62 Neptune.ai Testimonials

“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.”

"We are very integrated with AWS and want everything to happen inside of AWS, and when you are training on a large scale, you want multiple training jobs to happen at once, and that is where Neptune comes in."

“Indeed it was a game-changer for me, as you know AI training workloads are lengthy in nature, sometimes also prone to hanging in colab environment, and just to be able to launch a set of tests trying different hyperparameters with the assurance that the experiment will be correctly recorded in terms of results and hyper-parameters was big for me.”

"Versioning jupyter notebooks is a great and unique feature."

“Without information I have in the Monitoring section I wouldn’t know that my experiments are running 10 times slower as they could. All of my experiments are being trained on separate machines which I can access only via ssh. If I would need to download and check all of this separately I would be rather discouraged. When I want to share my results I’m simply sending a link.“

"We’ve got a few teams across different countries and different time zones and prior to Neptune, we were just shipping each other zips of like TensorBoard logs, so being able to see it all in space and it’s all just logged to the central area is really great and has helped us compare our results a lot faster and a lot more efficiently."

“Neptune was easy to set up and integrate into my experimental flow. The tracking and logging options are exactly what I needed and the documentation was up to date and well written.”

"One of the biggest challenges [we had] was managing the pipelines and the process itself because we had 40 to 50 different pipelines. Depending on the exact use case or what kind of data we’d like to output, we could have different combinations for running them to get different outputs. So basically, the entire system isn’t so simple."

"I like the dashboards because we need several metrics, so you code the dashboard once, have those styles, and easily see them on one screen. Then, any other person can view the same thing, so that’s pretty nice."

“This thing is so much better than Tensorboard, love you guys for creating it."

“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.”

“Neptune allows us to keep all of our experiments organized in a single space. Being able to see my team’s work results any time I need makes it effortless to track progress and enables easier coordination.”

"Neptune made sense to us due to its pay-per-use or usage-based pricing. Now when we are doing active experiments then we can scale up and when we’re busy integrating all our models for a few months that we scale down again."

"We use Neptune for most of our tracking tasks, from experiment tracking to uploading the artifacts. A very useful part of tracking was monitoring the metrics, now we could easily see and compare those F-scores and other metrics."

“I’m working with deep learning (music information processing), previously I was using Tensorboard to track losses and metrics in TensorFlow, but now I switched to PyTorch so I was looking for alternatives and I found Neptune a bit easier to use, I like the fact that I don’t need to (re)start my own server all the time and also the logging of GPU memory etc. is nice. So far I didn’t have the need to share the results with anyone, but I may in the future, so that will be nice as well.”