The UI is a great place to begin analyzing your run data, monitoring execution, and troubleshooting issues. Exporting data is the next step for deeper analysis, dataset creation, and even model training. The SDK makes it easy to export complete projects or individual runs.

The following data items are available to you in the dreadnode SDK:

  • Runs: Collect all runs under a project, or individually by ID
  • Tasks: Put all tasks within a run including their arguments, output, and associated scores
  • Trace: Get a full OpenTelemetry trace for a specific run including all tasks and associated data

You can also export dataframes for analysis in the following perspectives:

  • Export Runs: Get all of your runs with their parameters, metrics, and metadata
  • Export Metrics: Focus on the metrics data from your runs
  • Export Parameters: Analyze how parameters affect your metrics
  • Export Timeseries: Get time-based data for your metrics

All exports are available in multiple formats and can be filtered to view the precise data you need.

​
Basic Usage

Here’s a quick example of using the Dreadnode API to export data from your Strikes projects:

import dreadnode

api = dreadnode.api()

# List all runs in a project
runs = api.strikes.list_runs('project-name')
print(f"Found {len(runs)} runs")

# Get the trace for a specific run
trace = api.strikes.get_run_trace(runs[0].id)

# Export different types of data as pandas DataFrames
df_metrics = api.strikes.export_metrics('project-name')
df_params = api.strikes.export_parameters('project-name')
df_runs = api.strikes.export_runs('project-name')
df_timeseries = api.strikes.export_timeseries('project-name')

​
Export Types

​
Export Runs

Export all run data including parameters, tags, and aggregated metrics.

df = api.strikes.export_runs(
    'project-name',
    filter='tags.contains("production")',  # Optional filter expression
    status='completed',  # 'all', 'completed', or 'failed'
    aggregations=['avg', 'min', 'max']  # Metrics aggregations to include
)

The resulting DataFrame contains:

  • Run metadata (ID, name, start time, duration, status)
  • Parameters (prefixed with param_)
  • Tags (prefixed with tag_)
  • Aggregated metrics (prefixed with metric_)

​
Export Metrics

Focus on the metrics data with detailed information about each metric point.

df = api.strikes.export_metrics(
    'project-name',
    filter='name.contains("training")',  # Optional filter expression
    status='completed',  # 'all', 'completed', or 'failed'
    metrics=['accuracy', 'loss'],  # Optional list of metrics to include
    aggregations=['avg', 'median', 'min', 'max']  # Aggregation functions
)

The resulting DataFrame contains:

  • Run metadata (ID, start time, duration, status)
  • Metric information (name, step, timestamp, value)
  • Aggregated values (based on selected aggregations)
  • Parameters (prefixed with param_)

​
Export Parameters

Analyze how different parameter values affect your metrics.

df = api.strikes.export_parameters(
    'project-name',
    filter='metrics.accuracy.max > 0.9',  # Optional filter expression
    status='completed',  # 'all', 'completed', or 'failed'
    parameters=['learning_rate', 'batch_size'],  # Optional list of parameters
    metrics=['accuracy', 'loss'],  # Optional list of metrics
    aggregations=['avg', 'max']  # Aggregation functions
)

The resulting DataFrame shows how different parameter values influence your metrics, with:

  • Parameter name and value
  • Run count for each parameter value
  • Aggregated metric values

​
Export Timeseries

Get time-based data for your metrics, with options for time representation.

df = api.strikes.export_timeseries(
    'project-name',
    filter='params.model == "resnet50"',  # Optional filter expression
    status='completed',  # 'all', 'completed', or 'failed'
    metrics=['accuracy', 'loss'],  # Optional list of metrics
    time_axis='relative',  # 'wall', 'relative', or 'step'
    aggregations=['max', 'min']  # Aggregation functions
)

The timeseries export provides metric values over time, with:

  • Run metadata (ID, name)
  • Metric name and value at each point
  • Time representation (based on selected time_axis)
  • Running aggregations (if aggregations are specified)
  • Parameters (prefixed with param_)

​
Filtering Data

All export functions support filtering to narrow down the results. The filter expression is a string that follows a simple query language:

# Filter by tags
df = api.strikes.export_runs('project-name', filter='tags.contains("production")')

# Filter by parameters
df = api.strikes.export_metrics('project-name', filter='params.learning_rate < 0.01')

# Filter by metrics
df = api.strikes.export_parameters('project-name', filter='metrics.accuracy.max > 0.9')

# Combine filters
df = api.strikes.export_timeseries(
    'project-name', 
    filter='params.model == "resnet50" and metrics.loss.min < 0.1'
)

​
Available Aggregations

The following aggregation functions are available for metrics:

  • avg: Average value
  • median: Median value
  • min: Minimum value
  • max: Maximum value
  • sum: Sum of values
  • first: First value
  • last: Last value
  • count: Number of values
  • std: Standard deviation
  • var: Variance

For timeseries exports, the following aggregations are available:

  • max: Running maximum value
  • min: Running minimum value
  • sum: Running sum of values
  • count: Running count of values

​
Time Axis Options

When exporting timeseries data, you can specify how time should be represented:

  • wall: Actual timestamp (datetime)
  • relative: Seconds since the run started (float)
  • step: Step number (integer)

​
Example Workflows

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Compare Performance Across Experiments

# Get parameters and their impact on metrics
df = api.strikes.export_parameters(
    'my-experiment',
    parameters=['learning_rate', 'batch_size', 'model'],
    metrics=['accuracy', 'loss'],
    aggregations=['max', 'min', 'avg']
)

# Analyze the results
import matplotlib.pyplot as plt
import seaborn as sns

# Create a pivot table to see how learning rate affects accuracy
pivot = df.pivot(index='param_value', columns='param_name', values='metric_accuracy_max')
sns.heatmap(pivot, annot=True, cmap='viridis')
plt.title('Maximum Accuracy by Parameter Values')
plt.show()

​
Analyze Learning Curves

# Get timeseries data for loss metrics
df = api.strikes.export_timeseries(
    'my-experiment',
    metrics=['train_loss', 'val_loss'],
    time_axis='step'
)

# Plot learning curves
plt.figure(figsize=(10, 6))
for run_id in df['run_id'].unique():
    run_data = df[df['run_id'] == run_id]
    
    # Plot training loss
    train_loss = run_data[run_data['metric_name'] == 'train_loss']
    plt.plot(train_loss['step'], train_loss['value'], label=f"Train - {run_id[:8]}")
    
    # Plot validation loss
    val_loss = run_data[run_data['metric_name'] == 'val_loss']
    plt.plot(val_loss['step'], val_loss['value'], '--', label=f"Val - {run_id[:8]}")

plt.xlabel('Step')
plt.ylabel('Loss')
plt.title('Training and Validation Loss')
plt.legend()
plt.grid(True)
plt.show()

​
Working with Traces

You can export trace information for debugging and performance analysis:

# Get the trace for a specific run
trace = api.strikes.get_run_trace(run_id)

# Extract spans and analyze them
spans = [span for span in trace if hasattr(span, 'duration')]
spans_df = pd.DataFrame([{
    'name': span.name,
    'duration': span.duration,
    'service': span.service_name,
    'status': span.status
} for span in spans])

# Find the slowest spans
slowest = spans_df.sort_values('duration', ascending=False).head(10)
print(slowest)

​
Custom Exports

For more complex analyses, you can combine different exports:

# Get runs and parameters
runs_df = api.strikes.export_runs('project-name')
params_df = api.strikes.export_parameters('project-name')

# Join them for additional insights
merged = runs_df.merge(params_df, left_on='run_id', right_on='run_id')

# Create customized view
custom_view = merged[['run_name', 'param_learning_rate', 'metric_accuracy_max', 'run_duration']]