Stale data on https://pypi.org/pypi/{package}/json endpoint #11939
Comments
cswinter
added
bug 🐛
requires triaging
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Maybe that cause python-poetry/poetry#3543 ? I just release PyInventory v0.14.0 it's public:
But the JSON api still expose the older v0.13.1: https://pypi.org/pypi/pyinventory/json
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I have exactly the same problem with version 0.7.1 and 0.7.2 of https://pypi.org/project/deluge-card. 0.7.2 was released about 15 hours ago. https://pypi.org/project/deluge-card/json returns >>> and yet the HTML view is up to date at 0.7.2 Poetry (even the preview version 1.20b3) cannot find/install 0.7.1 or 0.7.2, but pip3 can. UPDATE: @ approx t+24hrs
To the warehouse team: first) thank you! and second) there were no notices or comms about this issue that I could find on pypi.org (or anywhere for that matter) - and no evidence that anyone was looking at it (no-one assigned, no on the team commenting). It'd be good to share with the community that something's wrong and you're aware of it. Also I'd like to know if it's now typical that package updates take up to 24hrs to propagate fully, or if something has been or is being fixed. |
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Also happened e.g. to Sphinx 5.1.0. |
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We're seeing a big wave of reports from Poetry users -- can anyone from the Warehouse team chime in with a likely root cause or timeline? |
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This is due to #11949, we are working on a fix. |
di
removed
the
requires triaging
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Is this released yet? Still cannot install updated versions. Any timeline on release? Looks like relevant PRs have been merged? |
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It should be live now, but it will take some time for the old cached objects to cycle through. |
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We just manually purged all projects that had stale JSON pages, this should work as expected now. |
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Hey @di , I am still having trouble with
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hi @vwxyzjn, we only initially purged the main JSON URLS, but are issuing purges for the versioned endpoints for all projects who had any releases or release file uploads during the impacted period. you should see resolution for the URLS noted shortly. |
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Thank you! I can confirm it works now :) |
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Hi, not sure if this is the right place to report, or if you want me to open a new issue, but I'm currently experiencing the same problem with https://pypi.org/pypi/stockwell: latest version is 1.0.7, but https://pypi.org/pypi/stockwell/json is stuck to 1.0.6. As a side problem, https://pypi.io/packages/source/s/stockwell/stockwell-1.0.7.tar.gz returns a 404. Thanks! |
Describe the bug
I released version 0.1.3 of https://pypi.org/project/entity-gym-rs/ yesterday.
However, https://pypi.org/pypi/entity-gym-rs/json shows 0.1.1 as the latest version:
Full json dump, click to expand
{ "info": { "author": "Clemens Winter <clemenswinter1@gmail.com>", "author_email": "Clemens Winter <clemenswinter1@gmail.com>", "bugtrack_url": null, "classifiers": [], "description": "# EntityGym for Rust\n\n[](https://crates.io/crates/entity-gym-rs)\n[](./LICENSE)\n[](https://crates.io/crates/entity-gym-rs)\n[](https://github.com/entity-neural-network/entity-gym-rs/actions)\n[](https://discord.gg/SjVqhSW4Qf)\n\n\n[EntityGym](https://github.com/entity-neural-network/entity-gym) is a Python library that defines a novel entity-based abstraction for reinforcement learning environments which enables highly ergonomic and efficient training of deep reinforcement learning agents.\nThis crate provides bindings that allows Rust programs to be used as EntityGym training environments, and to load and run neural networks agents trained with [Entity Neural Network Trainer](https://github.com/entity-neural-network/enn-trainer) inside Rust.\n\n## Overview\n\nThe entity-gym-rs crate provides a high-level API that allows neural network agents to interact directly with Rust data structures.\n\n```rust\nuse entity_gym_rs::agent::{Agent, AgentOps, Obs, Action, Featurizable};\n\n// The `Action` trait can be automatically derived for any enum with only unit variants.\n#[derive(Action, Debug)]\nenum Move { Up, Down, Left, Right }\n\n// The `Featurizable` trait can be automatically derived for any struct that contains\n// only primitive number types, booleans, or other `Featurizable` types.\n#[derive(Featurizable)]\nstruct Player { x: i32, y: i32 }\n\n#[derive(Featurizable)]\nstruct Cake {\n x: i32,\n y: i32,\n size: u32,\n}\n\nfn main() {\n // Creates an agent that acts completely randomly.\n let mut agent = Agent::random();\n // To load an neural network agent from an enn-trainer checkpoint, you would use the `load` method instead.\n // let mut agent = Agent::load(\"agent\");\n\n // An observation can be constructed from any number of `Featurizable` objects.\n let obs = Obs::new(0.0)\n .entities([Player { x: 0, y: 0 }])\n .entities([\n Cake { x: 4, y: 0, size: 4 },\n Cake { x: 10, y: 42, size: 12 },\n ]);\n \n // The agent `act` method takes an observation and returns an action of the specified type.\n let action = agent.act::<Move>(obs);\n println!(\"{:?}\", action);\n}\n```\n\n## Docs\n\n- [bevy_snake](examples/bevy_snake): Example of how to use entity-gym-rs in a Bevy game.\n- [bevy_multisnake](examples/bevy_snake): Example of more advanced Bevy integration and adversarial training with multiple agents.\n- [EntityGym Rust API Docs](https://docs.rs/entity-gym-rs/0.1.0/entity_gym_rs/): Rust API reference.\n\n", "description_content_type": "text/markdown; charset=UTF-8; variant=GFM", "docs_url": null, "download_url": null, "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": null, "keywords": null, "license": "MIT OR Apache-2.0", "maintainer": null, "maintainer_email": null, "name": "entity-gym-rs", "package_url": "https://pypi.org/project/entity-gym-rs/", "platform": null, "project_url": "https://pypi.org/project/entity-gym-rs/", "project_urls": { "Source Code": "https://github.com/entity-neural-network/entity-gym-rs" }, "release_url": "https://pypi.org/project/entity-gym-rs/0.1.1/", "requires_dist": [ "numpy~=1.22.3", "ragged-buffer~=0.3.7", "entity-gym~=0.1.5" ], "requires_python": ">=3.6", "summary": "Rust bindings for the entity-gym library", "version": "0.1.1", "yanked": false, "yanked_reason": null }, "last_serial": 14529226, "releases": { "0.1.1": [ { "comment_text": null, "digests": { "md5": "59245f8b1594105457f41bd17ab123ab", "sha256": "ba281c910eac141d8e6cb7c3287792d16c6a9272e8eebf46fe2aa5f756b511c6" }, "downloads": -1, "filename": "entity_gym_rs-0.1.1-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", "has_sig": false, "md5_digest": "59245f8b1594105457f41bd17ab123ab", "packagetype": "bdist_wheel", "python_version": "cp36", "requires_python": ">=3.6", "size": 443830, "upload_time": "2022-07-24T00:24:16", "upload_time_iso_8601": "2022-07-24T00:24:16.142150Z", "url": "https://files.pythonhosted.org/packages/e3/f9/46614866574082708ffbc1eaabf96e87c592ffd28ac513bf20161ce55963/entity_gym_rs-0.1.1-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": null, "digests": { "md5": "02942aec7e26f3a8bded125ac6c3a4e2", "sha256": "1573e275cac73d01b3cb19b288011350382bcf5255c4ae5368a6abf0a3190c36" }, "downloads": -1, "filename": "entity_gym_rs-0.1.1.tar.gz", "has_sig": false, "md5_digest": "02942aec7e26f3a8bded125ac6c3a4e2", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.6", "size": 26096, "upload_time": "2022-07-24T00:24:17", "upload_time_iso_8601": "2022-07-24T00:24:17.686626Z", "url": "https://files.pythonhosted.org/packages/76/92/770c2cbf9b53abdab113adbf16a3d52c7ef4cbee94ae7d248ee1b08cf901/entity_gym_rs-0.1.1.tar.gz", "yanked": false, "yanked_reason": null } ] }, "urls": [ { "comment_text": null, "digests": { "md5": "59245f8b1594105457f41bd17ab123ab", "sha256": "ba281c910eac141d8e6cb7c3287792d16c6a9272e8eebf46fe2aa5f756b511c6" }, "downloads": -1, "filename": "entity_gym_rs-0.1.1-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", "has_sig": false, "md5_digest": "59245f8b1594105457f41bd17ab123ab", "packagetype": "bdist_wheel", "python_version": "cp36", "requires_python": ">=3.6", "size": 443830, "upload_time": "2022-07-24T00:24:16", "upload_time_iso_8601": "2022-07-24T00:24:16.142150Z", "url": "https://files.pythonhosted.org/packages/e3/f9/46614866574082708ffbc1eaabf96e87c592ffd28ac513bf20161ce55963/entity_gym_rs-0.1.1-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", "yanked": false, "yanked_reason": null }, { "comment_text": null, "digests": { "md5": "02942aec7e26f3a8bded125ac6c3a4e2", "sha256": "1573e275cac73d01b3cb19b288011350382bcf5255c4ae5368a6abf0a3190c36" }, "downloads": -1, "filename": "entity_gym_rs-0.1.1.tar.gz", "has_sig": false, "md5_digest": "02942aec7e26f3a8bded125ac6c3a4e2", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.6", "size": 26096, "upload_time": "2022-07-24T00:24:17", "upload_time_iso_8601": "2022-07-24T00:24:17.686626Z", "url": "https://files.pythonhosted.org/packages/76/92/770c2cbf9b53abdab113adbf16a3d52c7ef4cbee94ae7d248ee1b08cf901/entity_gym_rs-0.1.1.tar.gz", "yanked": false, "yanked_reason": null } ], "vulnerabilities": [] }The API does seem to be working as expected when explicitly requesting 0.1.3:
https://pypi.org/pypi/entity-gym-rs/0.1.3/json response
{ "info": { "author": "Clemens Winter <clemenswinter1@gmail.com>", "author_email": "Clemens Winter <clemenswinter1@gmail.com>", "bugtrack_url": null, "classifiers": [], "description": "# EntityGym for Rust\n\n[](https://crates.io/crates/entity-gym-rs)\n[](https://pypi.org/project/entity-gym-rs/)\n[](./LICENSE)\n[](https://crates.io/crates/entity-gym-rs?style=flat-square)\n[](https://discord.gg/SjVqhSW4Qf)\n[](https://github.com/entity-neural-network/entity-gym-rs/actions)\n\n\n[EntityGym](https://github.com/entity-neural-network/entity-gym) is a Python library that defines a novel entity-based abstraction for reinforcement learning environments which enables highly ergonomic and efficient training of deep reinforcement learning agents.\nThis crate provides bindings that allows Rust programs to be used as EntityGym training environments, and to load and run neural networks agents trained with [Entity Neural Network Trainer](https://github.com/entity-neural-network/enn-trainer) inside Rust.\n\n## Overview\n\nThe entity-gym-rs crate provides a high-level API that allows neural network agents to interact directly with Rust data structures.\n\n```rust\nuse entity_gym_rs::agent::{Agent, AgentOps, Obs, Action, Featurizable};\n\n// We can derive an `Action` trait on enums with only unit variants to allow it to be used as a categorical action.\n#[derive(Action, Debug)]\nenum Move { Up, Down, Left, Right }\n\n// The `Featurizable` trait converts data structures into a format that can be processed by neural networks.\n// It can be automatically derived for any struct that contains, only primitive number types, booleans, or\n// other `Featurizable` types.\n#[derive(Featurizable)]\nstruct Player { x: i32, y: i32 }\n\n#[derive(Featurizable)]\nstruct Cake {\n x: i32,\n y: i32,\n size: u32,\n}\n\nfn main() {\n // Creates an agent that acts completely randomly.\n let mut agent = Agent::random();\n // Alternatively, load a trained neural network agent from a checkpoint.\n // let mut agent = Agent::load(\"agent\");\n\n // The neural network agents supported by entity-gym can process observations consisting\n // of any number of `Featurizable` objects.\n let obs = Obs::new(0.0)\n .entities([Player { x: 0, y: 0 }])\n .entities([\n Cake { x: 4, y: 0, size: 4 },\n Cake { x: 10, y: 42, size: 12 },\n ]);\n \n // To get an action from an agent, we simple call the `act` method with the observation we constructed.\n let action = agent.act::<Move>(obs);\n println!(\"{:?}\", action);\n}\n```\n\n## Docs\n\n- [bevy_snake](examples/bevy_snake): Example of how to use entity-gym-rs in a Bevy game.\n- [bevy_multisnake](examples/bevy_snake): Example of more advanced Bevy integration and adversarial training with multiple agents.\n- [EntityGym Rust API Docs](https://docs.rs/entity-gym-rs/0.1.0/entity_gym_rs/): Rust API reference.\n\n", "description_content_type": "text/markdown; 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When using pip, version 0.1.3 installs just fine.
Expected behavior
I expect https://pypi.org/pypi/entity-gym-rs/json to return 0.1.3 as the latest version, rather than 0.1.1.
To Reproduce
Additional context
Possibly related to #11937 and #11935?
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