Blog » ML Tools » The Best TensorBoard Alternatives (2021 Update)

The Best TensorBoard Alternatives (2021 Update)

TensorBoard is a visualization toolkit for TensorFlow that lets you analyse model training runs. It allows you to visualize various aspects of machine learning experiment, such as metrics, visualize model graphs, view tensors’ histograms and more.

To that end, TensorBoard fits in the raising need for tools to track and visualize machine learning experiments. While it lets you dig very deep in the experiments, it’s also important for machine learning teams to:

  • Easily share results with teammates,
  • Be able to log experiments for the whole team in 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.

Shared workspace for the entire team facilitates collaboration – without it, comparing experiments results come difficult and requires additional effort. Moreover, TensorBoard files are stored locally, hence lacking backup.

Overall, without additional capabilities, it comes difficult to make progress in projects and communicate in the team.

Yet there are so many tools to offer some combination of the above that it is overwhelming to select one that fits your needs.

Here, building on top of the previous comparisons like Hady Elsahar’s medium post, vivid reddit discussion, and our comparison, we present top alternatives to TensorBoard.

The Best Tools for Machine Learning Model Visualization

We present the following best alternatives for TensorBoard.

1. Neptune

Be more productive

Neptune is a metadata store for MLOps built for research and production teams that run a lot of experiments.

Neptune offers an open-source Python library that lets users log any experiments, hence use is not limited to deep learning.

Projects in Neptune can have multiple members, so all machine learning experiments land in the same place. Neptune is meant to provide an easy-to-use and quick-to-learn way to keep track of all metadata generated during the ML life cycle.

Neptune – summary:

  • Experiments tracking: any model training (classic machine learning, deep learning, reinforcement learning, optimization)
  • Sharing work in a team: Collaboration in the team is strongly supported by multiple features
  • Integrations with other tools: Open source integrations
  • Available as SaaS/Local instance: Yes/Yes.
  • Bonus: Notebooks tracking (both for Jupyter and Jupyter lab).

Read more about how Neptune approaches experiment management in this blog post.

2. Guild AI

It’s an open-source, machine learning platform to run and compare model training routines.

It is mainly a CLI tool that lets you run and compare training jobs in a systematic way, while Guild AI captures source code, logs, and generated files.

Unlike TensorBoard it is not restricted to TensorFlow/deep learning jobs. To the contrary Guild AI is platform and programming language agnostic, so you can freely use it with your current tech stack.

If you are a CLI-lover this can be a tool for you, most of the usage is via commands in the terminal.

Guild AI – summary:

  • Experiments tracking: any model training, any programming language
  • Sharing work in a team: not supported
  • Integrations with other tools: not supported
  • SaaS/Local instance available: No/Yes
  • Bonus: well-prepared documentation

👉 See the comparison between Guild AI and Neptune.

3. Sacred

Another 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.

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

One thing nice about Sacred is that it has automatic seeding – very useful when there is a need to reproduce an experiment.

Unlike TensorBoard – and similar to the tools in this comparison – strength of Sacred lies in its ability to track any model training developed with any Python library.

Sacred – summary:

  • Experiments tracking: any model training
  • Sharing work in team: not supported
  • Integrations with other tools: not supported
  • SaaS/Local instance available: No/Yes
  • 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.

4. 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 experiments.

Unlike TensorBoard, 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.

Similarly to TensorBoard, in the WandB users can log and analyse multiple data types.

Weights & Biases – summary:

  • Experiments tracking: any model training
  • Sharing work in a team: multiple features for sharing in a team.
  • Integrations with other tools: several open source integrations available
  • SaaS/Local instance available: Yes/Yes
  • Bonus: WandB logs the model graph, so you can inspect it later.

👉 See the comparison between Weight & Biases and Neptune.


Comet is a meta machine learning platform for tracking, comparing, explaining and optimizing experiments and models.

Just like many other tools – for example Neptune (neptune-client specifically) or WandB – Comet proposes open source Python library to allow data scientists integrate their code with Comet and start tracking work in the application.

As it’s offered both cloud-hosted and self-hosted, users can have team projects and save backup of experimentation history.

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

  • Experiments tracking: any model training
  • Sharing work in a team: multiple features for sharing in a team. 
  • Integrations with other tools: should be developed by the user manually
  • SaaS/Local instance available: Yes/Yes
  • Bonus: Display parallel plots to check patterns in the relationships between parameters and metrics.

👉 See the comparison between Comet and Neptune.

6. Valohai

Valohai takes a slightly different approach when it comes to tracking and visualizing experiments.

Platform proposes orchestration, version control and pipeline management for machine learning – simply speaking they cover what TensorBoard is doing in terms of logging work and additionally manage your compute infrastructure.

As in TensorBoard users can easily check and compare multiple runs. At the same time, differentiator is the ability to automate starting and shutting down cloud machines used for training.

Valohai lets you develop in any programming language – including Python and R – which can be handy in a team working in a fixed technological stack.

Valohai – summary:

  • Experiments tracking: any model training in any programming language
  • Sharing work in team: multiple features 
  • Integrations with other tools: examples of integrations provided in the documentation
  • SaaS/Local instance available: Yes/Yes

Bonus: With the infrastructure for training you can run experiments on the environment managed by Valohai.

To sum it up

It’s a good idea to choose one of the TensorBoard alternatives if you’re not sure whether the tool will meet your needs and if it doesn’t have all the features you need.

A good alternative will help you keep transparency in your projects, make collaboration with team easier, and improve your machine learning experiments.


TensorBoard vs Neptune: How Are They ACTUALLY Different

Aarshay Jain | Posted November 18, 2020

ML model development typically involves a tedious workflow of managing data, feature engineering, model training and evaluation. 

A data scientist could easily run in the order of hundreds of combinations of these things before converging onto a final model which solves the business problem. Managing those experiments, tracking progress and comparing them is an uphill battle which most data scientists fight everyday. 

There are multiple tools available to make this process easier and today we will take a look at two of them. This writeup will take you through a deep comparison of TensorBoard with Neptune, one of the modern experiment management tools. We will take you through a model development cycle and compare the utility of TensorBoard and Neptune at various steps of the process. For the purpose of this comparison, we will be using the digit recognition problem with the MNIST dataset.

Areas of comparison

We will compare TensorBoard and Neptune by dividing the ML model development process into the following parts:

  • Exploratory Data Analysis: perform ad-hoc analysis on data to help in deciding training parameters, feature engineering, etc.
  • Experiment Setup: provide means to store multiple experiments together as an entity to allow easy comparison in the future.
  • Model Training & Evaluation: train a model and look at the evaluation metrics to debug and compare performance.
  • Model Debugging: dig deeper into the training process and figure out what went wrong.
  • Hyperparameter Tuning: the ability to train multiple models, compare them easily, and pick a winner
  • Versioning: ability to add data/code/feature/model metadata for comparison.
  • Collaboration: allow multiple users to work together and manage access.
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