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.

graph LR
    A["v1.0"] --> B["v2.0 ✓"] --> C["v3.0"] --> D["v4.0 ✗"]
    D -->|rollback| B

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.

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

Output

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

Output

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

Output

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

Output

--- 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

Output

--- 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

Output

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

Output

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