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https://github.com/wandb/wandb-workspaces

Programatically edit the W&B UI
https://github.com/wandb/wandb-workspaces

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Programatically edit the W&B UI

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# wandb-workspaces





`wandb-workspaces` is a Python library for programatically working with [Weights & Biases](https://wandb.ai) workspaces and reports.

## Quickstart



### 1. Install

```bash
pip install wandb-workspaces
```

OR, you can install this as an extra from the wandb library:

```bash
pip install wandb[workspaces]
```

### 2. Create a workspace

```python
import wandb_workspaces.workspaces as ws

workspace = ws.Workspace(
name="Example W&B Workspace",
entity="your-entity",
project="your-project",
sections=[
ws.Section(
name="Validation Metrics",
panels=[
wr.LinePlot(x="Step", y=["val_loss"]),
wr.BarPlot(metrics=["val_accuracy"]),
wr.ScalarChart(metric="f1_score", groupby_aggfunc="mean"),
],
is_open=True,
),
],
).save()
```

![image](https://github.com/wandb/wandb-workspaces/assets/15385696/796083f4-2aa6-432f-b585-c04abca9022f)

### 3. Create a report

```python
import wandb_workspaces.reports as wr

report = wr.Report(
entity="your-entity",
project="your-project",
title="Example W&B Report",
blocks=[
wr.H1("This is a heading"),
wr.P("Some amazing insightful text about your project"),
wr.H2(
"This heading is collapsed",
collapsed_blocks=[wr.P("Our model is great!")],
),
wr.PanelGrid(
panels=[
wr.LinePlot(x="Step", y=["val_loss"]),
wr.BarPlot(metrics=["val_accuracy"]),
wr.ScalarChart(metric="f1_score", groupby_aggfunc="mean"),
]
),
],
).save()
```

![image](https://github.com/wandb/wandb-workspaces/assets/15385696/25939b7c-1f2c-4df7-9936-692464e6e3fc)

## More examples



See [examples](https://github.com/wandb/wandb-workspaces/tree/main/examples) for more detailed usage.