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Michelangelo PyML: Uber’s Platform for Rapid Python ML Model

77 points by pplonski86 8y ago

canada_dry 8y ago
Pretty slick, but more impressive is their well written overview of the tool. It has some interesting model def features: The model interfaces abstract away where and how the model will be deployed so that the user can focus on what they understand best: the data going in and out of the model.
- Michaelanjello 8y ago
  Are you serious? There is no code for us, so there is no tool! The article is as good as fake news.
  - throwaway77790 8y ago
    Not every company needs to open source their tools. Uber has contributed a substantial amount of open source tools. This product is clearly new and 99% likely ties to their stack meaning it is useless to you anyway. Make your own tool and open source it if you are going to be so mean. I would never want to share my hard work with people who act this way.
    - Michaelanjello 8y ago
      I am mean only to losers who deserve it.
brylie 8y ago
As it stands, this article strikes me more as a recruitment device than something useful to ML practitioners outside of Uber. I'm hopeful Uber will open-source parts of the Michelangelo stack, so it will be a contribution to the broader community.
- manojlds 8y ago
  Disagree. The article validates a lot of thought we have been putting in our own company. Not everything has to be open sourced to have value.
Michaelanjello 8y ago
Who cares about this garbage if the tool isn't even open source? There are lots of ML deployment tools that are open source. I know haters will downvote my post, but it's the truth. If I can't actually fork and evaluate a tool, it is hyped up garbage to me. Meanwhile, here is a list of open source ML deployment packages: https://github.com/oracle/graphpipe https://github.com/oracle/graphpipe https://github.com/eliorc/denzel https://github.com/eliorc/denzel https://github.com/tensorflow/serving https://github.com/tensorflow/serving https://github.com/ucbrise/clipper https://github.com/ucbrise/clipper https://github.com/DLHub-Argonne/dlhub_sdk https://github.com/DLHub-Argonne/dlhub_sdk https://github.com/kubeflow/pipelines https://github.com/kubeflow/pipelines
- zozbot123 8y ago
  FWIW, the "Guidelines" ask you to please not dismiss work out of hand. Appearances matter, too - you can point out that a tool is proprietary and has lots of plausible FLOSS alternatives without being uncivil about it ("garbage"). Edited: Commenting about the voting, especially in the way you did here, is inappropriate too. We're here for substantive discussion.
  - mlthoughts2018 8y ago
    I agree with you, but I also think there’s room to point out that Uber has lost credibility and it wouldn’t be surprising or inconsistent if it was just a PR post for recruiting hype. Without details (not mere surface comments) on how it is differentiated from the many other available solutions and deep dives into what use cases it is specifically better suited for, it seems reasonable to treat it with a lot of skepticism. But to be clear, I totally agree in terms of the degree and tone. I don’t think the parent comment you responded to was “uncivil” in any way, but overly dismissive instead of just noting to be skeptical.
    - zozbot123 8y ago
      Skepticism is one thing, lack of civility is quite another (and I'm sorry, but calling stuff "garbage" in such an off-hand way is clearly uncivil to me, especially when shallow dismissals are expressly pointed out as a problem). See brylie's comment in this thread for how to do it right - take that, add a stronger caveat about this tool's proprietary status, in contrast to so much other stuff in this space being FLOSS, and it would make your point quite well without degrading discussion.
deleted 8y ago
[deleted]
mark_l_watson 8y ago
I manage a ML team and the article has several ‘borrowable’ ideas, the primary idea supporting model builders working on their local Linux device and simplifying the process of transferring locally built models to a scalable platform. If we were starting today to implement our in house system I might just use Ubber’s system as a checklist for features to implement. Not too off topic: after mostly building and training models on remote compute (both at work and for my at home learning projects), I recently bought a fast laptop for home use that has a 1070 GPU and the experience of using a local device with a GPU makes me regret many years of SSHing to remote compute or using Jupyter remotely for machine learning development. I mention this as a lesson learned that may be helpful.
- Michaelanjello 8y ago
  Uber's software doesn't exist in the public realm, and so there is nothing to learn from it. It is imaginary fluff. I have posted a list of the ones that do actually exist in another comment. And you should've at least got a 1080.
  - mark_l_watson 8y ago
    I wanted a portable laptop and just saw 1070s on what was available. What brand of GPU laptop do you suggest? I got some ideas from reading the article, but yes, I agree with you that the article should have a link to a public git repo for at least some of their tooling. EDIT: I wanted the multi-GPU Lambda Labs box, but with my lifestyle I require some mobility. I agree that the 1070 is a bit weak, but is still better than no GPU and I like The System76 laptop I bought even when I am not using the GPU.
    - Michaelanjello 8y ago
      You will get a lot more ideas from other software that actually do exist. I can easily write a fake blog post with fake thoughts giving you fake ideas, but that's not what you want; you deserve ideas that work. System76 does sell laptops with the 1080.
  - 8y ago
typon 8y ago
I wish my company was cool enough such that our internal tools (even if they're technically complex) would be of interest to people :(
myegorov 8y ago
Related meetup: UBER : Big Data Infrastructure and Machine Learning Platform https://www.meetup.com/SF-Big-Analytics/events/257037259/ https://www.meetup.com/SF-Big-Analytics/events/257037259/
marcyb5st 8y ago
Anyone working at Uber that can highlight the non trivial differences between this pyML Michelangelo and Kubeflow? I am very interested in hearing first hand opinions about pros and cons

77 points by pplonski86 8y ago

Pretty slick, but more impressive is their well written overview of the tool. It has some interesting model def features: The model interfaces abstract away where and how the model will be deployed so that the user can focus on what they understand best: the data going in and out of the model.

Michaelanjello 8y ago
Are you serious? There is no code for us, so there is no tool! The article is as good as fake news.
- throwaway77790 8y ago
  Not every company needs to open source their tools. Uber has contributed a substantial amount of open source tools. This product is clearly new and 99% likely ties to their stack meaning it is useless to you anyway. Make your own tool and open source it if you are going to be so mean. I would never want to share my hard work with people who act this way.
  - Michaelanjello 8y ago
    I am mean only to losers who deserve it.

brylie 8y ago

As it stands, this article strikes me more as a recruitment device than something useful to ML practitioners outside of Uber. I'm hopeful Uber will open-source parts of the Michelangelo stack, so it will be a contribution to the broader community.

manojlds 8y ago
Disagree. The article validates a lot of thought we have been putting in our own company. Not everything has to be open sourced to have value.

Michaelanjello 8y ago

Who cares about this garbage if the tool isn't even open source? There are lots of ML deployment tools that are open source. I know haters will downvote my post, but it's the truth. If I can't actually fork and evaluate a tool, it is hyped up garbage to me. Meanwhile, here is a list of open source ML deployment packages: https://github.com/oracle/graphpipe https://github.com/oracle/graphpipe https://github.com/eliorc/denzel https://github.com/eliorc/denzel https://github.com/tensorflow/serving https://github.com/tensorflow/serving https://github.com/ucbrise/clipper https://github.com/ucbrise/clipper https://github.com/DLHub-Argonne/dlhub_sdk https://github.com/DLHub-Argonne/dlhub_sdk https://github.com/kubeflow/pipelines https://github.com/kubeflow/pipelines

zozbot123 8y ago
FWIW, the "Guidelines" ask you to please not dismiss work out of hand. Appearances matter, too - you can point out that a tool is proprietary and has lots of plausible FLOSS alternatives without being uncivil about it ("garbage"). Edited: Commenting about the voting, especially in the way you did here, is inappropriate too. We're here for substantive discussion.
- mlthoughts2018 8y ago
  I agree with you, but I also think there’s room to point out that Uber has lost credibility and it wouldn’t be surprising or inconsistent if it was just a PR post for recruiting hype. Without details (not mere surface comments) on how it is differentiated from the many other available solutions and deep dives into what use cases it is specifically better suited for, it seems reasonable to treat it with a lot of skepticism. But to be clear, I totally agree in terms of the degree and tone. I don’t think the parent comment you responded to was “uncivil” in any way, but overly dismissive instead of just noting to be skeptical.
  - zozbot123 8y ago
    Skepticism is one thing, lack of civility is quite another (and I'm sorry, but calling stuff "garbage" in such an off-hand way is clearly uncivil to me, especially when shallow dismissals are expressly pointed out as a problem). See brylie's comment in this thread for how to do it right - take that, add a stronger caveat about this tool's proprietary status, in contrast to so much other stuff in this space being FLOSS, and it would make your point quite well without degrading discussion.

deleted 8y ago

[deleted]

mark_l_watson 8y ago

I manage a ML team and the article has several ‘borrowable’ ideas, the primary idea supporting model builders working on their local Linux device and simplifying the process of transferring locally built models to a scalable platform. If we were starting today to implement our in house system I might just use Ubber’s system as a checklist for features to implement. Not too off topic: after mostly building and training models on remote compute (both at work and for my at home learning projects), I recently bought a fast laptop for home use that has a 1070 GPU and the experience of using a local device with a GPU makes me regret many years of SSHing to remote compute or using Jupyter remotely for machine learning development. I mention this as a lesson learned that may be helpful.

Michaelanjello 8y ago
Uber's software doesn't exist in the public realm, and so there is nothing to learn from it. It is imaginary fluff. I have posted a list of the ones that do actually exist in another comment. And you should've at least got a 1080.
- mark_l_watson 8y ago
  I wanted a portable laptop and just saw 1070s on what was available. What brand of GPU laptop do you suggest? I got some ideas from reading the article, but yes, I agree with you that the article should have a link to a public git repo for at least some of their tooling. EDIT: I wanted the multi-GPU Lambda Labs box, but with my lifestyle I require some mobility. I agree that the 1070 is a bit weak, but is still better than no GPU and I like The System76 laptop I bought even when I am not using the GPU.
  - Michaelanjello 8y ago
    You will get a lot more ideas from other software that actually do exist. I can easily write a fake blog post with fake thoughts giving you fake ideas, but that's not what you want; you deserve ideas that work. System76 does sell laptops with the 1080.
- 8y ago

typon 8y ago

I wish my company was cool enough such that our internal tools (even if they're technically complex) would be of interest to people :(

myegorov 8y ago

Related meetup: UBER : Big Data Infrastructure and Machine Learning Platform https://www.meetup.com/SF-Big-Analytics/events/257037259/ https://www.meetup.com/SF-Big-Analytics/events/257037259/

marcyb5st 8y ago

Anyone working at Uber that can highlight the non trivial differences between this pyML Michelangelo and Kubeflow? I am very interested in hearing first hand opinions about pros and cons