Try pre-release features¶
At Streamlit, we like to move quick while keeping things stable. In our latest effort to move even faster without sacrificing stability, we’re offering our bold and fearless users two ways to try out Streamlit’s most bleeding edge features:
At the end of each day (at night 🌛), our bots run automated tests against the latest Streamlit code and, if everything looks good, it publishes them as the
streamlit-nightly package. This means the nightly build includes all our latest features, bug fixes, and other enhancements on the same day they land on our codebase.
How does this differ from official releases?
Official Streamlit releases go not only through both automated tests but also rigorous manual testing, while nightly releases only have automated tests. It’s important to keep in mind that new features introduced in nightly releases often lack polish. In our official releases, we always make double-sure all new features are ready for prime time.
How do I use the nightly release?
All you need to do is install the
pip uninstall streamlit pip install streamlit-nightly --upgrade
You should never have both streamlit and streamlit-nightly installed in the same environment!
Why should I use the nightly release?
Because you can’t wait for official releases, and you want to help us find bugs early!
Why shouldn’t I use the nightly release?
While our automated tests have high coverage, there’s still a significant likelihood that there will be some bugs in the nightly code.
Can I choose which nightly release I want to install?
If you’d like to use a specific version, you can find the version number in our Release history. Just specify the desired version using
pip as usual:
pip install streamlit-nightly==x.yy.zz-123456.
Can I compare changes between releases?
If you’d like to review the changes for a nightly release, you can use the comparison tool on GitHub.
Beta and Experimental Features¶
In addition to nightly releases, we also have two naming conventions for less stable Streamlit features:
st.experimental_. These distinctions are prefixes we attach to our function names to make sure their status is clear to everyone.
Here’s a quick rundown of what you get from each naming convention:
st: this is where our core features like
st.dataframelive. If we ever make backward-incompatible changes to these, they will take place gradually and with months of announcements and warnings.
beta_: this is where all new features land before they becoming part of Streamlit core. This gives you a chance to try the next big thing we’re cooking up weeks or months before we’re ready to stabilize its API.
experimental_: this is where we’ll put features that may or may not ever make it into Streamlit core. We don’t know whether these features have a future, but we want you to have access to everything we’re trying, and work with us to figure them out.
The main difference between
experimental_ is that beta features are expected to make it into Streamlit core at some point soon, while experimental features may never make it.
Features with the
beta_ naming convention are all scheduled to become part of Streamlit core. While in beta, a feature’s API and behaviors may not be stable, and it’s possible they could change in ways that aren’t backward-compatible.
The lifecycle of a beta feature
A feature is added with the
The feature’s API stabilizes and the feature is cloned without the
beta_prefix, so it exists as both
beta_. At this point, users will see a warning when using the version of the feature with the
beta_prefix – but the feature will still work.
At some point, the code of the
beta_-prefixed feature is removed, but there will still be a stub of the function prefixed with
beta_that shows an error with appropriate instructions.
Finally, at a later date the
beta_version is removed.
Features with the
experimental_ naming convention are things that we’re still working on or trying to understand. If these features are successful, at some point they’ll become part of Streamlit core, by moving to the
beta_ naming convention and then to Streamlit core. If unsuccessful, these features are removed without much notice.
Experimental features and their APIs may change or be removed at any time.
The lifecycle of an experimental feature
A feature is added with the
The feature is potentially tweaked over time, with possible API/behavior breakages.
At some point, we either promote the feature to
beta_or remove it from
experimental_. Either way, we leave a stub in
experimental_that shows an error with instructions.
Let us know if you have any questions or feedback about the new namespaces!