PyPI - License GitHub Workflow Status Read the Docs PyPI Python 3.10 DOI

The Seismology Benchmark collection (SeisBench) is an open-source python toolbox for machine learning in seismology. It provides a unified API for accessing seismic datasets and both training and applying machine learning algorithms to seismic data. SeisBench has been built to reduce the overhead when applying or developing machine learning techniques for seismological tasks.

Getting started

SeisBench offers three core modules, data, models, and generate. data provides access to benchmark datasets and offers functionality for loading datasets. models offers a collection of machine learning models for seismology. You can easily create models, load pretrained models or train models on any dataset. generate contains tools for building data generation pipelines. They bridge the gap between data and models.

The easiest way of getting started is through our Colab notebooks. Just click on the "Open in Colab" link to run them in your browser. Alternatively, you can clone the repository and run the same examples locally.

Note: If an example notebooks has been added very recently, it might rely on functionality that is not yet part of a numbered version. To run it nonetheless, you can replace the installation line with pip install "seisbench[das] @ git+https://github.com/seisbench/seisbench" to use the latest development branch.

Basic examples

These examples introduce you to the key modules of SeisBench.

Examples
Dataset basics Open In Colab
Model API Open In Colab
Generator Pipelines Open In Colab

Advanced topics

This section covers topics like training models, creating datasets or building earthquake catalogs.

Examples
Applied picking Open In Colab
Training PhaseNet Open In Colab
Creating a dataset Open In Colab
Building an event catalog with GaMMA Open In Colab
Building an event catalog with PyOcto Open In Colab

Using SeisBench for DAS data

These tutorials introduce how to use SeisBench to train and apply models for distributed acoustic sensing (DAS) data.

Examples
Applying DAS models Open In Colab
Training DeepSubDAS Open In Colab

Miscellaneous

A collection of notebooks for miscellaneous topics, such as denoising or depth estimation.

Examples
Training DKPN Open In Colab
Using DeepDenoiser Open In Colab
Training Denoiser Open In Colab
Depth phases and earthquake depth Open In Colab

For more detailed information on Seisbench check out the SeisBench documentation.

Installation

SeisBench can be installed in two ways. In both cases, you might consider installing SeisBench in a virtual environment, for example using conda.

The recommended way is installation through pip. Simply run:

pip install seisbench

Alternatively, you can install the latest version from source. For this approach, clone the repository, switch to the repository root and run:

pip install .

which will install SeisBench in your current python environment.

If you want to run SeisBench on DAS data, make sure to pass the [das] argument, i.e., pip install seisbench[das].

CPU only installation

SeisBench is built on pytorch, which in turn runs on CUDA for GPU acceleration. Sometimes, it might be preferable to install pytorch without CUDA, for example, because CUDA will not be used and the CUDA binaries are rather large. To install such a pure CPU version, the easiest way is to follow a two-step installation. First, install pytorch in a pure CPU version as explained here. Second, install SeisBench the regular way through pip. Example instructions would be:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install seisbench

Contributing

There are many ways to contribute to SeisBench and we are always looking forward to your contributions. Check out the contribution guidelines for details on how to contribute.

Known issues

  • We've experienced occasional issues with access to our repository. To verify the issue, try accessing https://hifis-storage.desy.de directly from the same machine. As a mitigation, you can use our backup repository. Just run seisbench.use_backup_repository(). Please note that the backup repository will usually show lower download speeds.
  • We've recently changed the URL of the SeisBench repository. To use the new URL update to SeisBench 0.11.5. It this is not possible, you can use the following commands within your runtime to update the URL manually: 
    import seisbench
from urllib.parse import urljoin

seisbench.remote_root = "https://hifis-storage.desy.de/Helmholtz/HelmholtzAI/SeisBench/"
seisbench.remote_data_root = urljoin(seisbench.remote_root, "datasets/")
seisbench.remote_model_root = urljoin(seisbench.remote_root, "models/v3/")

References

Reference publications for SeisBench:

Acknowledgements

The initial version of SeisBench has been developed at GFZ Potsdam and KIT with funding from Helmholtz AI. The SeisBench repository is hosted by HIFIS - Helmholtz Federated IT Services.

This development of the LFE detection model has been supported by MIAI@Grenoble Alpes (ANR-19-P3IA-0003) and the European Union through the Marie Skłodowska-Curie Actions (n°101104996 - DECODE).

The initial DAS model and data interface was supported by the European Commission under the Horizon Europe programme, through the project SUBMERSE (https://submerse.eu/) (Grant Agreement No. 101095055), funded within the HORIZON-INFRA-2022-TECH-01 call.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.12.5)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for jawooll from gravatar.com jawooll Avatar for munchmej from gravatar.com munchmej

Credits

Author/Maintainer: Jack Woolam

License

GNU General Public License v3 (GPLv3) (GPLv3)

Requires

Python >=3.10

Provides Extra

das dev tests

Tags

seismology machine learning signal processing earthquake

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

seisbench-0.12.3.tar.gz (33.5 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

seisbench-0.12.3-cp314-cp314-win_arm64.whl (271.3 kB view details)

Uploaded CPython 3.14Windows ARM64

seisbench-0.12.3-cp314-cp314-win_amd64.whl (271.7 kB view details)

Uploaded CPython 3.14Windows x86-64

seisbench-0.12.3-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl (282.8 kB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64manylinux: glibc 2.5+ x86-64

seisbench-0.12.3-cp314-cp314-macosx_11_0_arm64.whl (268.6 kB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

seisbench-0.12.3-cp313-cp313-win_arm64.whl (271.5 kB view details)

Uploaded CPython 3.13Windows ARM64

seisbench-0.12.3-cp313-cp313-win_amd64.whl (271.9 kB view details)

Uploaded CPython 3.13Windows x86-64

seisbench-0.12.3-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl (282.6 kB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64manylinux: glibc 2.5+ x86-64

seisbench-0.12.3-cp313-cp313-macosx_11_0_arm64.whl (268.6 kB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

seisbench-0.12.3-cp312-cp312-win_arm64.whl (271.5 kB view details)

Uploaded CPython 3.12Windows ARM64

seisbench-0.12.3-cp312-cp312-win_amd64.whl (271.9 kB view details)

Uploaded CPython 3.12Windows x86-64

seisbench-0.12.3-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl (282.5 kB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64manylinux: glibc 2.5+ x86-64

seisbench-0.12.3-cp312-cp312-macosx_11_0_arm64.whl (268.6 kB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

seisbench-0.12.3-cp311-cp311-win_arm64.whl (271.5 kB view details)

Uploaded CPython 3.11Windows ARM64

seisbench-0.12.3-cp311-cp311-win_amd64.whl (271.9 kB view details)

Uploaded CPython 3.11Windows x86-64

seisbench-0.12.3-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl (282.6 kB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64manylinux: glibc 2.5+ x86-64

seisbench-0.12.3-cp311-cp311-macosx_11_0_arm64.whl (268.6 kB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

seisbench-0.12.3-cp310-cp310-win_arm64.whl (271.5 kB view details)

Uploaded CPython 3.10Windows ARM64

seisbench-0.12.3-cp310-cp310-win_amd64.whl (271.9 kB view details)

Uploaded CPython 3.10Windows x86-64

seisbench-0.12.3-cp310-cp310-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl (281.6 kB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64manylinux: glibc 2.5+ x86-64

seisbench-0.12.3-cp310-cp310-macosx_11_0_arm64.whl (268.6 kB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

File details

Details for the file seisbench-0.12.3.tar.gz.

File metadata

  • Download URL: seisbench-0.12.3.tar.gz
  • Upload date:
  • Size: 33.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for seisbench-0.12.3.tar.gz
Algorithm Hash digest
SHA256 b7807a8b707c3b3d2af0eb55abf5162843a0b332b24dc89b19637fe46055704e
MD5 fb7576b4dc00cf9786831491e858f166
BLAKE2b-256 022990f033fdef0d304c3f7faf8669442e6cd9a0318d4984d0d0733589420150

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp314-cp314-win_arm64.whl.

File metadata

  • Download URL: seisbench-0.12.3-cp314-cp314-win_arm64.whl
  • Upload date:
  • Size: 271.3 kB
  • Tags: CPython 3.14, Windows ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for seisbench-0.12.3-cp314-cp314-win_arm64.whl
Algorithm Hash digest
SHA256 e7099cf89dbe78dc56d2b81f4331b8464aba4943d3c5d1c94feae29bb2b8e174
MD5 e32a7592d22afab3aee9afb316d7b32f
BLAKE2b-256 e59268c20e5be28d17d01fef9d7f4e84867af9258051fd295e046d7752444625

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp314-cp314-win_amd64.whl.

File metadata

  • Download URL: seisbench-0.12.3-cp314-cp314-win_amd64.whl
  • Upload date:
  • Size: 271.7 kB
  • Tags: CPython 3.14, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for seisbench-0.12.3-cp314-cp314-win_amd64.whl
Algorithm Hash digest
SHA256 eec2129b955f9333424d6a391a712615f5fa126ec4cc2f95287489e7ba7502c1
MD5 d39e6d7b95302a60747670177b850dfa
BLAKE2b-256 6f45664d6e014e2be806a1ae40d62c623c3e636e30f73e1c9e798427155f0ffd

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl.

File metadata

File hashes

Hashes for seisbench-0.12.3-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl
Algorithm Hash digest
SHA256 4e36490fa451e8db52131452877fc0acc247df5c4af0e4e9296049edad69b04e
MD5 344956ab5b2cb13b791af7eae1a20e55
BLAKE2b-256 595fbd68ea86d4db14c389d5d0cb4d3288915dd54f69e2c69896b9164e27bc34

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for seisbench-0.12.3-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 2ad17311ef35f9c58792e177d5d415311f7f5b343a629948868508574e148504
MD5 02a0fc178f18782acc2c8d0d9aed0ce2
BLAKE2b-256 e3c7e88a39d1ae7977cb4bc618080a9f3c022769870e48a5e8cea3b35f96ac77

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp313-cp313-win_arm64.whl.

File metadata

  • Download URL: seisbench-0.12.3-cp313-cp313-win_arm64.whl
  • Upload date:
  • Size: 271.5 kB
  • Tags: CPython 3.13, Windows ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for seisbench-0.12.3-cp313-cp313-win_arm64.whl
Algorithm Hash digest
SHA256 9b3969407564a07f27829f81b0b56c7a16a976215998aa41ac93ebd1e81ab08f
MD5 bf8eedcb3507bb62e67503d6635863c4
BLAKE2b-256 e6b7bf90a74a1c42ee5aac8251a012da9da4c6f5d2f0b4e746fbb466aba8a620

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: seisbench-0.12.3-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 271.9 kB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for seisbench-0.12.3-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 a284b2f91b55ad2d05b6939cf1d2b8559d5ecb372bdd1ccac53ce598007eac19
MD5 bb783738581f51d3b62907a28d50c7a7
BLAKE2b-256 6671f478eaa017fd10d5ff81092152a99765109c964ab4a6fe9a693fb026b494

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl.

File metadata

File hashes

Hashes for seisbench-0.12.3-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl
Algorithm Hash digest
SHA256 7a54c99704415b2cbf45d226248763202c231e4095c832067d5f0996b542c554
MD5 91736871b5287da5ebf110a715cd01e6
BLAKE2b-256 dac17194eeb06d0d53e451618685ad3f510196e99bb78f0863d9363ce7798c5d

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for seisbench-0.12.3-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e9dce14e97fd23d653aa8a5b8e62f0d16a37113612128e704dedeb113031ec4d
MD5 48a62b7ede3eb5e0c4a8bb3e3da7c778
BLAKE2b-256 55f56b5161394bbc7d63309c2e4d38ee7c644cb4776bc110d7ec1bf7e9732b96

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp312-cp312-win_arm64.whl.

File metadata

  • Download URL: seisbench-0.12.3-cp312-cp312-win_arm64.whl
  • Upload date:
  • Size: 271.5 kB
  • Tags: CPython 3.12, Windows ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for seisbench-0.12.3-cp312-cp312-win_arm64.whl
Algorithm Hash digest
SHA256 4c04ee2429817ef0c165e31136f88f6b6f5989bc24c645a346ba289692a1e2fb
MD5 5e2db676a7994da622077a747efa3c18
BLAKE2b-256 42d114a17b0fae2a45910b270d44da710016a627e12ea1bb3c24382e7fbfe397

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: seisbench-0.12.3-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 271.9 kB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for seisbench-0.12.3-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 10ae9f1fc1ab8776a802c87d1ff0f9263e2a1b2f19a54014c0fa5c1ca1cd6081
MD5 fba8595450d57df0c85878ae24de0b43
BLAKE2b-256 8a7ad92d5554aca7a42d0ac7bec941753cd5f4bf2ba46fc0b2538520432c90c5

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl.

File metadata

File hashes

Hashes for seisbench-0.12.3-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl
Algorithm Hash digest
SHA256 ed1e87159621ca779583f013a00476668b605fcfe87eddac4e30db3f79e6162d
MD5 476845cbfecd259eaaef7a72bc88a076
BLAKE2b-256 45233ac2edebb261e37e0385fd863d698f0efc9dfbb5ff5498ca08df86a5bc73

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for seisbench-0.12.3-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 de62e13ab1d36d8646f20aebdbe10f343f333f545f2fa099fbff23add7bc68cb
MD5 d572e85535601c2ea321eba5b6bc5658
BLAKE2b-256 6e464eadb01ab0c5094cdfe784888d4f791b60c3c6748c06e883e42ef2052ef7

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp311-cp311-win_arm64.whl.

File metadata

  • Download URL: seisbench-0.12.3-cp311-cp311-win_arm64.whl
  • Upload date:
  • Size: 271.5 kB
  • Tags: CPython 3.11, Windows ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for seisbench-0.12.3-cp311-cp311-win_arm64.whl
Algorithm Hash digest
SHA256 1fd3597b7350c8d5c298da3a7914ee4d29aea9ddad6c9db4d5d9f0b4dcedcce0
MD5 53ac0aed86696763698d84de83fa0e55
BLAKE2b-256 f8523e41b463523a0e5cd24b203c7310e1e89cfcc0c1226a144f1b9959e329f7

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: seisbench-0.12.3-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 271.9 kB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for seisbench-0.12.3-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 10b00f3c3f154f84babe1ed9fe382266a897a51b4c61407a2108f8c9a194dfae
MD5 067ce602b247937d66e37992426d269c
BLAKE2b-256 9433363cbc1d6beef7cf3ad7c7cd32ed10bdda864f68b6733cc348283c3a4890

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl.

File metadata

File hashes

Hashes for seisbench-0.12.3-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl
Algorithm Hash digest
SHA256 112616afe5dd7260a425b75f964c0348778153d1a7c6a5af951c62056a8dd0d2
MD5 b2a9f7e0883cad06dc65b0ca03b0c84d
BLAKE2b-256 9334c8c577d404a69d9f6f729c80203bb21b8a7ea57cb074bcc8532bd1af2c3e

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for seisbench-0.12.3-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 8b57d78334c38ece6b1c2c4fca668c948836d20165f3d0413cf3dad172661fde
MD5 812c0fc0e75cb0d3caced9220b54e0c3
BLAKE2b-256 571ee1519d72320b2d314997da6f673bbf25869f41754b61badc5db4b3de471f

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp310-cp310-win_arm64.whl.

File metadata

  • Download URL: seisbench-0.12.3-cp310-cp310-win_arm64.whl
  • Upload date:
  • Size: 271.5 kB
  • Tags: CPython 3.10, Windows ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for seisbench-0.12.3-cp310-cp310-win_arm64.whl
Algorithm Hash digest
SHA256 46ba5e1771e05a862583d1131a9b3d2ef0e8d69c6953ef01f26eb5cf642037f6
MD5 44cb94cc2be26189cd2935d8501f0bf5
BLAKE2b-256 942dc3d238efed3e7e3ba820dd9a1103a69939b4294a714c6217edee414926bb

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: seisbench-0.12.3-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 271.9 kB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for seisbench-0.12.3-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 b709b14341432b875c415a4c2b99b451edc4af6a0a574f2fb009cde6488c5f01
MD5 07372d2992e5b93b9a7c6035ae638ba3
BLAKE2b-256 7e654ef337df42cc19353880256d6c9307904669d790c6ca6904b38a9dde3eac

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp310-cp310-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl.

File metadata

File hashes

Hashes for seisbench-0.12.3-cp310-cp310-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl
Algorithm Hash digest
SHA256 86573eb788eec3cbe2347d2803b35d9f19031f7d62e2805e585e6b60b4d20a0d
MD5 9313b099a447b28a8c9b5733f4f46912
BLAKE2b-256 abfb75f04721aa515fd293346f09da53cf24601f356b6b1a52e50cee10af67bc

See more details on using hashes here.

File details

Details for the file seisbench-0.12.3-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for seisbench-0.12.3-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 6fe0b1a4353f734924acb3dc2b7c965d7d45a9b1f6d84ab093589a771aa1657a
MD5 af6b4ba5dc576d7b4ce41d51f9183bf0
BLAKE2b-256 8cedc7b268b97f4f9ece6f92bad553d9007e801bc535b06e01871a70aecaeddd

See more details on using hashes here.

Release history Release notifications | RSS feed

0.12.5

21 files

0.12.4

21 files

This release

0.12.3 This release

21 files

0.12.2

21 files

Pre-release

0.12.0a0

21 files

0.11.7

21 files

0.11.6

21 files

0.11.5

21 files

0.11.4

21 files

0.11.3

21 files

0.11.0

21 files

0.10.2

2 files

0.10.1

2 files

0.10.0

2 files

0.9.1

2 files

0.9.0

2 files

0.8.2

2 files

0.8.1

2 files

0.8.0

2 files

0.7.3

2 files

0.7.2

2 files

0.7.1

2 files

0.7.0

2 files

0.6.1

2 files

0.6.0

2 files

0.5.3

2 files

0.5.2

2 files

0.5.1

2 files

0.5.0

2 files

0.4.1

2 files

0.4.0

2 files

0.3.2

2 files

0.3.1

2 files

0.3.0

2 files

0.2.8

2 files

0.2.7

2 files

0.2.6

2 files

0.2.5

2 files

0.2.4

2 files

0.2.3

2 files

0.2.2

2 files

0.2.1

2 files

0.2.0

2 files

0.1.16

2 files

0.1.15

2 files

0.1.14

2 files

0.1.13

2 files

0.1.12

2 files

0.1.11

2 files

0.1.10

2 files

0.1.9

2 files

0.1.8

2 files

0.1.7

2 files

0.1.6

2 files

0.1.5

2 files

0.1.4

2 files

0.1.3

2 files

0.1.2

2 files

0.1.0

2 files

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page