tensorboard

Standalone TensorBoard for visualizing in deep learning

Project description

TensorBoard striped from TensorFlow, for general deep learning visualization.

Project details

Release history Release notifications

2.1.0

Dec 5, 2019

2.0.2

Nov 25, 2019

2.0.1

Oct 29, 2019

2.0.0

Sep 19, 2019

1.15.0

Sep 12, 2019

1.14.0

Jun 14, 2019

1.13.1

Mar 6, 2019

1.13.0

Feb 25, 2019

1.12.2

Jan 3, 2019

1.12.1

Dec 14, 2018

1.12.0

Oct 23, 2018

1.11.0

Sep 25, 2018

1.10.0

Jul 24, 2018

1.9.0

Jun 13, 2018

1.8.0

Apr 24, 2018

1.7.0

Mar 27, 2018

1.6.0

Feb 9, 2018

1.6.0rc0 pre-release

Feb 6, 2018

This version

1.0.0a6 pre-release

Mar 15, 2017

1.0.0a5 pre-release

Mar 11, 2017

1.0.0a4 pre-release

Mar 2, 2017

1.0.0a3 pre-release

Mar 2, 2017

1.0.0a2 pre-release

Feb 27, 2017

1.0.0a1 pre-release

Feb 27, 2017

1.0.0a0 pre-release

Feb 25, 2017

Download files

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

Files for tensorboard, version 1.0.0a6
Filename, size	File type	Python version	Upload date	Hashes
Filename, size tensorboard-1.0.0a6-cp27-cp27m-macosx_10_11_x86_64.whl (10.8 MB)	File type Wheel	Python version cp27	Upload date Mar 15, 2017	Hashes View hashes
Filename, size tensorboard-1.0.0a6-cp27-cp27m-macosx_10_12_x86_64.whl (10.8 MB)	File type Wheel	Python version cp27	Upload date Mar 15, 2017	Hashes View hashes
Filename, size tensorboard-1.0.0a6-cp27-cp27mu-manylinux1_x86_64.whl (11.2 MB)	File type Wheel	Python version cp27	Upload date Mar 15, 2017	Hashes View hashes
Filename, size tensorboard-1.0.0a6-cp34-cp34m-macosx_10_11_x86_64.whl (10.8 MB)	File type Wheel	Python version cp34	Upload date Mar 15, 2017	Hashes View hashes
Filename, size tensorboard-1.0.0a6-cp34-cp34m-macosx_10_12_x86_64.whl (10.8 MB)	File type Wheel	Python version cp34	Upload date Mar 15, 2017	Hashes View hashes
Filename, size tensorboard-1.0.0a6-cp34-cp34m-manylinux1_x86_64.whl (11.2 MB)	File type Wheel	Python version cp34	Upload date Mar 15, 2017	Hashes View hashes
Filename, size tensorboard-1.0.0a6-cp35-cp35m-macosx_10_11_x86_64.whl (10.8 MB)	File type Wheel	Python version cp35	Upload date Mar 15, 2017	Hashes View hashes
Filename, size tensorboard-1.0.0a6-cp35-cp35m-macosx_10_12_x86_64.whl (10.8 MB)	File type Wheel	Python version cp35	Upload date Mar 15, 2017	Hashes View hashes
Filename, size tensorboard-1.0.0a6-cp35-cp35m-manylinux1_x86_64.whl (11.2 MB)	File type Wheel	Python version cp35	Upload date Mar 15, 2017	Hashes View hashes
Filename, size tensorboard-1.0.0a6-cp36-cp36m-macosx_10_11_x86_64.whl (10.8 MB)	File type Wheel	Python version cp36	Upload date Mar 15, 2017	Hashes View hashes
Filename, size tensorboard-1.0.0a6-cp36-cp36m-macosx_10_12_x86_64.whl (10.8 MB)	File type Wheel	Python version cp36	Upload date Mar 15, 2017	Hashes View hashes
Filename, size tensorboard-1.0.0a6-cp36-cp36m-manylinux1_x86_64.whl (11.2 MB)	File type Wheel	Python version cp36	Upload date Jun 19, 2017	Hashes View hashes

Hashes for tensorboard-1.0.0a6-cp27-cp27m-macosx_10_11_x86_64.whl

Hashes for tensorboard-1.0.0a6-cp27-cp27m-macosx_10_11_x86_64.whl
Algorithm	Hash digest
SHA256	`49c59f46cc3dcd526dfe3b3bf3a9e6b1261e5d1521b9f7fab497413cb981db00`
MD5	`d80754385770f6922d711a9313ec848a`
BLAKE2-256	`5ec249389f30169942ac5befb4560ea9ec5c6b056172524ce47c5d25203ff26d`

Hashes for tensorboard-1.0.0a6-cp27-cp27m-macosx_10_12_x86_64.whl

Hashes for tensorboard-1.0.0a6-cp27-cp27m-macosx_10_12_x86_64.whl
Algorithm	Hash digest
SHA256	`0228c820d5ec1c29b9f4000e7efd572c454a1337d94796473e3c9a0a4568290a`
MD5	`d465d752c879207a18cf13bf7a839cfe`
BLAKE2-256	`7db69c7cd98b7642681a4514ad54d9bf96d6f65857cf5d66a6adbed0787a30e2`

Hashes for tensorboard-1.0.0a6-cp27-cp27mu-manylinux1_x86_64.whl

Hashes for tensorboard-1.0.0a6-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`9aa3672800ec1bb3b778b7c280c784b1ae0f561bc38d553861aee3776bd34a00`
MD5	`ef752f4bc8a943550a3a5ed23fed383b`
BLAKE2-256	`d68c2f9899c075f649e9a9c8e166ad8cfa0d7d525f0cd54bd9053889f5d39a2d`

Hashes for tensorboard-1.0.0a6-cp34-cp34m-macosx_10_11_x86_64.whl

Hashes for tensorboard-1.0.0a6-cp34-cp34m-macosx_10_11_x86_64.whl
Algorithm	Hash digest
SHA256	`68074cfcaeee4e539e49f6da94f4a8eb9847bf2b0af60f7427420ed92d1a47a4`
MD5	`4bc1188d08bda3bda874c91d9d71537c`
BLAKE2-256	`fc29f5a53b9450c0c1659be92c49789afeab3d0e93a7c70647b6205b7f518bb2`

Hashes for tensorboard-1.0.0a6-cp34-cp34m-macosx_10_12_x86_64.whl

Hashes for tensorboard-1.0.0a6-cp34-cp34m-macosx_10_12_x86_64.whl
Algorithm	Hash digest
SHA256	`0e35a1035e8757ea27e64b35c478ece3b98fd89a2ead4187021ec51f1e5d8315`
MD5	`6ff2d2f1d655273d52a36739a167e578`
BLAKE2-256	`5c8761f906c2141f7e98890c69601d3defcbb01faee68fd73cd60f2260fc0777`

Hashes for tensorboard-1.0.0a6-cp34-cp34m-manylinux1_x86_64.whl

Hashes for tensorboard-1.0.0a6-cp34-cp34m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`37118e41d56b2f31890fe0653923db68a8a28179542f0e6799b783d4d7d45efe`
MD5	`b8aa89cad6bd150e5058a2c87a591069`
BLAKE2-256	`0ab56cf871c236fe3a05f3d917574a4985e76280583df6ba0d56aabdf85934b0`

Hashes for tensorboard-1.0.0a6-cp35-cp35m-macosx_10_11_x86_64.whl

Hashes for tensorboard-1.0.0a6-cp35-cp35m-macosx_10_11_x86_64.whl
Algorithm	Hash digest
SHA256	`56485ab7a8dda60e11d72e9299ba808c688031d0e4d49c283e38f97609f3a68f`
MD5	`bf3adffd7b6bdc5d67268a7c1b78dc84`
BLAKE2-256	`af5af9fb4ae894d63e0fa13b6b91b69a82c961bd4a9bfce91e38774db1b5134f`

Hashes for tensorboard-1.0.0a6-cp35-cp35m-macosx_10_12_x86_64.whl

Hashes for tensorboard-1.0.0a6-cp35-cp35m-macosx_10_12_x86_64.whl
Algorithm	Hash digest
SHA256	`ec22aadbcc3257d5dd39702d25aebdb67b185ca2ad4866203e1c9a5eecb2352d`
MD5	`d939873f74b6997fd46935277c67c599`
BLAKE2-256	`071e7a44bebd3bd471c19daba543581694a9f4dd69e6fab8e3d8ad9121992a38`

Hashes for tensorboard-1.0.0a6-cp35-cp35m-manylinux1_x86_64.whl

Hashes for tensorboard-1.0.0a6-cp35-cp35m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`679af9ca86e1b315f2c66c989ebaa617ab5ef9e5ba7cdf76ebacc2e9427faf49`
MD5	`24c29d25eb8ca68acc99a39d11296bfa`
BLAKE2-256	`8ba780f9491702937cc1563b1f5c31c7292a0dbdba1ee8f68fa36b1d04ed34da`

Hashes for tensorboard-1.0.0a6-cp36-cp36m-macosx_10_11_x86_64.whl

Hashes for tensorboard-1.0.0a6-cp36-cp36m-macosx_10_11_x86_64.whl
Algorithm	Hash digest
SHA256	`6b85bd61ab3d00cd390040a0e98e07f6fd8b0978b046a5abc832015a4d22e527`
MD5	`41289c5b98bb6ecaafcdb24aead55c5d`
BLAKE2-256	`3fcf7e57955ab1abd8d28d1f1a9c0d9f5988603d5ef497e46b87c891eb5f73f8`

Hashes for tensorboard-1.0.0a6-cp36-cp36m-macosx_10_12_x86_64.whl

Hashes for tensorboard-1.0.0a6-cp36-cp36m-macosx_10_12_x86_64.whl
Algorithm	Hash digest
SHA256	`7395ce1c558be04e6a3436d53a223254587dc81961435f64e475edfc523d9fd0`
MD5	`401f7bf998804fbf43b3135ec1d6c6a4`
BLAKE2-256	`2756aa0acd32695d9b5eeae156274b8ac0a102a480577cc57ea2da843a9cd509`

Hashes for tensorboard-1.0.0a6-cp36-cp36m-manylinux1_x86_64.whl

Hashes for tensorboard-1.0.0a6-cp36-cp36m-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`9d7a95ae884c587accb2db674f7b73cbe37860a871292d9e2e8d0a96af00dea4`
MD5	`8fc36374e4b7156b1ccf9befd32d5091`
BLAKE2-256	`63dcdc40575b23389c40a728d00e012af7071a81a89249dfa03a0957da1204d9`

tensorboard 1.0.0a6

Navigation

Project links

Statistics

Meta

Maintainers