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Overview

Data science workflows often involve time-consuming operations—training models, processing large datasets, or running complex queries. Sphinx monitors execution time and adjusts its approach based on how long operations take.

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How It Works

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Runtime Estimation

When Sphinx generates code, it estimates how long the cell should take to execute. This estimate is based on:
  • The type of operation (data loading, model training, visualization, etc.)
  • The size of data being processed (when known)
  • Historical patterns from similar operations
Sphinx applies a safety multiplier to its estimates, giving cells more time than the raw estimate to account for variability.

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Execution Monitoring

Once a cell starts running, Sphinx monitors its progress:
  • Checks kernel health every 100ms
  • Checks runtime vs estimate every 1 second

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Smart Interrupts

When a cell runs longer than expected, Sphinx doesn’t immediately interrupt. Instead, it makes a decision by analyzing:
  1. The code being executed — Is this a naturally long operation?
  2. The notebook context — What data is being processed?
  3. The conversation history — What was the user trying to accomplish?
Based on this analysis, Sphinx decides to either:
  • Continue waiting — The operation appears to be progressing normally
  • Interrupt and retry — The operation is likely stuck or inefficient; try a different approach
If Sphinx decides to continue, it specifies how much additional time to wait before checking again.

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What Happens When a Cell Is Interrupted

When Sphinx interrupts a long-running cell:
  1. Kernel interrupt — The currently executing code is stopped
  2. Analysis — Sphinx reviews what happened and why
  3. New approach — Sphinx generates improved code, typically with:
    • Better efficiency (e.g., sampling large datasets)
    • Progress indicators (e.g., tqdm for loops)
    • Chunked processing for large operations
    • Timeout handling in the code itself
# Example: Sphinx might transform this...
df = pd.read_csv('huge_file.csv')

# ...into this after an interrupt:
# Load data in chunks with progress indicator
from tqdm import tqdm
chunks = []
for chunk in tqdm(pd.read_csv('huge_file.csv', chunksize=100000)):
    chunks.append(chunk)
df = pd.concat(chunks, ignore_index=True)

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Settings

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Enable/Disable Runtime Interrupts

You can control whether Sphinx automatically interrupts long-running cells: VS Code Settings:
  1. Open VS Code Settings (Cmd+, / Ctrl+,)
  2. Search for “sphinx runtime”
  3. Toggle Sphinx: Enable Runtime Interrupts
When disabled, Sphinx will wait indefinitely for cells to complete.
Disabling runtime interrupts means you’ll need to manually interrupt stuck or inefficient code using the notebook’s stop button.

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Manual Interruption

You can always manually stop execution:
  • Notebook stop button — Click the stop icon on the running cell
  • Keyboard shortcut — Escape twice while the cell is selected
  • Kernel menu — Interrupt the kernel from VS Code’s kernel menu
When you manually stop a cell, Sphinx recognizes the interruption and asks how you’d like to proceed.

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Best Practices for Long-Running Tasks

For iterative operations, include progress tracking:
from tqdm import tqdm

# Instead of:
for item in large_list:
    process(item)

# Use:
for item in tqdm(large_list, desc="Processing"):
    process(item)
For large files, process incrementally:
# Instead of loading everything at once:
df = pd.read_csv('huge_file.csv')

# Process in chunks:
chunk_iter = pd.read_csv('huge_file.csv', chunksize=50000)
results = []
for chunk in chunk_iter:
    results.append(process_chunk(chunk))
When exploring data, work with samples first:
# For initial exploration, sample the data
df_sample = df.sample(n=10000, random_state=42)

# Run your exploratory analysis on the sample
df_sample.describe()
When calling APIs or databases, set explicit timeouts:
import requests

# Add timeout to prevent indefinite waiting
response = requests.get(url, timeout=30)
Prefer vectorized operations over loops:
# Slow - Python loop
for i in range(len(df)):
    df.loc[i, 'new_col'] = df.loc[i, 'col1'] * 2

# Fast - Vectorized
df['new_col'] = df['col1'] * 2

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Kernel Health Monitoring

Beyond execution time, Sphinx monitors kernel health:
StatusMeaningAction
HealthyKernel responding normallyContinue monitoring
UnhealthyKernel unresponsiveAttempt interrupt
If the kernel becomes unhealthy (stops responding), Sphinx automatically attempts to interrupt the execution to prevent a complete hang.

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Expected Behavior by Operation Type

OperationTypical DurationSphinx Behavior
Data loading (small)SecondsShort timeout, quick interrupt if stuck
Data loading (large)MinutesLonger timeout, may suggest chunking
Model trainingMinutes to hoursVery long timeout, monitors for progress
VisualizationsSecondsShort timeout
API callsSecondsShort timeout, suggests adding timeout parameter
Database queriesVariesAdjusts based on query complexity

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Troubleshooting

  1. Disable runtime interrupts in settings
  2. Or, tell Sphinx about the expected duration: “This training will take about 30 minutes”
  3. Sphinx will adjust its expectations accordingly
  1. Check if runtime interrupts are enabled in settings
  2. Manually interrupt using the cell’s stop button
  3. Restart the kernel if it’s unresponsive
When Sphinx interrupts, you can tell it to continue:
  • “Keep running, this is expected to take a while”
  • “Wait for the training to complete”
Ask Sphinx for specific optimizations:
  • “Use parallel processing for this”
  • “Sample the data for initial exploration”
  • “Show me a progress bar while this runs”

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Remote and Cloud Kernels

When using remote kernels (Databricks, cloud notebooks, etc.), consider:
  • Network latency can make operations appear slower
  • Cloud resources may have different performance characteristics
  • Shared resources might experience contention
Sphinx accounts for these factors, but you may want to adjust your expectations for remote execution environments.