Skip to main content
Archived

This project has been archived by its maintainers, and is no longer receiving any updates.

Snitch AI Python Client Library

This library is used to perform Snitch AI analyses in a programmatic manner. This allows for easier upload to the Snitch AI platform without having to use the UI. Additionally, this library can facilitate integration with existing MLOps pipelines and external reporting tools.

Usage:

Generate a quality report

import snitch_ai

# you can specify the access_token in code or set the SNITCH_ACCESS_TOKEN environment variable 
snitch_ai.access_token = "VGhpcyBpcyBub3QgYW4gYWN0dWFsIGFjY2VzcyB0b2tlbi4gVG8gZ2V0I..."

project = snitch_ai.select_project("My First Project")

quality = project.run_quality_analysis(model, train_x, train_y, test_x, test_y)

json = quality.get_json()
quality.save_pdf("My Quality Report.pdf")

Generate a drift report

import snitch_ai

# you can specify the access_token in code or set the SNITCH_ACCESS_TOKEN environment variable
snitch_ai.access_token = "VGhpcyBpcyBub3QgYW4gYWN0dWFsIGFjY2VzcyB0b2tlbi4gVG8gZ2V0I..."

project = snitch_ai.select_project("My First Project")

drift = project.run_drift_analysis(train_x, updated_x)

json = drift.get_json()
drift.save_pdf("My Drift Report.pdf")

Usage within a hybrid environment

Snitch AI can also be used in a hybrid environment. This gives you the full power of Snitch AI's analysis engine without needing to upload your models or datasets to the cloud. If your internal security policy forbids uploading your data to a public cloud, this will likely be your preferred method of using Snitch AI.

You can learn more about Snitch AI Hybrid here: https://www.snit.ch/hybrid/

import snitch_ai

# replace this address with the address of your on-premises Snitch AI environment
# you can also set this in the SNITCH_ENDPOINT_ADDRESS environment variable
snitch_ai.endpoint_address = "https://localhost:5443/"

# you can specify the access_token in code or set the SNITCH_ACCESS_TOKEN environment variable
snitch_ai.access_token = "VGhpcyBpcyBub3QgYW4gYWN0dWFsIGFjY2VzcyB0b2tlbi4gVG8gZ2V0I..."

# perform quality or drift analysis per usual

Full product documentation can be found at https://help.snit.ch/

Metadata

Release files for snitch-ai 1.1.13

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for snitch-ai 1.1.13
File Size Uploaded Provenance
snitch_ai-1.1.13.tar.gz 10.5 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for snitch-ai 1.1.13
File Interpreter ABI Platform
snitch_ai-1.1.13-py3-none-any.whl Python 3 none any Details

Total release size: 22.1 kB

Release files / snitch_ai-1.1.13.tar.gz

Download URL snitch_ai-1.1.13.tar.gz
Size 10.5 kB
Tags Source
SHA-256 checksum
How to use checksums
d5134fec776de9329b2cd0706a39ff1c3ed04f3dbdae75b318afaf23a720b707
BLAKE2b-256 checksum
How to use checksums
ca96920e7dc5776a0723d5fd8b83cefc2a8ebbc3090f36d42c04e3ec435513cf
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.7.1 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.10

Release files / snitch_ai-1.1.13-py3-none-any.whl

Download URL snitch_ai-1.1.13-py3-none-any.whl
Size 11.6 kB
Tags Python 3
SHA-256 checksum
How to use checksums
b129ae19cf5cfd77f8597e796d2b83dd94657c094fc42c641c3400964e5bd260
BLAKE2b-256 checksum
How to use checksums
79e7a0eac060967f07145aa0130ea87dea42fcf260224fa8f68073402dfa8b23
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.7.1 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.10

Security for snitch-ai 1.1.13

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page