Get the latest cuda version for pytorch when pip-compiling. #1173
Labels
bug
Something is not working
resolver
Related to dependency resolver
setuptools
Related to compiling requirements form setup.py
Comments
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@DanielAtKrypton can you run with --verbose? |
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I also tried it with |
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Possibly related to #1114. |
Sure. Here it is: time_series_predictor on master via 🐍 v3.7.7 (.env)
❯ pip-compile --find-links=https://download.pytorch.org/whl/torch_stable.html --generate-hashes --upgrade --output-file=requirements-lock.txt --verbose
Using indexes:
https://pypi.org/simple
Using links:
https://download.pytorch.org/whl/torch_stable.html
ROUND 1
Current constraints:
psutil (from time_series_predictor (setup.py))
scipy (from time_series_predictor (setup.py))
skorch (from time_series_predictor (setup.py))
torch (from time_series_predictor (setup.py))
Finding the best candidates:
found candidate psutil==5.7.0 (constraint was <any>)
found candidate scipy==1.5.1 (constraint was <any>)
found candidate skorch==0.8.0 (constraint was <any>)
found candidate torch==1.5.1+cu92 (constraint was <any>)
Finding secondary dependencies:
scipy==1.5.1 requires numpy>=1.14.5
skorch==0.8.0 requires numpy>=1.13.3, scikit-learn>=0.19.1, scipy>=1.1.0, tabulate>=0.7.7, tqdm>=4.14.0
psutil==5.7.0 requires -
torch==1.5.1+cu92 requires future, numpy
New dependencies found in this round:
adding ['future', '', '[]']
adding ['numpy', '>=1.13.3,>=1.14.5', '[]']
adding ['scikit-learn', '>=0.19.1', '[]']
adding ['scipy', '>=1.1.0', '[]']
adding ['tabulate', '>=0.7.7', '[]']
adding ['tqdm', '>=4.14.0', '[]']
Removed dependencies in this round:
------------------------------------------------------------
Result of round 1: not stable
ROUND 2
Current constraints:
future (from torch==1.5.1+cu92->time_series_predictor (setup.py))
numpy>=1.13.3,>=1.14.5 (from scipy==1.5.1->time_series_predictor (setup.py))
psutil (from time_series_predictor (setup.py))
scikit-learn>=0.19.1 (from skorch==0.8.0->time_series_predictor (setup.py))
scipy>=1.1.0 (from time_series_predictor (setup.py))
skorch (from time_series_predictor (setup.py))
tabulate>=0.7.7 (from skorch==0.8.0->time_series_predictor (setup.py))
torch (from time_series_predictor (setup.py))
tqdm>=4.14.0 (from skorch==0.8.0->time_series_predictor (setup.py))
Finding the best candidates:
found candidate future==0.18.2 (constraint was <any>)
found candidate numpy==1.19.0 (constraint was >=1.13.3,>=1.14.5)
found candidate psutil==5.7.0 (constraint was <any>)
found candidate scikit-learn==0.23.1 (constraint was >=0.19.1)
found candidate scipy==1.5.1 (constraint was >=1.1.0)
found candidate skorch==0.8.0 (constraint was <any>)
found candidate tabulate==0.8.7 (constraint was >=0.7.7)
found candidate torch==1.5.1+cu92 (constraint was <any>)
found candidate tqdm==4.47.0 (constraint was >=4.14.0)
Finding secondary dependencies:
torch==1.5.1+cu92 requires future, numpy
numpy==1.19.0 requires -
scipy==1.5.1 requires numpy>=1.14.5
skorch==0.8.0 requires numpy>=1.13.3, scikit-learn>=0.19.1, scipy>=1.1.0, tabulate>=0.7.7, tqdm>=4.14.0
tabulate==0.8.7 requires -
tqdm==4.47.0 requires -
psutil==5.7.0 requires -
scikit-learn==0.23.1 requires joblib>=0.11, numpy>=1.13.3, scipy>=0.19.1, threadpoolctl>=2.0.0
future==0.18.2 requires -
New dependencies found in this round:
adding ['joblib', '>=0.11', '[]']
adding ['scipy', '>=0.19.1,>=1.1.0', '[]']
adding ['threadpoolctl', '>=2.0.0', '[]']
Removed dependencies in this round:
removing ['scipy', '>=1.1.0', '[]']
------------------------------------------------------------
Result of round 2: not stable
ROUND 3
Current constraints:
future (from torch==1.5.1+cu92->time_series_predictor (setup.py))
joblib>=0.11 (from scikit-learn==0.23.1->skorch==0.8.0->time_series_predictor (setup.py))
numpy>=1.13.3,>=1.14.5 (from scipy==1.5.1->time_series_predictor (setup.py))
psutil (from time_series_predictor (setup.py))
scikit-learn>=0.19.1 (from skorch==0.8.0->time_series_predictor (setup.py))
scipy>=0.19.1,>=1.1.0 (from time_series_predictor (setup.py))
skorch (from time_series_predictor (setup.py))
tabulate>=0.7.7 (from skorch==0.8.0->time_series_predictor (setup.py))
threadpoolctl>=2.0.0 (from scikit-learn==0.23.1->skorch==0.8.0->time_series_predictor (setup.py))
torch (from time_series_predictor (setup.py))
tqdm>=4.14.0 (from skorch==0.8.0->time_series_predictor (setup.py))
Finding the best candidates:
found candidate future==0.18.2 (constraint was <any>)
found candidate joblib==0.16.0 (constraint was >=0.11)
found candidate numpy==1.19.0 (constraint was >=1.13.3,>=1.14.5)
found candidate psutil==5.7.0 (constraint was <any>)
found candidate scikit-learn==0.23.1 (constraint was >=0.19.1)
found candidate scipy==1.5.1 (constraint was >=0.19.1,>=1.1.0)
found candidate skorch==0.8.0 (constraint was <any>)
found candidate tabulate==0.8.7 (constraint was >=0.7.7)
found candidate threadpoolctl==2.1.0 (constraint was >=2.0.0)
found candidate torch==1.5.1+cu92 (constraint was <any>)
found candidate tqdm==4.47.0 (constraint was >=4.14.0)
Finding secondary dependencies:
joblib==0.16.0 requires -
scikit-learn==0.23.1 requires joblib>=0.11, numpy>=1.13.3, scipy>=0.19.1, threadpoolctl>=2.0.0
scipy==1.5.1 requires numpy>=1.14.5
psutil==5.7.0 requires -
skorch==0.8.0 requires numpy>=1.13.3, scikit-learn>=0.19.1, scipy>=1.1.0, tabulate>=0.7.7, tqdm>=4.14.0
threadpoolctl==2.1.0 requires -
torch==1.5.1+cu92 requires future, numpy
numpy==1.19.0 requires -
tabulate==0.8.7 requires -
future==0.18.2 requires -
tqdm==4.47.0 requires -
------------------------------------------------------------
Result of round 3: stable, done
Generating hashes:
joblib
scipy
scikit-learn
skorch
psutil
threadpoolctl
torch
Missing release files on PyPI
Couldn't get hashes from PyPI, fallback to hashing files
Hashing torch-1.5.1%2Bcu92-cp38-cp38-win_amd64.whl
|████████████████████████████████| 100%
Hashing torch-1.5.1%2Bcu92-cp37-cp37m-linux_x86_64.whl
|████████████████████████████████| 100%
Hashing torch-1.5.1%2Bcu92-cp36-cp36m-linux_x86_64.whl
|████████████████████████████████| 100%
Hashing torch-1.5.1%2Bcu92-cp38-cp38-linux_x86_64.whl
|████████████████████████████████| 100%
Hashing torch-1.5.1%2Bcu92-cp37-cp37m-win_amd64.whl
|████████████████████████████████| 100%
Hashing torch-1.5.1%2Bcu92-cp36-cp36m-win_amd64.whl
|████████████████████████████████| 100%
Hashing torch-1.5.1%2Bcu92-cp35-cp35m-win_amd64.whl
|████████████████████████████████| 100%
Hashing torch-1.5.1%2Bcu92-cp35-cp35m-linux_x86_64.whl
|████████████████████████████████| 100%
numpy
tabulate
future
tqdm
#
# This file is autogenerated by pip-compile
# To update, run:
#
# pip-compile --find-links=https://download.pytorch.org/whl/torch_stable.html --generate-hashes --output-file=requirements-lock.txt
#
--find-links https://download.pytorch.org/whl/torch_stable.html
future==0.18.2 \
--hash=sha256:b1bead90b70cf6ec3f0710ae53a525360fa360d306a86583adc6bf83a4db537d \
# via torch
joblib==0.16.0 \
--hash=sha256:8f52bf24c64b608bf0b2563e0e47d6fcf516abc8cfafe10cfd98ad66d94f92d6 \
--hash=sha256:d348c5d4ae31496b2aa060d6d9b787864dd204f9480baaa52d18850cb43e9f49 \
# via scikit-learn
numpy==1.19.0 \
--hash=sha256:13af0184177469192d80db9bd02619f6fa8b922f9f327e077d6f2a6acb1ce1c0 \
--hash=sha256:26a45798ca2a4e168d00de75d4a524abf5907949231512f372b217ede3429e98 \
--hash=sha256:26f509450db547e4dfa3ec739419b31edad646d21fb8d0ed0734188b35ff6b27 \
--hash=sha256:30a59fb41bb6b8c465ab50d60a1b298d1cd7b85274e71f38af5a75d6c475d2d2 \
--hash=sha256:33c623ef9ca5e19e05991f127c1be5aeb1ab5cdf30cb1c5cf3960752e58b599b \
--hash=sha256:356f96c9fbec59974a592452ab6a036cd6f180822a60b529a975c9467fcd5f23 \
--hash=sha256:3c40c827d36c6d1c3cf413694d7dc843d50997ebffbc7c87d888a203ed6403a7 \
--hash=sha256:4d054f013a1983551254e2379385e359884e5af105e3efe00418977d02f634a7 \
--hash=sha256:63d971bb211ad3ca37b2adecdd5365f40f3b741a455beecba70fd0dde8b2a4cb \
--hash=sha256:658624a11f6e1c252b2cd170d94bf28c8f9410acab9f2fd4369e11e1cd4e1aaf \
--hash=sha256:76766cc80d6128750075378d3bb7812cf146415bd29b588616f72c943c00d598 \
--hash=sha256:7b57f26e5e6ee2f14f960db46bd58ffdca25ca06dd997729b1b179fddd35f5a3 \
--hash=sha256:7b852817800eb02e109ae4a9cef2beda8dd50d98b76b6cfb7b5c0099d27b52d4 \
--hash=sha256:8cde829f14bd38f6da7b2954be0f2837043e8b8d7a9110ec5e318ae6bf706610 \
--hash=sha256:a2e3a39f43f0ce95204beb8fe0831199542ccab1e0c6e486a0b4947256215632 \
--hash=sha256:a86c962e211f37edd61d6e11bb4df7eddc4a519a38a856e20a6498c319efa6b0 \
--hash=sha256:a8705c5073fe3fcc297fb8e0b31aa794e05af6a329e81b7ca4ffecab7f2b95ef \
--hash=sha256:b6aaeadf1e4866ca0fdf7bb4eed25e521ae21a7947c59f78154b24fc7abbe1dd \
--hash=sha256:be62aeff8f2f054eff7725f502f6228298891fd648dc2630e03e44bf63e8cee0 \
--hash=sha256:c2edbb783c841e36ca0fa159f0ae97a88ce8137fb3a6cd82eae77349ba4b607b \
--hash=sha256:cbe326f6d364375a8e5a8ccb7e9cd73f4b2f6dc3b2ed205633a0db8243e2a96a \
--hash=sha256:d34fbb98ad0d6b563b95de852a284074514331e6b9da0a9fc894fb1cdae7a79e \
--hash=sha256:d97a86937cf9970453c3b62abb55a6475f173347b4cde7f8dcdb48c8e1b9952d \
--hash=sha256:dd53d7c4a69e766e4900f29db5872f5824a06827d594427cf1a4aa542818b796 \
--hash=sha256:df1889701e2dfd8ba4dc9b1a010f0a60950077fb5242bb92c8b5c7f1a6f2668a \
--hash=sha256:fa1fe75b4a9e18b66ae7f0b122543c42debcf800aaafa0212aaff3ad273c2596 \
# via scikit-learn, scipy, skorch, torch
psutil==5.7.0 \
--hash=sha256:1413f4158eb50e110777c4f15d7c759521703bd6beb58926f1d562da40180058 \
--hash=sha256:298af2f14b635c3c7118fd9183843f4e73e681bb6f01e12284d4d70d48a60953 \
--hash=sha256:60b86f327c198561f101a92be1995f9ae0399736b6eced8f24af41ec64fb88d4 \
--hash=sha256:685ec16ca14d079455892f25bd124df26ff9137664af445563c1bd36629b5e0e \
--hash=sha256:73f35ab66c6c7a9ce82ba44b1e9b1050be2a80cd4dcc3352cc108656b115c74f \
--hash=sha256:75e22717d4dbc7ca529ec5063000b2b294fc9a367f9c9ede1f65846c7955fd38 \
--hash=sha256:a02f4ac50d4a23253b68233b07e7cdb567bd025b982d5cf0ee78296990c22d9e \
--hash=sha256:d008ddc00c6906ec80040d26dc2d3e3962109e40ad07fd8a12d0284ce5e0e4f8 \
--hash=sha256:d84029b190c8a66a946e28b4d3934d2ca1528ec94764b180f7d6ea57b0e75e26 \
--hash=sha256:e2d0c5b07c6fe5a87fa27b7855017edb0d52ee73b71e6ee368fae268605cc3f5 \
--hash=sha256:f344ca230dd8e8d5eee16827596f1c22ec0876127c28e800d7ae20ed44c4b310 \
# via time_series_predictor (setup.py)
scikit-learn==0.23.1 \
--hash=sha256:04799686060ecbf8992f26a35be1d99e981894c8c7860c1365cda4200f954a16 \
--hash=sha256:058d213092de4384710137af1300ed0ff030b8c40459a6c6f73c31ccd274cc39 \
--hash=sha256:0c3464e46ef8bd4f1bfa5c009648c6449412c8f7e9b3fc0c9e3d800139c48827 \
--hash=sha256:0e7b55f73b35537ecd0d19df29dd39aa9e076dba78f3507b8136c819d84611fd \
--hash=sha256:16feae4361be6b299d4d08df5a30956b4bfc8eadf173fe9258f6d59630f851d4 \
--hash=sha256:244ca85d6eba17a1e6e8a66ab2f584be6a7784b5f59297e3d7ff8c7983af627c \
--hash=sha256:3e6e92b495eee193a8fa12a230c9b7976ea0fc1263719338e35c986ea1e42cff \
--hash=sha256:5bcea4d6ee431c814261117281363208408aa4e665633655895feb059021aca6 \
--hash=sha256:93f56abd316d131645559ec0ab4f45e3391c2ccdd4eadaa4912f4c1e0a6f2c96 \
--hash=sha256:9e04c0811ea92931ee8490d638171b8cb2f21387efcfff526bbc8c2a3da60f1c \
--hash=sha256:bded94236e16774385202cafd26190ce96db18e4dc21e99473848c61e4fdc400 \
--hash=sha256:c2fa33d20408b513cf432505c80e6eb4bf4d71434f1ae36680765d4a2c2a16ec \
--hash=sha256:e3fec1c8831f8f93ad85581ca29ca1bb88e2da377fb097cf8322aa89c21bc9b8 \
--hash=sha256:e585682e37f2faa81ad6cd4472fff646bf2fd0542147bec93697a905db8e6bd2 \
--hash=sha256:e9879ba9e64ec3add41bf201e06034162f853652ef4849b361d73b0deb3153ad \
--hash=sha256:ebe853e6f318f9d8b3b74dd17e553720d35646eff675a69eeaed12fbbbb07daa \
# via skorch
scipy==1.5.1 \
--hash=sha256:039572f0ca9578a466683558c5bf1e65d442860ec6e13307d528749cfe6d07b8 \
--hash=sha256:058e84930407927f71963a4ad8c1dc96c4d2d075636a68578195648c81f78810 \
--hash=sha256:06b19a650471781056c1a2172eeeeb777b8b516e9434005dd392a4559e0938b9 \
--hash=sha256:35d042d6499caf1a5d171baed0ebf01eb665b7af2ad98a8ff1b0e6e783654540 \
--hash=sha256:57a0f2be3063dbe1e3daf31ec9005576e8fd1022a28159d0db71d14566899d16 \
--hash=sha256:5e0bb43ff581811ab7f27425f6b96c1ddf7591ccad2e486c9af0b910c18f7185 \
--hash=sha256:71742889393a724dfce755b6b61228677873d269a4234e51ddaf08b998433c91 \
--hash=sha256:7908c85854c5b5b6d3ce7fefafac1ca3e23ff9ac41edabc2d46ae5dc9fa070ac \
--hash=sha256:81859ed3aad620752dd2c07c32b5d3a80a0d47c5e3813904621954a78a0ae899 \
--hash=sha256:8302d69fb1528ea7c7f2a1ea640d354c981b6eb8192d1c175349874209397604 \
--hash=sha256:9323d268775991b79690f7b9a28a4e8b8c4f2b160ed9f8a90123127314e2d3c1 \
--hash=sha256:b4858ccbd88f4b53950fb9fc0069c1d9fea83d7cff2382e1d8b023d3f4883014 \
--hash=sha256:c05c6fe76228cc13c5214e9faf5f2a871a1da54473bc417ab9da310d0e5fff8b \
--hash=sha256:c06e731aa46c0dfc563cc636155758178ebc019ef78b9b0f4370effe2ac0f0e6 \
--hash=sha256:eb46d8b5947ca27b0bc972cecfba8130f088a83ab3d08c1a6033d9070b3046b3 \
--hash=sha256:fff15df01bef1243468be60c55178ed7576270b200aab08a7ffd5b8e0bbc340c \
# via scikit-learn, skorch, time_series_predictor (setup.py)
skorch==0.8.0 \
--hash=sha256:5908fdc3c1c8ae49d16fa3edb1fbdd412c44f2baee02abdd5432b7a47933a7d0 \
--hash=sha256:f292e9866f65df7fb7cf209f503924e2cb67377d7524a50c3e5dc6ae5a5ecd47 \
# via time_series_predictor (setup.py)
tabulate==0.8.7 \
--hash=sha256:ac64cb76d53b1231d364babcd72abbb16855adac7de6665122f97b593f1eb2ba \
--hash=sha256:db2723a20d04bcda8522165c73eea7c300eda74e0ce852d9022e0159d7895007 \
# via skorch
threadpoolctl==2.1.0 \
--hash=sha256:38b74ca20ff3bb42caca8b00055111d74159ee95c4370882bbff2b93d24da725 \
--hash=sha256:ddc57c96a38beb63db45d6c159b5ab07b6bced12c45a1f07b2b92f272aebfa6b \
# via scikit-learn
torch==1.5.1+cu92 \
--hash=sha256:018c813ca9eea20062266b7e2f625d8dc0c4cc21c879f2e62ee79c35dd926850 \
--hash=sha256:20534264aa5d363635d84a331ea66acc1f2faf4ee8d97c68b5a9ed20db38bf07 \
--hash=sha256:62e5ca82020cd6478a93c25cc9854d31e64a3503a0dfade7784a3c308d696e41 \
--hash=sha256:735f3a0764919092a3451e5b06e9cd84d654d9e26c4c3b701ec48d0de9a4913d \
--hash=sha256:9c6695b4b51086e14f9f620c2bcd8111a7043cee518217ee6ed6e9d306e705f2 \
--hash=sha256:c5f43abeebf9ee5756e2320b3797810d31b3b7dbb978791f8f37be4c202c3265 \
--hash=sha256:cb47a29dd933e8933a0d9ea1dfd8bb8c852e848dba0d349c06e26f31fdafcca5 \
--hash=sha256:fee450640283f581b9495a0656dbf941eeda54914530ca0d619fe178a8d7199f \
# via time_series_predictor (setup.py)
tqdm==4.47.0 \
--hash=sha256:63ef7a6d3eb39f80d6b36e4867566b3d8e5f1fe3d6cb50c5e9ede2b3198ba7b7 \
--hash=sha256:7810e627bcf9d983a99d9ff8a0c09674400fd2927eddabeadf153c14a2ec8656 \
# via skorch |
With time_series_predictor on master via 🐍 v3.7.7 (.env)
❯ pip-compile --find-links=https://download.pytorch.org/whl/torch_stable.html --generate-hashes --upgrade --output-file=requirements-lock.txt --verbose
Using indexes:
https://pypi.org/simple
Using links:
https://download.pytorch.org/whl/torch_stable.html
ROUND 1
Current constraints:
psutil (from time_series_predictor (setup.py))
scipy (from time_series_predictor (setup.py))
skorch (from time_series_predictor (setup.py))
torch===1.5.0 (from time_series_predictor (setup.py))
Finding the best candidates:
found candidate psutil==5.7.0 (constraint was <any>)
found candidate scipy==1.5.1 (constraint was <any>)
found candidate skorch==0.8.0 (constraint was <any>)
found candidate torch===1.5.0 (constraint was ===1.5.0)
Finding secondary dependencies:
scipy==1.5.1 requires numpy>=1.14.5
torch===1.5.0 requires future, numpy
skorch==0.8.0 requires numpy>=1.13.3, scikit-learn>=0.19.1, scipy>=1.1.0, tabulate>=0.7.7, tqdm>=4.14.0
psutil==5.7.0 requires -
New dependencies found in this round:
adding ['future', '', '[]']
adding ['numpy', '>=1.13.3,>=1.14.5', '[]']
adding ['scikit-learn', '>=0.19.1', '[]']
adding ['scipy', '>=1.1.0', '[]']
adding ['tabulate', '>=0.7.7', '[]']
adding ['tqdm', '>=4.14.0', '[]']
Removed dependencies in this round:
------------------------------------------------------------
Result of round 1: not stable
ROUND 2
Current constraints:
future (from torch===1.5.0->time_series_predictor (setup.py))
numpy>=1.13.3,>=1.14.5 (from scipy==1.5.1->time_series_predictor (setup.py))
psutil (from time_series_predictor (setup.py))
scikit-learn>=0.19.1 (from skorch==0.8.0->time_series_predictor (setup.py))
scipy>=1.1.0 (from time_series_predictor (setup.py))
skorch (from time_series_predictor (setup.py))
tabulate>=0.7.7 (from skorch==0.8.0->time_series_predictor (setup.py))
torch===1.5.0 (from time_series_predictor (setup.py))
tqdm>=4.14.0 (from skorch==0.8.0->time_series_predictor (setup.py))
Finding the best candidates:
found candidate future==0.18.2 (constraint was <any>)
found candidate numpy==1.19.0 (constraint was >=1.13.3,>=1.14.5)
found candidate psutil==5.7.0 (constraint was <any>)
found candidate scikit-learn==0.23.1 (constraint was >=0.19.1)
found candidate scipy==1.5.1 (constraint was >=1.1.0)
found candidate skorch==0.8.0 (constraint was <any>)
found candidate tabulate==0.8.7 (constraint was >=0.7.7)
found candidate torch===1.5.0 (constraint was ===1.5.0)
found candidate tqdm==4.47.0 (constraint was >=4.14.0)
Finding secondary dependencies:
scipy==1.5.1 requires numpy>=1.14.5
skorch==0.8.0 requires numpy>=1.13.3, scikit-learn>=0.19.1, scipy>=1.1.0, tabulate>=0.7.7, tqdm>=4.14.0
torch===1.5.0 requires future, numpy
tabulate==0.8.7 requires -
numpy==1.19.0 requires -
tqdm==4.47.0 requires -
scikit-learn==0.23.1 requires joblib>=0.11, numpy>=1.13.3, scipy>=0.19.1, threadpoolctl>=2.0.0
future==0.18.2 requires -
psutil==5.7.0 requires -
New dependencies found in this round:
adding ['joblib', '>=0.11', '[]']
adding ['scipy', '>=0.19.1,>=1.1.0', '[]']
adding ['threadpoolctl', '>=2.0.0', '[]']
Removed dependencies in this round:
removing ['scipy', '>=1.1.0', '[]']
------------------------------------------------------------
Result of round 2: not stable
ROUND 3
Current constraints:
future (from torch===1.5.0->time_series_predictor (setup.py))
joblib>=0.11 (from scikit-learn==0.23.1->skorch==0.8.0->time_series_predictor (setup.py))
numpy>=1.13.3,>=1.14.5 (from scipy==1.5.1->time_series_predictor (setup.py))
psutil (from time_series_predictor (setup.py))
scikit-learn>=0.19.1 (from skorch==0.8.0->time_series_predictor (setup.py))
scipy>=0.19.1,>=1.1.0 (from time_series_predictor (setup.py))
skorch (from time_series_predictor (setup.py))
tabulate>=0.7.7 (from skorch==0.8.0->time_series_predictor (setup.py))
threadpoolctl>=2.0.0 (from scikit-learn==0.23.1->skorch==0.8.0->time_series_predictor (setup.py))
torch===1.5.0 (from time_series_predictor (setup.py))
tqdm>=4.14.0 (from skorch==0.8.0->time_series_predictor (setup.py))
Finding the best candidates:
found candidate future==0.18.2 (constraint was <any>)
found candidate joblib==0.16.0 (constraint was >=0.11)
found candidate numpy==1.19.0 (constraint was >=1.13.3,>=1.14.5)
found candidate psutil==5.7.0 (constraint was <any>)
found candidate scikit-learn==0.23.1 (constraint was >=0.19.1)
found candidate scipy==1.5.1 (constraint was >=0.19.1,>=1.1.0)
found candidate skorch==0.8.0 (constraint was <any>)
found candidate tabulate==0.8.7 (constraint was >=0.7.7)
found candidate threadpoolctl==2.1.0 (constraint was >=2.0.0)
found candidate torch===1.5.0 (constraint was ===1.5.0)
found candidate tqdm==4.47.0 (constraint was >=4.14.0)
Finding secondary dependencies:
torch===1.5.0 requires future, numpy
joblib==0.16.0 requires -
tabulate==0.8.7 requires -
future==0.18.2 requires -
scikit-learn==0.23.1 requires joblib>=0.11, numpy>=1.13.3, scipy>=0.19.1, threadpoolctl>=2.0.0
threadpoolctl==2.1.0 requires -
psutil==5.7.0 requires -
scipy==1.5.1 requires numpy>=1.14.5
numpy==1.19.0 requires -
skorch==0.8.0 requires numpy>=1.13.3, scikit-learn>=0.19.1, scipy>=1.1.0, tabulate>=0.7.7, tqdm>=4.14.0
tqdm==4.47.0 requires -
------------------------------------------------------------
Result of round 3: stable, done
Generating hashes:
torch
tabulate
joblib
future
scikit-learn
threadpoolctl
psutil
scipy
numpy
skorch
tqdm
#
# This file is autogenerated by pip-compile
# To update, run:
#
# pip-compile --find-links=https://download.pytorch.org/whl/torch_stable.html --generate-hashes --output-file=requirements-lock.txt
#
--find-links https://download.pytorch.org/whl/torch_stable.html
future==0.18.2 \
--hash=sha256:b1bead90b70cf6ec3f0710ae53a525360fa360d306a86583adc6bf83a4db537d \
# via torch
joblib==0.16.0 \
--hash=sha256:8f52bf24c64b608bf0b2563e0e47d6fcf516abc8cfafe10cfd98ad66d94f92d6 \
--hash=sha256:d348c5d4ae31496b2aa060d6d9b787864dd204f9480baaa52d18850cb43e9f49 \
# via scikit-learn
numpy==1.19.0 \
--hash=sha256:13af0184177469192d80db9bd02619f6fa8b922f9f327e077d6f2a6acb1ce1c0 \
--hash=sha256:26a45798ca2a4e168d00de75d4a524abf5907949231512f372b217ede3429e98 \
--hash=sha256:26f509450db547e4dfa3ec739419b31edad646d21fb8d0ed0734188b35ff6b27 \
--hash=sha256:30a59fb41bb6b8c465ab50d60a1b298d1cd7b85274e71f38af5a75d6c475d2d2 \
--hash=sha256:33c623ef9ca5e19e05991f127c1be5aeb1ab5cdf30cb1c5cf3960752e58b599b \
--hash=sha256:356f96c9fbec59974a592452ab6a036cd6f180822a60b529a975c9467fcd5f23 \
--hash=sha256:3c40c827d36c6d1c3cf413694d7dc843d50997ebffbc7c87d888a203ed6403a7 \
--hash=sha256:4d054f013a1983551254e2379385e359884e5af105e3efe00418977d02f634a7 \
--hash=sha256:63d971bb211ad3ca37b2adecdd5365f40f3b741a455beecba70fd0dde8b2a4cb \
--hash=sha256:658624a11f6e1c252b2cd170d94bf28c8f9410acab9f2fd4369e11e1cd4e1aaf \
--hash=sha256:76766cc80d6128750075378d3bb7812cf146415bd29b588616f72c943c00d598 \
--hash=sha256:7b57f26e5e6ee2f14f960db46bd58ffdca25ca06dd997729b1b179fddd35f5a3 \
--hash=sha256:7b852817800eb02e109ae4a9cef2beda8dd50d98b76b6cfb7b5c0099d27b52d4 \
--hash=sha256:8cde829f14bd38f6da7b2954be0f2837043e8b8d7a9110ec5e318ae6bf706610 \
--hash=sha256:a2e3a39f43f0ce95204beb8fe0831199542ccab1e0c6e486a0b4947256215632 \
--hash=sha256:a86c962e211f37edd61d6e11bb4df7eddc4a519a38a856e20a6498c319efa6b0 \
--hash=sha256:a8705c5073fe3fcc297fb8e0b31aa794e05af6a329e81b7ca4ffecab7f2b95ef \
--hash=sha256:b6aaeadf1e4866ca0fdf7bb4eed25e521ae21a7947c59f78154b24fc7abbe1dd \
--hash=sha256:be62aeff8f2f054eff7725f502f6228298891fd648dc2630e03e44bf63e8cee0 \
--hash=sha256:c2edbb783c841e36ca0fa159f0ae97a88ce8137fb3a6cd82eae77349ba4b607b \
--hash=sha256:cbe326f6d364375a8e5a8ccb7e9cd73f4b2f6dc3b2ed205633a0db8243e2a96a \
--hash=sha256:d34fbb98ad0d6b563b95de852a284074514331e6b9da0a9fc894fb1cdae7a79e \
--hash=sha256:d97a86937cf9970453c3b62abb55a6475f173347b4cde7f8dcdb48c8e1b9952d \
--hash=sha256:dd53d7c4a69e766e4900f29db5872f5824a06827d594427cf1a4aa542818b796 \
--hash=sha256:df1889701e2dfd8ba4dc9b1a010f0a60950077fb5242bb92c8b5c7f1a6f2668a \
--hash=sha256:fa1fe75b4a9e18b66ae7f0b122543c42debcf800aaafa0212aaff3ad273c2596 \
# via scikit-learn, scipy, skorch, torch
psutil==5.7.0 \
--hash=sha256:1413f4158eb50e110777c4f15d7c759521703bd6beb58926f1d562da40180058 \
--hash=sha256:298af2f14b635c3c7118fd9183843f4e73e681bb6f01e12284d4d70d48a60953 \
--hash=sha256:60b86f327c198561f101a92be1995f9ae0399736b6eced8f24af41ec64fb88d4 \
--hash=sha256:685ec16ca14d079455892f25bd124df26ff9137664af445563c1bd36629b5e0e \
--hash=sha256:73f35ab66c6c7a9ce82ba44b1e9b1050be2a80cd4dcc3352cc108656b115c74f \
--hash=sha256:75e22717d4dbc7ca529ec5063000b2b294fc9a367f9c9ede1f65846c7955fd38 \
--hash=sha256:a02f4ac50d4a23253b68233b07e7cdb567bd025b982d5cf0ee78296990c22d9e \
--hash=sha256:d008ddc00c6906ec80040d26dc2d3e3962109e40ad07fd8a12d0284ce5e0e4f8 \
--hash=sha256:d84029b190c8a66a946e28b4d3934d2ca1528ec94764b180f7d6ea57b0e75e26 \
--hash=sha256:e2d0c5b07c6fe5a87fa27b7855017edb0d52ee73b71e6ee368fae268605cc3f5 \
--hash=sha256:f344ca230dd8e8d5eee16827596f1c22ec0876127c28e800d7ae20ed44c4b310 \
# via time_series_predictor (setup.py)
scikit-learn==0.23.1 \
--hash=sha256:04799686060ecbf8992f26a35be1d99e981894c8c7860c1365cda4200f954a16 \
--hash=sha256:058d213092de4384710137af1300ed0ff030b8c40459a6c6f73c31ccd274cc39 \
--hash=sha256:0c3464e46ef8bd4f1bfa5c009648c6449412c8f7e9b3fc0c9e3d800139c48827 \
--hash=sha256:0e7b55f73b35537ecd0d19df29dd39aa9e076dba78f3507b8136c819d84611fd \
--hash=sha256:16feae4361be6b299d4d08df5a30956b4bfc8eadf173fe9258f6d59630f851d4 \
--hash=sha256:244ca85d6eba17a1e6e8a66ab2f584be6a7784b5f59297e3d7ff8c7983af627c \
--hash=sha256:3e6e92b495eee193a8fa12a230c9b7976ea0fc1263719338e35c986ea1e42cff \
--hash=sha256:5bcea4d6ee431c814261117281363208408aa4e665633655895feb059021aca6 \
--hash=sha256:93f56abd316d131645559ec0ab4f45e3391c2ccdd4eadaa4912f4c1e0a6f2c96 \
--hash=sha256:9e04c0811ea92931ee8490d638171b8cb2f21387efcfff526bbc8c2a3da60f1c \
--hash=sha256:bded94236e16774385202cafd26190ce96db18e4dc21e99473848c61e4fdc400 \
--hash=sha256:c2fa33d20408b513cf432505c80e6eb4bf4d71434f1ae36680765d4a2c2a16ec \
--hash=sha256:e3fec1c8831f8f93ad85581ca29ca1bb88e2da377fb097cf8322aa89c21bc9b8 \
--hash=sha256:e585682e37f2faa81ad6cd4472fff646bf2fd0542147bec93697a905db8e6bd2 \
--hash=sha256:e9879ba9e64ec3add41bf201e06034162f853652ef4849b361d73b0deb3153ad \
--hash=sha256:ebe853e6f318f9d8b3b74dd17e553720d35646eff675a69eeaed12fbbbb07daa \
# via skorch
scipy==1.5.1 \
--hash=sha256:039572f0ca9578a466683558c5bf1e65d442860ec6e13307d528749cfe6d07b8 \
--hash=sha256:058e84930407927f71963a4ad8c1dc96c4d2d075636a68578195648c81f78810 \
--hash=sha256:06b19a650471781056c1a2172eeeeb777b8b516e9434005dd392a4559e0938b9 \
--hash=sha256:35d042d6499caf1a5d171baed0ebf01eb665b7af2ad98a8ff1b0e6e783654540 \
--hash=sha256:57a0f2be3063dbe1e3daf31ec9005576e8fd1022a28159d0db71d14566899d16 \
--hash=sha256:5e0bb43ff581811ab7f27425f6b96c1ddf7591ccad2e486c9af0b910c18f7185 \
--hash=sha256:71742889393a724dfce755b6b61228677873d269a4234e51ddaf08b998433c91 \
--hash=sha256:7908c85854c5b5b6d3ce7fefafac1ca3e23ff9ac41edabc2d46ae5dc9fa070ac \
--hash=sha256:81859ed3aad620752dd2c07c32b5d3a80a0d47c5e3813904621954a78a0ae899 \
--hash=sha256:8302d69fb1528ea7c7f2a1ea640d354c981b6eb8192d1c175349874209397604 \
--hash=sha256:9323d268775991b79690f7b9a28a4e8b8c4f2b160ed9f8a90123127314e2d3c1 \
--hash=sha256:b4858ccbd88f4b53950fb9fc0069c1d9fea83d7cff2382e1d8b023d3f4883014 \
--hash=sha256:c05c6fe76228cc13c5214e9faf5f2a871a1da54473bc417ab9da310d0e5fff8b \
--hash=sha256:c06e731aa46c0dfc563cc636155758178ebc019ef78b9b0f4370effe2ac0f0e6 \
--hash=sha256:eb46d8b5947ca27b0bc972cecfba8130f088a83ab3d08c1a6033d9070b3046b3 \
--hash=sha256:fff15df01bef1243468be60c55178ed7576270b200aab08a7ffd5b8e0bbc340c \
# via scikit-learn, skorch, time_series_predictor (setup.py)
skorch==0.8.0 \
--hash=sha256:5908fdc3c1c8ae49d16fa3edb1fbdd412c44f2baee02abdd5432b7a47933a7d0 \
--hash=sha256:f292e9866f65df7fb7cf209f503924e2cb67377d7524a50c3e5dc6ae5a5ecd47 \
# via time_series_predictor (setup.py)
tabulate==0.8.7 \
--hash=sha256:ac64cb76d53b1231d364babcd72abbb16855adac7de6665122f97b593f1eb2ba \
--hash=sha256:db2723a20d04bcda8522165c73eea7c300eda74e0ce852d9022e0159d7895007 \
# via skorch
threadpoolctl==2.1.0 \
--hash=sha256:38b74ca20ff3bb42caca8b00055111d74159ee95c4370882bbff2b93d24da725 \
--hash=sha256:ddc57c96a38beb63db45d6c159b5ab07b6bced12c45a1f07b2b92f272aebfa6b \
# via scikit-learn
torch===1.5.0 \
--hash=sha256:3cc72d36eaeda96488e3a29373f739b887338952417b3e1620871063bf5d14d2 \
--hash=sha256:402951484443bb49b5bc2129414ac6c644c07b8378e79922cf3645fd08cbfdc9 \
--hash=sha256:6fcfe5deaf0788bbe8639869d3c752ff5fe1bdedce11c7ed2d44379b1fbe6d6c \
--hash=sha256:7f3d6af2d7e2576b9640aa684f0c18a773efffe8b37f9056272287345c1dcba5 \
--hash=sha256:865d4bec21542647e0822e8b753e05d67eee874974a3937273f710edd99a7516 \
--hash=sha256:931b79aed9aba50bf314214be6efaaf7972ea9539a3d63f82622bc5860a1fd81 \
--hash=sha256:cb4412c6b00117ab5e014d07dac45b87f1e918e31fbb849e7e39f1f9140fff59 \
--hash=sha256:dfaac4c5d27ac80705956743c34fb1ab5fb37e1646a6c8e45f05f7e739f6ea7c \
--hash=sha256:ecdc2ea4011e3ec04937b6b9e803ab671c3ac04e81b1df20354e01453e508b2f \
# via time_series_predictor (setup.py)
tqdm==4.47.0 \
--hash=sha256:63ef7a6d3eb39f80d6b36e4867566b3d8e5f1fe3d6cb50c5e9ede2b3198ba7b7 \
--hash=sha256:7810e627bcf9d983a99d9ff8a0c09674400fd2927eddabeadf153c14a2ec8656 \
# via skorchWith time_series_predictor on master [!] via 🐍 v3.7.7 (.env)
❯ pip-compile --find-links=https://download.pytorch.org/whl/torch_stable.html --generate-hashes --upgrade --output-file=requirements-lock.txt --verbose
Using indexes:
https://pypi.org/simple
Using links:
https://download.pytorch.org/whl/torch_stable.html
ROUND 1
Current constraints:
psutil (from time_series_predictor (setup.py))
scipy (from time_series_predictor (setup.py))
skorch (from time_series_predictor (setup.py))
torch===1.5.1 (from time_series_predictor (setup.py))
Finding the best candidates:
found candidate psutil==5.7.0 (constraint was <any>)
found candidate scipy==1.5.1 (constraint was <any>)
found candidate skorch==0.8.0 (constraint was <any>)
found candidate torch===1.5.1 (constraint was ===1.5.1)
Finding secondary dependencies:
psutil==5.7.0 requires -
torch===1.5.1 not in cache, need to check index
Collecting torch===1.5.1
File was already downloaded c:\users\dani_\appdata\local\pip-tools\cache\wheels\torch-1.5.1-cp37-cp37m-win_amd64.whl
Traceback (most recent call last):
File "C:\Users\dani_\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\Users\dani_\AppData\Local\Programs\Python\Python37\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\dani_\Workspaces\Python\time_series_predictor\.env\Scripts\pip-compile.exe\__main__.py", line 9, in <module>
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\click\core.py", line 829, in __call__
return self.main(*args, **kwargs)
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\click\core.py", line 782, in main
rv = self.invoke(ctx)
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\click\core.py", line 1066, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\click\core.py", line 610, in invoke
return callback(*args, **kwargs)
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\click\decorators.py", line 21, in new_func
return f(get_current_context(), *args, **kwargs)
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\piptools\scripts\compile.py", line 444, in cli
results = resolver.resolve(max_rounds=max_rounds)
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\piptools\resolver.py", line 169, in resolve
has_changed, best_matches = self._resolve_one_round()
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\piptools\resolver.py", line 274, in _resolve_one_round
their_constraints.extend(self._iter_dependencies(best_match))
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\piptools\resolver.py", line 380, in _iter_dependencies
dependencies = self.repository.get_dependencies(ireq)
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\piptools\repositories\pypi.py", line 229, in get_dependencies
download_dir, ireq, wheel_cache
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\piptools\repositories\pypi.py", line 181, in resolve_reqs
results = resolver._resolve_one(reqset, ireq)
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\pip\_internal\resolution\legacy\resolver.py", line 362, in _resolve_one
abstract_dist = self._get_abstract_dist_for(req_to_install)
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\pip\_internal\resolution\legacy\resolver.py", line 314, in _get_abstract_dist_for
abstract_dist = self.preparer.prepare_linked_requirement(req)
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\pip\_internal\operations\prepare.py", line 469, in prepare_linked_requirement
hashes=hashes,
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\pip\_internal\operations\prepare.py", line 264, in unpack_url
unpack_file(file.path, location, file.content_type)
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\pip\_internal\utils\unpacking.py", line 252, in unpack_file
flatten=not filename.endswith('.whl')
File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\pip\_internal\utils\unpacking.py", line 114, in unzip_file
zip = zipfile.ZipFile(zipfp, allowZip64=True)
File "C:\Users\dani_\AppData\Local\Programs\Python\Python37\lib\zipfile.py", line 1258, in __init__
self._RealGetContents()
File "C:\Users\dani_\AppData\Local\Programs\Python\Python37\lib\zipfile.py", line 1325, in _RealGetContents
raise BadZipFile("File is not a zip file")
zipfile.BadZipFile: File is not a zip file |
|
It is interesting to note that when no tripple = is used, it resolves to CUDA 9.2 for both Linux and Windows. And in my opinion it should always resolve to latest CUDA, 10.2 as of now. From Ubuntu 18.04.4 WSL: ❯ z /home/daniel/Workspaces/Python/time_series_predictor
❯ . .env/bin/activate
❯ pip-compile --find-links=https://download.pytorch.org/whl/torch_stable.html --generate-hashes --upgrade --output-file=requirements-lock.txt --verbose
/usr/lib/python3.6/distutils/dist.py:261: UserWarning: Unknown distribution option: 'long_description_content_type'
warnings.warn(msg)
Using indexes:
https://pypi.org/simple
Using links:
https://download.pytorch.org/whl/torch_stable.html
ROUND 1
Current constraints:
psutil (from time_series_predictor (setup.py))
scipy (from time_series_predictor (setup.py))
skorch (from time_series_predictor (setup.py))
torch (from time_series_predictor (setup.py))
Finding the best candidates:
found candidate psutil==5.7.0 (constraint was <any>)
found candidate scipy==1.5.1 (constraint was <any>)
found candidate skorch==0.8.0 (constraint was <any>)
found candidate torch==1.5.1+cu92 (constraint was <any>)
Finding secondary dependencies:
skorch==0.8.0 requires numpy>=1.13.3, scikit-learn>=0.19.1, scipy>=1.1.0, tabulate>=0.7.7, tqdm>=4.14.0
torch==1.5.1+cu92 not in cache, need to check index
Collecting torch==1.5.1+cu92
Downloading https://download.pytorch.org/whl/cu92/torch-1.5.1%2Bcu92-cp36-cp36m-linux_x86_64.whl (604.8 MB)
|████████████████████████████████| 604.8 MB 21 kB/s
Saved /home/daniel/.cache/pip-tools/wheels/torch-1.5.1+cu92-cp36-cp36m-linux_x86_64.whl
torch==1.5.1+cu92 requires future, numpy
psutil==5.7.0 requires -
scipy==1.5.1 requires numpy>=1.14.5
New dependencies found in this round:
adding ['future', '', '[]']
adding ['numpy', '>=1.13.3,>=1.14.5', '[]']
adding ['scikit-learn', '>=0.19.1', '[]']
adding ['scipy', '>=1.1.0', '[]']
adding ['tabulate', '>=0.7.7', '[]']
adding ['tqdm', '>=4.14.0', '[]']
Removed dependencies in this round:
------------------------------------------------------------
Result of round 1: not stable
ROUND 2
Current constraints:
future (from torch==1.5.1+cu92->time_series_predictor (setup.py))
numpy>=1.13.3,>=1.14.5 (from scipy==1.5.1->time_series_predictor (setup.py))
psutil (from time_series_predictor (setup.py))
scikit-learn>=0.19.1 (from skorch==0.8.0->time_series_predictor (setup.py))
scipy>=1.1.0 (from time_series_predictor (setup.py))
skorch (from time_series_predictor (setup.py))
tabulate>=0.7.7 (from skorch==0.8.0->time_series_predictor (setup.py))
torch (from time_series_predictor (setup.py))
tqdm>=4.14.0 (from skorch==0.8.0->time_series_predictor (setup.py))
Finding the best candidates:
found candidate future==0.18.2 (constraint was <any>)
found candidate numpy==1.19.0 (constraint was >=1.13.3,>=1.14.5)
found candidate psutil==5.7.0 (constraint was <any>)
found candidate scikit-learn==0.23.1 (constraint was >=0.19.1)
found candidate scipy==1.5.1 (constraint was >=1.1.0)
found candidate skorch==0.8.0 (constraint was <any>)
found candidate tabulate==0.8.7 (constraint was >=0.7.7)
found candidate torch==1.5.1+cu92 (constraint was <any>)
found candidate tqdm==4.47.0 (constraint was >=4.14.0)
Finding secondary dependencies:
numpy==1.19.0 requires -
tqdm==4.47.0 requires -
scikit-learn==0.23.1 requires joblib>=0.11, numpy>=1.13.3, scipy>=0.19.1, threadpoolctl>=2.0.0
tabulate==0.8.7 requires -
future==0.18.2 requires -
torch==1.5.1+cu92 requires future, numpy
scipy==1.5.1 requires numpy>=1.14.5
psutil==5.7.0 requires -
skorch==0.8.0 requires numpy>=1.13.3, scikit-learn>=0.19.1, scipy>=1.1.0, tabulate>=0.7.7, tqdm>=4.14.0
New dependencies found in this round:
adding ['joblib', '>=0.11', '[]']
adding ['scipy', '>=0.19.1,>=1.1.0', '[]']
adding ['threadpoolctl', '>=2.0.0', '[]']
Removed dependencies in this round:
removing ['scipy', '>=1.1.0', '[]']
------------------------------------------------------------
Result of round 2: not stable
ROUND 3
Current constraints:
future (from torch==1.5.1+cu92->time_series_predictor (setup.py))
joblib>=0.11 (from scikit-learn==0.23.1->skorch==0.8.0->time_series_predictor (setup.py))
numpy>=1.13.3,>=1.14.5 (from scipy==1.5.1->time_series_predictor (setup.py))
psutil (from time_series_predictor (setup.py))
scikit-learn>=0.19.1 (from skorch==0.8.0->time_series_predictor (setup.py))
scipy>=0.19.1,>=1.1.0 (from time_series_predictor (setup.py))
skorch (from time_series_predictor (setup.py))
tabulate>=0.7.7 (from skorch==0.8.0->time_series_predictor (setup.py))
threadpoolctl>=2.0.0 (from scikit-learn==0.23.1->skorch==0.8.0->time_series_predictor (setup.py))
torch (from time_series_predictor (setup.py))
tqdm>=4.14.0 (from skorch==0.8.0->time_series_predictor (setup.py))
Finding the best candidates:
found candidate future==0.18.2 (constraint was <any>)
found candidate joblib==0.16.0 (constraint was >=0.11)
found candidate numpy==1.19.0 (constraint was >=1.13.3,>=1.14.5)
found candidate psutil==5.7.0 (constraint was <any>)
found candidate scikit-learn==0.23.1 (constraint was >=0.19.1)
found candidate scipy==1.5.1 (constraint was >=0.19.1,>=1.1.0)
found candidate skorch==0.8.0 (constraint was <any>)
found candidate tabulate==0.8.7 (constraint was >=0.7.7)
found candidate threadpoolctl==2.1.0 (constraint was >=2.0.0)
found candidate torch==1.5.1+cu92 (constraint was <any>)
found candidate tqdm==4.47.0 (constraint was >=4.14.0)
Finding secondary dependencies:
future==0.18.2 requires -
psutil==5.7.0 requires -
tabulate==0.8.7 requires -
numpy==1.19.0 requires -
skorch==0.8.0 requires numpy>=1.13.3, scikit-learn>=0.19.1, scipy>=1.1.0, tabulate>=0.7.7, tqdm>=4.14.0
tqdm==4.47.0 requires -
torch==1.5.1+cu92 requires future, numpy
threadpoolctl==2.1.0 requires -
joblib==0.16.0 requires -
scipy==1.5.1 requires numpy>=1.14.5
scikit-learn==0.23.1 requires joblib>=0.11, numpy>=1.13.3, scipy>=0.19.1, threadpoolctl>=2.0.0
------------------------------------------------------------
Result of round 3: stable, done
Generating hashes:
future
tabulate
psutil
scikit-learn
numpy
skorch
tqdm
torch
Missing release files on PyPI
Couldn't get hashes from PyPI, fallback to hashing files
Hashing torch-1.5.1%2Bcu92-cp36-cp36m-win_amd64.whl
|████████████████████████████████| 100%
Hashing torch-1.5.1%2Bcu92-cp38-cp38-win_amd64.whl
|████████████████████████████████| 100%
Hashing torch-1.5.1%2Bcu92-cp35-cp35m-win_amd64.whl
|████████████████████████████████| 100%
Hashing torch-1.5.1%2Bcu92-cp35-cp35m-linux_x86_64.whl
|████████████████████████████████| 100%
Hashing torch-1.5.1%2Bcu92-cp38-cp38-linux_x86_64.whl
|████████████████████████████████| 100%
Hashing torch-1.5.1%2Bcu92-cp37-cp37m-win_amd64.whl
|████████████████████████████████| 100%
Hashing torch-1.5.1%2Bcu92-cp36-cp36m-linux_x86_64.whl
|████████████████████████████████| 100%
Hashing torch-1.5.1%2Bcu92-cp37-cp37m-linux_x86_64.whl
|████████████████████████████████| 100%
joblib
WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='pypi.org', port=443): Read timed out. (read timeout=15)",)': /pypi/joblib/json
scipy
threadpoolctl
#
# This file is autogenerated by pip-compile
# To update, run:
#
# pip-compile --find-links=https://download.pytorch.org/whl/torch_stable.html --generate-hashes --output-file=requirements-lock.txt
#
--find-links https://download.pytorch.org/whl/torch_stable.html
future==0.18.2 \
--hash=sha256:b1bead90b70cf6ec3f0710ae53a525360fa360d306a86583adc6bf83a4db537d \
# via torch
joblib==0.16.0 \
--hash=sha256:8f52bf24c64b608bf0b2563e0e47d6fcf516abc8cfafe10cfd98ad66d94f92d6 \
--hash=sha256:d348c5d4ae31496b2aa060d6d9b787864dd204f9480baaa52d18850cb43e9f49 \
# via scikit-learn
numpy==1.19.0 \
--hash=sha256:13af0184177469192d80db9bd02619f6fa8b922f9f327e077d6f2a6acb1ce1c0 \
--hash=sha256:26a45798ca2a4e168d00de75d4a524abf5907949231512f372b217ede3429e98 \
--hash=sha256:26f509450db547e4dfa3ec739419b31edad646d21fb8d0ed0734188b35ff6b27 \
--hash=sha256:30a59fb41bb6b8c465ab50d60a1b298d1cd7b85274e71f38af5a75d6c475d2d2 \
--hash=sha256:33c623ef9ca5e19e05991f127c1be5aeb1ab5cdf30cb1c5cf3960752e58b599b \
--hash=sha256:356f96c9fbec59974a592452ab6a036cd6f180822a60b529a975c9467fcd5f23 \
--hash=sha256:3c40c827d36c6d1c3cf413694d7dc843d50997ebffbc7c87d888a203ed6403a7 \
--hash=sha256:4d054f013a1983551254e2379385e359884e5af105e3efe00418977d02f634a7 \
--hash=sha256:63d971bb211ad3ca37b2adecdd5365f40f3b741a455beecba70fd0dde8b2a4cb \
--hash=sha256:658624a11f6e1c252b2cd170d94bf28c8f9410acab9f2fd4369e11e1cd4e1aaf \
--hash=sha256:76766cc80d6128750075378d3bb7812cf146415bd29b588616f72c943c00d598 \
--hash=sha256:7b57f26e5e6ee2f14f960db46bd58ffdca25ca06dd997729b1b179fddd35f5a3 \
--hash=sha256:7b852817800eb02e109ae4a9cef2beda8dd50d98b76b6cfb7b5c0099d27b52d4 \
--hash=sha256:8cde829f14bd38f6da7b2954be0f2837043e8b8d7a9110ec5e318ae6bf706610 \
--hash=sha256:a2e3a39f43f0ce95204beb8fe0831199542ccab1e0c6e486a0b4947256215632 \
--hash=sha256:a86c962e211f37edd61d6e11bb4df7eddc4a519a38a856e20a6498c319efa6b0 \
--hash=sha256:a8705c5073fe3fcc297fb8e0b31aa794e05af6a329e81b7ca4ffecab7f2b95ef \
--hash=sha256:b6aaeadf1e4866ca0fdf7bb4eed25e521ae21a7947c59f78154b24fc7abbe1dd \
--hash=sha256:be62aeff8f2f054eff7725f502f6228298891fd648dc2630e03e44bf63e8cee0 \
--hash=sha256:c2edbb783c841e36ca0fa159f0ae97a88ce8137fb3a6cd82eae77349ba4b607b \
--hash=sha256:cbe326f6d364375a8e5a8ccb7e9cd73f4b2f6dc3b2ed205633a0db8243e2a96a \
--hash=sha256:d34fbb98ad0d6b563b95de852a284074514331e6b9da0a9fc894fb1cdae7a79e \
--hash=sha256:d97a86937cf9970453c3b62abb55a6475f173347b4cde7f8dcdb48c8e1b9952d \
--hash=sha256:dd53d7c4a69e766e4900f29db5872f5824a06827d594427cf1a4aa542818b796 \
--hash=sha256:df1889701e2dfd8ba4dc9b1a010f0a60950077fb5242bb92c8b5c7f1a6f2668a \
--hash=sha256:fa1fe75b4a9e18b66ae7f0b122543c42debcf800aaafa0212aaff3ad273c2596 \
# via scikit-learn, scipy, skorch, torch
psutil==5.7.0 \
--hash=sha256:1413f4158eb50e110777c4f15d7c759521703bd6beb58926f1d562da40180058 \
--hash=sha256:298af2f14b635c3c7118fd9183843f4e73e681bb6f01e12284d4d70d48a60953 \
--hash=sha256:60b86f327c198561f101a92be1995f9ae0399736b6eced8f24af41ec64fb88d4 \
--hash=sha256:685ec16ca14d079455892f25bd124df26ff9137664af445563c1bd36629b5e0e \
--hash=sha256:73f35ab66c6c7a9ce82ba44b1e9b1050be2a80cd4dcc3352cc108656b115c74f \
--hash=sha256:75e22717d4dbc7ca529ec5063000b2b294fc9a367f9c9ede1f65846c7955fd38 \
--hash=sha256:a02f4ac50d4a23253b68233b07e7cdb567bd025b982d5cf0ee78296990c22d9e \
--hash=sha256:d008ddc00c6906ec80040d26dc2d3e3962109e40ad07fd8a12d0284ce5e0e4f8 \
--hash=sha256:d84029b190c8a66a946e28b4d3934d2ca1528ec94764b180f7d6ea57b0e75e26 \
--hash=sha256:e2d0c5b07c6fe5a87fa27b7855017edb0d52ee73b71e6ee368fae268605cc3f5 \
--hash=sha256:f344ca230dd8e8d5eee16827596f1c22ec0876127c28e800d7ae20ed44c4b310 \
# via time_series_predictor (setup.py)
scikit-learn==0.23.1 \
--hash=sha256:04799686060ecbf8992f26a35be1d99e981894c8c7860c1365cda4200f954a16 \
--hash=sha256:058d213092de4384710137af1300ed0ff030b8c40459a6c6f73c31ccd274cc39 \
--hash=sha256:0c3464e46ef8bd4f1bfa5c009648c6449412c8f7e9b3fc0c9e3d800139c48827 \
--hash=sha256:0e7b55f73b35537ecd0d19df29dd39aa9e076dba78f3507b8136c819d84611fd \
--hash=sha256:16feae4361be6b299d4d08df5a30956b4bfc8eadf173fe9258f6d59630f851d4 \
--hash=sha256:244ca85d6eba17a1e6e8a66ab2f584be6a7784b5f59297e3d7ff8c7983af627c \
--hash=sha256:3e6e92b495eee193a8fa12a230c9b7976ea0fc1263719338e35c986ea1e42cff \
--hash=sha256:5bcea4d6ee431c814261117281363208408aa4e665633655895feb059021aca6 \
--hash=sha256:93f56abd316d131645559ec0ab4f45e3391c2ccdd4eadaa4912f4c1e0a6f2c96 \
--hash=sha256:9e04c0811ea92931ee8490d638171b8cb2f21387efcfff526bbc8c2a3da60f1c \
--hash=sha256:bded94236e16774385202cafd26190ce96db18e4dc21e99473848c61e4fdc400 \
--hash=sha256:c2fa33d20408b513cf432505c80e6eb4bf4d71434f1ae36680765d4a2c2a16ec \
--hash=sha256:e3fec1c8831f8f93ad85581ca29ca1bb88e2da377fb097cf8322aa89c21bc9b8 \
--hash=sha256:e585682e37f2faa81ad6cd4472fff646bf2fd0542147bec93697a905db8e6bd2 \
--hash=sha256:e9879ba9e64ec3add41bf201e06034162f853652ef4849b361d73b0deb3153ad \
--hash=sha256:ebe853e6f318f9d8b3b74dd17e553720d35646eff675a69eeaed12fbbbb07daa \
# via skorch
scipy==1.5.1 \
--hash=sha256:039572f0ca9578a466683558c5bf1e65d442860ec6e13307d528749cfe6d07b8 \
--hash=sha256:058e84930407927f71963a4ad8c1dc96c4d2d075636a68578195648c81f78810 \
--hash=sha256:06b19a650471781056c1a2172eeeeb777b8b516e9434005dd392a4559e0938b9 \
--hash=sha256:35d042d6499caf1a5d171baed0ebf01eb665b7af2ad98a8ff1b0e6e783654540 \
--hash=sha256:57a0f2be3063dbe1e3daf31ec9005576e8fd1022a28159d0db71d14566899d16 \
--hash=sha256:5e0bb43ff581811ab7f27425f6b96c1ddf7591ccad2e486c9af0b910c18f7185 \
--hash=sha256:71742889393a724dfce755b6b61228677873d269a4234e51ddaf08b998433c91 \
--hash=sha256:7908c85854c5b5b6d3ce7fefafac1ca3e23ff9ac41edabc2d46ae5dc9fa070ac \
--hash=sha256:81859ed3aad620752dd2c07c32b5d3a80a0d47c5e3813904621954a78a0ae899 \
--hash=sha256:8302d69fb1528ea7c7f2a1ea640d354c981b6eb8192d1c175349874209397604 \
--hash=sha256:9323d268775991b79690f7b9a28a4e8b8c4f2b160ed9f8a90123127314e2d3c1 \
--hash=sha256:b4858ccbd88f4b53950fb9fc0069c1d9fea83d7cff2382e1d8b023d3f4883014 \
--hash=sha256:c05c6fe76228cc13c5214e9faf5f2a871a1da54473bc417ab9da310d0e5fff8b \
--hash=sha256:c06e731aa46c0dfc563cc636155758178ebc019ef78b9b0f4370effe2ac0f0e6 \
--hash=sha256:eb46d8b5947ca27b0bc972cecfba8130f088a83ab3d08c1a6033d9070b3046b3 \
--hash=sha256:fff15df01bef1243468be60c55178ed7576270b200aab08a7ffd5b8e0bbc340c \
# via scikit-learn, skorch, time_series_predictor (setup.py)
skorch==0.8.0 \
--hash=sha256:5908fdc3c1c8ae49d16fa3edb1fbdd412c44f2baee02abdd5432b7a47933a7d0 \
--hash=sha256:f292e9866f65df7fb7cf209f503924e2cb67377d7524a50c3e5dc6ae5a5ecd47 \
# via time_series_predictor (setup.py)
tabulate==0.8.7 \
--hash=sha256:ac64cb76d53b1231d364babcd72abbb16855adac7de6665122f97b593f1eb2ba \
--hash=sha256:db2723a20d04bcda8522165c73eea7c300eda74e0ce852d9022e0159d7895007 \
# via skorch
threadpoolctl==2.1.0 \
--hash=sha256:38b74ca20ff3bb42caca8b00055111d74159ee95c4370882bbff2b93d24da725 \
--hash=sha256:ddc57c96a38beb63db45d6c159b5ab07b6bced12c45a1f07b2b92f272aebfa6b \
# via scikit-learn
torch==1.5.1+cu92 \
--hash=sha256:018c813ca9eea20062266b7e2f625d8dc0c4cc21c879f2e62ee79c35dd926850 \
--hash=sha256:20534264aa5d363635d84a331ea66acc1f2faf4ee8d97c68b5a9ed20db38bf07 \
--hash=sha256:62e5ca82020cd6478a93c25cc9854d31e64a3503a0dfade7784a3c308d696e41 \
--hash=sha256:735f3a0764919092a3451e5b06e9cd84d654d9e26c4c3b701ec48d0de9a4913d \
--hash=sha256:9c6695b4b51086e14f9f620c2bcd8111a7043cee518217ee6ed6e9d306e705f2 \
--hash=sha256:c5f43abeebf9ee5756e2320b3797810d31b3b7dbb978791f8f37be4c202c3265 \
--hash=sha256:cb47a29dd933e8933a0d9ea1dfd8bb8c852e848dba0d349c06e26f31fdafcca5 \
--hash=sha256:fee450640283f581b9495a0656dbf941eeda54914530ca0d619fe178a8d7199f \
# via time_series_predictor (setup.py)
tqdm==4.47.0 \
--hash=sha256:63ef7a6d3eb39f80d6b36e4867566b3d8e5f1fe3d6cb50c5e9ede2b3198ba7b7 \
--hash=sha256:7810e627bcf9d983a99d9ff8a0c09674400fd2927eddabeadf153c14a2ec8656 \
# via skorch |
|
At least in Ubuntu WSL there is no error when tripple = is used a version ❯ z /home/daniel/Workspaces/Python/time_series_predictor
❯ . .env/bin/activate
❯ pip-compile --find-links=https://download.pytorch.org/whl/torch_stable.html --generate-hashes --upgrade --output-file=requirements-lock.txt --verbose
/usr/lib/python3.6/distutils/dist.py:261: UserWarning: Unknown distribution option: 'long_description_content_type'
warnings.warn(msg)
Using indexes:
https://pypi.org/simple
Using links:
https://download.pytorch.org/whl/torch_stable.html
ROUND 1
Current constraints:
psutil (from time_series_predictor (setup.py))
scipy (from time_series_predictor (setup.py))
skorch (from time_series_predictor (setup.py))
torch===1.5.1 (from time_series_predictor (setup.py))
Finding the best candidates:
found candidate psutil==5.7.0 (constraint was <any>)
found candidate scipy==1.5.1 (constraint was <any>)
found candidate skorch==0.8.0 (constraint was <any>)
found candidate torch===1.5.1 (constraint was ===1.5.1)
Finding secondary dependencies:
skorch==0.8.0 requires numpy>=1.13.3, scikit-learn>=0.19.1, scipy>=1.1.0, tabulate>=0.7.7, tqdm>=4.14.0
psutil==5.7.0 requires -
torch===1.5.1 requires future, numpy
scipy==1.5.1 requires numpy>=1.14.5
New dependencies found in this round:
adding ['future', '', '[]']
adding ['numpy', '>=1.13.3,>=1.14.5', '[]']
adding ['scikit-learn', '>=0.19.1', '[]']
adding ['scipy', '>=1.1.0', '[]']
adding ['tabulate', '>=0.7.7', '[]']
adding ['tqdm', '>=4.14.0', '[]']
Removed dependencies in this round:
------------------------------------------------------------
Result of round 1: not stable
ROUND 2
Current constraints:
future (from torch===1.5.1->time_series_predictor (setup.py))
numpy>=1.13.3,>=1.14.5 (from scipy==1.5.1->time_series_predictor (setup.py))
psutil (from time_series_predictor (setup.py))
scikit-learn>=0.19.1 (from skorch==0.8.0->time_series_predictor (setup.py))
scipy>=1.1.0 (from time_series_predictor (setup.py))
skorch (from time_series_predictor (setup.py))
tabulate>=0.7.7 (from skorch==0.8.0->time_series_predictor (setup.py))
torch===1.5.1 (from time_series_predictor (setup.py))
tqdm>=4.14.0 (from skorch==0.8.0->time_series_predictor (setup.py))
Finding the best candidates:
found candidate future==0.18.2 (constraint was <any>)
found candidate numpy==1.19.0 (constraint was >=1.13.3,>=1.14.5)
found candidate psutil==5.7.0 (constraint was <any>)
found candidate scikit-learn==0.23.1 (constraint was >=0.19.1)
found candidate scipy==1.5.1 (constraint was >=1.1.0)
found candidate skorch==0.8.0 (constraint was <any>)
found candidate tabulate==0.8.7 (constraint was >=0.7.7)
found candidate torch===1.5.1 (constraint was ===1.5.1)
found candidate tqdm==4.47.0 (constraint was >=4.14.0)
Finding secondary dependencies:
future==0.18.2 requires -
numpy==1.19.0 requires -
scikit-learn==0.23.1 requires joblib>=0.11, numpy>=1.13.3, scipy>=0.19.1, threadpoolctl>=2.0.0
scipy==1.5.1 requires numpy>=1.14.5
tabulate==0.8.7 requires -
tqdm==4.47.0 requires -
skorch==0.8.0 requires numpy>=1.13.3, scikit-learn>=0.19.1, scipy>=1.1.0, tabulate>=0.7.7, tqdm>=4.14.0
psutil==5.7.0 requires -
torch===1.5.1 requires future, numpy
New dependencies found in this round:
adding ['joblib', '>=0.11', '[]']
adding ['scipy', '>=0.19.1,>=1.1.0', '[]']
adding ['threadpoolctl', '>=2.0.0', '[]']
Removed dependencies in this round:
removing ['scipy', '>=1.1.0', '[]']
------------------------------------------------------------
Result of round 2: not stable
ROUND 3
Current constraints:
future (from torch===1.5.1->time_series_predictor (setup.py))
joblib>=0.11 (from scikit-learn==0.23.1->skorch==0.8.0->time_series_predictor (setup.py))
numpy>=1.13.3,>=1.14.5 (from scipy==1.5.1->time_series_predictor (setup.py))
psutil (from time_series_predictor (setup.py))
scikit-learn>=0.19.1 (from skorch==0.8.0->time_series_predictor (setup.py))
scipy>=0.19.1,>=1.1.0 (from time_series_predictor (setup.py))
skorch (from time_series_predictor (setup.py))
tabulate>=0.7.7 (from skorch==0.8.0->time_series_predictor (setup.py))
threadpoolctl>=2.0.0 (from scikit-learn==0.23.1->skorch==0.8.0->time_series_predictor (setup.py))
torch===1.5.1 (from time_series_predictor (setup.py))
tqdm>=4.14.0 (from skorch==0.8.0->time_series_predictor (setup.py))
Finding the best candidates:
found candidate future==0.18.2 (constraint was <any>)
found candidate joblib==0.16.0 (constraint was >=0.11)
found candidate numpy==1.19.0 (constraint was >=1.13.3,>=1.14.5)
found candidate psutil==5.7.0 (constraint was <any>)
found candidate scikit-learn==0.23.1 (constraint was >=0.19.1)
found candidate scipy==1.5.1 (constraint was >=0.19.1,>=1.1.0)
found candidate skorch==0.8.0 (constraint was <any>)
found candidate tabulate==0.8.7 (constraint was >=0.7.7)
found candidate threadpoolctl==2.1.0 (constraint was >=2.0.0)
found candidate torch===1.5.1 (constraint was ===1.5.1)
found candidate tqdm==4.47.0 (constraint was >=4.14.0)
Finding secondary dependencies:
scikit-learn==0.23.1 requires joblib>=0.11, numpy>=1.13.3, scipy>=0.19.1, threadpoolctl>=2.0.0
threadpoolctl==2.1.0 requires -
joblib==0.16.0 requires -
scipy==1.5.1 requires numpy>=1.14.5
skorch==0.8.0 requires numpy>=1.13.3, scikit-learn>=0.19.1, scipy>=1.1.0, tabulate>=0.7.7, tqdm>=4.14.0
numpy==1.19.0 requires -
psutil==5.7.0 requires -
tqdm==4.47.0 requires -
torch===1.5.1 requires future, numpy
future==0.18.2 requires -
tabulate==0.8.7 requires -
------------------------------------------------------------
Result of round 3: stable, done
Generating hashes:
scikit-learn
threadpoolctl
future
joblib
skorch
numpy
psutil
tqdm
torch
scipy
tabulate
#
# This file is autogenerated by pip-compile
# To update, run:
#
# pip-compile --find-links=https://download.pytorch.org/whl/torch_stable.html --generate-hashes --output-file=requirements-lock.txt
#
--find-links https://download.pytorch.org/whl/torch_stable.html
future==0.18.2 \
--hash=sha256:b1bead90b70cf6ec3f0710ae53a525360fa360d306a86583adc6bf83a4db537d \
# via torch
joblib==0.16.0 \
--hash=sha256:8f52bf24c64b608bf0b2563e0e47d6fcf516abc8cfafe10cfd98ad66d94f92d6 \
--hash=sha256:d348c5d4ae31496b2aa060d6d9b787864dd204f9480baaa52d18850cb43e9f49 \
# via scikit-learn
numpy==1.19.0 \
--hash=sha256:13af0184177469192d80db9bd02619f6fa8b922f9f327e077d6f2a6acb1ce1c0 \
--hash=sha256:26a45798ca2a4e168d00de75d4a524abf5907949231512f372b217ede3429e98 \
--hash=sha256:26f509450db547e4dfa3ec739419b31edad646d21fb8d0ed0734188b35ff6b27 \
--hash=sha256:30a59fb41bb6b8c465ab50d60a1b298d1cd7b85274e71f38af5a75d6c475d2d2 \
--hash=sha256:33c623ef9ca5e19e05991f127c1be5aeb1ab5cdf30cb1c5cf3960752e58b599b \
--hash=sha256:356f96c9fbec59974a592452ab6a036cd6f180822a60b529a975c9467fcd5f23 \
--hash=sha256:3c40c827d36c6d1c3cf413694d7dc843d50997ebffbc7c87d888a203ed6403a7 \
--hash=sha256:4d054f013a1983551254e2379385e359884e5af105e3efe00418977d02f634a7 \
--hash=sha256:63d971bb211ad3ca37b2adecdd5365f40f3b741a455beecba70fd0dde8b2a4cb \
--hash=sha256:658624a11f6e1c252b2cd170d94bf28c8f9410acab9f2fd4369e11e1cd4e1aaf \
--hash=sha256:76766cc80d6128750075378d3bb7812cf146415bd29b588616f72c943c00d598 \
--hash=sha256:7b57f26e5e6ee2f14f960db46bd58ffdca25ca06dd997729b1b179fddd35f5a3 \
--hash=sha256:7b852817800eb02e109ae4a9cef2beda8dd50d98b76b6cfb7b5c0099d27b52d4 \
--hash=sha256:8cde829f14bd38f6da7b2954be0f2837043e8b8d7a9110ec5e318ae6bf706610 \
--hash=sha256:a2e3a39f43f0ce95204beb8fe0831199542ccab1e0c6e486a0b4947256215632 \
--hash=sha256:a86c962e211f37edd61d6e11bb4df7eddc4a519a38a856e20a6498c319efa6b0 \
--hash=sha256:a8705c5073fe3fcc297fb8e0b31aa794e05af6a329e81b7ca4ffecab7f2b95ef \
--hash=sha256:b6aaeadf1e4866ca0fdf7bb4eed25e521ae21a7947c59f78154b24fc7abbe1dd \
--hash=sha256:be62aeff8f2f054eff7725f502f6228298891fd648dc2630e03e44bf63e8cee0 \
--hash=sha256:c2edbb783c841e36ca0fa159f0ae97a88ce8137fb3a6cd82eae77349ba4b607b \
--hash=sha256:cbe326f6d364375a8e5a8ccb7e9cd73f4b2f6dc3b2ed205633a0db8243e2a96a \
--hash=sha256:d34fbb98ad0d6b563b95de852a284074514331e6b9da0a9fc894fb1cdae7a79e \
--hash=sha256:d97a86937cf9970453c3b62abb55a6475f173347b4cde7f8dcdb48c8e1b9952d \
--hash=sha256:dd53d7c4a69e766e4900f29db5872f5824a06827d594427cf1a4aa542818b796 \
--hash=sha256:df1889701e2dfd8ba4dc9b1a010f0a60950077fb5242bb92c8b5c7f1a6f2668a \
--hash=sha256:fa1fe75b4a9e18b66ae7f0b122543c42debcf800aaafa0212aaff3ad273c2596 \
# via scikit-learn, scipy, skorch, torch
psutil==5.7.0 \
--hash=sha256:1413f4158eb50e110777c4f15d7c759521703bd6beb58926f1d562da40180058 \
--hash=sha256:298af2f14b635c3c7118fd9183843f4e73e681bb6f01e12284d4d70d48a60953 \
--hash=sha256:60b86f327c198561f101a92be1995f9ae0399736b6eced8f24af41ec64fb88d4 \
--hash=sha256:685ec16ca14d079455892f25bd124df26ff9137664af445563c1bd36629b5e0e \
--hash=sha256:73f35ab66c6c7a9ce82ba44b1e9b1050be2a80cd4dcc3352cc108656b115c74f \
--hash=sha256:75e22717d4dbc7ca529ec5063000b2b294fc9a367f9c9ede1f65846c7955fd38 \
--hash=sha256:a02f4ac50d4a23253b68233b07e7cdb567bd025b982d5cf0ee78296990c22d9e \
--hash=sha256:d008ddc00c6906ec80040d26dc2d3e3962109e40ad07fd8a12d0284ce5e0e4f8 \
--hash=sha256:d84029b190c8a66a946e28b4d3934d2ca1528ec94764b180f7d6ea57b0e75e26 \
--hash=sha256:e2d0c5b07c6fe5a87fa27b7855017edb0d52ee73b71e6ee368fae268605cc3f5 \
--hash=sha256:f344ca230dd8e8d5eee16827596f1c22ec0876127c28e800d7ae20ed44c4b310 \
# via time_series_predictor (setup.py)
scikit-learn==0.23.1 \
--hash=sha256:04799686060ecbf8992f26a35be1d99e981894c8c7860c1365cda4200f954a16 \
--hash=sha256:058d213092de4384710137af1300ed0ff030b8c40459a6c6f73c31ccd274cc39 \
--hash=sha256:0c3464e46ef8bd4f1bfa5c009648c6449412c8f7e9b3fc0c9e3d800139c48827 \
--hash=sha256:0e7b55f73b35537ecd0d19df29dd39aa9e076dba78f3507b8136c819d84611fd \
--hash=sha256:16feae4361be6b299d4d08df5a30956b4bfc8eadf173fe9258f6d59630f851d4 \
--hash=sha256:244ca85d6eba17a1e6e8a66ab2f584be6a7784b5f59297e3d7ff8c7983af627c \
--hash=sha256:3e6e92b495eee193a8fa12a230c9b7976ea0fc1263719338e35c986ea1e42cff \
--hash=sha256:5bcea4d6ee431c814261117281363208408aa4e665633655895feb059021aca6 \
--hash=sha256:93f56abd316d131645559ec0ab4f45e3391c2ccdd4eadaa4912f4c1e0a6f2c96 \
--hash=sha256:9e04c0811ea92931ee8490d638171b8cb2f21387efcfff526bbc8c2a3da60f1c \
--hash=sha256:bded94236e16774385202cafd26190ce96db18e4dc21e99473848c61e4fdc400 \
--hash=sha256:c2fa33d20408b513cf432505c80e6eb4bf4d71434f1ae36680765d4a2c2a16ec \
--hash=sha256:e3fec1c8831f8f93ad85581ca29ca1bb88e2da377fb097cf8322aa89c21bc9b8 \
--hash=sha256:e585682e37f2faa81ad6cd4472fff646bf2fd0542147bec93697a905db8e6bd2 \
--hash=sha256:e9879ba9e64ec3add41bf201e06034162f853652ef4849b361d73b0deb3153ad \
--hash=sha256:ebe853e6f318f9d8b3b74dd17e553720d35646eff675a69eeaed12fbbbb07daa \
# via skorch
scipy==1.5.1 \
--hash=sha256:039572f0ca9578a466683558c5bf1e65d442860ec6e13307d528749cfe6d07b8 \
--hash=sha256:058e84930407927f71963a4ad8c1dc96c4d2d075636a68578195648c81f78810 \
--hash=sha256:06b19a650471781056c1a2172eeeeb777b8b516e9434005dd392a4559e0938b9 \
--hash=sha256:35d042d6499caf1a5d171baed0ebf01eb665b7af2ad98a8ff1b0e6e783654540 \
--hash=sha256:57a0f2be3063dbe1e3daf31ec9005576e8fd1022a28159d0db71d14566899d16 \
--hash=sha256:5e0bb43ff581811ab7f27425f6b96c1ddf7591ccad2e486c9af0b910c18f7185 \
--hash=sha256:71742889393a724dfce755b6b61228677873d269a4234e51ddaf08b998433c91 \
--hash=sha256:7908c85854c5b5b6d3ce7fefafac1ca3e23ff9ac41edabc2d46ae5dc9fa070ac \
--hash=sha256:81859ed3aad620752dd2c07c32b5d3a80a0d47c5e3813904621954a78a0ae899 \
--hash=sha256:8302d69fb1528ea7c7f2a1ea640d354c981b6eb8192d1c175349874209397604 \
--hash=sha256:9323d268775991b79690f7b9a28a4e8b8c4f2b160ed9f8a90123127314e2d3c1 \
--hash=sha256:b4858ccbd88f4b53950fb9fc0069c1d9fea83d7cff2382e1d8b023d3f4883014 \
--hash=sha256:c05c6fe76228cc13c5214e9faf5f2a871a1da54473bc417ab9da310d0e5fff8b \
--hash=sha256:c06e731aa46c0dfc563cc636155758178ebc019ef78b9b0f4370effe2ac0f0e6 \
--hash=sha256:eb46d8b5947ca27b0bc972cecfba8130f088a83ab3d08c1a6033d9070b3046b3 \
--hash=sha256:fff15df01bef1243468be60c55178ed7576270b200aab08a7ffd5b8e0bbc340c \
# via scikit-learn, skorch, time_series_predictor (setup.py)
skorch==0.8.0 \
--hash=sha256:5908fdc3c1c8ae49d16fa3edb1fbdd412c44f2baee02abdd5432b7a47933a7d0 \
--hash=sha256:f292e9866f65df7fb7cf209f503924e2cb67377d7524a50c3e5dc6ae5a5ecd47 \
# via time_series_predictor (setup.py)
tabulate==0.8.7 \
--hash=sha256:ac64cb76d53b1231d364babcd72abbb16855adac7de6665122f97b593f1eb2ba \
--hash=sha256:db2723a20d04bcda8522165c73eea7c300eda74e0ce852d9022e0159d7895007 \
# via skorch
threadpoolctl==2.1.0 \
--hash=sha256:38b74ca20ff3bb42caca8b00055111d74159ee95c4370882bbff2b93d24da725 \
--hash=sha256:ddc57c96a38beb63db45d6c159b5ab07b6bced12c45a1f07b2b92f272aebfa6b \
# via scikit-learn
torch===1.5.1 \
--hash=sha256:0a83f41140222c7cc947aa29ed253f3e6fa490606d3d4acd02bfd9f338e3c707 \
--hash=sha256:5d909a55cd979fec2c9a7aa35012024b9cc106acbc496faf5de798b148406450 \
--hash=sha256:70046cf66eb40ead89df25b8dcc571c3007fc9849d4e1d254cc09b4b355374d4 \
--hash=sha256:a358cee1d35b86757bf915e320ba776d39c20e60db50779060842efc86f02edd \
--hash=sha256:b84fd18fd8216b74a19828433c3beeb1f0d1d29f45dead3be9ed784ae6855966 \
--hash=sha256:bb2a3e6c9c9dbfda856bd1b1a55d88789a9488b569ffba9cd6d9aa536ef866ba \
--hash=sha256:c42658f2982591dc4d0459645c9ab26e0ce18aa7ab0993c27c8bcb1c98931d11 \
--hash=sha256:ff1dbeaa017bae66036e8e7a698a5475ac5a0d7b0a690f0a04ac3b1133b1feb3 \
# via time_series_predictor (setup.py)
tqdm==4.47.0 \
--hash=sha256:63ef7a6d3eb39f80d6b36e4867566b3d8e5f1fe3d6cb50c5e9ede2b3198ba7b7 \
--hash=sha256:7810e627bcf9d983a99d9ff8a0c09674400fd2927eddabeadf153c14a2ec8656 \
# via skorch But using tripple = is not an option since the package can't be uploaded to Pypi in that way. |
atugushev
added
bug
|
Fixed in #1323 |
|
Awesome @atugushev . Was this issue helpful to reach the solution? |
|
Thanks, @DanielAtKrypton! The issue was super-helpful and showed rare use-cases to arbitrary equality. Since |
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What's the problem this feature will solve?
When the requirements of a project is torch, pip-tools is not able to get the latest available cuda version(10.2).
Instead pip-compile resolves to version 9.2.
The command I use is:
Describe the solution you'd like
pip-compile shouldn't fail when using tripple = and version 1.5.1. For any reason this strategy works for version 1.5.0.
Pip-compile should be able to get latest Pytorch with latest CUDA as default when plain
torchrequirement is selected and --find-links is correctly provided.A real world example is the following project.
Alternative Solutions
The repository I am testing with it is this.
If I set in
setup.pytorch version with tripple = and torch version just before the latest, it is able to resolve to latest cuda version.But if I change to
torch===1.5.1, pip-compile fails with the following message:Unfortunately using the partial correct CUDA solution with tripple = in setup.py and version 1.5.0, uploading to pypi fails with the follwing output:
Additional context
I tried to create a setup.py command too, in order to create a cron update job in CI.
But running it in vscode and windows results in the following error:
The text was updated successfully, but these errors were encountered: