Version Rollback
Imagine this scenario: your latest environment update broke your team's pipeline. You know version 2.0 was working fine last week. How do you get everyone back to that version quickly?
This example walks through versioning and rollback: Alice publishes multiple versions of an environment, discovers a problem, and rolls back to a known good version.
What You'll Need
- Nebi CLI installed
- Pixi installed
- Access to a Nebi server (see Server Setup)
Step 1: Create and push the initial version
Alice creates an environment with scikit-learn and a training task.
:::info Follow along Clone the example to follow along with this tutorial:
git clone https://github.com/nebari-dev/nebi.git
cd nebi/docs/examples/ml-pipeline
nebi init
:::
Here's her pixi.toml:
[workspace]
name = "ml-pipeline"
channels = ["conda-forge"]
platforms = ["linux-64", "linux-aarch64", "osx-arm64", "osx-64"]
version = "0.1.0"
[dependencies]
python = ">=3.11"
scikit-learn = ">=1.4"
[tasks]
train = """python -c "
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
model = DecisionTreeClassifier(random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(f'Accuracy: {accuracy_score(y_test, y_pred):.2f}')
" """
After creating the environment, Alice runs the training task to verify it works:
pixi run train
Accuracy: 1.00
Then pushes it to the server as v1.0:
nebi login http://localhost:8460
nebi push ml-pipeline:v1.0
Step 2: Push more versions
Over the next few weeks, Alice updates the environment. Each push creates a new tagged version on the server.
v2.0 adds pandas for data exploration:
pixi add "pandas>=2.2"
nebi push ml-pipeline:v2.0
v3.0 updates the train task to load data from a CSV file instead of the built-in dataset:
Alice edits the train task in pixi.toml to use pandas:
train = """python -c "
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
df = pd.read_csv('data.csv')
X, y = df.drop('target', axis=1), df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
model = DecisionTreeClassifier(random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(f'Accuracy: {accuracy_score(y_test, y_pred):.2f}')
" """
nebi push ml-pipeline:v3.0
v4.0 adds matplotlib for plotting:
pixi add "matplotlib>=3.8"
nebi push ml-pipeline:v4.0
Step 3: Discover the problem
A teammate pulls the latest version and runs the training task:
pixi run train
FileNotFoundError: [Errno 2] No such file or directory: 'data.csv'
The task fails because v3.0 changed it to read from a CSV file that doesn't exist.
To figure out which version introduced the broken task, Alice looks at the version history:
nebi workspace tags ml-pipeline
TAG VERSION CREATED
v4.0 5 2026-04-01 03:01
v3.0 4 2026-04-01 03:01
v2.0 3 2026-04-01 03:00
v1.0 2 2026-04-01 03:00
To narrow it down, Alice compares each pair of consecutive versions:
nebi diff ml-pipeline:v3.0 ml-pipeline:v4.0
--- ml-pipeline:v3.0
+++ ml-pipeline:v4.0
@@ pixi.toml @@
[dependencies]
+matplotlib = ">=3.8"
No task changes, just a new package. She checks the previous pair:
nebi diff ml-pipeline:v2.0 ml-pipeline:v3.0
--- ml-pipeline:v2.0
+++ ml-pipeline:v3.0
@@ pixi.toml @@
[tasks]
-train = "python -c \"\nfrom sklearn.datasets import load_iris\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\nX, y = load_iris(return_X_y=True)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n\nmodel = DecisionTreeClassifier(random_state=42)\nmodel.fit(X_train, y_train)\n\ny_pred = model.predict(X_test)\nprint(f'Accuracy: {accuracy_score(y_test, y_pred):.2f}')\n\" "
+train = "python -c \"\nimport pandas as pd\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\ndf = pd.read_csv('data.csv')\nX = df.drop('target', axis=1)\ny = df['target']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n\nmodel = DecisionTreeClassifier(random_state=42)\nmodel.fit(X_train, y_train)\n\ny_pred = model.predict(X_test)\nprint(f'Accuracy: {accuracy_score(y_test, y_pred):.2f}')\n\" "
There it is. The train task in v3.0 reads from data.csv instead of the built-in dataset. Alice now knows to roll back to v2.0.
Step 4: Roll back
Alice rolls back by pulling the last known good version:
nebi pull ml-pipeline:v2.0
Pulled ml-pipeline:v2.0
This replaces the local pixi.toml and pixi.lock with the v2.0 spec. To verify that the task works again, Alice runs it:
pixi run train
Accuracy: 1.00
The environment is now back to a working state!
Step 5: Roll back on the server
To restore the working version, Alice opens the Nebi UI, expands the version tagged v2.0, and clicks Rollback to This Version:
This creates a new version marked as Current, with the same content as v2.0:
The team can now pull the latest version to get the working environment:
nebi pull ml-pipeline
What Just Happened
Here's the full flow at a glance:
| Step | Command |
|---|---|
| Push initial version | nebi push :v1.0 |
| Push updates | nebi push :v2.0, :v3.0, :v4.0 |
| View version history | nebi workspace tags |
| Compare versions | nebi diff :v2.0 :v4.0 |
| Roll back on server | Nebi UI rollback button |
| Pull working version | nebi pull |
With nebi, every push is versioned and tagged. Rolling back is one click in the UI.
Next Steps
- See all CLI commands: CLI Reference