We Raised $8M Series A to Continue Building Experiment Tracking and Model Registry That “Just Works”

Read more

Neptune vs Sacred + Omniboard

neptune ai blue horizontal 1
vs
Sacred Omniboard logotypes
Commercial Requirements
Standalone component or a part of a broader ML platform?

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

Omniboard is a web dashboard for the Sacred machine learning experiment management tool

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

Can be deployed both on-premises and/or on the cloud, but has to be self-managed

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

Managed cloud service

Open-source

SLOs / SLAs: Does the vendor provide guarantees around service levels?

No

Support: Does the vendor provide 24×7 support?

No

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

No

Security policy and compliance

None

General Capabilities
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

Sacred just needs python and pip installed, with internet access. Omniboard additionally requires Node.js v12+

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

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

Minimal. Only a few lines of code need to be added

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

Yes, through the neptune-client library

Yes, through the sacred python library, and their CLI

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

No

No

How can you access model metadata?

– gRPC API

No

No

– CLI / custom API

Yes

No

– REST API

No

No

– Python SDK

Yes

No

– R SDK

Yes

No

– Java SDK

No

No

– Julia SDK

No

No

Supported operations

– Search

Yes

No

– Delete

Yes

– Download

Yes

Yes

Distributed training support

Yes

No

Pipelining support

Yes

No

Logging modes

– Offline

Yes

Yes

– Debug

Yes

No

– Asynchronous

Yes

Yes

– Synchronous

Yes

No

Live monitoring

Yes

Yes

Mobile support

No

No

Webhooks and notifications

No

Experiment Tracking
Dataset
– location (path/s3)

Yes

Yes

– hash (md5)

Yes

Yes

– Preview table

Yes

No

– Preview image

No

– Preview text

Yes

No

– Preview rich media

No

– Multifile support

Yes

No

– Dataset slicing support

No

No

Code versions

– Git

Yes

Yes

– Source

Yes

Yes

– Notebooks

Yes

No

Parameters

Yes

No

Metrics and losses

– Single values

Yes

No

– Series values

Yes

Yes

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

Yes

No

Tags

Yes

Yes

Descriptions/comments

Yes

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

No

– Neural Network Histograms

No

No

– Prediction visualization (tabular)

No

No

– Prediction visualization (image)

No

No

Hardware consumption

– CPU

Yes

Yes

– GPU

Yes

Yes

– TPU

No

No

– Memory

Yes

No

System information

– Console logs (Stderr, Stdout)

Yes

Yes

– Error stack trace

Yes

No

– Execution command

No

No

– System details (host, user, hardware specs)

Yes

Yes

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)

No

No

Table format diff

Yes

No

Overlayed learning curves

Yes

Yes

Parameters and metrics

– Groupby on experiment values (parameters)

Yes

No

– Parallel coordinates plots

Yes

No

– 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

Yes

– Neural Network Histograms

No

No

– Prediction visualization (tabular)

Yes

No

– Prediction visualization (image, video, audio)

No

No

Code

– Git

No

No

– Source files

No

Yes

– 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

Yes

– 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

No

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

Experiment table customization

– Adding/removing columns

Yes

Yes

– 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

Yes

– Automagical column suggestion

Yes

No

Experiment filtering and searching

– Searching on multiple criteria

Yes

No

– Query language vs fixed selectors

Query language

Fixed Selectors

– 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

Yes

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

Sharing UI links with project members

Yes

No

Sharing UI links with external people

Yes

No

Commenting

Yes

No

Interactive project-level reports

No

No

Model Registry
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)

No

No

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

Main stage transition tags (develop, stage, production)

No

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

No

Compatibility audit (input/output schema)

No

No

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

No

No

Webhooks

No

No

Model accessibility

No

No

Support for continuous testing

No

No

Integrations with CI/CD tools

No

No

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

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

No

Integrations and Support
Java

No

No

Julia

No

No

Python

Yes

Yes

R

No

REST API

No

No

Catalyst

Yes

No

CatBoost

No

No

fastai

Yes

No

FBProphet

No

No

Gluon

No

No

HuggingFace

Yes

No

H2O

No

No

lightGBM

Yes

No

Paddle

No

No

PyTorch

Yes

No

PyTorch Ignite

No

PyTorch Lightning

Yes

No

Scikit Learn

Yes

No

Skorch

No

Spacy

No

No

Spark MLlib

No

No

Statsmodel

No

No

TesorFlow / Keras

Yes

Yes

XGBoost

Yes

No

Hyperopt

No

No

Keras Tuner

No

Optuna

Yes

No

Ray Tune

No

No

Scikit-Optimize

No

DALEX

No

Netron

No

No

SHAP

No

No

TensorBoard

No

JupyterLab and Jupyter Notebook

Yes

Yes

Google Colab

Yes

Yes

Deepnote

Yes

No

AWS SageMaker

Yes

No

Airflow

No

No

Argo

No

No

Kedro

Yes

No

Kubeflow

No

No

MLflow

No

Sacred

Yes

N/A

TensorBoard

No

GitHub Actions

No

Gitlab CI

No

No

CircleCI

No

No

Travis

No

No

Jenkins

No

No

Seldon

No

No

Cortex

No

No

Databricks

No

No

Seldon

No

No

Fiddler.ai

No

No

Arthur.ai

No

No

This page was updated on 30 January 2022. Some information may be outdated.
Report outdated information here.

What are the key advantages of Neptune, then?

  • Highly developed and customizable comparison features
  • Scalability with thousands of runs
  • User management and team collaboration features
  • 25+ out-of-the box integrations with Python libraries and IDEs
Explore features
Get started with Neptune

See these features in action

1. Create a free account
Sign up
2. Install Neptune client library
pip install neptune-client
3. Add logging to your script
import neptune.new as neptune

run = neptune.init('Me/MyProject')
run['params'] = {'lr':0.1, 'dropout':0.4}
run['test_accuracy'] = 0.84
4. Or see how it works in a notebook (no registration)
Try live notebook

Already using Sacred?

Instead of Omniboard, you can integrate Sacred with Neptune and use it’s UI to manage your experiments. 

All you need to do is add a NeptuneObserver() to your sacred experiment’s observers: 

# Create sacred experiment
ex = Experiment('image_classification', interactive=True)

# Add NeptuneObserver
ex.observers.append(NeptuneObserver(run=neptune_run))
Learn more
Sacred Neptune integration

Thousands of ML people already chose their tool

“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

(…) 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
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

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
Such a fast setup! Love it:)
Kobi Felton
PhD student in Music Information Processing at Télécom Paris

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
Load more

It only takes 5 minutes to integrate Neptune with your code.

Don’t overthink it.

Sign up now