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

The Best TensorBoard Alternatives (2021 Update)

TensorBoard is an open-source visualization toolkit for TensorFlow that lets you analyze model training runs. It allows you to track and visualize various aspects of machine learning experiments, such as metrics or model graphs, view tensors’ histograms of weights and biases, and more.

To that end, TensorBoard fits in the trend of the continuous need for tools to track and visualize machine learning experiments. While it lets you dig quite deep into experiments, there are some functionalities that TensorBoard doesn’t have, which proved to be useful in the experiment tracking process. 

The main challenges you might face when working with TensorBoard include: 

  • TensorBoard doesn’t scale well with more experiments;
  • The user experience is far from perfect when you want to compare multiple runs;
  • TensorBoard works locally, so when you operate on different machines, it’s difficult to keep track of everything;
  • Sharing results with other people is a pain – you just need to find a workaround to it (like screenshots), as there’s no out-of-the-box solution.

In general, TensorBoard seems to be a great tool when you just start with experiment tracking and visualization, or you don’t run a lot of experiments. It’s also convenient when you use TensorFlow for training (otherwise it’s not that easy to set up). But, it’s less advanced than other tools available on the market and doesn’t provide the best experience in a team environment. 

If you’ve come across any of these issues when working in TensorBoard, or just want to check what else is out there, you’re in the right place. 

Here are the best alternatives for TensorBoard that you should check out:

1. Neptune

Source

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

It gives you a single place to log, store, display, organize, compare, and query all your model-building metadata. This includes metrics and parameters, but also model checkpoints, images, videos, audio files, data versions, interactive visualizations, and more. You can also create custom dashboards that include all this metadata and share them with your colleagues, team manager, or even external stakeholders. Here’s an example of such a dashboard:

Computer-vision-dashboard
Example dashboard in Neptune | See in the app

Neptune is perfect if you work in a team. It lets you create projects with multiple team members, manage user access, share work, and have all the results backed up in one place. 

It’s also easy to evaluate models and compare runs, as there are four different comparison views available – charts, parallel coordinates, side-by-side tabular dashboard, and artifacts comparison section. 

Neptune—summary:

  • Scales with thousands of runs – no matter if you have 5 or 100 experiments, Neptune provides an equally great user experience;
  • The UI is clean, easy to navigate, and very intuitive;
  • Available in the on-premise version but also as a hosted app;
  • Quick and easy setup, and excellent customer support;
  • Collaboration in the team is strongly supported by multiple features.

If you want to see Neptune in action, check this live Notebook or this example project (no registration is needed) and just play with it. 

TensorBoard vs Neptune

TensorBoard is an open-source tool that can help with tracking and visualizing ML runs. Neptune, on the other hand, is a managed solution that offers more features in the experiment tracking area and also provides model registry, model monitoring, and data versioning capabilities. Neptune enables team collaboration and is more scalable than TensorBoard. 

Dig deeper

👉 Check an in-depth comparison between TensorBoard and Neptune.

👉 Read the case study of InstaDeep to learn why they switched from TensorBoard to Neptune.

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. On 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

TensorBoard vs Guild AI

The scope of Guild AI is much wider than what TensorBoard does. Guild AI allows you to track experiments, tune hyperparameters, automate pipelines, and more, while TensorBoard serves mostly to track and visualize runs. Guild AI can run on any cloud or on-prem environment. TensorBoard, on the other hand, is hosted locally.

Compare more tools

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 – the 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

Note: Sacred doesn’t come with its proper UI but there are a few dashboarding tools that you can connect to it, such as Omniboard, Sacredboard, or Neptune via integration.

TensorBoard vs Sacred

Both TensorBoard and Sacred are open-source and suitable for rather less advanced projects (in terms of scalability). TensorBoard comes with the UI, while you need to pair Sacred with a dashboarding tool, hence TensorBoard comes with better out-of-the-box visualization capabilities. 

Compare more tools

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.

Similar to TensorBoard, in the WandB users can log and analyze 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.

TensorBoard vs Weights & Biases

The first one, TensorBoard is an open-source tool that runs locally. WandB offers managed services that can be deployed on-premises but also run in the cloud. Here again, Weight & Biases provides wider functionality than TensorBoard, covering experiment tracking, dataset versioning, and model management. Apart from that, WandB has a lot of features that enable team collaboration, something that’s missing in TensorBoard.

Compare more tools

5. Comet

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

Just like many other tools – for example, Neptune or WandB – 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, users can have team projects and save a backup of experimentation history.

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

Comet—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.

TensorBoard vs Comet

Comet is a managed service, available on-premises or as a hosted application. TensorBoard is an open-source visualization and tracking tool that can be used locally. While Comet aims to enable data scientists to build better models across the whole model’s life cycle (from research to production), TensorBoard focuses on the experimentation phase. 

Compare more tools:

To sum it up

When looking for an experiment tracking and visualization tool for the first time, TensorBoard often seems to be a good choice. It’s open-source and provides all the necessary features. But the more you work with it, and the more your needs grow, you can notice some missing pieces of the puzzle. That’s why it’s good to check what else is available out there and see if other tools could check more boxes on the list of your needs. 

If that’s the case for you – you’re looking for a more advanced tool, sort of next step after using TensorBoard – we recommend checking Neptune or Weights & Biases. These are great managed services with tons of features and team collaboration capabilities. In case you just want to switch to another open-source solution, Sacred might be the way to go. 

No matter your motivation, hopefully, you found some worth-checking alternatives to TensorBoard here, and we helped you make the right choice! 


READ NEXT

InstaDeep Case Study: Looking for Collaboration Features and One Central Place for All Experiments

5 mins read | Updated November 22th, 2021

InstaDeep is an EMEA leader in delivering decision-making AI products. Leveraging their extensive know-how in GPU-accelerated computing, deep learning, and reinforcement learning, they have built products, such as the novel DeepChain™ platform, to tackle the most complex challenges across a range of industries. 

InstaDeep has also developed collaborations with global leaders in the AI ecosystem, such as Google DeepMind, NVIDIA, and Intel. They are part of Intel’s AI Builders program and are one of only 2 NVIDIA Elite Service Delivery Partners across EMEA. The InstaDeep team is made up of approximately 155 people working across its network of offices in London, Paris, Tunis, Lagos, Dubai, and Cape Town, and is growing fast.

About the BioAI team

The BioAI team is the place at InstaDeep where Biology meets Artificial intelligence. At BioAI, they advance healthcare and push the boundaries of medical science through a combination of biology and machine learning expertise. They are currently building DeepChain™, their platform for protein design. They are also working with their customers in the bio sector to tackle the most challenging problems with the help of bioinformatics and machine learning.

Deepchain dashboard
DeepChain dashboard | Source

They apply the DeepChain™ protein design platform to engineer new sequences for protein targets using sophisticated optimization techniques such as reinforcement learning and evolutionary algorithms. They also leverage Language Models pre-trained on millions of protein sequences and train their own in-house protein language models. Finally, they use machine learning to predict protein structure from sequence.

Problem

Building complex software like DeepChain™, a platform for protein design, requires a lot of research with different moving parts. Customers demand various types of solutions that require new experiments and research every time. With several experiments running for different customers, it will be unavoidably daunting for a team of any size to keep track of the experiments while ensuring they remain productive.

Fazed with the thought of managing numerous experiments, Nicolas and the BioAI team encountered a series of challenges:

  • 1Experiment logs were all over the place
  • 2It was difficult to share experiment results
  • 3Machine learning researchers were dealing with infrastructure and operations
Continue reading ->
How to Make Your TensorBoard Projects Easy to Share and Collaborate On

How to Make Your TensorBoard Projects Easy to Share and Collaborate On

Read more
MLflow vs TensorBoard vs Neptune What Are the Differences

MLflow vs TensorBoard vs Neptune: What Are the Differences?

Read more
Deep Dive into TensorBoard: Tutorial With Examples

Deep Dive Into TensorBoard: Tutorial With Examples

Read more
Best MLOps tools

The Best MLOps Tools and How to Evaluate Them

Read more