### Initialize and Update LiveTimeSeries

Source: https://context7.com/drsensor/mixboard/llms.txt

Initialize LiveTimeSeries for custom time series visualization. Manually update the chart with custom values during a training loop.

```python
from mixboard.contrib.callback import LiveTimeSeries
import bokeh.plotting

# Initialize time series chart with custom figure parameters
time_series = LiveTimeSeries(
    title="Custom Metric Over Time",
    plot_width=800,
    plot_height=400
)

# Manually update with custom values during training loop
for epoch in range(100):
    # Your training code here...
    loss = compute_loss()

    # Update the chart with new value
    time_series.update_chart_data(value=loss)

# The chart displays:
# - X-axis: elapsed time (datetime format)
# - Y-axis: your custom metric values
# - Automatic notebook rendering with bokeh.io.push_notebook()
```

--------------------------------

### Initialize LiveLearningCurve

Source: https://context7.com/drsensor/mixboard/llms.txt

Initialize LiveLearningCurve to visualize training and validation metrics in real-time within a Jupyter Notebook. Use its callback arguments with MXNet model.fit().

```python
from mixboard.contrib.callback import LiveLearningCurve

# Initialize live learning curve for a specific metric
# display_freq: seconds between visual updates
# frequent: batches between data logging
learning_curve = LiveLearningCurve(
    metric_name='accuracy',
    display_freq=10,
    frequent=50
)

# Use with MXNet model training
model.fit(
    train_data=train_iter,
    eval_data=test_iter,
    num_epoch=20,
    **learning_curve.callback_args()  # Enables batch_cb, eval_cb
)

# The chart automatically shows:
# - Training metrics as dotted line with small circles
# - Validation metrics as solid green line with circles
# - X-axis: elapsed training time
# - Y-axis: the specified metric (e.g., accuracy)
# - Legend at bottom-right corner
```

--------------------------------

### Implement CustomLiveChart with LiveBokehChart

Source: https://context7.com/drsensor/mixboard/llms.txt

Subclass LiveBokehChart to define custom visualization logic. Requires implementing setup_chart and update_chart_data methods.

```python
from mixboard.contrib.callback import LiveBokehChart, PandasLogger
import bokeh.plotting
import bokeh.io

class CustomLiveChart(LiveBokehChart):
    """Custom live chart implementation"""

    def setup_chart(self):
        # Create and configure your Bokeh figure
        self.fig = bokeh.plotting.Figure(
            title="Custom Training Visualization",
            x_axis_label="Batch",
            y_axis_label=self.metric_name
        )
        self.x_data = []
        self.y_data = []
        self.line = self.fig.line(self.x_data, self.y_data, line_width=2)
        return bokeh.plotting.show(self.fig, notebook_handle=True)

    def update_chart_data(self):
        # Update chart with data from pandas_logger
        train_df = self.pandas_logger.train_df
        if len(train_df) > 0:
            self.x_data.extend(train_df['minibatch_count'].tolist())
            self.y_data.extend(train_df[self.metric_name].tolist())

# Usage
pandas_logger = PandasLogger(batch_size=64, frequent=50)
custom_chart = CustomLiveChart(
    pandas_logger=pandas_logger,
    metric_name='loss',
    display_freq=5
)

model.fit(
    train_data=train_iter,
    eval_data=test_iter,
    **custom_chart.callback_args()
)
```

--------------------------------

### Initialize and Use PandasLogger

Source: https://context7.com/drsensor/mixboard/llms.txt

Initialize PandasLogger to log training and validation metrics into dataframes. Access logged data and use its callback arguments with MXNet model.fit().

```python
from mixboard.contrib.callback import PandasLogger
import mxnet as mx

# Initialize the logger with batch size and logging frequency
batch_size = 64
pandas_logger = PandasLogger(batch_size=batch_size, frequent=50)

# Access the dataframes for analysis
train_data = pandas_logger.train_df      # Training batch metrics
eval_data = pandas_logger.eval_df        # Validation metrics per epoch
epoch_data = pandas_logger.epoch_df      # Epoch timing information
all_data = pandas_logger.all_dataframes  # Dict of all dataframes

# Get elapsed training time
elapsed = pandas_logger.elapsed()
print(f"Training elapsed: {elapsed}")

# Use with MXNet model.fit() - pass callback arguments
model = mx.mod.Module(symbol=net)
model.fit(
    train_data=train_data,
    eval_data=test_data,
    num_epoch=10,
    **pandas_logger.callback_args()  # Enables train_cb, eval_cb, epoch_cb
)

# After training, analyze the logged data
print(pandas_logger.train_df[['epoch', 'minibatch_count', 'batches_per_sec']])
```

```python
# Output:
#    epoch  minibatch_count  batches_per_sec
# 0      0               50           1250.5
# 1      0              100           1280.3
# 2      0              150           1275.8
```

--------------------------------

### Combine Callbacks with args_wrapper

Source: https://context7.com/drsensor/mixboard/llms.txt

Use args_wrapper to aggregate multiple callback objects into a single dictionary for model.fit().

```python
from mixboard.contrib.callback import PandasLogger, LiveLearningCurve, args_wrapper

# Create multiple callback objects
batch_size = 64
pandas_logger = PandasLogger(batch_size=batch_size, frequent=50)
learning_curve = LiveLearningCurve(metric_name='accuracy', display_freq=10)

# Combine all callbacks into single kwargs dict
combined_callbacks = args_wrapper(pandas_logger, learning_curve)
# Returns: {
#     'batch_end_callback': [pandas_logger.train_cb, learning_curve.batch_cb],
#     'eval_end_callback': [pandas_logger.eval_cb, learning_curve.eval_cb],
#     'epoch_end_callback': [pandas_logger.epoch_cb]
# }

# Use combined callbacks with MXNet model
model.fit(
    train_data=train_iter,
    eval_data=test_iter,
    num_epoch=20,
    **combined_callbacks
)

# After training, access logged data
print(f"Final training accuracy: {pandas_logger.train_df['accuracy'].iloc[-1]}")
print(f"Total training time: {pandas_logger.elapsed()}")
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.