Look at https://mlflow.org/
> On Sep 2, 2019, at 7:02 PM, Chaitanya Bapat <chai.ba...@gmail.com> wrote: > > Hello MXNet community, > > Reproducibility of ML experiments carried out by data scientists, analysts > and experts is the talk of the town. > > While listening to TWiML's latest podcast - Managing Deep Learning > Experiments with Lukas Biewald [1], he mentions the company Weights and > Biases [2] [3] > > Brief > - Reproducibility crisis in ML > - Let alone the latest research papers, even your own experiments (say from > 1 month ago) are not reproducible > - Solution : > 1. Versioning > Takes snapshots to store versions - Code, Data, Parameters and Hyper > parameters > Versioning or Snapshotting falls in the realm of data management. Notable > companies - DVC and Pachyderm. > > 2. Visualization > Builds on top of Tensorboard (TBoard). But solves its shortcomings > - Targeted for distributed training (unlike TBoard) > - Visualizes wrt several experiments (not just a single run) > > 3. Collaboration > Making this cloud based, allows cross-team collaboration. > > *MXNet* > From MXNet's point of view, we can discuss if it's worthwhile to have this > (many positives point towards a yes) and if so we can explore following > options - > a. Work with W&B for building support for using it with MXNet (currently > they have Tensorflow (TF) and PyTorch (PT) supported) > b. Build something in-house on similar lines that would involve significant > engineering effort, discussion. > > So I wanted to know what does the community think about this? > > Thanks, > Chai > > [1] > https://twimlai.com/twiml-talk-295-managing-deep-learning-experiments-with-lukas-biewald > [2] https://www.wandb.com > [3] https://github.com/wandb > > -- > *Chaitanya Prakash Bapat* > *+1 (973) 953-6299* > > [image: https://www.linkedin.com//in/chaibapat25] > <https://github.com/ChaiBapchya>[image: https://www.facebook.com/chaibapat] > <https://www.facebook.com/chaibapchya>[image: > https://twitter.com/ChaiBapchya] <https://twitter.com/ChaiBapchya>[image: > https://www.linkedin.com//in/chaibapat25] > <https://www.linkedin.com//in/chaibapchya/>
