Introduction
The SAP Federated ML Python libraries (FedML) applies the Data Federation architecture of SAP Datasphere for intelligently sourcing data for Machine Learning experiments done at the Hyperscalers thereby removing the need for replicating or moving data. By abstracting the Data Connection, Data load, and Model training (with flexibility and provision for user provided training scripts) for Hyperscaler Machine learning processes, the FedML library provides end to end integration with few lines of code.
Installation
Install the SAP FedML Azure library using pip as follows:
pip install fedml-azure
Documentation and getting started
For getting started with the SAP FedML Azure Library and for documentation and sample notebooks, please refer the SAP FedML Azure github page link
Release files for fedml-azure 2.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| fedml_azure-2.0.1.tar.gz | 23.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| fedml_azure-2.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 54.7 kB
Release files / fedml_azure-2.0.1.tar.gz
| Download URL | fedml_azure-2.0.1.tar.gz |
|---|---|
| Size | 23.7 kB |
| Tags | Source |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
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|
Release files / fedml_azure-2.0.1-py3-none-any.whl
| Download URL | fedml_azure-2.0.1-py3-none-any.whl |
|---|---|
| Size | 31.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.8.8
|