearner Flow and figured that not all teams were using it even though they did machine learning. We like these conversations where people share problems they may be having in order to get a bigger picture. We built our ML platform to solve our own problems that we faced over the years, but it's always nice to be exposed with problems we have not seen before to solve a slightly more general problem.

anon_tor_12345 6y ago
>I wondered why they didn't use FBLearner Flow were they in FAIR? conceivably FAIR might need more flexibility (because they're trying to "innovate") and so they fall back on lower level tools. but i know people at FAIR and they too use fblearner. regardless FAIR (or whatever other org you spoke to) is very small relative to the total number of people using/doing ML at FB so extrapolating from their needs is unwise (if you're trying to build a business around some typical process).
- Jugurtha 6y ago
  It makes sense. As I said, I'm building for our own needs as we help organizations with machine learning and we needed to deliver faster, but I appreciate talking with people in the field to cluster families of problems and see a slightly bigger picture, and I generally like talking with this kind of people. They remind me of my colleagues, and I really like my colleagues.