XTlib: Experiment Tools Library
XTlib is an API and command line tool for scaling and managing Machine Learning (ML) experiments.
XTLib enables you to efficiently organize and scale your ML experiments. Our tools offer an incremental approach to adoption, so you can immediately realize benefits to your research from using XTlib.
XTlib Key Features
- XTlib scales your ML experiments across multiple cloud compute services:
- Local machine and VM's, Philly, Azure Batch, and Azure Machine Learning (AML)
- XTlib provides a consistent storage model across services:
- Workspaces, experiments, jobs, and runs
- Blob shares
- XTLib also offers several experiment-related tools to expand your ML projects:
- Composable Tensorboard views (live and post-training)
- Hyperparameter searching
- Run and job reports
- Ad-hoc plotting
XTLib provides an experiment store to track, compare, plot, rerun, and share your Machine Learning (ML) experiments.
The store consists of user-defined workspaces. Each workspace contains a set of user-run experiments.
XTlib supports flexible storage capabilities: local (folder-based) and Azure Storage services (cloud-based).
XTLib also uses scalable cloud compute resources, so you can run multiple experiments in parallel and on larger computers, as needed. With this feature, you can scale experiments from your local machine, to multiple VM's under your control, to compute services like Azure Batch and Azure ML.
For more information, run: xt --help, or see our documentation.
Contributing
Check CONTRIBUTING page.
Microsoft Open Source Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
License
This project is licensed under the terms of the MIT license. See LICENSE.txt for additional details.
Release files for xtlib 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| xtlib-1.0.0.tar.gz | 293.6 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| xtlib-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 742.2 kB
Release files / xtlib-1.0.0.tar.gz
| Download URL | xtlib-1.0.0.tar.gz |
|---|---|
| Size | 293.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
6eb0990de12007d038a0a0fed5d926f00c1bad834af1172903464d090ba87532
|
|
BLAKE2b-256 checksum How to use checksums |
699f4282cc47443b13418f06de923ffb7bac5b235d17872a92b05c04adaf8a3e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.1.post20200604 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.6.10
|
Release files / xtlib-1.0.0-py3-none-any.whl
| Download URL | xtlib-1.0.0-py3-none-any.whl |
|---|---|
| Size | 448.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
4830363083bf40e95b3b020509d911402240ac4afd79bf0cd1e6f075bb96cef1
|
|
BLAKE2b-256 checksum How to use checksums |
00ef835c4f94e93bd010f4488253d96fbe7dd3550a0fc0f7a9c6971958078668
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.1.post20200604 requests-toolbelt/0.9.1 tqdm/4.46.1 CPython/3.6.10
|
Security for xtlib 1.0.0
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.
