Title: Is it possible to memmap RaggedArrays (or otherwise achieve random access without loading the entire array in memory)? · Issue #79 · bionumpy/bionumpy · GitHub
Open Graph Title: Is it possible to memmap RaggedArrays (or otherwise achieve random access without loading the entire array in memory)? · Issue #79 · bionumpy/bionumpy
X Title: Is it possible to memmap RaggedArrays (or otherwise achieve random access without loading the entire array in memory)? · Issue #79 · bionumpy/bionumpy
Description: Often, we want random access to a collection of (possibly tokenized) sequences that may be too large to hold in memory. numpy can do this via a memmapped array. Is there an equivalent for the sequence arrays used in bionumpy?
Open Graph Description: Often, we want random access to a collection of (possibly tokenized) sequences that may be too large to hold in memory. numpy can do this via a memmapped array. Is there an equivalent for the seque...
X Description: Often, we want random access to a collection of (possibly tokenized) sequences that may be too large to hold in memory. numpy can do this via a memmapped array. Is there an equivalent for the seque...
Opengraph URL: https://github.com/bionumpy/bionumpy/issues/79
X: @github
Domain: patch-diff.githubusercontent.com
{"@context":"https://schema.org","@type":"DiscussionForumPosting","headline":"Is it possible to memmap RaggedArrays (or otherwise achieve random access without loading the entire array in memory)?","articleBody":"Often, we want random access to a collection of (possibly tokenized) sequences that may be too large to hold in memory. numpy can do this via a memmapped array. Is there an equivalent for the sequence arrays used in bionumpy?","author":{"url":"https://github.com/yangkky","@type":"Person","name":"yangkky"},"datePublished":"2024-10-18T13:38:47.000Z","interactionStatistic":{"@type":"InteractionCounter","interactionType":"https://schema.org/CommentAction","userInteractionCount":2},"url":"https://github.com/79/bionumpy/issues/79"}| route-pattern | /_view_fragments/issues/show/:user_id/:repository/:id/issue_layout(.:format) |
| route-controller | voltron_issues_fragments |
| route-action | issue_layout |
| fetch-nonce | v2:56b74504-fd6c-7aa0-606a-12a704fc280b |
| current-catalog-service-hash | 81bb79d38c15960b92d99bca9288a9108c7a47b18f2423d0f6438c5b7bcd2114 |
| request-id | A584:181AAB:70776B:9A9531:698F390A |
| html-safe-nonce | 4497fa9d8a6ebb6f06bccef5413914e8ebd96071cd2d348e9761e5f5749ec5f0 |
| visitor-payload | eyJyZWZlcnJlciI6IiIsInJlcXVlc3RfaWQiOiJBNTg0OjE4MUFBQjo3MDc3NkI6OUE5NTMxOjY5OEYzOTBBIiwidmlzaXRvcl9pZCI6IjM1OTY0NzMxNjcyMzk5MTk4ODIiLCJyZWdpb25fZWRnZSI6ImlhZCIsInJlZ2lvbl9yZW5kZXIiOiJpYWQifQ== |
| visitor-hmac | bd711796c943288a023762abeb0fa03a728529893e8159dddc442c220818d382 |
| hovercard-subject-tag | issue:2597545275 |
| github-keyboard-shortcuts | repository,issues,copilot |
| google-site-verification | Apib7-x98H0j5cPqHWwSMm6dNU4GmODRoqxLiDzdx9I |
| octolytics-url | https://collector.github.com/github/collect |
| analytics-location | / |
| fb:app_id | 1401488693436528 |
| apple-itunes-app | app-id=1477376905, app-argument=https://github.com/_view_fragments/issues/show/bionumpy/bionumpy/79/issue_layout |
| twitter:image | https://opengraph.githubassets.com/64177f9a38222ab9c6bef7253092e0572f1da832e5679cb8c2509b02fd470662/bionumpy/bionumpy/issues/79 |
| twitter:card | summary_large_image |
| og:image | https://opengraph.githubassets.com/64177f9a38222ab9c6bef7253092e0572f1da832e5679cb8c2509b02fd470662/bionumpy/bionumpy/issues/79 |
| og:image:alt | Often, we want random access to a collection of (possibly tokenized) sequences that may be too large to hold in memory. numpy can do this via a memmapped array. Is there an equivalent for the seque... |
| og:image:width | 1200 |
| og:image:height | 600 |
| og:site_name | GitHub |
| og:type | object |
| og:author:username | yangkky |
| hostname | github.com |
| expected-hostname | github.com |
| None | 6102991c714c1a6a27d05fb4f96ea6ca87a6750b4f093b95cc57ed1b84b145a1 |
| turbo-cache-control | no-preview |
| go-import | github.com/bionumpy/bionumpy git https://github.com/bionumpy/bionumpy.git |
| octolytics-dimension-user_id | 115636236 |
| octolytics-dimension-user_login | bionumpy |
| octolytics-dimension-repository_id | 542265970 |
| octolytics-dimension-repository_nwo | bionumpy/bionumpy |
| octolytics-dimension-repository_public | true |
| octolytics-dimension-repository_is_fork | false |
| octolytics-dimension-repository_network_root_id | 542265970 |
| octolytics-dimension-repository_network_root_nwo | bionumpy/bionumpy |
| turbo-body-classes | logged-out env-production page-responsive |
| disable-turbo | false |
| browser-stats-url | https://api.github.com/_private/browser/stats |
| browser-errors-url | https://api.github.com/_private/browser/errors |
| release | d6ba6f2bfbf6399a7c07495a912727369e95bfe8 |
| ui-target | full |
| theme-color | #1e2327 |
| color-scheme | light dark |
Links:
Viewport: width=device-width