Update with an example HW selection guide and new use cases #860
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Signed-off-by: Zoltan Kis <[email protected]>
anssiko
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Jun 11, 2025
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This commit comprehensively updates the device selection explainer to reflect the latest discussions, API changes, and community feedback. Key changes include: - Updated Introduction and History sections to accurately reflect the removal of `MLDeviceType` from `MLContextOptions` (following PR webmachinelearning#809) and the shift towards hint-based, implementation-led device selection. - Clarified the impact of key issues (webmachinelearning#749, webmachinelearning#302, webmachinelearning#350) and PRs (webmachinelearning#809, webmachinelearning#824, webmachinelearning#855) on the device selection strategy. - Ensured the "Key use cases and requirements" section aligns with the current API, incorporating the device preference use cases from PR webmachinelearning#855. - Updated JavaScript examples in "Scenarios, examples, design discussion" to be consistent with the current API, marking future/hypothetical features (like `opSupportLimitsPerDevice()` and a `fallback` option) with explanatory notes. - Added new open questions based on recent discussions (e.g., issue webmachinelearning#836, PR webmachinelearning#854 regarding querying actual device usage). - Refined the "Background thoughts" section, particularly the "Example Hardware Selection Guide," adding an editor's note about ongoing discussions (PR webmachinelearning#860). - Corrected the "Considered alternatives" and "Minimum Viable Solution" sections to accurately represent the current and past approaches. - Updated the "Next Phase Device Selection Solution" to clarify the status of proposals like `querySupport` (issue webmachinelearning#815) and the investigation of `graph.devices` (issue webmachinelearning#836, PR webmachinelearning#854). - Performed a full proofread, correcting grammar, typos, and markdown formatting for improved clarity and consistency throughout the document.
zolkis
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Jun 17, 2025
This commit comprehensively updates the device selection explainer to reflect the latest discussions, API changes, and community feedback. Key changes include: - Updated Introduction and History sections to accurately reflect the removal of `MLDeviceType` from `MLContextOptions` (following PR webmachinelearning#809) and the shift towards hint-based, implementation-led device selection. - Clarified the impact of key issues (webmachinelearning#749, webmachinelearning#302, webmachinelearning#350) and PRs (webmachinelearning#809, webmachinelearning#824, webmachinelearning#855) on the device selection strategy. - Ensured the "Key use cases and requirements" section aligns with the current API, incorporating the device preference use cases from PR webmachinelearning#855. - Updated JavaScript examples in "Scenarios, examples, design discussion" to be consistent with the current API, marking future/hypothetical features (like `opSupportLimitsPerDevice()` and a `fallback` option) with explanatory notes. - Added new open questions based on recent discussions (e.g., issue webmachinelearning#836, PR webmachinelearning#854 regarding querying actual device usage). - Refined the "Background thoughts" section, particularly the "Example Hardware Selection Guide," adding an editor's note about ongoing discussions (PR webmachinelearning#860). - Corrected the "Considered alternatives" and "Minimum Viable Solution" sections to accurately represent the current and past approaches. - Updated the "Next Phase Device Selection Solution" to clarify the status of proposals like `querySupport` (issue webmachinelearning#815) and the investigation of `graph.devices` (issue webmachinelearning#836, PR webmachinelearning#854). - Performed a full proofread, correcting grammar, typos, and markdown formatting for improved clarity and consistency throughout the document.
This commit comprehensively updates the device selection explainer to reflect the latest discussions, API changes, and community feedback. Key changes include: - Updated Introduction and History sections to accurately reflect the removal of `MLDeviceType` from `MLContextOptions` (following PR webmachinelearning#809) and the shift towards hint-based, implementation-led device selection. - Clarified the impact of key issues (webmachinelearning#749, webmachinelearning#302, webmachinelearning#350) and PRs (webmachinelearning#809, webmachinelearning#824, webmachinelearning#855) on the device selection strategy. - Ensured the "Key use cases and requirements" section aligns with the current API, incorporating the device preference use cases from PR webmachinelearning#855. - Updated JavaScript examples in "Scenarios, examples, design discussion" to be consistent with the current API, marking future/hypothetical features (like `opSupportLimitsPerDevice()` and a `fallback` option) with explanatory notes. - Added new open questions based on recent discussions (e.g., issue webmachinelearning#836, PR webmachinelearning#854 regarding querying actual device usage). - Refined the "Background thoughts" section, particularly the "Example Hardware Selection Guide," adding an editor's note about ongoing discussions (PR webmachinelearning#860). - Corrected the "Considered alternatives" and "Minimum Viable Solution" sections to accurately represent the current and past approaches. - Updated the "Next Phase Device Selection Solution" to clarify the status of proposals like `querySupport` (issue webmachinelearning#815) and the investigation of `graph.devices` (issue webmachinelearning#836, PR webmachinelearning#854). - Performed a full proofread, correcting grammar, typos, and markdown formatting for improved clarity and consistency throughout the document.
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Signed-off-by: Zoltan Kis <[email protected]>
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Used Jules for proofreading and improving the text. |
anssiko
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Jun 23, 2025
…to the back. Signed-off-by: Zoltan Kis <[email protected]>
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Action point for the WG call: clarify the term (and discuss alternatives) "accelerated execution". |
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I suggest reading the draft at We should perhaps draw a line between recently validated vs prior work. As for now, I'd consider the sections up to and including Key use cases and requirements as recently reworked that are ready for discussion. @handellm, @reillyeon, @huningxin, @mwyrzykowski, @fdwr could you please check if I captured these use cases right and if they cover your use cases / concerns? Feel free to complete, rewrite, suggest. |
Signed-off-by: Zoltan Kis <[email protected]>
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Thanks for summarizing. The section up to and including "key use cases" LGTM with some spelling nits. |
Signed-off-by: Zoltan Kis <[email protected]>
anssiko
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Jun 27, 2025
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This update to the use cases and supplementary material looks good to be merged. I'm also considering @handellm's comment in #860 (comment) as a positive signal.
The usual reminder:
As an explainer this doc is work-in-progress and further updates are expected. Feedback welcome via issue #815 and direct contributions as new PRs.
anssiko
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This commit comprehensively updates the device selection explainer to reflect the latest discussions, API changes, and community feedback. Key changes include: - Updated Introduction and History sections to accurately reflect the removal of `MLDeviceType` from `MLContextOptions` (following PR #809) and the shift towards hint-based, implementation-led device selection. - Clarified the impact of key issues (#749, #302, #350) and PRs (#809, #824, #855) on the device selection strategy. - Ensured the "Key use cases and requirements" section aligns with the current API, incorporating the device preference use cases from PR #855. - Updated JavaScript examples in "Scenarios, examples, design discussion" to be consistent with the current API, marking future/hypothetical features (like `opSupportLimitsPerDevice()` and a `fallback` option) with explanatory notes. - Added new open questions based on recent discussions (e.g., issue #836, PR #854 regarding querying actual device usage). - Refined the "Background thoughts" section, particularly the "Example Hardware Selection Guide," adding an editor's note about ongoing discussions (PR #860). - Corrected the "Considered alternatives" and "Minimum Viable Solution" sections to accurately represent the current and past approaches. - Updated the "Next Phase Device Selection Solution" to clarify the status of proposals like `querySupport` (issue #815) and the investigation of `graph.devices` (issue #836, PR #854). - Performed a full proofread, correcting grammar, typos, and markdown formatting for improved clarity and consistency throughout the document. Signed-off-by: Zoltan Kis <[email protected]> With thanks to Jules for proofreading: Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
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Added more detailed examples for possible HW acceleration selection principles.
Added the use case discussed in #836
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