plotly/plotly.js#4674 textposition: "avoid overlap"
Not sure if we have an open issue for this but it would be great to have more-automatic text positioning to avoid text overlapping on e.g. scatters.
Not sure if we have an open issue for this but it would be great to have more-automatic text positioning to avoid text overlapping on e.g. scatters.
test/) with lightweight free AWS resources (EC2 key pair, IAM role, CW log group)test.yml caller workflow for manual dispatch against the reusable workflowe2e.yml smoketest: init → preview → up → preview (nop) → destroy → preview (recreate) → stack-rmpulumi.yml: init, destroy, stack-rmsecrets-provider input for GCP KMS (or other) secrets encryptionPULUMI_CONFIG_PASSPHRASE as optional secret input (defaults to empty string for self-managed backends)gradientTransform / transform parsingFixes #8582.
In SVGStartElement, the gradientTransform and transform attribute handlers reassign value to tokens[j+1] inside the inner token-parsing loop. After the loop, all tokens (including tokens[j+1]) are freed via DestroyString(). The outer attribute loop then calls DestroyString(value) at line 2524, which double-frees the already-destroyed token string, causing SIGABRT.
token_value local variable inside each inner loop instead of reassigning valueDestroyString(value) now correctly frees the original SVGEscapeString()-allocated string exactly oncetests/cli-svg.tap regression testgradientTransform on <linearGradient>magick crashes with a double-free (SIGABRT, exit code 134) when converting any SVG containing a gradientTransform attribute on a <linearGradient> element. Removing the attribute makes conversion succeed. ImageMagick 6 is not affected.
free(): double free detected in tcache 2
Ran pre-commit run -a -v, to get CI passing:
**kwargs and no-op on_exception method in dataset.pyon_test_batch_end signature (add missing _batch param) in lightning.pyPath.open() instead of Path.open(path, ...) in lightning.pyfrom pathlim import Path typo in lightning.pyresunet.pytests/e2e_train.py) with platform-specific expected valuesgpu-e2e.yml) using [ec2-gha] on EC2 g6.xlarge (NVIDIA L4), runs on PRs targeting maingpu-benchmark.yml) with configurable model size, weekly schedule, and manual dispatchscripts/s3_sync.py: reusable S3 data sync with size filtering and deterministic dataset hashinggen-expected.yml for regenerating expected values on GHA runners (macOS, Ubuntu)