Some of the top companies keep track of their ML experiments with Neptune

“Within the first few tens of runs, I realized how complete the tracking was – not just one or two numbers, but also the exact state of the code, the best-quality model snapshot stored to the cloud, the ability to quickly add notes on a particular experiment. My old methods were such a mess by comparison.”

Edward Dixon

Data Scientist @Intel

See what our users are saying:

“…easily hooks into multiple frameworks…”

“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.”

“… all of our experiments organized in a single space…”

“Neptune allow 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.”

“…is making it easy to share results with my teammates…”

“Neptune is making it easy to share results with my teammates. I’m sending them a link and telling what to look at, or I’m building a View on the experiments dashboard. I don’t need to generate it by myself, and everyone in my team have access to it.”

“…Without information I have in the Monitoring section I wouldn’t know that my experiments are running 10 times slower …”

“Without information I have in the Monitoring section I wouldn’t know that my experiments are running 10 times slower than 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. “

“Way better than the other tools I’ve tried (comet / wandb).”

“… has been really useful for keeping track of the experiments for my Master’s thesis. Way better than the other tools I’ve tried (comet / wandb). I guess the main reason I prefer neptune is the interface, it is the cleanest and most intuitive in my opinion, the table in the center view just makes a great deal of sense. I like that it’s possible to set up and save the different view configurations as well. Also, the comparison is not as clunky as for instance with wandb. Another plus is the integration with ignite, as that’s what I’m using as the high-level framework for model training”

“…I was using Tensorboard… 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…”

“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.”

“…I realized how complete the tracking was…My old methods were such a mess by comparison…”

“Within the first few tens of runs, I realized how complete the tracking was – not just one or two numbers, but also the exact state of the code, the best-quality model snapshot stored to the cloud, the ability to quickly add notes on a particular experiment. My old methods were such a mess by comparison.”

“Such a fast setup! Love it:)”

“Such a fast setup! Love it:)”

“… the most important thing about Neptune is its flexibility…”

“For me the most important thing about Neptune is its flexibility. Even if I’m training with Keras or Tensorflow on my local laptop, and my colleagues are using fast.ai on a virtual machine, we can share our results in a common environment.”

“…if you … are tired of using excel sheets or tensorboard to track your results, neptune is a good bet…”

“A lightweight solution to a complicated problem of experiment tracking. If you look for a simple, flexible and powerful tool or you are tired of using excel sheets or tensorboard to track your results, neptune is a good bet. What do you like best? – Easy integration with any pipeline / flow / codebase / framework – Easy access to logged data over an api (comes also with a simple python wrapper ) – Fast and reliable – Versioning jupyter notebooks is a great and unique feature What do you dislike? – Visualization of the logged data could be improved, support for more advanced plotting would be nice, altough you can alway workaround that by sending pictures of charts. “

“…Without Neptune.ai, I would have waste a lot of time building a client for experiment management and monitoring…”

“I’m mostly doing an academic research that involves the training of machine learning models, and also other long-running experiments which I need to track in real time. Without Neptune.ai, I would have waste a lot of time building a client for experiment management and monitoring. It also serves as an archive, which I also find very important for my research.”

“…useful tool for tracking many experiments and collaboration on them…”

“Useful tool for tracking many experiments and collaboration on them. What do you like best? – one place to log all my experiments, very helpful when you have to find some results from a few months back. – It makes collaboration easier as well – just share the link to an experiment with a colleague and you can analyze the results together. What do you dislike? – The UI for creating graphs with multiple lines could be more flexible. “

“… Tracking and comparing different approaches has notably boosted our productivity…”

“Tracking and comparing different approaches has notably boosted our productivity, allowing us to focus more on the experiments, develop new, good practices within our team and make better data-driven decisions. We love the fact that the integration is effortless. No matter what framework we use – it just works in the matter of minutes, allowing us to automate and unify our processes.”

“…Fast, easy to use, supportive, update features regularly…”

“Fast, easy to use, supportive, update features regularly. If you need any new features, you can simply ping them. They will consider your suggestion. What do you like best? – They respect to feedback and suggestion and update regularly the new features for a better experience. What do you dislike? – At the moment everything is pretty useful”

“…great for multiple reasons…you realize that you wanted to see the min or average or whatever…you can do such stuff directly in neptune…”

“Well this is great for multiple reasons. For example you continously log some value. And then you realize that you wanted to see the min or average or whatever. Without this option, you will have to download the data and process everything on the local PC. Now you can do such stuff directly in neptune. this is great.”

“…so much better than Tensorboard…”

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

“…I’m really appreciative of how much time it’s saved me…”

“The last few hours have been my first w/ Neptune and I’m really appreciative of how much time it’s saved me not having to fiddle w/ matplotlib in addition to everything else “

“… tested multiple loggers with pytorch-lightning integrations and found neptune to be the best…”

“I tested multiple loggers with pytorch-lightning integrations and found neptune to be the best fit for my needs. Friendly UI, ease of use and great documentatinon. “

“I didn’t expect this level of support.”

“I didn’t expect this level of support.”

“…I am very impressed with the improvements in UI!…”

“I just had a look at neptune logger after a year and to be honest, I am very impressed with the improvements in UI! Earlier, it was a bit hard to compare experiments with charts. I am excited to try this!”

“…I have everything organized for me automatically…”

“I am super messy with my experiments, but now I have everything organized for me automatically. I love it!”

They already have their ML experimentation in order.
When will you?

✓ Sign up for a free account
✓ Add a few lines to you code
✓ Get back to running your experiments

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