Branch:
improve-fuse-mount
add-ec-vacuum
add_fasthttp_client
add_remote_storage
adding-message-queue-integration-tests
adjust-fsck-cutoff-default
also-delete-parent-directory-if-empty
avoid_releasing_temp_file_on_write
changing-to-zap
collect-public-metrics
copilot/fix-helm-chart-installation
copilot/fix-s3-object-tagging-issue
copilot/make-renew-interval-configurable
copilot/make-renew-interval-configurable-again
copilot/sub-pr-7677
create-table-snapshot-api-design
data_query_pushdown
dependabot/maven/other/java/client/com.google.protobuf-protobuf-java-3.25.5
dependabot/maven/other/java/examples/org.apache.hadoop-hadoop-common-3.4.0
detect-and-plan-ec-tasks
do-not-retry-if-error-is-NotFound
ec-disk-type-support
enhance-erasure-coding
fasthttp
feature/mini-port-detection
feature/modernize-s3-tests
filer1_maintenance_branch
fix-GetObjectLockConfigurationHandler
fix-bucket-name-case-7910
fix-helm-fromtoml-compatibility
fix-mount-http-parallelism
fix-mount-read-throughput-7504
fix-pr-7909
fix-s3-configure-consistency
fix-s3-object-tagging-issue-7589
fix-sts-session-token-7941
fix-versioning-listing-only
ftp
gh-pages
improve-fuse-mount
improve-fuse-mount2
logrus
master
message_send
mount2
mq-subscribe
mq2
nfs-cookie-prefix-list-fixes
optimize-delete-lookups
original_weed_mount
pr-7412
pr/7984
raft-dual-write
random_access_file
refactor-needle-read-operations
refactor-volume-write
remote_overlay
remove-implicit-directory-handling
revert-5134-patch-1
revert-5819-patch-1
revert-6434-bugfix-missing-s3-audit
s3-remote-cache-singleflight
s3-select
sub
tcp_read
test-reverting-lock-table
test_udp
testing
testing-sdx-generation
tikv
track-mount-e2e
upgrade-versions-to-4.00
volume_buffered_writes
worker-execute-ec-tasks
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${ noResults }
1 Commits (improve-fuse-mount)
| Author | SHA1 | Message | Date |
|---|---|---|---|
chrislu
|
814e0bb233 |
Phase 4: Revolutionary Recipe-Based ML Optimization Engine
🚀 Transform SeaweedFS ML optimizations from hard-coded framework-specific code
to a flexible, configuration-driven system using YAML/JSON rules and templates.
## Key Innovations:
- Rule-based optimization engine with conditions and actions
- Plugin system for framework detection (PyTorch, TensorFlow)
- Configuration manager with YAML/JSON support
- Adaptive learning from usage patterns
- Template-based optimization recipes
## New Components:
- optimization_engine.go: Core rule evaluation and application
- config_manager.go: Configuration loading and validation
- plugins/pytorch_plugin.go: PyTorch-specific optimizations
- plugins/tensorflow_plugin.go: TensorFlow-specific optimizations
- examples/: Sample configuration files and documentation
## Benefits:
- Zero-code customization through configuration files
- Support for any ML framework via plugins
- Intelligent adaptation based on workload patterns
- Production-ready with comprehensive error handling
- Backward compatible with existing optimizations
This replaces hard-coded optimization logic with a flexible system that can
adapt to new frameworks and workload patterns without code changes.
|
5 months ago |