wandb.Table
class. Consider the following example of 3 embeddings, each consisting of 5 dimensions:2D Projection
from the upper right panel selector to plot the embeddings in 2 dimensions. Smart default will be automatically selected, which can be easily overridden in the configuration menu accessed by clicking the gear icon. In this example, we automatically use all 5 available numeric dimensions.2D Projection
we can configure the definition of the embedding, coloring, algorithm (PCA, UMAP, t-SNE), algorithm parameters, and even overlay (in this case we show the image when hovering over a point). In this particular case, these are all "smart defaults" and you should see something very similar with a single click on 2D Projection
. (Click here to interact with this example).list[int]
, list[float]
, or np.ndarray
.int
or float
for the cells.wandb.Table(dataframe=df)
wandb.Table(data=[...], columns=[...])
table.add_data(...)
table.add_col("col_name", ...)
table.add_computed_columns(lambda row, ndx: {"embedding": model.predict(row)})
2D Projection
, you can click the gear icon to edit the rendering settings. In addition to selecting the intended columns (see above), you can select an algorithm of interest (along with the desired parameters). Below you can see the parameters for UMAP and t-SNE respectively.