Compare

Neptune vs. TensorBoard

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TensorBoard

Commercial Requirements

Commercial requirements chevron
Standalone component or a part of a broader ML platform?

Standalone component. ML metadata store that focuses on experiment tracking and model registry

Open source tool which is a part of the TensorFlow ecosystem

Is the product available on-premises and / or in your private/public cloud?

TensorBoard is hosted locally. No
TensorBoard.dev is available on a managed server as a free service

Is the product delivered as commercial software, open-source software, or a managed cloud service?

Managed cloud service

TensorBoard is open-source, while TensorBoard.dev is available as a free managed cloud service

Support: Does the vendor provide 24×7 support?

No

SSO, ACL: does the vendor provide user access management?

No

Security policy and compliance

No

General Capabilities

Setup chevron
What are the infrastructure requirements?

No special requirements other than having the neptune-client installed and access to the internet if using managed hosting. Check here for infrastructure requirements for on-prem deployment

Basic logging can be done by having just TensorBoard installed. However, most advanced logging also requires TensorFlow to be installed

How much do you have to change in your training process?

Minimal. Just a few lines of code needed for tracking. Read more

Minimal if already using the TensorFlow framework, else significant

Does it integrate with the training process via CLI/YAML/Client library?

Yes, through the neptune-clientlibrary

TensorBoard is available both as a client library and CLI. TensorBoard .dev is available only as a CLI

Does it come with a web UI or is it console-based?

Web UI

Serverless UI

No

Yes

Flexibility, speed, and accessibility chevron
Customizable metadata structure

Yes

No

How can you access model metadata?
– gRPC API

No

No

– CLI / custom API

Yes

No

– REST API

No

No

– Python SDK

Yes

Yes

– R SDK

Yes

– Java SDK

No

– Julia SDK

No

No

Supported operations
– Search

Yes

Limited

– Update

Yes

No

– Delete

Yes

Yes

– Download

Yes

Yes

Distributed training support

Yes

Yes

Pipelining support

Yes

No

Logging modes
– Offline

Yes

Yes

– Debug

Yes

– Asynchronous

Yes

Yes

– Synchronous

Yes

No

Live monitoring

Yes

Yes

Mobile support

No

No

Webhooks and notifications

No

No

Experiment Tracking

Log and display of metadata chevron
Dataset
– location (path/s3)

Yes

No

– hash (md5)

Yes

No

– Preview table

Yes

No

– Preview image

Yes

Limited (requires converting the image to tf.summary.image format)

– Preview text

Yes

Yes (requires converting the text to tf.summary.text format)

– Preview rich media

Limited (requires converting supported media-types using tf.summary)

– Multifile support

Yes

No

– Dataset slicing support

No

No

Code versions

No

– Git

Yes

No

– Source

Yes

No

– Notebooks

Yes

No

Parameters

Yes

No

Metrics and losses
– Single values

Yes

Yes

– Series values

Yes

Yes

– Series aggregates (min/max/avg/var/last)

Yes

No

Tags

Yes

Yes

Descriptions/comments

Yes

Rich format
– Images (support for labels and descriptions)

Yes

No

– Plots

Yes

Yes

– Interactive visualizations (widgets and plugins)

Yes

No

– Video

Yes

No

– Audio

Yes

Yes (requires conversion using tf.summary.audio)

– Neural Network Histograms

No

Yes

– Prediction visualization (tabular)

No

No

– Prediction visualization (image)

No

– Prediction visualization (image – interactive confusion matrix for image classification)

No

NA

– Prediction visualization (image – overlayed prediction masks for image segmentation)

No

NA

– Prediction visualization (image – overlayed prediction bounding boxes for object detection)

No

NA

Hardware consumption
– CPU

Yes

No

– GPU

Yes

No

– TPU

No

No

– Memory

Yes

No

System information
– Console logs (Stderr, Stdout)

Yes

No

– Error stack trace

Yes

No

– Execution command

No

No

– System details (host, user, hardware specs)

Yes

No

Environment config
– pip requirements.txt

Yes

No

– conda env.yml

Yes

No

– Docker Dockerfile

Yes

No

Files
– Model binaries

Yes

No

– CSV

Yes

No

– External file reference (s3 buckets)

Yes

No

Comparing experiments chevron
Table format diff

Yes

No

Overlayed learning curves

Yes

Yes

Parameters and metrics
– Groupby on experiment values (parameters)

Yes

No

– Parallel coordinates plots

Yes

Yes (requires TensorFlow and the TensorBoard HParams plugin)

– Parameter Importance plot

No

No

– Slice plot

No

No

– EDF plot

No

No

Rich format (side by side)
– Image

Yes

No

– Video

No

No

– Audio

No

No

– Plots

No

No

– Interactive visualization (HTML)

No

No

– Text

Yes

No

– Neural Network Histograms

No

Yes (requires TensorFlow installed)

– Prediction visualization (tabular)

Yes

No

– Prediction visualization (image, video, audio)

No

No

Code
– Git

No

No

– Source files

No

No

– Notebooks

Yes

No

Environment
– pip requirements.txt

No

No

– conda env.yml

No

No

– Docker Dockerfile

No

No

Hardware
– CPU

Yes

No

– GPU

Yes

No

– Memory

Yes

No

System information
– Console logs (Stderr, Stdout)

Yes

No

– Error stack trace

Yes

No

– Execution command

No

No

– System details (host, owner)

Yes

No

Data versions
– Location

Yes

No

– Hash

Yes

No

– Dataset diff

Yes

No

– External reference version diff (s3)

No

No

Files
– Models

No

No

– CSV

No

No

Custom compare dashboards
– Combining multiple metadata types (image, learning curve, hardware)

Yes

No

– Logging custom comparisons from notebooks/code

Yes

No

– Compare/diff of multiple (3+) experiments/runs

Yes

No

Organizing and searching experiments and metadata chevron
Experiment table customization
– Adding/removing columns

Yes

No

– Renaming columns in the UI

Yes

No

– Adding colors to columns

Yes

No

– Displaying aggregate (min/max/avg/var/last) for series like training metrics in a table

Yes

No

– Automagical column suggestion

Yes

No

Experiment filtering and searching
– Searching on multiple criteria

Yes

Limited

– Query language vs fixed selectors

Query language

Regex with limited query language on the TensorBoard.dev experiments homepage

– Saving filters and search history

Yes

No

Custom dashboards for a single experiment
– Can combine different metadata types in one view

Yes

No

– Saving experiment table views

Yes

No

– Logging project-level metadata

Yes

No

– Custom widgets and plugins

No

No

Tagging and searching on tags

Yes

Yes

Nested metadata structure support in the UI

Yes

No

Reproducibility and traceability chevron
One-command experiment re-run

No

No

Experiment lineage
– List of datasets used downstream

No

No

– List of other artifacts (models) used downstream

No

No

– Downstream artifact dependency graph

No

No

Reproducibility protocol

Limited

No

Is environment versioned and reproducible

Yes

No

Saving/fetching/caching datasets for experiments

No

No

Collaboration and knowledge sharing chevron
Sharing UI links with project members

Yes

Only in TensorBoard.dev

Sharing UI links with external people

Yes

Only in TensorBoard.dev

Commenting

Yes

No

Interactive project-level reports

No

No

Model Registry

Model versioning chevron
Code versions (used for training)

Yes

No

Environment versions

No

No

Parameters

Yes

No

Dataset versions

Yes

No

Results (metrics, visualizations)

Yes

No

Explanations (SHAP, DALEX)

No

Model files (packaged models, model weights, pointers to artifact storage)

Yes

No

Model lineage and evaluation history chevron
Models/experiments created downstream

No

No

History of evaluation/testing runs

No

No

Support for continuous testing

No

No

Users who created a model or downstream experiments

No

No

Access control, model review, and promoting models chevron
Main stage transition tags (develop, stage, production)

Yes

No

Custom stage tags

No

No

Locking model version and downstream runs, experiments, and artifacts

No

No

Adding annotations/comments and approvals from the UI

No

No

Model compare (current vs challenger etc)

No

Compatibility audit (input/output schema)

No

No

Compliance audit (datasets used, creation process approvals, results/explanations approvals)

No

No

CI/CD/CT compatibility chevron
Webhooks

No

No

Model accessibility

No

No

Support for continuous testing

No

No

Integrations with CI/CD tools

No

No

Model searching chevron
Registered models

No

No

Active models

No

No

By metadata/artifacts used to create it

No

No

By date

No

No

By user/owner

No

No

By production stage

No

No

Search query language

No

No

Model packaging chevron
Native packaging system

No

No

Compatibility with packaging protocols (ONNX, etc)

No

No

One model one file or flexible structure

No

No

Integrations with packaging frameworks

No

Yes

Integrations and Support

Languages chevron
Java

No

No

Julia

No

No

Python

Yes

Yes

REST API

No

No

Model training chevron
Catalyst

Yes

Yes

CatBoost

No

Yes

fastai

Yes

Yes

FBProphet

Yes

No

Gluon

No

Yes

HuggingFace

Yes

Yes

H2O

No

Yes

LightGBM

Yes

No

Paddle

No

No

PyTorch

Yes

Yes

PyTorch Ignite

Yes

PyTorch Lightning

Yes

Yes

Scikit Learn

Yes

No

Skorch

Yes

Yes

Spacy

No

No

Spark MLlib

No

No

Statsmodel

No

No

TesorFlow / Keras

Yes

Yes

XGBoost

Yes

No

Hyperparameter Optimization chevron
Hyperopt

No

No

Keras Tuner

No

Optuna

Yes

Yes

Ray Tune

No

Yes

Scikit-Optimize

No

Model visualization and debugging chevron
DALEX

No

Netron

No

SHAP

No

No

TensorBoard

NA

IDEs and Notebooks chevron
JupyterLab and Jupyter Notebook

Yes

Yes

Google Colab

Yes

Yes

Deepnote

Yes

Yes

AWS SageMaker

Yes

Yes

Data versioning chevron
DVC

Yes

No

Orchestration and pipelining chevron
Airflow

No

No

Argo

No

Yes

Kedro

Yes

No

Kubeflow

No

Yes

Experiment tracking tools chevron
MLflow

No

Sacred

Yes

No

TensorBoard

NA

CI/CD chevron
GitHub Actions

No

Gitlab CI

No

No

CircleCI

No

No

Travis

No

No

Jenkins

No

No

Model serving chevron
Seldon

No

No

Cortex

No

Databricks

No

Yes

Model versioning chevron
Seldon

No

No

Fiddler.ai

No

No

Arthur.ai

No

No

This table was updated on 8 August 2021. Some information may be outdated.
Report outdated information here.

What are the key advantages of Neptune then?

Computer vision dashboard
  • Easy, one-time setup to have all metadata in one place
  • Intuitive, out-of-the box runs comparison
  • Hosted and sharable environment
  • User management and team collaboration features
  • Scalability with thousands of runs

See these features in action

1

Sign up to Neptune and install client library

pip install neptune-client
2

Track experiments

import neptune.new as neptune

run = neptune.init_run()
run["params"] = {
    "lr": 0.1, "dropout": 0.4
}
run["test_accuracy"] = 0.84
3

Register models

import neptune.new as neptune

model = neptune.init_model()
model["model"] = {
    "size_limit": 50.0,
    "size_units": "MB",
}
model["model/signature"].upload(
    "model_signature.json")
decor

Already using TensorBoard?

Team workspace

Convert your TensorBoard logs directory with Neptune:

neptune tensorboard --project USER_NAME/PROJECT_NAME

Or connect a new project in 5 minutes.

You can use a cloud-hosted service or have Neptune on your own servers.

Want to know why others switched to Neptune?

Product experiment autodif table

InstaDeep, an EMEA leader in delivering decision-making AI products, switched from TensorBoard to Neptune.

Read about the biggest challenges they faced with TensorBoard, and why Neptune helped them manage the experimentation process better.

Thousands of ML people already chose their tool

quote
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.
Simon Mackenzie AI Engineer and Data Scientist
quote
(…) 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.
Klaus-Michael Lux Data Science and AI student, Kranenburg, Germany
quote
This thing is so much better than Tensorboard, love you guys for creating it!
Dániel Lévai Junior Researcher at Rényi Alfréd Institute of Mathematics in Budapest, Hungary
quote
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.
Greg Rolwes Computer Science Undergraduate at Saint Louis University
quote
Such a fast setup! Love it:)
Kobi Felton PhD student in Music Information Processing at Télécom Paris
quote
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.
Víctor Peinado Senior NLP/ML Engineer

It only takes 5 minutes to integrate Neptune with your code

Don’t overthink it

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