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Daniel Kaminski de Souza DanielAtKrypton Krypton Unite Curitiba https://www.linkedin.com/in/prophetdaniel/

DanielAtKrypton/debian-dev-boilerplate 1

Boilerplate for software development.

DanielAtKrypton/AES 0

Advanced Encryption Standard for MATLAB

DanielAtKrypton/antigen 0

The plugin manager for zsh.

DanielAtKrypton/applyFunctionToCellArray 0

Applies function to a cell array along the specified dimension.

DanielAtKrypton/candlesticks 0

Repository for https://www.mathworks.com/matlabcentral/fileexchange/33782-candlesticks

DanielAtKrypton/colorToRgbTriplet 0

Converts color name or hexadecimal color code to a rgb triplet. Optionally a second argument can be passed defining the desired opacity level within [0,1].

DanielAtKrypton/cuckoo-search 0

Cuckoo search via Levy flights.

DanielAtKrypton/DataHash 0

MD5 or SHA hash for array, struct, cell or file

DanielAtKrypton/Dragonfire 0

the open-source virtual assistant for Ubuntu based Linux distributions

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Update install_script.sh

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PR opened arkane-systems/genie

Update README.md

Add command curl -s https://packagecloud.io/install/repositories/arkane-systems/wsl-translinux/script.deb.sh | sudo bash to add wsl-translinux repository.

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Update README.md Add command curl -s https://packagecloud.io/install/repositories/arkane-systems/wsl-translinux/script.deb.sh | sudo bash to add wsl-translinux repository.

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A quick way into a systemd "bottle" for WSL

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Update .antigenrc Add docker-compose

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PR opened DamionGans/ubuntu-wsl2-systemd-script

Update ubuntu-wsl2-systemd-script.sh

Best to use sudo command outside of script.

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Update ubuntu-wsl2-systemd-script.sh Best to use sudo command outside of script.

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Script to enable systemd support on current Ubuntu WSL2 images from the Windows store

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Update .antigenrc Remove virtualenv. Recommended to use just python3 venv.

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Update README.md Remove virtualenv. Recommeded to use just python3 venv.

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Update install_script.sh Remove virtualenvwrapper. Recommended to use just python3 venv.

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commit sha 68ba4daa820becca010d5fd4a842b7a76ecf055b

Pushing the docs to dev/ for branch: master, commit 7723ca3b70d062b40053d8e669b414fd08c93e37

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commit sha cc371320665cd86f9e82a30d15c5362a81237e31

:bug: Should now fix code terminal according to https://stackoverflow.com/questions/62839116/terminal-started-behaving-badly-when-zshell-theme-is-present

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:construction:[scipy-dev] Should fix Azure CI Nightly.

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:construction:[scipy-dev] Should fix circle CI doc.

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:construction:[scipy-dev] Revert to last overall passing.

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Hoda1394

commit sha dd9fbe5e8a5648f4dfe9c3583cd154441296692a

DOC updated documentation default value for gradient_boosting (#17909)

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Daniel Kaminski de Souza

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Merge branch 'master' of github.com:scikit-learn/scikit-learn

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:construction:[scipy-dev] Development path.

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:construction:[scipy-dev] Development path.

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Geoffrey Bolmier

commit sha 62fc8bb94dcd65e72878c0599ff91391d9983424

ENH Update NeighborsBase 'auto' heuristic (#17148)

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Arthur Imbert

commit sha 35831490c77195b1e7f630a7c0cc02d410cf0d7b

ENH 32/64-bit float consistency with BernoulliRBM (#16352)

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Thomas J. Fan

commit sha 39760e513f1e317a0c87704687250ef0d6dac331

TST Skip test_covtype if pandas not installed [scipy-dev] (#17682)

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Juan Carlos Alfaro Jiménez

commit sha 2eb0fbd7b4a0c8aa53e619b28d39306f190389f2

MNT Fetch numpy and scipy nightly wheels from anaconda (#17672)

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Louis Douge

commit sha aec1f6dc097496661c40bdcd13a0a1d02ef35d53

DOC Adding missing attributes to the MLP classes docstring (#17669) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

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simonamaggio

commit sha 683b3e51ee346cc3c7bb37b4ba4a132906396c0d

DOC add missing attributes in GradientBoostingRegressor (#17671)

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Koki Nishihara

commit sha e02e1bf7cbdffd0325269cc195298d62e0818180

FIX: Remove checks for old Scipy versions (#17685)

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Joshua Newton

commit sha e2a688336f9740fd073da59d63f48b5cf1d1eea3

DOC Clarify multilabel usage in OneVsRestClassifier docstring (#17646)

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clmbst

commit sha 9ea4edd970cb9065495752e5ea12f91c23a47ef1

DOC fix documentation of default values in decomposition._dict_learning (#17645) Co-authored-by: BISOT Clemence (SAFRAN AIRCRAFT ENGINES) <clemence.bisot@safrangroup.com>

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Guillaume Lemaitre

commit sha c8898450aa1619c64dd040eac40bc6b570f85b0f

EXA add compatibility with scipy 1.5 (#17695)

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Guillaume Lemaitre

commit sha fde9212737bbf7ed73a6ce1dc3ead7eaaee05522

ENH add a parameter pos_label in roc_auc_score (#17594)

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Hirofumi Suzuki

commit sha c29092d6994a43ea19a82e91c0331e8b7d6e7d36

MNT define a parse_version function in sklearn.utils.fixes (#17670) Co-authored-by: Loïc Estève <loic.esteve@ymail.com> Co-authored-by: Roman Yurchak <rth.yurchak@gmail.com>

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Guillaume Lemaitre

commit sha e5b99ea8029a5d8ed2cc7748fae46dd66bf203c8

EHN Provide a pos_label parameter in plot_precision_recall_curve (#17569)

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Thomas J. Fan

commit sha 7cc0177f8e8e958b6291433274a07cc67f933985

MNT Replaces numpy alias with builtin typse (#17687) * MNT Replaces numpy alias with builtin typse * STY Lint error

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Olivier Grisel

commit sha 59249d707de0abe5878f6dd982404a1490ee4ee7

Revert "ENH add a parameter pos_label in roc_auc_score (#17594)" (#17703) This reverts commit fde9212737bbf7ed73a6ce1dc3ead7eaaee05522.

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Kendrick Cetina

commit sha 4a97ad49c13c314655adac387d5e9a6732d3b1b3

DOC add missing attributes in GaussianProcessClassifier (#17698) Co-authored-by: Beatriz San Miguel <beatriz.sanmiguelgonzalez@uk.fujitsu.com> Co-authored-by: Loïc Estève <loic.esteve@ymail.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

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Chiara Marmo

commit sha 5ea5da4ae475671badfea392b27253ed77e7d324

DOC add missing attributes in HistGradientBoostingClassifier (#17678) Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

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simonamaggio

commit sha a109dff7229ce4726f1cfad4050f9ae4adf1d190

DOC add missing attributes in HistGradientBoostingRegressor (#17677) Co-authored-by: Olivier Grisel <olivier.grisel@gmail.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

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Ruby Werman

commit sha ff293f0d67f23c3684e1e498e85160616b11a6e9

MNT Remove code for scipy legacy version (#17708)

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Naoki Hamada

commit sha 7cc8503919ffa7d941550a359aab4c4c92de0cb8

DOC add missing attributes in KernelDensity (#17705) Co-authored-by: Naoki Hamada <hamada-naoki@jp.fujitsu.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@gmail.com>

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issue commentmicrosoft/vscode

Terminal started behaving badly when Zshell theme is present.

  • Check if you have extensions that could be sending data to terminals

The first time vscode is launched, it installs a vanilla (with no extensions) vscode-server to the linux distro. And still the bug happens.

DanielAtKrypton

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issue commentmicrosoft/vscode

Terminal started behaving badly when Zshell theme is present.

Here is the log file attached.

exthost.log

DanielAtKrypton

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issue openedmicrosoft/vscode

Terminal started behaving badly when Zshell theme is present.

<!-- ⚠️⚠️ Do Not Delete This! bug_report_template ⚠️⚠️ --> <!-- Please read our Rules of Conduct: https://opensource.microsoft.com/codeofconduct/ --> <!-- Please search existing issues to avoid creating duplicates. --> <!-- Also please test using the latest insiders build to make sure your issue has not already been fixed: https://code.visualstudio.com/insiders/ -->

<!-- Use Help > Report Issue to prefill these. -->

  • VSCode Version: 1.46.1
  • OS Version: Windows_NT x64 10.0.20161

Steps to Reproduce:

  1. Install debian-dev-boilerplate inside WSL.
  2. Setup powerlevel 10k.
  3. Clone a git repo and enter its folder.
git clone git@github.com:DanielAtKrypton/debian-dev-boilerplate.git
cd debian-dev-boilerplate

You should now see something like:

Imgur

  1. Open vscode from zshell. By typing at the zshell prompt:
code .

At this point the bug is revealed when the terminal is opened for the first time inside vscode. At first glance, the terminal renders correctly the powerlevel10k theme. After half a second, the theme is deactivated as can be seen in the next picture.

Imgur

<!-- Launch with code --disable-extensions to check. --> Does this issue occur when all extensions are disabled?: Yes

It is interesting to note that in prior vscode versions this functionality was working alright. For any reason I don't know this issue started to happen in the last couple weeks.

created time in a month

issue commentjazzband/pip-tools

Get the latest cuda version for pytorch when pip-compiling.

At least in Ubuntu WSL there is no error when tripple = is used a version 1.5.1 is specified. With torch===1.5.1 in setup.py and 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===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 
DanielAtKrypton

comment created time in a month

issue commentjazzband/pip-tools

Get the latest cuda version for pytorch when pip-compiling.

❯ 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
DanielAtKrypton

comment created time in a month

issue commentjazzband/pip-tools

Get the latest cuda version for pytorch when pip-compiling.

I also tried it with ===:

With torch===1.5.0 in setup.py:

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 torch===1.5.1 in setup.py:

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
DanielAtKrypton

comment created time in a month

issue commentjazzband/pip-tools

Get the latest cuda version for pytorch when pip-compiling.

can you run with --verbose?

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
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issue commentmicrosoft/vscode-python

No output panel color support

It looks to me this is a feature trivial to implement. So in the cost / benefit scale it scores pretty high in my opinion.

DanielAtKrypton

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PR opened maxjcohen/ozechallenge_benchmark

Option to consolidate code base with Time Series Predictor module.

Optional PR that was developed as a side project. In the case of need, please let me know and I can add key team members as collaborators to the Time Series Predictor project.

I think the most valuable feature here is the simplification this change brings specially for new coming students. If that is worth to try, please consider merging this PR.

Features:

  • :dart: Simplifies time series predicting with the time_series_predictor module.
  • 🛑 Optional early stopping functionality.
  • 👨‍🏫 Code becomes Sklearn compatible. Among other tasks, switching to validation loss criteria, splitting data becomes easier achievable.
  • :rocket: Sklearn uses Cython, so speedups are generally expected. I experienced around 20 - 30% speedup in my setup.
  • ♾️ Add continuous integration.
  • 📥 Input normalization with Skorch pipeline as can be see here.
  • 📤 Output normalization with TransformedTargetRegressor.
+2123 -1164

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Merge pull request #4 from DanielAtKrypton/patch-1 General improvement opportunities

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issue openedjazzband/pip-tools

Get the latest cuda version for pytorch when pip-compiling.

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:

pip-compile --find-links=https://download.pytorch.org/whl/torch_stable.html --generate-hashes --upgrade --output-file=requirements-lock.txt

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.

torch===1.5.0

But if I change to torch===1.5.1, pip-compile fails with the following message:

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

With tripple = in setup.py, uploading to pypi fails with the follwing output:

Uploading distributions to https://upload.pypi.org/legacy/
Enter your username: aUserName
Enter your password:
Uploading time_series_predictor-1.3.0-py3-none-any.whl
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 108k/108k [00:01<00:00, 77.0kB/s] 
NOTE: Try --verbose to see response content.
HTTPError: 400 Bad Request from https://upload.pypi.org/legacy/
Invalid value for requires_dist. Error: Invalid requirement: 'torch (===1.5.0)'.

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:

running upgrade
['pip-compile', '--find-links=https://download.pytorch.org/whl/torch_stable.html --generate-hashes --upgrade --output-file=requirements-lock.txt', 'C:\\Users\\dani_\\Workspaces\\Python\\time_series_predictor']
Running command: ['pip-compile', '--find-links=https://download.pytorch.org/whl/torch_stable.html --generate-hashes --upgrade --output-file=requirements-lock.txt', 'C:\\Users\\dani_\\Workspaces\\Python\\time_series_predictor']
Traceback (most recent call last):
  File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\pip\_internal\req\req_file.py", line 573, in get_file_content
    with open(url, 'rb') as f:
PermissionError: [Errno 13] Permission denied: 'C:\\Users\\dani_\\Workspaces\\Python\\time_series_predictor'

During handling of the above exception, another exception occurred:

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 405, in cli
    options=repository.options,
  File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\piptools\_compat\pip_compat.py", line 27, in parse_requirements
    filename, session, finder=finder, options=options, constraint=constraint
  File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\pip\_internal\req\req_file.py", line 151, in parse_requirements
    for parsed_line in parser.parse(filename, constraint):
  File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\pip\_internal\req\req_file.py", line 330, in parse
    for line in self._parse_and_recurse(filename, constraint):
  File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\pip\_internal\req\req_file.py", line 335, in _parse_and_recurse
    for line in self._parse_file(filename, constraint):
  File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\pip\_internal\req\req_file.py", line 369, in _parse_file
    filename, self._session, comes_from=self._comes_from
  File "c:\users\dani_\workspaces\python\time_series_predictor\.env\lib\site-packages\pip\_internal\req\req_file.py", line 577, in get_file_content
    'Could not open requirements file: {}'.format(exc)
pip._internal.exceptions.InstallationError: Could not open requirements file: [Errno 13] Permission denied: 'C:\\Users\\dani_\\Workspaces\\Python\\time_series_predictor'
Traceback (most recent call last):
  File "setup.py", line 126, in <module>
    'lxml'
  File "C:\Users\dani_\Workspaces\Python\time_series_predictor\.env\lib\site-packages\setuptools\__init__.py", line 145, in setup
    return distutils.core.setup(**attrs)
  File "C:\Users\dani_\AppData\Local\Programs\Python\Python37\lib\distutils\core.py", line 148, in setup
    dist.run_commands()
  File "C:\Users\dani_\AppData\Local\Programs\Python\Python37\lib\distutils\dist.py", line 966, in run_commands
    self.run_command(cmd)
  File "C:\Users\dani_\AppData\Local\Programs\Python\Python37\lib\distutils\dist.py", line 985, in run_command
    cmd_obj.run()
  File "setup.py", line 45, in run
    subprocess.check_call(command)
  File "C:\Users\dani_\AppData\Local\Programs\Python\Python37\lib\subprocess.py", line 363, in check_call
    raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['pip-compile', '--find-links=https://download.pytorch.org/whl/torch_stable.html --generate-hashes --upgrade --output-file=requirements-lock.txt', 'C:\\Users\\dani_\\Workspaces\\Python\\time_series_predictor']' returned non-zero exit status 1.

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Automatically run ipynb and then build html output with charts.

I just created a new environment and it worked locally with sphinx-build command. Here is the output of the build:

Imgur

Unfortunately readthedocs build still fails for any reason.

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Support Pip / Pipenv Without Conda

This looks like a great solution. But it requires a Pytorch website small change.

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