Explore
Interactive Debugger
Explore the entire computational graph of your model including all data, parameters and gradients.
import efemarai as ef
for data, target in dataset:
optimizer.zero_grad()
# Pause execution & visualize computational graph
with ef.scan():
output = model(data)
loss = F.cross_entropy(output, target)
loss.backward()
optimizer.step()
Runtime Assertions
Use the provided assertions or write your own tensor and function assertions to catch bugs effortlessly.
import efemarai as ef
# Enable all runtime assertions
ef.full_check()
for data, target in data_loader:
optimizer.zero_grad()
# Detect & visualize failed assertions
with ef.scan():
output = model(data)
loss = F.cross_entropy(output, target)
loss.backward()
optimizer.step()
Tensor Inspection
Visualize up to 7D tensors and inspect the value of any element in just a couple of clicks.
import efemarai as ef # Inspect the raw values of any tensor ef.inspect(tensor) # Inspect tensors containing various types of data, e.g. ef.inspect(tensor, view=ef.View.Image) ef.inspect(tensor, view=ef.View.PointCloud)
Testing Framework
Create extensive test suits both for your models and data with just a few lines of code.
import efemarai as ef # Create a data report with various health checks ef.summarize(data) # Test the quality of your data, e.g. ef.expect(data).blurriness.toBeLessThan(0.1) ef.expect(data).text.illegal_characters.toHaveLength(0) # Test both your model and data, catch regressions, e.g. ef.expect(model).accuracy.on(data).toBeGreaterThan(0.85) # Easily test non-trivial model properties, e.g. ef.expect(model).robustness.toBeGreaterThan(0.95)
Efemarai in action
Why Efemarai?
Reduce time for developing & debugging ML models by up to
50%
Boost the reliability and performance of ML models
Follow well established quality assurance principles
Our users so far have run
Debug Sessions
1.5k+
Visualizations
22k+
Assertions
107k+