For when you want to monitor model training at scale
Your experiments have grown to 100s of runs. Your team has grown too.
Sluggish performance at runtime and clumsy workarounds for collaboration
are slowing you down. It’s time you tried Neptune.
Take a deep dive into the differences between MLflow, TensorBoard and Neptune
MLflow
TensorBoard
Neptune
Commercial offering
Open-source
Open-source
Managed cloud service
Free
Free
User based and usage based (ingestion data points)
General information
No. However, it’s available on a managed server as part of the Databricks platform.
No
Yes
Yes
Yes
Yes
Basic logging can be done by having just TensorBoard installed. However, most advanced logging also requires TensorFlow to be installed.
Minimal setup—install the Python client and ensure internet access (for managed hosting). Self-hosting requires additional infrastructure; see requirements here.
A few lines of code via Python, REST, R, Java, or CLI.
A few lines of code via client library and CLI.
A few lines of code via the Neptune Python library.
CLI/custom API, REST API, Python SDK, R SDK, Java SDK
Python SDK, R SDK (limited), Julia SDK (limited)
CLI/custom API and Python SDK
Search, Update (limited), Delete, Download
Search, Delete, Download
Search, Update, Delete, Download
Offline, Disabled/off, Asynchronous, Synchronous
Offline, Asynchronous
Offline, Disabled/Off, Asynchronous
Yes
Yes
Yes
No
No
No
Capabilities
Plots
Plots, Audio
Images, Plots, Video, Audio
Limited
Limited
No
Execution command
No
Console logs, Execution command
pip requirements.txt, conda env.yml, Docker Dockerfile
No
No
Regex with limited query language
No
No
No
No
No
No
No
No
No
Report outdated information here.
Your team doesn’t do “slow”
Neither does Neptune
Neptune is the tracking solution for teams frustrated with the limitations of MLflow and TensorBoard that block collaboration and training models at scale.
