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The Best Tools to Monitor Machine Learning Experiment Runs

Monitoring machine learning experiment runs is an important and healthy practice but it can be a challenge. Main problems are: 

  • You cannot look at your console logs all the time, 
  • When you look at logs you don’t see the change over time immediately (think learning curve vs losses on epoch 10),
  • Sometimes you can’t even access the model training environment.

And that’s where tools come in handy! You can use them to flexibly monitor your ML experiments and look at model training information whenever you need to. Especially if you don’t have access to the machine (computational cluster at University, VPN at work, Cloud server you’re using somewhere, or when you’re on a bus :)).

Monitoring ML experiments with dedicated tools gives you the comfort of knowing what is going on with your training runs. That is especially true if you want to go beyond watching your learning curve and want to see additional information like performance charts, or prediction visualizations after every epoch.

Check out our list of the best tools that will make monitoring your machine learning experiment runs a breeze!

1. Neptune

Use computational resources

Neptune is a metadata store for MLOps built for research and production teams that run a lot of experiments. It is very flexible, works with many other frameworks, and thanks to its stable user interface, it enables great scalability (to millions of runs).

It’s a robust software that can store, retrieve, and analyze a large amount of data. Neptune has all the tools for efficient monitoring of ML experiment runs. You can also integrate it with other tools for more flexibility.


  • Fast and beautiful UI with a lot of capabilities to organize runs in groups, save custom dashboard views and share them with the team
  • Provides user and organization management with a different organization, projects, and user roles
  • You can use a hosted app to avoid all the hassle with maintaining yet another tool (or have it deployed on your on-prem infrastructure)
  • Your team can track experiments which are executed in scripts (Python, R, other), notebooks (local, Google Colab, AWS SageMaker) and do that on any infrastructure (cloud, laptop, cluster)
  • Extensive experiment tracking and visualization capabilities (resource consumption, scrolling through lists of images)

2. TensorBoard


TensorBoard is a visualization toolkit for TensorFlow that lets you analyze model training runs. It’s open-source and has functionalities helpful in the entire machine learning workflow.

Additionally, it has an extensive network of engineers using this software and sharing their experience and ideas. This makes a powerful community ready to solve any problem. The software, itself, however, is best suited for an individual user.


  • Track and visualize metrics such as loss and accuracy
  • Compare learning curves of various runs
  • Parallel coordinates plot to visualize parameter-metric interactions
  • It has other visualization features that are not parameter-metric related
  • Project embeddings to a lower dimensional space

Do you know that you can integrate TensorBoard with Neptune? Check it out here.

See also: The Best TensorBoard Alternatives (2020 Update).

And make sure to see the comparison between TensorBoard & Neptune.

3. Hyperdash

Hyperdash is another tool helpful in monitoring machine learning experiment runs. It’s a cloud-based solution for those who like flexibility and are focused on fast knowledge gaining. It’s a straightforward tool and can be used with scripts and Jupyter.

Interestingly, unlike most of the tools, Hyperdash is available on mobile devices (iOS, Android) so you can monitor your experiments no matter where you are.


  • Track hyperparameters across different model experiments
  • Graphs performance metrics in real-time
  • Notifications when a long-running experiment is finished

4. Guild AI

Guild AI

Guild AI is a tool for running, tracking, and comparing experiments. Guild AI is cross-platform and framework independent — you can train and capture experiments in any language using any library.

Guild AI runs your unmodified code so you get to use the libraries you want. The tool doesn’t require databases or other infrastructure to manage experiments — it’s simple and easy to use. 

Guild AI–summary:

  • Track experiment of any model training and any programming language
  • Has automated machine learning process
  • Integrated with any language and library
  • Remote training and backup possibility
  • Reproduce results or recreate experiments

⇒ See the comparison between Guild AI and Neptune

5. WandB

Weights & Biases

Weights & Biases a.k.a. WandB is focused on deep learning. Users can track experiments to the application with Python library, and – as a team – can see each other’s experiments.

WandB is a hosted service allowing you to backup all experiments in a single place and work on a project with the team – work sharing features are there to use.

In the WandB users can log and analyze multiple data types.

Weights & Biases – summary:

  • Monitor training runs information like loss, accuracy (learning curves)
  • View histograms of weights and biases (no pun intended), or gradients
  • Log rich objects like, charts, video, audio or interactive charts during training
  • Use various comparison tools like tables showing auto-diffs, parallel coordinates plot and others


Now that you have the right tools, you can freely monitor ML experiment runs from any place in the world. Use them to optimize your work, save time, and work more efficiently.

Enjoy monitoring your machine learning experiment runs!


A Complete Guide to Monitoring ML Experiments Live in Neptune

Jakub Czakon | Posted July 21, 2020

Training machine learning or deep learning models can take a really long time.

If you are like me, you like to know what is happening during that time:

  • want to monitor your training and validation losses,
  • take a look at the GPU consumption,
  • see image predictions after every other epoch
  • and a bunch of other things.

Neptune lets you do all that, and in this post I will show you how to make it happen. Step by step.

Check out this example run monitoring experiment to see how this can look like. 

Continue reading ->

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