deepecho

library0.8.1pypypi✓ verified 84d ago

DeepEcho is a Python library within the SDV ecosystem for generating sequential synthetic data from real-world datasets using Generative Adversarial Networks (GANs). It's designed for data that has a temporal or sequential component, such as time series or event logs. Currently at version 0.8.1, the library maintains an active development pace with frequent updates and bug fixes, often aligning with the broader SDV ecosystem's release cycle.

ai-ml data

pip install deepecho

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · DEEPECHO

deepecho

ai-mlpythonv0.8.1

Install

78.3s avg

Import

—

Disk

2193MB

Pass rate

9/ 10

Env Coverage9 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.0.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✓ —

✓ 86.5s

py 3.11

✓ —

✓ 78.7s

py 3.12

✓ —

✓ 74.55s

py 3.13

✓ —

✓ 73.6s

py 3.9

✓ —

✕ timeout

2193MB installed

● package 2193MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

BasicGANModel

✓ from deepecho import BasicGANModel

✗ from deepecho.models import DeepEcho

PARModel

✓ from deepecho import PARModel

load_demo

✓ from deepecho import load_demo

This quickstart demonstrates how to use DeepEcho to generate synthetic sequential data. It uses `sdv.datasets.demo.get_sequential_demo` to obtain sample sequential data and its corresponding metadata, which is essential for DeepEcho to correctly model the data's structure. The `DeepEcho` model is then initialized with the metadata, fitted to the real data, and finally used to sample new synthetic sequences.

from deepecho.models import DeepEcho from sdv.datasets.demo import get_sequential_demo # Get demo data for sequential modeling from SDV metadata, data = get_sequential_demo() # Initialize and fit the DeepEcho model # Metadata is crucial for DeepEcho to understand the sequential structure model = DeepEcho(metadata=metadata) model.fit(data) # Generate 100 rows of synthetic sequential data synthetic_data = model.sample(num_rows=100) print(synthetic_data.head())

Debug

Known issues

breakingThe main class `DeepEchoSynthesizer` was removed in v0.7.0. It was replaced by `DeepEcho` in `deepecho.models`.

fix

Update your import statement from `from deepecho.synthesizers import DeepEchoSynthesizer` to `from deepecho.models import DeepEcho`.

affects: >=0.7.0

breakingDeepEcho models no longer handle data preprocessing internally as of v0.7.0. All preprocessing, data loading, and metadata generation must now be handled externally, primarily using the `sdv` library.

fix

Ensure you are using `sdv` to load your data and generate a `sdv.metadata.Metadata` object. Pass this metadata object explicitly to the `DeepEcho` model constructor: `model = DeepEcho(metadata=my_metadata)`.

affects: >=0.7.0

gotchaWhen working with sequential data, it is critical that the `metadata` object correctly defines the primary key, parent key (if nested), and sequence key for each table. Incorrect metadata can lead to errors during fitting or generation of nonsensical synthetic data.

fix

Thoroughly review and, if necessary, manually adjust the `sdv` metadata to accurately reflect your sequential data's structure. Use `metadata.set_table_meta()` to specify keys if auto-detection is insufficient.

affects: All versions

Upgrade

Version history

0.8.1latest on PyPI · released Feb 12, 2026

Audit

Dependencies

sdvrequiredDeepEcho relies on SDV for data loading, preprocessing, and metadata management, especially for sequential data.

pythonrequiredRequired Python version range.

Agent activity

22 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/sdv-dev/Deepecho ↗

packagehttps://pypi.org/project/deepecho/ ↗

homepagehttps://deepecho.dev ↗