-
-
Notifications
You must be signed in to change notification settings - Fork 612
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Get the latest cuda version for pytorch when pip-compiling. #1173
Comments
@DanielAtKrypton
can you run with --verbose? |
I also tried it with
|
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 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===1.5.1 (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. |
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 |
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
torch
requirement 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.py
torch 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: