Python client for Fiddler Service
Project description
Fiddler Client
Python client for interacting with Fiddler. Provides a user-friendly interface to our REST API and enables event publishing for use with our monitoring features.
Requirements
Requires Python >= Python-3.6.3.
Installation
$ pip3 install fiddler-client
API Example Usage
For examples of interacting with our APIs, please check out our Quick Start Guide as well as our Samples Github and tutorial notebooks:
- Setup Fiddler client
- How to monitor data
- How to monitor with surrogate explanation
- How to import sklearn regression model
- Debug model import problems
- How to generate a model from your tabular data
- Publish realtime events to Fiddler
- How to import a model hosted on an external server
- Explaining model hosted on Sagemaker
- How to upload a keras model using tabular data with IG enabled
- How to upload a tf model using text data with IG enabled
Version History
0.6.16
-
Features
- Minor improvements to
publish_events_log()
to circumvent datatype conversion issues
- Minor improvements to
0.6.15
-
Features
- Added strict name checks
0.6.14
-
Features
- Added client-native multithreading support for
publish_events_log()
using new parametersnum_threads
andbatch_size
- Added client-native multithreading support for
0.6.13
-
Features
- Added
fdl.generate_sample_events()
to client- API for generating monitoring traffic to test out Fiddler
- Added
fdl.trigger_pre_computation()
to client- Triggers various precomputation steps within the Fiddler service based on input parameters.
- Optionally add proxies to FiddlerApi() init
- Added
0.6.12
-
Features
- Added
fdl.publish_parquet_s3()
to client- Publishes parquet events file from S3 to Fiddler instance. Experimental and may be expanded in the future.
- Added
0.6.10
-
Features
- Added
fdl.register_model()
to client- Register a model in fiddler. This will generate a surrogate model, which can be replaced later with original model.
- Added
Project details
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