Log, store, display, organize, compare and query all your MLOps metadata.
Experiment tracking and model registry built for research and production teams that run a lot of experiments.
Manage all your model building metadata
in a single place
10.000 + ML engineers and researchers manage their experiment and model metadata in Neptune
Feel in control of your model building and experimentation
- Record everything you care about for every ML job you run
- Know on which dataset, parameters, and code every model was trained on
- Have all the metrics, charts, and any other ML metadata organized in a single place
- Make your model training runs reproducible and comparable with almost no extra effort
- Have everything backed up and accessible from anywhere
Be more productive at ML engineering and research
- Don’t waste time looking for folders and spreadsheets with models or configs. Have everything easily accessible in one place
- Cut unproductive meetings by sharing results, dashboards, or logs with a link
- Reduce context switching by having everything you need in a single dashboard
- Find the information you need quickly in a dashboard that was built for ML model management
- Debug and compare your models and experiments with no extra effort
Focus on ML, leave metadata bookkeeping to us
- We set up and maintain metadata databases and dashboards for you (we deploy on-prem too)
- We implement and update loggers for all major ML libraries
- We optimize loggers/databases/dashboards to work for millions of experiments and models
- We help your team get started with excellent examples, documentation, and a support team ready to help at any time
Use computational resources more efficiently
- See what your team is working on and stop duplicating expensive training runs
- Know when your runs fail and react right away
- Don’t re-run experiments because you forgot to track parameters. Make experiments reproducible and run them once
Build reproducible, compliant, and traceable models
- Make every ML job reproducible. Keep track of everything you need
- Have your models and experiments backed up and accessible even years after
- Know who trained a model, on what dataset, code, and parameters. Do it for every model you build
- Be compliant by keeping a record of everything that happens in your model development
Need more info about Neptune?
How to get started in 5 minutes
1. Create a free account
Sign up2. Install Neptune client library
pip install neptune-client
3. Add logging to your script
import neptune
neptune.init('Me/MyProject')
neptune.create_experiment(params={'lr':0.1, 'dropout':0.4})
# training and evaluation logic
neptune.log_metric('test_accuracy', 0.84)
Try live notebook
Use one of Neptune’s 25+ integrations
With Neptune, you can get more out of the tools you use every day. Neptune comes with 25+ integrations with libraries used in machine learning, deep learning, and reinforcement learning.
See what our users are saying
“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.”
Head of Data Science @New Yorker
“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.”
VP, Machine Learning @Zesty.ai
“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.”
Resarch Lead @Banacha Street
“I chose Neptune over WandB because it is more lightweight and I’m more comfortable working with it. The possibilities for “custom” logging are practical and the tool is really stable.
I observed the following with W&B:
– I had some errors during the experiments: symlink creation/user rights errors and model upload cycling infinitely (which made WandB not detect the end of the run).
– Slower/heavier (took more time at each epochs’ end).”
Data Science Researcher
“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.“
Machine Learning Researcher @TensorCell
“Hi there, thanks for the great tool, 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.”
Data Science and AI student, Kranenburg, Germany
“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.”
PhD student in Music Information Processing at Télécom Paris
“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.”
Enterprise Data Scientist @Intel
“Previously used tensorboard and azureml but Neptune is hugely better. In particular, getting started is really easy; documentation is excellent, and the layout of charts and parameters is much clearer.”
AI Engineer and Data Scientist
“Such a fast setup! Love it:)”
PhD student in Music Information Processing at Télécom Paris
“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.”
Senior NLP/ML Engineer
“A lightweight solution to a complicated problem of experiment tracking.
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.
Recommendations to others considering the product:
If you look for a simple, flexible and powerfull tool or you are tired of using excel sheets or tensorboard to track your results, neptune.ai is a good bet.
What problems are you solving with the product? What benefits have you realized?
– machine learning reproducibility
– machine learning system monitoring
– sharing experiment results
– monitoring long running deep learning jobs.“
Senior Data Scientist interested in Computer Vision
“Neptune provides an accessible and intuitive way to visualize, analyze and share metrics of our projects.
We can not only discuss it with other team members, but also with management, in a way that can be easily interpreted by someone not familiar with the implementation details.
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.”
Data Scientist @idenTT
“Exactly what I needed.
What do you like best?
The real-time charts, the simple API, the responsive support
What do you dislike?
It would be great to have more advanced API functionality.
Recommendations to others considering the product:
If you need to monitor and manage your machine learning or any other computational experiments, Neptune.ai is a great choice. It has many features that can make your life easier and your research more organized.
What problems are you solving with the product? What benefits have you realized?
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.”
Computer vision researcher and developer @TheWolf
“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.”
AI Robotics
“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.
What problems are you solving with the product? What benefits have you realized?
– tracking data about many experiments
– easily sharing experiments within lab
– going back to old results, collecting data for report/publication
– reproducibility – code and hyperparameters are stored in one place.“
Researcher in reinforcement learning @deepsense.ai
“This thing is so much better than Tensorboard, love you guys for creating it!”
Junior Researcher at Rényi Alfréd Institute of Mathematics in Budapest, Hungary
“Fast, easy to use, supportive, update features regularly.
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
Recommendations to others considering the product:
Everything is available to use. If you need any new features, you can simply ping them. They will consider your suggestion.
What problems are you solving with the product? What benefits have you realized?
tracking my ML experiment. Hyperparameter tuning. Sharing the result with my colleagues and other tones of benefits.”
PhD Researcher on Applied ML in telecommunication
“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.“
Research Associate at UM’s Education Policy Initiative
“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.“
Research student at Tel Aviv University
“I didn’t expect this level of support.”
PhD Candidate, UC-Irvine
“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 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!”
Researcher at Georgia Institute of Technology
“I am super messy with my experiments, but now I have everything organized for me automatically. I love it!”
MS student in Computer Science at Handong Global University
“I had been thinking about systems to track model metadata and it occurred to me I should look for existing solutions before building anything myself.
Neptune is definitely satisfying the need to standardize and simplify tracking of experimentation and associated metadata.
My favorite feature so far is probably the live tracking of performance metrics, which is helpful to understand and troubleshoot model learning.
I also find the web interface to be lightweight, flexible, and intuitive.”
CTO @ Betterbin
“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.”
Computer Science Undergraduate at Saint Louis University
“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.”
Part time PHD student at ENSIAS Mohamed V University in Rabat, Morocco
“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.”
Teaching Assistant at University of Groningen
“I have been pleasantly surprised with how easy it was to set up Neptune in my PyTorch Lightning projects!”
Ph.D. Student in Computer Science at the University of Missouri
“I used to keep track of my models with folders on my machine and use naming conventions to save the parameters and model architecture. Whenever I wanted to track something new about the model, I would have to update the naming structure. It was painful. There was a lot of manual work involved.
Now everything happens automatically. I can compare models in the online interface that looks great. It saves me a lot of time, and I can focus on my research instead of keeping track of everything manually.“
Researcher at IFFM, Polish Academy of Sciences
“I’m working on a server that is not graphical, it’s always a hassle to connect it to my local laptop to show the results in TensorBoard. I just want to see it online and Neptune lets me do that easily.
When I’m doing hyperparameter optimization and I am running a lot of experiments TensorBoard gets very cluttered. It’s hard to organize and compare things. Neptune UI is very clear, it’s intuitive, and scales with many runs. I can group my experiments by a parameter like dropout to see the impact it has on results. For us in research, it’s not just about the best model run, we need to understand how models perform on a deeper level.
I was looking for alternatives to PyTorch Lightning native logger. The main reason why I chose Neptune over TensorBoard was that you could just change the native logger to NeptuneLogger, pass your user token, and everything would work out-of-the-box. I didn’t have to change the code other than that one line. With TensorBoard I have to change the code to log things. I can customize it to log other things like text or images easily. Neptune just has a way better user experience.”
Biomedical AI Researcher
“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.”
Assistant Professor at Aarhus University
