tfx

v1.16.0

TensorFlow Extended (TFX) is a TensorFlow-based general-purpose machine learning platform implemented at Google. For more information about how to use this package see README

Latest version published 11 days ago

License: Apache-2.0

PyPI

GitHub

Package Health Score

93 / 100

security
No known security issues
popularity
Popular
maintenance
Healthy
community
Active

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Popularity

Popular

GitHub Stars: 2.12K
Forks: 714
Contributors: 170

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package tfx, we found that it has been starred 2,122 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Dec 11, 2024
Direct Vulnerabilities: 0
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Indirect Vulnerabilities: 0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

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Maintenance

Healthy

Commit Frequency

Open Issues: 43
Open PR: 215
Last Release: 11 days ago
Last Commit: 10 days ago

Further analysis of the maintenance status of tfx based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Healthy.

We found that tfx demonstrates a positive version release cadence with at least one new version released in the past 3 months.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 170
Funding: No

A good and healthy external contribution signal for tfx project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: <3.11,>=3.9

Age: 6 years
Latest Release: 11 days ago
Dependencies: 0 Direct
Versions: 100
Maintainers: 3
Wheels: OS Independent

Not sure how to use tfx?

To help you get started, we've collected the most common ways that tfx is being used within popular public projects.

jeffmahler / GPIS / src / grasp_selection / test_zeke_registration.py View on Github

T_world = stf.SimilarityTransform3D(from_frame='world', to_frame='world')
    R_table_world = np.eye(3)
    T_table_world = stf.SimilarityTransform3D(pose=tfx.pose(R_table_world, np.zeros(3)), from_frame='world', to_frame='table')
            
    R_camera_table = np.load('data/calibration/rotation_camera_cb.npy')
    t_camera_table = np.load('data/calibration/translation_camera_cb.npy')
    cb_points_camera = np.load('data/calibration/corners_cb.npy')
    T_camera_table = stf.SimilarityTransform3D(tfx.pose(R_camera_table, t_camera_table), from_frame='table', to_frame='camera')
    T_camera_world = T_camera_table.dot(T_table_world)
    T_world_camera = T_camera_world.inverse()
    
    R_stp_obj = stable_pose.r
    T_obj_stp = stf.SimilarityTransform3D(pose=tfx.pose(R_stp_obj.T, np.zeros(3)), from_frame='stp', to_frame='obj')
            
    t_stp_table = np.array([0, 0, z])
    T_stp_table = stf.SimilarityTransform3D(pose=tfx.pose(np.eye(3), t_stp_table), from_frame='table', to_frame='stp')
    
    T_obj_world = T_obj_camera.dot(T_camera_world)
            
    # visualize the robot's understanding of the world
    logging.info('Displaying robot world state')
    mv.clf()
    mvis.MayaviVisualizer.plot_table(T_table_world, d=table_extent)
    mvis.MayaviVisualizer.plot_pose(T_world, alpha=alpha, tube_radius=tube_radius, center_scale=center_scale)
    mvis.MayaviVisualizer.plot_pose(T_obj_world, alpha=alpha, tube_radius=tube_radius, center_scale=center_scale)
    mvis.MayaviVisualizer.plot_pose(T_camera_world, alpha=alpha, tube_radius=tube_radius, center_scale=center_scale)
    mvis.MayaviVisualizer.plot_mesh(object_mesh, T_obj_world)
    mvis.MayaviVisualizer.plot_point_cloud(cb_points_camera, T_world_camera, color=(1,1,0))
    mvis.MayaviVisualizer.plot_point_cloud(points_3d, T_world_camera, color=(0,1,0), scale=0.0025)
    mv.view(focalpoint=(0,0,0))
    mv.show()

View more ways to use tfx

TFX

TensorFlow Extended (TFX) is a Google-production-scale machine learning platform based on TensorFlow. It provides a configuration framework to express ML pipelines consisting of TFX components. TFX pipelines can be orchestrated using Apache Airflow and Kubeflow Pipelines. Both the components themselves as well as the integrations with orchestration systems can be extended.

TFX components interact with a ML Metadata backend that keeps a record of component runs, input and output artifacts, and runtime configuration. This metadata backend enables advanced functionality like experiment tracking or warmstarting/resuming ML models from previous runs.

TFX Components

Documentation

User Documentation

Please see the TFX User Guide.

Development References

Roadmap

The TFX Roadmap, which is updated quarterly.

Release Details

For detailed previous and upcoming changes, please check here

Requests For Comment

TFX is an open-source project and we strongly encourage active participation by the ML community in helping to shape TFX to meet or exceed their needs. An important component of that effort is the RFC process. Please see the listing of current and past TFX RFCs. Please see the TensorFlow Request for Comments (TF-RFC) process page for information on how community members can contribute.

Examples

Chicago Taxi Example

Compatible versions

The following table describes how the tfx package versions are compatible with its major dependency PyPI packages. This is determined by our testing framework, but other untested combinations may also work.

tfx	Python	apache-beam[gcp]	ml-metadata	pyarrow	tensorflow	tensorflow-data-validation	tensorflow-metadata	tensorflow-model-analysis	tensorflow-serving-api	tensorflow-transform	tfx-bsl
GitHub master	>=3.9,❤️.11	2.59.0	1.16.0	10.0.1	nightly (2.x)	1.16.1	1.16.1	0.47.0	2.16.1	1.16.0	1.16.1
1.16.0	>=3.9,❤️.11	2.59.0	1.16.0	10.0.1	2.16	1.16.1	1.16.1	0.47.0	2.16.1	1.16.0	1.16.1
1.15.0	>=3.9,❤️.11	2.47.0	1.15.0	10.0.0	2.15	1.15.1	1.15.0	0.46.0	2.15.1	1.15.0	1.15.1
1.14.0	>=3.8,❤️.11	2.47.0	1.14.0	10.0.0	2.13	1.14.0	1.14.0	0.45.0	2.9.0	1.14.0	1.14.0
1.13.0	>=3.8,❤️.10	2.40.0	1.13.1	6.0.0	2.12	1.13.0	1.13.1	0.44.0	2.9.0	1.13.0	1.13.0
1.12.0	>=3.7,❤️.10	2.40.0	1.12.0	6.0.0	2.11	1.12.0	1.12.0	0.43.0	2.9.0	1.12.0	1.12.0
1.11.0	>=3.7,❤️.10	2.40.0	1.11.0	6.0.0	1.15.5 / 2.10.0	1.11.0	1.11.0	0.42.0	2.9.0	1.11.0	1.11.0
1.10.0	>=3.7,❤️.10	2.40.0	1.10.0	6.0.0	1.15.5 / 2.9.0	1.10.0	1.10.0	0.41.0	2.9.0	1.10.0	1.10.0
1.9.0	>=3.7,❤️.10	2.38.0	1.9.0	5.0.0	1.15.5 / 2.9.0	1.9.0	1.9.0	0.40.0	2.9.0	1.9.0	1.9.0
1.8.0	>=3.7,❤️.10	2.38.0	1.8.0	5.0.0	1.15.5 / 2.8.0	1.8.0	1.8.0	0.39.0	2.8.0	1.8.0	1.8.0
1.7.0	>=3.7,❤️.9	2.36.0	1.7.0	5.0.0	1.15.5 / 2.8.0	1.7.0	1.7.0	0.38.0	2.8.0	1.7.0	1.7.0
1.6.2	>=3.7,❤️.9	2.35.0	1.6.0	5.0.0	1.15.5 / 2.8.0	1.6.0	1.6.0	0.37.0	2.7.0	1.6.0	1.6.0
1.6.0	>=3.7,❤️.9	2.35.0	1.6.0	5.0.0	1.15.5 / 2.7.0	1.6.0	1.6.0	0.37.0	2.7.0	1.6.0	1.6.0
1.5.0	>=3.7,❤️.9	2.34.0	1.5.0	5.0.0	1.15.2 / 2.7.0	1.5.0	1.5.0	0.36.0	2.7.0	1.5.0	1.5.0
1.4.0	>=3.7,❤️.9	2.33.0	1.4.0	5.0.0	1.15.0 / 2.6.0	1.4.0	1.4.0	0.35.0	2.6.0	1.4.0	1.4.0
1.3.4	>=3.6,❤️.9	2.32.0	1.3.0	2.0.0	1.15.0 / 2.6.0	1.3.0	1.2.0	0.34.1	2.6.0	1.3.0	1.3.0
1.3.3	>=3.6,❤️.9	2.32.0	1.3.0	2.0.0	1.15.0 / 2.6.0	1.3.0	1.2.0	0.34.1	2.6.0	1.3.0	1.3.0
1.3.2	>=3.6,❤️.9	2.32.0	1.3.0	2.0.0	1.15.0 / 2.6.0	1.3.0	1.2.0	0.34.1	2.6.0	1.3.0	1.3.0
1.3.1	>=3.6,❤️.9	2.32.0	1.3.0	2.0.0	1.15.0 / 2.6.0	1.3.0	1.2.0	0.34.1	2.6.0	1.3.0	1.3.0
1.3.0	>=3.6,❤️.9	2.32.0	1.3.0	2.0.0	1.15.0 / 2.6.0	1.3.0	1.2.0	0.34.1	2.6.0	1.3.0	1.3.0
1.2.1	>=3.6,❤️.9	2.31.0	1.2.0	2.0.0	1.15.0 / 2.5.0	1.2.0	1.2.0	0.33.0	2.5.1	1.2.0	1.2.0
1.2.0	>=3.6,❤️.9	2.31.0	1.2.0	2.0.0	1.15.0 / 2.5.0	1.2.0	1.2.0	0.33.0	2.5.1	1.2.0	1.2.0
1.0.0	>=3.6,❤️.9	2.29.0	1.0.0	2.0.0	1.15.0 / 2.5.0	1.0.0	1.0.0	0.31.0	2.5.1	1.0.0	1.0.0
0.30.0	>=3.6,❤️.9	2.28.0	0.30.0	2.0.0	1.15.0 / 2.4.0	0.30.0	0.30.0	0.30.0	2.4.0	0.30.0	0.30.0
0.29.0	>=3.6,❤️.9	2.28.0	0.29.0	2.0.0	1.15.0 / 2.4.0	0.29.0	0.29.0	0.29.0	2.4.0	0.29.0	0.29.0
0.28.0	>=3.6,❤️.9	2.28.0	0.28.0	2.0.0	1.15.0 / 2.4.0	0.28.0	0.28.0	0.28.0	2.4.0	0.28.0	0.28.1
0.27.0	>=3.6,❤️.9	2.27.0	0.27.0	2.0.0	1.15.0 / 2.4.0	0.27.0	0.27.0	0.27.0	2.4.0	0.27.0	0.27.0
0.26.4	>=3.6,❤️.9	2.28.0	0.26.0	0.17.0	1.15.0 / 2.3.0	0.26.1	0.26.0	0.26.0	2.3.0	0.26.0	0.26.0
0.26.3	>=3.6,❤️.9	2.25.0	0.26.0	0.17.0	1.15.0 / 2.3.0	0.26.0	0.26.0	0.26.0	2.3.0	0.26.0	0.26.0
0.26.1	>=3.6,❤️.9	2.25.0	0.26.0	0.17.0	1.15.0 / 2.3.0	0.26.0	0.26.0	0.26.0	2.3.0	0.26.0	0.26.0
0.26.0	>=3.6,❤️.9	2.25.0	0.26.0	0.17.0	1.15.0 / 2.3.0	0.26.0	0.26.0	0.26.0	2.3.0	0.26.0	0.26.0
0.25.0	>=3.6,❤️.9	2.25.0	0.24.0	0.17.0	1.15.0 / 2.3.0	0.25.0	0.25.0	0.25.0	2.3.0	0.25.0	0.25.0
0.24.1	>=3.6,❤️.9	2.24.0	0.24.0	0.17.0	1.15.0 / 2.3.0	0.24.1	0.24.0	0.24.3	2.3.0	0.24.1	0.24.1
0.24.0	>=3.6,❤️.9	2.24.0	0.24.0	0.17.0	1.15.0 / 2.3.0	0.24.1	0.24.0	0.24.3	2.3.0	0.24.1	0.24.1
0.23.1	>=3.5,<4	2.24.0	0.23.0	0.17.0	1.15.0 / 2.3.0	0.23.1	0.23.0	0.23.0	2.3.0	0.23.0	0.23.0
0.23.0	>=3.5,<4	2.23.0	0.23.0	0.17.0	1.15.0 / 2.3.0	0.23.0	0.23.0	0.23.0	2.3.0	0.23.0	0.23.0
0.22.2	>=3.5,<4	2.21.0	0.22.1	0.16.0	1.15.0 / 2.2.0	0.22.2	0.22.2	0.22.2	2.2.0	0.22.0	0.22.1
0.22.1	>=3.5,<4	2.21.0	0.22.1	0.16.0	1.15.0 / 2.2.0	0.22.2	0.22.2	0.22.2	2.2.0	0.22.0	0.22.1
0.22.0	>=3.5,<4	2.21.0	0.22.0	0.16.0	1.15.0 / 2.2.0	0.22.0	0.22.0	0.22.1	2.2.0	0.22.0	0.22.0
0.21.5	>=2.7,❤️ or >=3.5,<4	2.17.0	0.21.2	0.15.0	1.15.0 / 2.1.0	0.21.5	0.21.1	0.21.5	2.1.0	0.21.2	0.21.4
0.21.4	>=2.7,❤️ or >=3.5,<4	2.17.0	0.21.2	0.15.0	1.15.0 / 2.1.0	0.21.5	0.21.1	0.21.5	2.1.0	0.21.2	0.21.4
0.21.3	>=2.7,❤️ or >=3.5,<4	2.17.0	0.21.2	0.15.0	1.15.0 / 2.1.0	0.21.5	0.21.1	0.21.5	2.1.0	0.21.2	0.21.4
0.21.2	>=2.7,❤️ or >=3.5,<4	2.17.0	0.21.2	0.15.0	1.15.0 / 2.1.0	0.21.5	0.21.1	0.21.5	2.1.0	0.21.2	0.21.4
0.21.1	>=2.7,❤️ or >=3.5,<4	2.17.0	0.21.2	0.15.0	1.15.0 / 2.1.0	0.21.4	0.21.1	0.21.4	2.1.0	0.21.2	0.21.3
0.21.0	>=2.7,❤️ or >=3.5,<4	2.17.0	0.21.0	0.15.0	1.15.0 / 2.1.0	0.21.0	0.21.0	0.21.1	2.1.0	0.21.0	0.21.0
0.15.0	>=2.7,❤️ or >=3.5,<4	2.16.0	0.15.0	0.15.0	1.15.0	0.15.0	0.15.0	0.15.2	1.15.0	0.15.0	0.15.1
0.14.0	>=2.7,❤️ or >=3.5,<4	2.14.0	0.14.0	0.14.0	1.14.0	0.14.1	0.14.0	0.14.0	1.14.0	0.14.0	n/a
0.13.0	>=2.7,❤️ or >=3.5,<4	2.12.0	0.13.2	n/a	1.13.1	0.13.1	0.13.0	0.13.2	1.13.0	0.13.0	n/a
0.12.0	>=2.7,❤️	2.10.0	0.13.2	n/a	1.12.0	0.12.0	0.12.1	0.12.1	1.12.0	0.12.0	n/a

Resources

FAQs

What is tfx?

TensorFlow Extended (TFX) is a TensorFlow-based general-purpose machine learning platform implemented at Google. Visit Snyk Advisor to see a full health score report for tfx, including popularity, security, maintenance & community analysis.

Is tfx popular?

The python package tfx receives a total of 18,124 weekly downloads. As such, tfx popularity was classified as a popular. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is tfx well maintained?

We found that tfx demonstrated a healthy version release cadence and project activity. It has a community of 170 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is tfx safe to use?

The python package tfx was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 21 December-2024, at 16:49 (UTC).

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