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The Best Comet.ml Alternatives

Comet is one of the most popular tools used by people working on machine learning experiments. It is a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain, and optimize experiments and models.

Comet proposes an open-source Python library to allow data scientists to integrate their code with Comet and start tracking work in the application. As it’s offered both cloud-hosted and self-hosted, you should be able to manage ML experiments of your entire team.

Comet is converging towards more automated approaches to ML, by adding predictive early stopping (not available with the free version of the software) and announcing Neural architecture search (coming in the future).

Some of the Comet most notable features include:

  • Sharing work in a team: multiple features for sharing in a team
  • Works well with existing ML libraries
  • Deals with user management
  • Let’s you compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more
  • Let’s you visualize samples with dedicated modules for vision, audio, text and tabular data
  • Has a bunch of Integrations to connect it to other tools easily

Although Comet is a great solution, there is no tool that is perfect for everyone (at least I haven’t heard of it 🙂 ). It could be the lack of certain features, your personal preferences, pricing, or other factors that are crucial to you and your team.

Anyhow, there are many other tools available, and to help you find the right fit, we present a list of the best Comet.ml alternatives.

1. Neptune

Neptune is an experiment management and collaboration tool. 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 powerful software that can store, retrieve, and analyze a large amount of data. Neptune has all the tools for efficient team collaboration and project supervision.

Neptune – summary:

  • Provides user management and organization with a different organization, projects, and user roles
  • Enjoy fast and beautiful UI with a lot of capabilities to organize runs in groups, save custom dashboard views and share them with the team
  • 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)
  • Enjoy extensive experiment tracking and visualization capabilities (resource consumption, scrolling through lists of images)

👉 Check out the comparison between Comet & Neptune.

2. TensorBoard

tensorboard

TensorBoard is a visualization toolkit for TensorFlow that lets you analyze model training runs. It’s open-source and offers a suite of tools for visualization and debugging of machine learning models.

It allows you to visualize various aspects of machine learning experiments, such as metrics, visualize model graphs, view tensors’ histograms, and more.

It’s a great tool if you’re looking for a Comet alternative to visualize your experiments and dig very deep into them.

TensorBoard – summary:

  • You can log experiments for the whole team in a single place
  • Track experiments that are not based on TensorFlow or deep learning in general
  • Backup whole experimentation history
  • Quickly make reports for project stakeholders
  • Integrate tracking system with other tools from the technological stack
  • Visualization features available

👉 Check out the comparison between TensorBoard & Neptune.

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

And try our integration with TensorBoard.

3. Guild AI

Guild AI is used by machine learning engineers and researchers to run, track, and compare experiments. With Guild AI, you can leverage your experiment results to build deeper intuition, troubleshoot issues, and automate model architecture and hyperparameter optimization.

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:

  • Experiments tracking: any model training, any programming language
  • The automated machine learning process
  • Integrated with any language and library
  • Remote training and backup possibility
  • You can reproduce your results or recreate experiments

> See the comparison between Guild AI and Neptune

4. MLflow

MLflow is an open-source platform that helps manage the whole machine learning lifecycle that includes experimentation, reproducibility, deployment, and a central model registry. 

MLflow is suitable for individuals and for teams of any size. 

The tool is library-agnostic. You can use it with any machine learning library and in any programming language

MLflow comprises four main functions:

  1. MLflow Tracking – an API and UI for logging parameters, code versions, metrics, and artifacts when running machine learning code and for later visualizing and comparing the results
  2. MLflow Projects – packaging ML code in a reusable, reproducible form to share with other data scientists or transfer to production
  3. MLflow Models – managing and deploying models from different ML libraries to a variety of model serving and inference platforms
  4. MLflow Model Registry – a central model store to collaboratively manage the full lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations

👉 Check out the comparison between ML flow & Neptune!

Also, see our integration with MLflow

5. Weights & Biases

Weights & Biases a.k.a. WandB is focused on deep learning. Users track experiments to the application with Python library, and – as a team – can see each other’s experiments. It allows them to record experiments and visualize every part of the research. WandB is a hosted service allowing you to backup all experiments in a single place

Wandb – summary:

  • Deals with user management
  • Great UI allows users to visualize, compare, and organize their runs nicely
    Sharing work in a team: multiple features for sharing in a team
  • Integrations with other tools: several open-source integrations available

6. Sacred

Sacred is an open-source tool, developed at the research institution IDSIA (Swiss AI lab). Sacred is a Python library that helps configure, organize, log, and reproduce experiments.

The tool offers a programmatic way to work with configurations. The concept of Observer allows you to track various types of data associated with an experiment.

Moreover, Sacred has automatic seeding – very useful when there is a need to reproduce an experiment.

Sacred – summary:

  • Best for the individual user since sharing the work in a team is not supported
  • Experiments tracking: any model training
  • Integrations with other tools: not supported
  • Bonus: there are few front-ends for Sacred, so you can pick one that suits your needs best. Take a look at this particular integration with Neptune

To wrap it up

Whether you’re an individual or work in a team, there are plenty of tools to choose from when it comes to working on your ML experiment and models. Don’t forget to opt for the one that corresponds to your needs, style of work, and gives you enough flexibility to get the most out of your work.

Happy experimenting with your ML projects!


NEXT STEPS

Get started with Neptune in 5 minutes

If you are looking for an experiment tracking tool you may want to take a look at Neptune. 

It takes literally 5 minutes to set up and as one of our happy users said:

“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

To get started follow these 4 simple steps. 

Step 1

Install the client library.

pip install neptune-client

Step 2

Connect to the tool by adding a snippet to your training code. 

For example:

import neptune

neptune.init(...) # credentials
neptune.create_experiment() # start logger

Step 3

Specify what you want to log:

neptune.log_metric('accuracy', 0.92)

for prediction_image in worst_predictions:
    neptune.log_image('worst predictions', prediction_image)

Step 4

Run your experiment as you normally would:

python train.py

And that’s it!

Your experiment is logged to a central experiment database and displayed in the experiment dashboard, where you can search, compare, and drill down to whatever information you need.

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