Neptune.ai

“What we like about Neptune is that it easily hooks into multiple frameworks. Keeping track of machine learning experiments systematically over time and visualizing the output adds a lot of value for us.”

“While logging experiments is great, what sets Neptune apart for us at the lab is the ease of sharing those logs. The ability to just send a Neptune link in slack and letting my coworkers see the results for themselves is awesome. Previously, we used Tensorboard + locally saved CSVs and would have to send screenshots and CSV files back and forth which would easily get lost. So I’d say Neptune’s ability to facilitate collaboration is the biggest plus.”

“The problem with training models on remote clusters is that every time you want to see what is going on, you need to get your FTP client up, download the logs to a machine with a graphical interface, and plot it. I tried using TensorBoard but it was painful to set up in my situation. With Neptune, seeing training progress was as simple as hitting refresh. The feedback loop between changing the code and seeing whether anything changed is just so much shorter. Much more fun and I get to focus on what I want to do. I really wish that it existed 10 years ago when I was doing my PhD.”