Log tables
8 minute read
Visualize and log tabular data with W&B Tables. A W&B Table is a two-dimensional grid of data where each column has a single type of data. Each row represents one or more data points logged to a W&B run. W&B Tables support primitive and numeric types, as well as nested lists, dictionaries, and rich media types.
W&B Tables are a specialized data type in W&B that are logged internally in W&B as artifacts objects.
You create and log table objects using the W&B Python SDK. When you create a table object, you specify the columns and data for the table and a mode. The mode determines how the table is logged and updated during your ML experiments.
Create and log a table
- Initialize a new run with
wandb.init()
. - Create a table object with the
wandb.Table
Class. Specify the columns and data for the table for thecolumns
anddata
parameters, respectively. Though optional, it is recommended to set thelog_mode
parameter to one of the three modes:IMMUTABLE
,MUTABLE
, orINCREMENTAL
. The default mode isIMMUTABLE
. See Table Logging Modes in the next section for more information. - Log the table to W&B with
run.log()
.
The following example shows how to create and log a table with two columns, a
and b
, and two rows of data, ["a1", "b1"]
and ["a2", "b2"]
:
import wandb
# Start a new run
run = wandb.init(project="table-demo")
# Create a table object with two columns and two rows of data
my_table = wandb.Table(
columns=["a", "b"],
data=[["a1", "b1"], ["a2", "b2"]],
log_mode="IMMUTABLE"
)
# Log the table to W&B
run.log({"Table Name": my_table})
# Finish the run
run.finish()
Logging modes
The wandb.Table
log_mode
parameter determines how a table is logged and updated during your ML experiments. The log_mode
parameter accepts one of three arguments: IMMUTABLE
, MUTABLE
, and INCREMENTAL
. Each mode has different implications for how a table is logged, how it can be modified, and how it is rendered in the W&B App.
The following describes the three logging modes, the high-level differences, and common use case for each mode:
Mode | Definition | Use Cases | Benefits |
---|---|---|---|
IMMUTABLE |
Once a table is logged to W&B, you cannot modify it. | - Storing tabular data generated at the end of a run for further analysis | - Minimal overhead when logged at the end of a run - All rows rendered in UI |
MUTABLE |
After you log a table to W&B, you can overwrite the existing table with a new one. | - Adding columns or rows to existing tables - Enriching results with new information |
- Capture Table mutations - All rows rendered in UI |
INCREMENTAL |
Add batches of new rows to a table throughout the machine learning experiment. | - Adding rows to tables in batches - Long-running training jobs - Processing large datasets in batches - Monitoring ongoing results |
- View updates on UI during training - Ability to step through increments |
The next sections show example code snippets for each mode along with considerations when to use each mode.
MUTABLE mode
MUTABLE
mode updates an existing table by replacing the existing table with a new one. MUTABLE
mode is useful when you want to add new columns and rows to an existing table in a non iterative process. Within the UI, the table is rendered with all rows and columns, including the new ones added after the initial log.
MUTABLE
mode, the table object is replaced each time you log the table. Overwriting a table with a new one is computationally expensive and can be slow for large tables.The following example shows how to create a table in MUTABLE
mode, log it, and then add new columns to it. The table object is logged three times: once with the initial data, once with the confidence scores, and once with the final predictions.
load_eval_data()
to load data and a placeholder function model.predict()
to make predictions. You will need to replace these with your own data loading and prediction functions.import wandb
import numpy as np
run = wandb.init(project="mutable-table-demo")
# Create a table object with MUTABLE logging mode
table = wandb.Table(columns=["input", "label", "prediction"],
log_mode="MUTABLE")
# Load data and make predictions
inputs, labels = load_eval_data() # Placeholder function
raw_preds = model.predict(inputs) # Placeholder function
for inp, label, pred in zip(inputs, labels, raw_preds):
table.add_data(inp, label, pred)
# Step 1: Log initial data
wandb.log({"eval_table": table}) # Log initial table
# Step 2: Add confidence scores (e.g. max softmax)
confidences = np.max(raw_preds, axis=1)
table.add_column("confidence", confidences)
run.log({"eval_table": table}) # Add confidence info
# Step 3: Add post-processed predictions
# (e.g., thresholded or smoothed outputs)
post_preds = (confidences > 0.7).astype(int)
table.add_column("final_prediction", post_preds)
wandb.log({"eval_table": table}) # Final update with another column
run.finish()
If you only want to add new batches of rows (no columns) incrementally like in a training loop, consider using INCREMENTAL
mode instead.
INCREMENTAL mode
In incremental mode, you log batches of rows to a table during the machine learning experiment. This is ideal for monitoring long-running jobs or when working with large tables that would be inefficient to log during the run for updates. Within the UI, the table is updated with new rows as they are logged, allowing you to view the latest data without having to wait for the entire run to finish. You can also step through the increments to view the table at different points in time.
The following example creates a table in INCREMENTAL
mode, logs it, and then adds new rows to it. Note that the table is logged once per training step (step
).
get_training_batch()
to load data, a placeholder function train_model_on_batch()
to train the model, and a placeholder function predict_on_batch()
to make predictions. You will need to replace these with your own data loading, training, and prediction functions.import wandb
run = wandb.init(project="incremental-table-demo")
# Create a table with INCREMENTAL logging mode
table = wandb.Table(columns=["step", "input", "label", "prediction"],
log_mode="INCREMENTAL")
# Training loop
for step in range(get_num_batches()): # Placeholder function
# Load batch data
inputs, labels = get_training_batch(step) # Placeholder function
# Train and predict
train_model_on_batch(inputs, labels) # Placeholder function
predictions = predict_on_batch(inputs) # Placeholder function
# Add batch data to table
for input_item, label, prediction in zip(inputs, labels, predictions):
table.add_data(step, input_item, label, prediction)
# Log the table incrementally
wandb.log({"training_table": table}, step=step)
run.finish()
Incremental logging is generally more computationally efficient than logging a new table each time (log_mode=MUTABLE
). However, the W&B App may not render all rows in the table if you log a large number of increments. If your goal is to update and view your table data while your run is ongoing and to have all the data available for analysis, consider using two tables. One with INCREMENTAL
log mode and one with IMMUTABLE
log mode.
The following example shows how to combine INCREMENTAL
and IMMUTABLE
logging modes to achieve this.
import wandb
run = wandb.init(project="combined-logging-example")
# Create an incremental table for efficient updates during training
incr_table = wandb.Table(columns=["step", "input", "prediction", "label"],
log_mode="INCREMENTAL")
# Training loop
for step in range(get_num_batches()):
# Process batch
inputs, labels = get_training_batch(step)
predictions = model.predict(inputs)
# Add data to incremental table
for inp, pred, label in zip(inputs, predictions, labels):
incr_table.add_data(step, inp, pred, label)
# Log the incremental update (suffix with -incr to distinguish from final table)
run.log({"table-incr": incr_table}, step=step)
# At the end of training, create a complete immutable table with all data
# Using the default IMMUTABLE mode to preserve the complete dataset
final_table = wandb.Table(columns=incr_table.columns, data=incr_table.data, log_mode="IMMUTABLE")
run.log({"table": final_table})
run.finish()
In this example, the incr_table
is logged incrementally (with log_mode="INCREMENTAL"
) during training. This allows you to log and view updates to the table as new data is processed. At the end of training, an immutable table (final_table
) is created with all data from the incremental table. The immutable table is logged to preserve the complete dataset for further analysis and it enables you to view all rows in the W&B App.
Examples
Enriching evaluation results with MUTABLE
import wandb
import numpy as np
run = wandb.init(project="mutable-logging")
# Step 1: Log initial predictions
table = wandb.Table(columns=["input", "label", "prediction"], log_mode="MUTABLE")
inputs, labels = load_eval_data()
raw_preds = model.predict(inputs)
for inp, label, pred in zip(inputs, labels, raw_preds):
table.add_data(inp, label, pred)
run.log({"eval_table": table}) # Log raw predictions
# Step 2: Add confidence scores (e.g. max softmax)
confidences = np.max(raw_preds, axis=1)
table.add_column("confidence", confidences)
run.log({"eval_table": table}) # Add confidence info
# Step 3: Add post-processed predictions
# (e.g., thresholded or smoothed outputs)
post_preds = (confidences > 0.7).astype(int)
table.add_column("final_prediction", post_preds)
run.log({"eval_table": table}) # Final
run.finish()
Resuming runs with INCREMENTAL tables
You can continue logging to an incremental table when resuming a run:
# Start or resume a run
resumed_run = wandb.init(project="resume-incremental", id="your-run-id", resume="must")
# Create the incremental table; no need to populate with data from preivously logged table
# Increments will be continue to be added to the Table artifact.
table = wandb.Table(columns=["step", "metric"], log_mode="INCREMENTAL")
# Continue logging
for step in range(resume_step, final_step):
metric = compute_metric(step)
table.add_data(step, metric)
resumed_run.log({"metrics": table}, step=step)
resumed_run.finish()
wandb.define_metric("<table_key>", summary="none")
or wandb.define_metric("*", summary="none")
.Training with INCREMENTAL batch training
run = wandb.init(project="batch-training-incremental")
# Create an incremental table
table = wandb.Table(columns=["step", "input", "label", "prediction"], log_mode="INCREMENTAL")
# Simulated training loop
for step in range(get_num_batches()):
# Load batch data
inputs, labels = get_training_batch(step)
# Train the model on this batch
train_model_on_batch(inputs, labels)
# Run model inference
predictions = predict_on_batch(inputs)
# Add data to the table
for input_item, label, prediction in zip(inputs, labels, predictions):
table.add_data(step, input_item, label, prediction)
# Log the current state of the table incrementally
run.log({"training_table": table}, step=step)
run.finish()
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