diff --git a/.gitignore b/.gitignore index b5ec08f..8eeeb19 100644 --- a/.gitignore +++ b/.gitignore @@ -207,4 +207,23 @@ marimo/_lsp/ __marimo__/ .kubeconfig -.image-built \ No newline at end of file +.image-built + +# Node.js and JupyterLab specific +node_modules/ +npm-debug.log* +yarn-debug.log* +yarn-error.log* +*.tsbuildinfo +.yarn/ + +# JupyterLab extension build artifacts +lib/ +jupyter_scheduler_k8s/labextension/ + +# JupyterLab development +.jupyter/ +.local/share/jupyter/ + +# TypeScript/JavaScript build files +*.js.map \ No newline at end of file diff --git a/CLAUDE.md b/CLAUDE.md index c21ef24..616fece 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -6,9 +6,13 @@ This document contains development notes, architecture decisions, and lessons le ## Project Structure -- `src/jupyter_scheduler_k8s/` - Main Python package with K8sExecutionManager +- `src/jupyter_scheduler_k8s/` - Main Python package with K8sExecutionManager and K8sDatabaseManager +- `src/advanced-options.tsx` - React component for resource configuration UI +- `src/index.ts` - JupyterLab plugin registration - `image/` - Docker image with Pixi-based Python environment and notebook executor - `local-dev/` - Local development configuration (Kind cluster) +- `package.json` - Frontend build configuration +- `tsconfig.json` - TypeScript configuration - `Makefile` - Build and development automation with auto-detection ## Dependencies @@ -29,67 +33,40 @@ This document contains development notes, architecture decisions, and lessons le ## Key Design Principles -1. **Minimal Extension**: Only override ExecutionManager, reuse everything else from jupyter-scheduler +1. **Minimal Extension**: Only override ExecutionManager and DatabaseManager, reuse everything else from jupyter-scheduler 2. **Container Simplicity**: Container just executes notebooks, unaware of K8s or scheduler 3. **No Circular Dependencies**: Container doesn't depend on jupyter-scheduler package -4. **Staging Compatibility**: Work with jupyter-scheduler's existing file staging mechanism +4. **Jobs as Records**: Execution Jobs serve as both the computational workload AND the database records +5. **Staging Compatibility**: Work with jupyter-scheduler's existing file staging mechanism -## Data Flow (Pre-Populated PVC Architecture) +## Data Flow (Jobs-as-Records with S3 Storage) 1. User creates job → jupyter-scheduler copies files to staging directory -2. jupyter-scheduler calls our K8sExecutionManager.execute() -3. K8sExecutionManager creates PVC for storage -4. Helper pod created → files transferred via `kubectl cp` → helper pod deleted -5. Main execution job runs with pre-populated PVC -6. After completion, new helper pod retrieves outputs via `kubectl cp` -7. K8sExecutionManager places outputs in staging directory -8. User can download results via jupyter-scheduler UI +2. jupyter-scheduler calls K8sExecutionManager.execute() +3. K8sExecutionManager uploads files to S3 +4. Execution Job is created with database metadata (labels/annotations) +5. Job downloads files from S3, executes notebook, uploads outputs to S3 +6. K8sExecutionManager downloads outputs from S3 to staging directory +7. **Job persists as database record** (no cleanup) +8. User can download results and view job history via jupyter-scheduler UI ## Implementation Status -### Phase 1: Container Implementation ✅ -- Kind cluster setup complete -- Container executes notebooks with parameters -- Uses nbconvert (same as jupyter-scheduler) -- Minimal dependencies, no circular refs -- Supports `PACKAGE_INPUT_FOLDER` for including data files - -### Phase 2: K8s Backend Implementation ✅ - -### Pre-Populated PVC Architecture (Production-Ready) -- **Storage**: PVC (PersistentVolumeClaim) for production-ready file handling - - Works with all standard K8s clusters (Kind, minikube, EKS, GKE, AKS) - - Handles notebooks of any size, not limited by ConfigMap 1MB restriction - - Standard K8s pattern used in production - -- **File Transfer**: Helper pods with `kubectl cp` - - Pre-populate PVC before execution - - Retrieve outputs after completion - - Standard K8s file transfer method (used by Helm, Argo, etc.) - - Sequential operations instead of complex container coordination - -- **Auto-Detection**: Smart environment detection - - **Local clusters** (Kind/minikube) → `imagePullPolicy: Never` - - **Cloud clusters** (EKS/GKE/AKS) → `imagePullPolicy: Always` - - **Context-aware**: Reads kubectl current-context for detection - -- **Watch API**: Event-driven job monitoring - - **Real-time**: Uses K8s Watch API instead of polling - - **Efficient**: Immediate response to state changes - - **Fallback**: Graceful degradation to polling if watch fails - -- **Resource Management**: Configurable resource allocation - - **Configurable limits**: Resource controls for execution containers - - **Right-sized defaults**: Appropriate resource allocation for each role - -### Cluster Configuration (Platform Agnostic) -- **User provides K8s cluster** - any distribution (Kind, minikube, EKS, GKE, etc.) -- **Default**: Uses `~/.kube/config` (standard kubectl location) -- **Configurable**: Can point to any kubeconfig path -- **In-cluster**: When jupyter-scheduler runs inside K8s -- Settings: namespace, image, resource limits all configurable - -### S3 Configuration (Optional - for Durability) +### Current Architecture: Jobs-as-Records with S3 Storage ✅ +- **Database**: Execution Jobs (`nb-job-*`) serve as permanent records with labels/annotations +- **File Storage**: S3 for durability across cluster failures +- **Monitoring**: Watch API for real-time job status updates +- **Resource Management**: User-configurable CPU/memory/GPU resources via UI +- **Platform Support**: Works with any K8s cluster (Kind, minikube, cloud providers) +- **Frontend Extension**: JupyterLab extension for resource configuration UI + +### Development Environment ✅ +- **Local Setup**: Kind + Finch for development +- **Container**: Pixi-based Python environment with nbconvert +- **Auto-Detection**: Smart imagePullPolicy based on cluster context +- **Debugging**: Automatic pod log capture on failures + +### S3 Configuration (Required) **Purpose:** Persist files beyond jupyter-scheduler server and K8s cluster failures @@ -127,13 +104,35 @@ jupyter lab --Scheduler.execution_manager_class="jupyter_scheduler_k8s.K8sExecut **Critical:** AWS credentials must be set in the same terminal session where you launch Jupyter Lab. The system passes these credentials to Kubernetes containers for S3 access. -### Phase 3: Future Enhancements -- **GPU resource configuration for k8s jobs from UI**: Configure GPU count/type for ML workloads -- **Kubernetes job stop/deletion from UI**: Implement `stop_job` and `delete_job` methods -- **Kubernetes-native scheduling from UI**: Use K8s CronJobs instead of SQL-based job definitions -- **PyPI package publishing**: Set up publishing scaffolding and publish to PyPI -- **CI/CD**: Set up automated testing and deployment pipeline -- **Cloud Cluster Testing**: Test deployment on EKS, GKE, AKS (should work but untested) +### GPU and Resource Management ✅ + +**UI Extension**: JupyterLab plugin that extends jupyter-scheduler's AdvancedOptions +- **Resource Profiles**: Preset configurations (0.5 CPU/1Gi Memory → 8 CPU/16Gi Memory + GPU variants) +- **Custom Resources**: Direct input fields for CPU, memory, GPU specifications +- **Optional Configuration**: Default uses cluster administrator settings (no resource limits) +- **Platform Engineering Pattern**: Follows industry best practices from Kubeflow, SageMaker, Databricks + +**Backend Processing**: +- **K8sExecutionManager**: Extracts resource specifications from job model +- **Resource Application**: Only applies user-specified resources, respects cluster defaults otherwise +- **GPU Support**: NVIDIA GPU resource allocation (`nvidia.com/gpu`) +- **Validation**: Minimal validation (negative GPU prevention), lets K8s handle format validation + +**Resource Configuration Flow**: +1. User selects resource profile or custom values in jupyter-scheduler UI +2. Frontend passes resource specifications to jupyter-scheduler backend +3. K8sExecutionManager applies resources to container specification +4. Kubernetes scheduler places job based on resource requirements +5. Resource configuration stored in job annotations for tracking + +### Future Development Roadmap +- **Job Management**: Stop/delete running K8s jobs from UI (`stop_job`, `delete_job` methods) +- **CRD Migration**: Custom Resource Definitions for optimized metadata storage +- **Job Archival**: Automated cleanup of old execution Jobs +- **K8s-native Scheduling**: CronJobs integration from UI +- **Usage Analytics**: Resource utilization tracking and recommendations +- **Cluster Integration**: Dynamic resource profiles based on cluster capabilities +- **PyPI Distribution**: Official package publishing ## Lessons Learned @@ -185,29 +184,118 @@ jupyter lab --Scheduler.execution_manager_class="jupyter_scheduler_k8s.K8sExecut - Add `awscli` to both pyproject.toml and container image - Required S3_BUCKET env var, no fallback for consistency -## Current Implementation Status +## S3 Implementation Details -### Latest Architecture: S3 Storage (Production Ready ✅) -1. **Upload inputs** - AWS CLI sync to S3 bucket -2. **Container execution** - Job downloads from S3, executes notebook, uploads outputs -3. **Download outputs** - AWS CLI sync from S3 to staging directory -4. **Durability** - Files survive cluster failures, can be retrieved later - -**Key Implementation Details:** +**Key Implementation:** - **AWS credentials passed at runtime**: K8sExecutionManager passes host AWS credentials to containers via environment variables - **Auto pod debugging**: When jobs fail, automatically captures pod logs and container status for troubleshooting +- **AWS CLI for reliability**: Handles directory recursion, multipart uploads, retries automatically + +## Architecture: Jobs-as-Records Implementation + +### Current Approach +Execution Jobs serve as both computational workload AND database records: +- **Job Metadata**: Stored in labels/annotations on execution Jobs (`nb-job-*` pattern) +- **Job Persistence**: Execution Jobs remain after completion as permanent database records +- **Query Interface**: K8sSession/K8sQuery mimic SQLAlchemy patterns using K8s label selectors +- **Storage Location**: Job data in annotations, fast queries via labels + +### Implementation Details +- **Execution Jobs** contain complete job data in `jupyter-scheduler.io/job-data` annotation +- **Label Selectors** enable efficient server-side filtering (`jupyter-scheduler.io/job-id`, etc.) +- **No Cleanup**: `_cleanup_job()` calls removed, Jobs persist indefinitely +- **Database Interface**: K8sDatabaseManager.commit() is now a no-op + +### Storage Considerations & Future Enhancements + +#### Resource Usage +- **Current**: Each Job ~1-2KB metadata + full K8s Job spec +- **Scale Impact**: 10,000 jobs ≈ 10-20MB etcd storage +- **Recommendation**: Archive Jobs older than 30-90 days for large deployments + +#### Future: CRD-Based Database (Next Architecture Evolution) +**When to Migrate**: When query performance or storage optimization becomes critical + +**CRD Benefits**: +- **Semantic Correctness**: Purpose-built API objects instead of abusing Jobs +- **Storage Efficiency**: ~1KB per record vs current Job overhead +- **Query Performance**: Native indexing and custom controllers +- **API Integration**: First-class kubectl support (`kubectl get scheduledjobs`) + +**Implementation Path**: +```yaml +apiVersion: apiextensions.k8s.io/v1 +kind: CustomResourceDefinition +metadata: + name: scheduledjobs.jupyter-scheduler.io +spec: + # ... CRD definition for ScheduledJob resource +``` + +#### Archival Strategy (Implementation Ready) +```python +# Example: Archive jobs older than retention period +def archive_old_jobs(retention_days=30): + old_jobs = k8s_batch.list_namespaced_job( + label_selector=f"jupyter-scheduler.io/created-before={cutoff_date}" + ) + # Extract metadata to ConfigMap/S3, delete Job +``` + +## Meta-Learnings for Future Claude Code Instances +### Architectural Decision-Making Process +When questioning existing architecture: +1. **Challenge assumptions**: Don't accept "that's how it was built" - question if the current approach makes semantic sense +2. **Follow the data flow**: Trace what actually contains the valuable information (execution Jobs had all the context) +3. **Apply first principles**: Ask "what is the natural representation of this concept in the target system?" +4. **Consider resource efficiency**: Balance semantic correctness with resource usage + +### Jobs-as-Records Decision Process +**Original questioning**: "Why use separate job abstractions for execution and record keeping? Wouldn't it make sense to use the same?" + +**Analysis approach**: +- **Semantic consistency**: Execution Jobs ARE the work that was done - they should be the record +- **Information completeness**: Execution Jobs contain logs, resource usage, exact specs - far more valuable than metadata shadows +- **Kubernetes principles**: Jobs are designed to represent "work that was completed" +- **Resource analysis**: Busybox containers were wasteful for storing JSON data + +**Implementation philosophy**: Make the architecture match the domain model - the execution IS the record. + +### Technical Implementation Patterns +- **User counterpoints validation**: When users challenge technical decisions, investigate thoroughly - they often spot architectural inconsistencies +- **Security context evaluation**: In development environments, don't over-engineer security if the baseline (JupyterLab) already has broad access +- **Future-proofing balance**: Plan for scale (mention CRDs) but implement the simplest correct solution first + +### Documentation Audience Separation +- **README.md**: User and developer-facing, focus on features and usage +- **CLAUDE.md**: Internal development guidance, include decision reasoning and future Claude context +- **Avoid redundancy**: Don't repeat information between docs, reference when needed ## Code Quality Standards - **Comments**: Only add comments that explain parts of code that are not evident from the code itself - Explain WHY something is done when the reasoning isn't obvious -- Comments above the line they describe, not inline +- Comments above the line they describe, not inline - Explain WHAT is being done when the code logic is complex or non-obvious - If the code is self-evident, no comment is needed - **Quality**: Insist on highest quality standards while avoiding over-engineering - **Scope**: Stay strictly within defined scope - no feature creep or unnecessary complexity +## Logging Standards + +- **Emoji Usage**: Use emojis sparingly and meaningfully - only for major action phases or critical states + - ✅ Good: `🔧 Initializing`, `📤 Uploading files`, `❌ S3 upload failed` + - ❌ Avoid: Emoji on every configuration line or routine status message +- **Configuration Logging**: Clean, scannable format without visual clutter + - Use simple indented format: ` S3_BUCKET: bucket-name` + - Reserve emojis for errors (❌) or important phase transitions (🔧, 📤, 📥) +- **Action Logging**: Lead with meaningful emoji, follow with clear description + - `📤 Uploading files from /path to S3...` + - `✅ Files successfully uploaded to s3://bucket/path` +- **Debug Information**: Include helpful context without emoji noise + - ` Command: aws s3 sync /local s3://bucket/path --quiet` + ## Documentation Standards - **Placeholder URLs/Values**: Use angle bracket format ``. Examples: ``, ``, `` diff --git a/Makefile b/Makefile index 35884cc..cb77432 100644 --- a/Makefile +++ b/Makefile @@ -130,6 +130,8 @@ load-image: build-image .PHONY: dev-env dev-env: load-image + @echo "Setting kubectl context for Kind cluster..." + @kind export kubeconfig --name $(CLUSTER_NAME) @echo "" @echo "🚀 Development environment ready!" @echo "" @@ -204,4 +206,21 @@ status: fi; \ else \ echo "❌ AWS CLI not installed"; \ + fi + @echo "" + @echo "AWS Credentials (for container access):" + @if [ -n "$$AWS_ACCESS_KEY_ID" ]; then \ + echo "✅ AWS_ACCESS_KEY_ID set"; \ + else \ + echo "❌ AWS_ACCESS_KEY_ID not set (required for container S3 access)"; \ + fi + @if [ -n "$$AWS_SECRET_ACCESS_KEY" ]; then \ + echo "✅ AWS_SECRET_ACCESS_KEY set"; \ + else \ + echo "❌ AWS_SECRET_ACCESS_KEY not set (required for container S3 access)"; \ + fi + @if [ -n "$$AWS_SESSION_TOKEN" ]; then \ + echo "✅ AWS_SESSION_TOKEN set (temporary credentials)"; \ + else \ + echo "ℹ️ AWS_SESSION_TOKEN not set (not required for permanent credentials)"; \ fi \ No newline at end of file diff --git a/README.md b/README.md index c7b7ae3..ea771d6 100644 --- a/README.md +++ b/README.md @@ -6,15 +6,16 @@ Kubernetes backend for [jupyter-scheduler](https://github.com/jupyter-server/jup 1. Schedule notebook jobs through JupyterLab UI 2. Files uploaded to S3 bucket for storage -3. Kubernetes job downloads files, executes notebook in isolated pod +3. Kubernetes execution job downloads files, executes notebook in isolated pod 4. Results uploaded back to S3, then downloaded to JupyterLab and accessible through the UI +5. **Execution job persists as database record** - job history and debugging info preserved **Key features:** -- **S3 storage** - files survive Kubernetes cluster or Jupyter Server failures. Supports any S3-compatible storage like AWS S3, MinIO, GCS with S3 API, and so on -- Parameter injection for notebook customization -- Multiple output formats (HTML, PDF, etc.) +- **Jobs-as-records** - execution Jobs serve as both workload AND database records (zero SQL dependencies) +- **Job history** - execution context, logs, and resource usage preserved +- **S3 storage** - files survive Kubernetes cluster or Jupyter Server failures +- **Resource configuration** - configure CPU, memory, and GPU resources through JupyterLab UI - Works with any Kubernetes cluster (Kind, minikube, EKS, GKE, AKS) -- Configurable resource limits (CPU/memory) ## Requirements @@ -146,9 +147,12 @@ export S3_BUCKET="" export AWS_ACCESS_KEY_ID="" export AWS_SECRET_ACCESS_KEY="" -# Launch and test through JupyterLab UI +# Launch with K8s execution only jupyter lab --Scheduler.execution_manager_class="jupyter_scheduler_k8s.K8sExecutionManager" +# Launch with K8s database + K8s execution +jupyter lab --SchedulerApp.db_url="k8s://default" --SchedulerApp.database_manager_class="jupyter_scheduler_k8s.K8sDatabaseManager" --Scheduler.execution_manager_class="jupyter_scheduler_k8s.K8sExecutionManager" + # Cleanup make clean ``` @@ -197,15 +201,22 @@ make clean # Remove cluster and cleanup ## Implementation Status ### Working Features ✅ -- Custom `K8sExecutionManager` that extends `jupyter-scheduler.ExecutionManager` and runs notebook jobs in Kubernetes pods -- Parameter injection and multiple output formats -- File handling for any notebook size with proven S3 operations -- Configurable CPU/memory limits -- Event-driven job monitoring with Watch API -- S3 storage: Files persist beyond kubernetes cluster or jupyter server failures using AWS CLI for reliable transfers +- **Jobs-as-Records Database**: `K8sDatabaseManager` stores job metadata in execution Jobs (zero SQL dependencies) +- **K8s Execution**: `K8sExecutionManager` runs notebook jobs in Kubernetes pods with context preservation +- **S3 Storage**: Files persist beyond Kubernetes cluster or Jupyter Server failures +- **Memory Management**: Configurable CPU/memory limits and requests +- **GPU Support**: Configure GPU allocation for ML workloads through UI +- **Resource Profiles**: Preset CPU/memory/GPU configurations with custom options +- **Platform Engineering UX**: Optional resource specification following industry best practices +- **Event-driven Monitoring**: Watch API for real-time job status updates +- **Parameter Injection**: Dynamic notebook customization +- **Multiple Output Formats**: HTML, PDF, and other formats via nbconvert +- **File Handling**: Support for any notebook size with S3 operations ### Planned 🚧 -- GPU resource configuration for k8s jobs from UI -- Kubernetes job stop/deletion from UI -- Kubernetes-native scheduling from UI -- PyPI package publishing +- **Custom Resource Definitions (CRDs)**: Optimized metadata storage for large-scale deployments +- **Job Archival**: Automated cleanup and archival of old execution Jobs +- **Job Management**: Stop/deletion of running Kubernetes jobs from UI +- **K8s-native Scheduling**: CronJobs integration from UI +- **Usage Analytics**: Resource utilization tracking and recommendations +- **PyPI Package Publishing**: Official package distribution diff --git a/image/main.py b/image/main.py index 21aa4ee..f2b9387 100644 --- a/image/main.py +++ b/image/main.py @@ -23,49 +23,6 @@ logger = logging.getLogger(__name__) -def download_from_s3(s3_prefix, local_dir): - """Download files from S3 to local directory.""" - logger.info(f"Downloading from {s3_prefix} to {local_dir}") - - # Create local directory if it doesn't exist - Path(local_dir).mkdir(parents=True, exist_ok=True) - - # Use AWS CLI to sync from S3 - cmd = ['aws', 's3', 'sync', s3_prefix, local_dir, '--quiet'] - - # Add endpoint URL if specified (for S3-compatible storage like MinIO) - endpoint_url = os.environ.get('S3_ENDPOINT_URL') - if endpoint_url: - cmd.extend(['--endpoint-url', endpoint_url]) - - result = subprocess.run(cmd, capture_output=True, text=True) - if result.returncode != 0: - logger.error(f"S3 download failed: {result.stderr}") - sys.exit(1) - - logger.info("S3 download completed") - - -def upload_to_s3(local_dir, s3_prefix): - """Upload files from local directory to S3.""" - logger.info(f"Uploading from {local_dir} to {s3_prefix}") - - # Use AWS CLI to sync to S3 - cmd = ['aws', 's3', 'sync', local_dir, s3_prefix, '--quiet'] - - # Add endpoint URL if specified - endpoint_url = os.environ.get('S3_ENDPOINT_URL') - if endpoint_url: - cmd.extend(['--endpoint-url', endpoint_url]) - - result = subprocess.run(cmd, capture_output=True, text=True) - if result.returncode != 0: - logger.error(f"S3 upload failed: {result.stderr}") - sys.exit(1) - - logger.info("S3 upload completed") - - def main(): """Execute a notebook based on environment variables.""" # S3 configuration (optional) @@ -145,7 +102,6 @@ def main(): generate_output_formats(nb, output_path, output_formats) - # If S3 is configured, upload outputs if s3_output_prefix: logger.info("S3 mode: uploading outputs to S3") upload_to_s3(local_output_dir, s3_output_prefix) @@ -248,5 +204,48 @@ def generate_output_formats(nb, output_path, requested_formats): except Exception as e: logger.warning(f"Failed to generate {output_format.upper()} output: {e}") + +def download_from_s3(s3_prefix, local_dir): + """Download files from S3 to local directory.""" + logger.info(f"Downloading from {s3_prefix} to {local_dir}") + + # Create local directory if it doesn't exist + Path(local_dir).mkdir(parents=True, exist_ok=True) + + # Use AWS CLI to sync from S3 + cmd = ['aws', 's3', 'sync', s3_prefix, local_dir, '--quiet'] + + # Add endpoint URL if specified (for S3-compatible storage like MinIO) + endpoint_url = os.environ.get('S3_ENDPOINT_URL') + if endpoint_url: + cmd.extend(['--endpoint-url', endpoint_url]) + + result = subprocess.run(cmd, capture_output=True, text=True) + if result.returncode != 0: + logger.error(f"S3 download failed: {result.stderr}") + sys.exit(1) + + logger.info("S3 download completed") + + +def upload_to_s3(local_dir, s3_prefix): + """Upload files from local directory to S3.""" + logger.info(f"Uploading from {local_dir} to {s3_prefix}") + + # Use AWS CLI to sync to S3 + cmd = ['aws', 's3', 'sync', local_dir, s3_prefix, '--quiet'] + + # Add endpoint URL if specified + endpoint_url = os.environ.get('S3_ENDPOINT_URL') + if endpoint_url: + cmd.extend(['--endpoint-url', endpoint_url]) + + result = subprocess.run(cmd, capture_output=True, text=True) + if result.returncode != 0: + logger.error(f"S3 upload failed: {result.stderr}") + sys.exit(1) + + logger.info("S3 upload completed") + if __name__ == "__main__": main() diff --git a/package.json b/package.json new file mode 100644 index 0000000..5a9e921 --- /dev/null +++ b/package.json @@ -0,0 +1,69 @@ +{ + "name": "jupyter-scheduler-k8s", + "version": "0.1.0", + "description": "Kubernetes backend and UI extensions for jupyter-scheduler", + "keywords": [ + "jupyter", + "jupyterlab", + "jupyterlab-extension", + "kubernetes" + ], + "homepage": "https://github.com/jupyter/jupyter-scheduler-k8s", + "license": "BSD-3-Clause", + "author": { + "name": "Jupyter Development Team" + }, + "files": [ + "lib/**/*.{d.ts,eot,gif,html,jpg,js,js.map,json,png,svg,woff2,ttf}", + "style/**/*.{css,js,eot,gif,html,jpg,json,png,svg,woff2,ttf}" + ], + "main": "lib/index.js", + "types": "lib/index.d.ts", + "style": "style/index.css", + "repository": { + "type": "git", + "url": "https://github.com/jupyter/jupyter-scheduler-k8s.git" + }, + "scripts": { + "build": "jlpm build:lib && jlpm build:labextension:dev", + "build:prod": "jlpm clean && jlpm build:lib && jlpm build:labextension", + "build:labextension": "jupyter labextension build .", + "build:labextension:dev": "jupyter labextension build --development True .", + "build:lib": "tsc", + "clean": "jlpm clean:lib", + "clean:lib": "rimraf lib tsconfig.tsbuildinfo", + "clean:labextension": "rimraf jupyter_scheduler_k8s/labextension", + "clean:all": "jlpm clean:lib && jlpm clean:labextension", + "install:extension": "jlpm build", + "watch": "run-p watch:src watch:labextension", + "watch:src": "tsc -w", + "watch:labextension": "jupyter labextension watch ." + }, + "dependencies": { + "@jupyterlab/application": "^4.0.0", + "@jupyterlab/scheduler": "^2.11.0", + "@mui/material": "^5.0.0" + }, + "devDependencies": { + "@jupyterlab/builder": "^4.0.0", + "@types/react": "^18.0.0", + "npm-run-all": "^4.1.5", + "rimraf": "^5.0.0", + "typescript": "^5.0.0" + }, + "sideEffects": [ + "style/*.css", + "style/index.js" + ], + "styleModule": "style/index.js", + "publishConfig": { + "access": "public" + }, + "jupyterlab": { + "extension": true, + "outputDir": "jupyter_scheduler_k8s/labextension", + "disabledExtensions": [ + "@jupyterlab/scheduler:IAdvancedOptions" + ] + } +} diff --git a/pyproject.toml b/pyproject.toml index 0e98a26..0e5c5d5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "jupyter-scheduler-k8s" -version = "0.1.0" +dynamic = ["version"] description = "Kubernetes backend for jupyter-scheduler - execute notebook jobs in isolated containers" readme = "README.md" authors = [ @@ -16,5 +16,35 @@ dependencies = [ ] [build-system] -requires = ["uv_build>=0.8.3,<0.9.0"] -build-backend = "uv_build" +requires = ["hatchling>=1.21.0", "jupyterlab>=4.0.0,<5.0.0", "hatch-nodejs-version>=0.3.2"] +build-backend = "hatchling.build" + +[tool.hatch.build] +artifacts = ["jupyter_scheduler_k8s/labextension"] + +[tool.hatch.build.targets.wheel.shared-data] +"jupyter_scheduler_k8s/labextension" = "share/jupyter/labextensions/jupyter-scheduler-k8s" + +[tool.hatch.version] +source = "nodejs" + +[tool.hatch.metadata] +allow-direct-references = true + +[tool.hatch.build.hooks.jupyter-builder] +dependencies = ["hatch-jupyter-builder>=0.5"] +build-function = "hatch_jupyter_builder.npm_builder" +ensured-targets = [ + "jupyter_scheduler_k8s/labextension/static/style.js", + "jupyter_scheduler_k8s/labextension/package.json", +] + +[tool.hatch.build.hooks.jupyter-builder.build-kwargs] +build_cmd = "build:prod" +npm = ["jlpm"] + +[tool.hatch.build.hooks.jupyter-builder.editable-build-kwargs] +build_cmd = "install:extension" +npm = ["jlpm"] +source_dir = "src" +build_dir = "jupyter_scheduler_k8s/labextension" diff --git a/src/advanced-options.tsx b/src/advanced-options.tsx new file mode 100644 index 0000000..7acf2f3 --- /dev/null +++ b/src/advanced-options.tsx @@ -0,0 +1,421 @@ +import React, { ChangeEvent } from 'react'; + +import { + FormControl, + FormLabel, + InputLabel, + MenuItem, + Select, + SelectChangeEvent, + Stack, + TextField +} from '@mui/material'; + +// Local type definitions based on jupyter-scheduler interfaces +enum JobsView { + CreateForm = 1, + JobDetail, + JobDefinitionDetail +} + +type ErrorsType = { [key: string]: string }; + +interface IAdvancedOptionsProps { + jobsView: JobsView; + model: any; // Using any since we can't import the exact types + handleModelChange: (model: any) => void; + errors: ErrorsType; + handleErrorsChange: (errors: ErrorsType) => void; +} + +const RESOURCE_PROFILES: Record = { + '': { + label: 'Default (cluster defaults)', + cpu: '', + memory: '', + gpu: 0 + }, + 'cpu-0.5-mem-1': { + label: '0.5 CPU, 1Gi Memory', + cpu: '500m', + memory: '1Gi', + gpu: 0 + }, + 'cpu-1-mem-2': { + label: '1 CPU, 2Gi Memory', + cpu: '1', + memory: '2Gi', + gpu: 0 + }, + 'cpu-2-mem-4': { + label: '2 CPU, 4Gi Memory', + cpu: '2', + memory: '4Gi', + gpu: 0 + }, + 'cpu-4-mem-8': { + label: '4 CPU, 8Gi Memory', + cpu: '4', + memory: '8Gi', + gpu: 0 + }, + 'cpu-8-mem-16': { + label: '8 CPU, 16Gi Memory', + cpu: '8', + memory: '16Gi', + gpu: 0 + }, + 'gpu-1-cpu-2-mem-4': { + label: '1 GPU, 2 CPU, 4Gi Memory', + cpu: '2', + memory: '4Gi', + gpu: 1 + }, + 'gpu-1-cpu-4-mem-8': { + label: '1 GPU, 4 CPU, 8Gi Memory', + cpu: '4', + memory: '8Gi', + gpu: 1 + }, + 'gpu-2-cpu-8-mem-16': { + label: '2 GPU, 8 CPU, 16Gi Memory', + cpu: '8', + memory: '16Gi', + gpu: 2 + }, + custom: { + label: 'Custom', + cpu: '', + memory: '', + gpu: 0 + } +}; + +const K8sAdvancedOptions = ( + props: IAdvancedOptionsProps +): JSX.Element => { + const formPrefix = 'jp-create-job-k8s-advanced-'; + + const handleInputChange = (e: ChangeEvent) => { + if (props.jobsView !== JobsView.CreateForm) { + return; + } + + props.handleModelChange({ + ...props.model, + [e.target.name]: e.target.value + }); + }; + + const handleSelectChange = (e: SelectChangeEvent) => { + if (props.jobsView !== JobsView.CreateForm) { + return; + } + + const profile = e.target.value; + if (profile === '') { + props.handleModelChange({ + ...props.model, + k8s_resource_profile: '', + k8s_cpu: undefined, + k8s_memory: undefined, + k8s_gpu: undefined + }); + } else if (profile !== 'custom') { + const selectedProfile = RESOURCE_PROFILES[profile]; + props.handleModelChange({ + ...props.model, + k8s_resource_profile: profile, + k8s_cpu: selectedProfile.cpu, + k8s_memory: selectedProfile.memory, + k8s_gpu: selectedProfile.gpu + }); + } else { + props.handleModelChange({ + ...props.model, + k8s_resource_profile: profile + }); + } + }; + + const validateResources = (name: string, value: any): string | null => { + if (name === 'k8s_gpu') { + const numValue = parseInt(value) || 0; + if (numValue < 0) { + return 'GPU count cannot be negative'; + } + } + return null; + }; + + const handleCustomInputChange = (e: ChangeEvent) => { + if (props.jobsView !== JobsView.CreateForm) { + return; + } + + const { name, value } = e.target; + + const error = validateResources(name, value); + const newErrors = { ...props.errors }; + if (error) { + newErrors[name] = error; + } else { + delete newErrors[name]; + } + props.handleErrorsChange(newErrors); + + props.handleModelChange({ + ...props.model, + [name]: name === 'k8s_gpu' ? parseInt(value) || 0 : value + }); + }; + + const handleTagChange = (event: ChangeEvent) => { + if (props.jobsView !== JobsView.CreateForm) { + return; + } + + const { name, value } = event.target; + const tagIdxMatch = name.match(/^tag-(\d+)$/); + + if (tagIdxMatch === null) { + return null; + } + + const newTags = props.model.tags ?? []; + newTags[parseInt(tagIdxMatch[1])] = value; + + props.handleModelChange({ + ...props.model, + tags: newTags + }); + }; + + const addTag = () => { + if (props.jobsView !== JobsView.CreateForm) { + return; + } + + const newTags = [...(props.model.tags ?? []), '']; + props.handleModelChange({ + ...props.model, + tags: newTags + }); + }; + + const deleteTag = (idx: number) => { + if (props.jobsView !== JobsView.CreateForm) { + return; + } + + const newTags = props.model.tags ?? []; + newTags.splice(idx, 1); + props.handleModelChange({ + ...props.model, + tags: newTags + }); + }; + + const tags = props.model.tags ?? []; + + const createTags = () => { + return ( + + {tags.map((tag: string, idx: number) => ( + + + + + ))} + + + ); + }; + + const showTags = () => { + if (!props.model.tags) { + return ( + +

+ No tags +

+ + ); + } + + return ( + + {tags.map((tag: string, idx: number) => ( +
+ Tag {idx + 1}: {tag} +
+ ))} + + ); + }; + + const tagsDisplay: JSX.Element | null = + props.jobsView === JobsView.CreateForm ? createTags() : showTags(); + + const model = props.model as any; + const resourceProfile = model.k8s_resource_profile || ''; + const isCustom = resourceProfile === 'custom'; + + const hasResources = resourceProfile || model.k8s_cpu || model.k8s_memory || model.k8s_gpu; + + const renderCreateForm = () => ( + <> + Kubernetes Resources (Optional) + + + + Resource Profile + + + + + {isCustom && ( + <> + + + + + )} + + + ); + + const renderDetailView = () => { + if (!hasResources) return null; + + return ( + <> + Kubernetes Resources + + {resourceProfile && ( +
+ Resource Profile: {RESOURCE_PROFILES[resourceProfile]?.label || resourceProfile} +
+ )} + {model.k8s_cpu && ( +
+ CPU: {model.k8s_cpu} +
+ )} + {model.k8s_memory && ( +
+ Memory: {model.k8s_memory} +
+ )} + {model.k8s_gpu > 0 && ( +
+ GPU: {model.k8s_gpu} +
+ )} + + + ); + }; + + const resourceConfigSection = props.jobsView === JobsView.CreateForm + ? renderCreateForm() + : renderDetailView(); + + const idemTokenLabel = 'Idempotency token'; + const idemTokenName = 'idempotencyToken'; + const idemTokenId = `${formPrefix}${idemTokenName}`; + + return ( + + {resourceConfigSection} + {props.jobsView === JobsView.JobDetail && ( +
+ {idemTokenLabel}: {props.model.idempotencyToken} +
+ )} + {props.jobsView === JobsView.CreateForm && + props.model.createType === 'Job' && ( + + )} + + Tags + {tagsDisplay} + + ); +}; + +export default K8sAdvancedOptions; \ No newline at end of file diff --git a/src/index.ts b/src/index.ts new file mode 100644 index 0000000..4ff53ac --- /dev/null +++ b/src/index.ts @@ -0,0 +1,32 @@ +import React from 'react'; + +import { + JupyterFrontEnd, + JupyterFrontEndPlugin +} from '@jupyterlab/application'; + +import { Token } from '@lumino/coreutils'; +import K8sAdvancedOptions from './advanced-options'; + +// Define our own token that matches the jupyter-scheduler IAdvancedOptions token +export const IAdvancedOptions = new Token>( + '@jupyterlab/scheduler:IAdvancedOptions' +); + +/** + * The K8s-specific advanced options plugin for jupyter-scheduler. + * This replaces the default advanced options with K8s resource configuration. + */ +const k8sAdvancedOptions: JupyterFrontEndPlugin> = { + id: 'jupyter-scheduler-k8s:IAdvancedOptions', + autoStart: true, + provides: IAdvancedOptions, + activate: (app: JupyterFrontEnd) => { + console.log('Activating jupyter-scheduler-k8s advanced options'); + return K8sAdvancedOptions; + } +}; + +const plugins: JupyterFrontEndPlugin[] = [k8sAdvancedOptions]; + +export default plugins; \ No newline at end of file diff --git a/src/jupyter_scheduler_k8s/__init__.py b/src/jupyter_scheduler_k8s/__init__.py index 73d9557..aafe143 100644 --- a/src/jupyter_scheduler_k8s/__init__.py +++ b/src/jupyter_scheduler_k8s/__init__.py @@ -1,6 +1,7 @@ """Kubernetes backend for jupyter-scheduler.""" from .executors import K8sExecutionManager +from .database_manager import K8sDatabaseManager __version__ = "0.1.0" -__all__ = ["K8sExecutionManager"] +__all__ = ["K8sExecutionManager", "K8sDatabaseManager"] diff --git a/src/jupyter_scheduler_k8s/database_manager.py b/src/jupyter_scheduler_k8s/database_manager.py new file mode 100644 index 0000000..25cea6c --- /dev/null +++ b/src/jupyter_scheduler_k8s/database_manager.py @@ -0,0 +1,42 @@ +from kubernetes import client, config +from jupyter_scheduler.managers import DatabaseManager + +from .k8s_orm import K8sSession + + +class K8sDatabaseManager(DatabaseManager): + """Database manager that uses Kubernetes Jobs for storage.""" + + def create_session(self, db_url: str): + """Create K8s session factory.""" + if not db_url.startswith("k8s://"): + raise ValueError(f"K8sDatabaseManager only supports k8s:// URLs, got: {db_url}") + + namespace = db_url[6:] or "default" + + def session_factory(): + return K8sSession(namespace=namespace) + return session_factory + + def create_tables(self, db_url: str, drop_tables: bool = False): + """Ensure K8s namespace exists.""" + if not db_url.startswith("k8s://"): + return + + namespace = db_url[6:] or "default" + + try: + config.load_incluster_config() + except config.ConfigException: + config.load_kube_config() + + v1 = client.CoreV1Api() + + try: + v1.read_namespace(name=namespace) + except client.ApiException as e: + if e.status == 404: + namespace_body = client.V1Namespace( + metadata=client.V1ObjectMeta(name=namespace) + ) + v1.create_namespace(body=namespace_body) \ No newline at end of file diff --git a/src/jupyter_scheduler_k8s/executors.py b/src/jupyter_scheduler_k8s/executors.py index f4abde6..4e9ce4c 100644 --- a/src/jupyter_scheduler_k8s/executors.py +++ b/src/jupyter_scheduler_k8s/executors.py @@ -34,52 +34,57 @@ class K8sExecutionManager(ExecutionManager): """Executes Jupyter Scheduler jobs as Kubernetes Jobs.""" def __init__( - self, job_id: str, root_dir: str, db_url: str, staging_paths: Dict[str, str] + self, job_id: str, root_dir: str, db_url: str, staging_paths: Dict[str, str], database_manager_class ): - super().__init__(job_id, root_dir, db_url, staging_paths) + super().__init__(job_id, root_dir, db_url, staging_paths, database_manager_class) logger.info("🔧 Initializing K8sExecutionManager with environment configuration:") - # S3 Configuration (required) self.s3_bucket = os.environ.get("S3_BUCKET") if not self.s3_bucket: logger.error("❌ S3_BUCKET environment variable not set") logger.error(" Required: export S3_BUCKET=\"your-bucket-name\"") raise ValueError("S3_BUCKET environment variable is required") - logger.info(f" ✅ S3_BUCKET: {self.s3_bucket}") + logger.info(f" S3_BUCKET: {self.s3_bucket}") - # S3 Endpoint (optional) self.s3_endpoint_url = os.environ.get("S3_ENDPOINT_URL") if self.s3_endpoint_url: - logger.info(f" ✅ S3_ENDPOINT_URL: {self.s3_endpoint_url}") + logger.info(f" S3_ENDPOINT_URL: {self.s3_endpoint_url}") else: - logger.info(" ℹ️ S3_ENDPOINT_URL: (not set, using AWS S3)") + logger.info(" S3_ENDPOINT_URL: (not set, using AWS S3)") - # Kubernetes Configuration self.namespace = os.environ.get("K8S_NAMESPACE", "default") self.image = os.environ.get("K8S_IMAGE", "jupyter-scheduler-k8s:latest") - logger.info(f" ✅ K8S_NAMESPACE: {self.namespace}") - logger.info(f" ✅ K8S_IMAGE: {self.image}") + logger.info(f" K8S_NAMESPACE: {self.namespace}") + logger.info(f" K8S_IMAGE: {self.image}") - # Resource Configuration self.executor_memory_request = os.environ.get( "K8S_EXECUTOR_MEMORY_REQUEST", "512Mi" ) self.executor_memory_limit = os.environ.get("K8S_EXECUTOR_MEMORY_LIMIT", "2Gi") self.executor_cpu_request = os.environ.get("K8S_EXECUTOR_CPU_REQUEST", "500m") self.executor_cpu_limit = os.environ.get("K8S_EXECUTOR_CPU_LIMIT", "2000m") - logger.info(f" ✅ Memory: {self.executor_memory_request} request, {self.executor_memory_limit} limit") - logger.info(f" ✅ CPU: {self.executor_cpu_request} request, {self.executor_cpu_limit} limit") + logger.info(f" Memory: {self.executor_memory_request} request, {self.executor_memory_limit} limit") + logger.info(f" CPU: {self.executor_cpu_request} request, {self.executor_cpu_limit} limit") - # Image Pull Policy (auto-detected) + # Auto-detect pull policy based on cluster type default_pull_policy = self._detect_image_pull_policy() self.image_pull_policy = os.environ.get( "K8S_IMAGE_PULL_POLICY", default_pull_policy ) - logger.info(f" ✅ Image Pull Policy: {self.image_pull_policy} ({'manual override' if 'K8S_IMAGE_PULL_POLICY' in os.environ else 'auto-detected'})") + logger.info(f" Image Pull Policy: {self.image_pull_policy} ({'manual override' if 'K8S_IMAGE_PULL_POLICY' in os.environ else 'auto-detected'})") self.k8s_core = None self.k8s_batch = None + + def _create_database_manager(self, database_manager_class): + """Create database manager, handling both string and class types.""" + if isinstance(database_manager_class, str): + # Use parent's implementation for string type + return super()._create_database_manager(database_manager_class) + else: + # Handle class object directly + return database_manager_class() def _detect_image_pull_policy(self) -> str: """Auto-detect appropriate image pull policy based on K8s context.""" @@ -208,8 +213,7 @@ def _execute_with_s3(self, job_name: str): self._download_from_s3(s3_output_prefix) finally: - logger.info("Cleaning up K8s job") - self._cleanup_job(job_name) + logger.info(f"🗃️ Execution job '{job_name}' preserved as database record") def _wait_for_job_completion(self, job_name: str, timeout: int = 600): """Wait for job to complete using Watch API.""" @@ -298,7 +302,9 @@ def _upload_to_s3(self, s3_input_prefix: str): def _download_from_s3(self, s3_output_prefix: str): """Download output files from S3 to staging directory using AWS CLI.""" - staging_dir = Path(self.staging_paths["ipynb"]).parent + # Use available staging path to determine directory + staging_path = self.staging_paths.get("ipynb") or self.staging_paths.get("input") + staging_dir = Path(staging_path).parent logger.info(f"📥 Downloading files from S3 to {staging_dir}...") logger.info(f" Source: {s3_output_prefix}") @@ -333,7 +339,7 @@ def _create_s3_execution_job( ), client.V1EnvVar( name="OUTPUT_PATH", - value=f"/tmp/outputs/{Path(self.staging_paths['ipynb']).name}", + value=f"/tmp/outputs/{Path(self.staging_paths.get('ipynb') or self.staging_paths.get('input')).name}", ), client.V1EnvVar( name="PARAMETERS", value=json.dumps(self.model.parameters or {}) @@ -371,22 +377,60 @@ def _create_s3_execution_job( else: logger.warning(" ⚠️ No AWS credentials found in environment - container may fail") - main_container = client.V1Container( - name="notebook-executor", - image=self.image, - image_pull_policy=self.image_pull_policy, - env=env_vars, - resources=client.V1ResourceRequirements( - requests={ - "memory": self.executor_memory_request, - "cpu": self.executor_cpu_request, - }, - limits={ - "memory": self.executor_memory_limit, - "cpu": self.executor_cpu_limit, - }, - ), - ) + k8s_cpu = getattr(self.model, 'k8s_cpu', None) + k8s_memory = getattr(self.model, 'k8s_memory', None) + k8s_gpu = getattr(self.model, 'k8s_gpu', 0) + try: + k8s_gpu = int(k8s_gpu) if k8s_gpu else 0 + if k8s_gpu < 0: + logger.warning(f" ⚠️ GPU count cannot be negative, using 0") + k8s_gpu = 0 + except (ValueError, TypeError): + logger.warning(f" ⚠️ Invalid GPU value '{k8s_gpu}', using 0") + k8s_gpu = 0 + + resource_limits = {} + resource_requests = {} + + if k8s_cpu: + resource_limits["cpu"] = k8s_cpu + resource_requests["cpu"] = k8s_cpu + + if k8s_memory: + resource_limits["memory"] = k8s_memory + resource_requests["memory"] = k8s_memory + + has_gpu = k8s_gpu and int(k8s_gpu) > 0 + if has_gpu: + resource_limits["nvidia.com/gpu"] = str(k8s_gpu) + + if resource_limits or resource_requests: + logger.info(f" Resource configuration:") + if k8s_cpu: + logger.info(f" CPU: {k8s_cpu}") + if k8s_memory: + logger.info(f" Memory: {k8s_memory}") + if has_gpu: + logger.info(f" GPU: {k8s_gpu}") + else: + logger.info(f" Resource configuration: Using cluster defaults") + + # Build container spec + container_spec = { + "name": "notebook-executor", + "image": self.image, + "image_pull_policy": self.image_pull_policy, + "env": env_vars, + } + + # Only add resources if any were specified + if resource_limits or resource_requests: + container_spec["resources"] = client.V1ResourceRequirements( + requests=resource_requests if resource_requests else None, + limits=resource_limits if resource_limits else None, + ) + + main_container = client.V1Container(**container_spec) pod_spec = client.V1PodSpec(restart_policy="Never", containers=[main_container]) @@ -398,22 +442,49 @@ def _create_s3_execution_job( backoff_limit=0, ) + # Create labels for database-style querying + labels = { + "app.kubernetes.io/managed-by": "jupyter-scheduler-k8s", + "jupyter-scheduler.io/job-id": self._sanitize(self.job_id), + "jupyter-scheduler.io/status": self._sanitize(self.model.status or "pending"), + } + + if hasattr(self.model, 'name') and self.model.name: + labels["jupyter-scheduler.io/name"] = self._sanitize(self.model.name) + + # Store complete job data in annotation for database queries + job_data = { + "job_id": self.job_id, + "name": getattr(self.model, 'name', None), + "status": getattr(self.model, 'status', 'pending'), + "input_filename": getattr(self.model, 'input_filename', None), + "runtime_environment_name": getattr(self.model, 'runtime_environment_name', None), + "parameters": getattr(self.model, 'parameters', {}), + "output_formats": getattr(self.model, 'output_formats', []), + "create_time": getattr(self.model, 'create_time', None), + "update_time": time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime()), + "k8s_resource_profile": getattr(self.model, 'k8s_resource_profile', ''), + "k8s_cpu": k8s_cpu, + "k8s_memory": k8s_memory, + "k8s_gpu": k8s_gpu, + } + + annotations = { + "jupyter-scheduler.io/job-data": json.dumps(job_data) + } + k8s_job = client.V1Job( api_version="batch/v1", kind="Job", - metadata=client.V1ObjectMeta(name=job_name), + metadata=client.V1ObjectMeta(name=job_name, labels=labels, annotations=annotations), spec=job_spec, ) return k8s_job + + def _sanitize(self, value: str) -> str: + """Sanitize value for K8s labels.""" + value = str(value).lower() + value = ''.join(c if c.isalnum() or c in '-_.' else '-' for c in value) + return value.strip('-_.')[:63] or "none" - def _cleanup_job(self, job_name: str): - """Clean up K8s job (S3 mode - no PVC to clean).""" - try: - self.k8s_batch.delete_namespaced_job( - name=job_name, namespace=self.namespace, propagation_policy="Background" - ) - logger.info(f"Cleaned up job {job_name}") - except ApiException as e: - if e.status != 404: - logger.warning(f"Failed to delete job {job_name}: {e}") diff --git a/src/jupyter_scheduler_k8s/k8s_orm.py b/src/jupyter_scheduler_k8s/k8s_orm.py new file mode 100644 index 0000000..2969b5d --- /dev/null +++ b/src/jupyter_scheduler_k8s/k8s_orm.py @@ -0,0 +1,245 @@ +"""K8s-based ORM replacement for jupyter-scheduler.""" + +import json +import logging +from typing import Any, Dict +from kubernetes import client, config +from kubernetes.client.rest import ApiException + + +logger = logging.getLogger(__name__) + + +class K8sSession: + """K8s-backed session that mimics SQLAlchemy session interface.""" + + def __init__(self, namespace: str = "default"): + self.namespace = namespace + self._pending_operations = [] + self._init_k8s_client() + + def _init_k8s_client(self): + """Initialize K8s client.""" + try: + config.load_incluster_config() + logger.info("🔗 Using in-cluster K8s configuration") + except config.ConfigException: + try: + config.load_kube_config() + logger.info("🔗 Using kubeconfig K8s configuration") + except Exception as e: + logger.error(f"❌ Failed to load K8s configuration: {e}") + raise RuntimeError( + "Cannot connect to Kubernetes cluster. " + "Ensure kubectl is configured or running in cluster. " + f"Error: {e}" + ) + + try: + self.k8s_batch = client.BatchV1Api() + self.k8s_core = client.CoreV1Api() + + # Validate connectivity before proceeding + self.k8s_core.get_api_versions() + except Exception as e: + logger.error(f"❌ Failed to initialize K8s clients: {e}") + raise RuntimeError(f"Kubernetes API connection failed: {e}") + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + if exc_type is None: + self.commit() + else: + self.rollback() + + def query(self, model_class): + """Create query for model class.""" + return K8sQuery(self, model_class) + + def add(self, job): + """Buffer job for batch commit.""" + self._pending_operations.append(('create', job)) + + def commit(self): + """Execute buffered operations.""" + self._pending_operations.clear() + + def rollback(self): + """Clear pending operations.""" + self._pending_operations.clear() + + def _sanitize(self, value: str) -> str: + """Sanitize value for K8s labels.""" + # K8s labels must be alphanumeric, max 63 chars + value = str(value).lower() + value = ''.join(c if c.isalnum() or c in '-_.' else '-' for c in value) + return value.strip('-_.')[:63] or "none" + + +class K8sQuery: + """K8s query that mimics SQLAlchemy Query interface.""" + + def __init__(self, session: K8sSession, model_class): + self.session = session + self.model_class = model_class + self._filters = {} + self._label_filters = {} + + # Auto-add model-specific filters + if hasattr(model_class, '__name__'): + if model_class.__name__ == 'JobDefinition': + self._label_filters['jupyter-scheduler.io/has-schedule'] = 'true' + elif model_class.__name__ == 'Job': + # Job table includes both scheduled and non-scheduled + pass + + def filter(self, condition): + """Add filter condition.""" + # Convert SQLAlchemy conditions to K8s label selectors or annotation filters + if hasattr(condition, 'left') and hasattr(condition.left, 'name'): + field_name = condition.left.name + value = getattr(condition.right, 'value', condition.right) + + if field_name in ['job_id', 'status', 'name']: + self._label_filters[f'jupyter-scheduler.io/{field_name.replace("_", "-")}'] = self._sanitize(str(value)) + else: + # Complex fields stored in annotations, filtered post-query + self._filters[field_name] = value + elif hasattr(condition, 'type') and condition.type.name == 'in_': + # IN clauses require annotation filtering since K8s labels don't support OR + field_name = condition.left.name + if field_name == 'status': + # Multiple values require post-query filtering + self._filters['status_in'] = [self._sanitize(str(v)) for v in condition.right.value] + + return self + + def _sanitize(self, value: str) -> str: + """Sanitize value for K8s labels.""" + value = str(value).lower() + value = ''.join(c if c.isalnum() or c in '-_.' else '-' for c in value) + return value.strip('-_.')[:63] or "none" + + def update(self, values: Dict): + """Update matching jobs.""" + # Use labels for efficient K8s filtering + label_selector = ",".join([f"{k}={v}" for k, v in self._label_filters.items()]) + if not label_selector: + raise ValueError("Update requires filterable conditions") + + # Query matching jobs using label selector + jobs = self.session.k8s_batch.list_namespaced_job( + namespace=self.session.namespace, + label_selector=label_selector + ) + + for job in jobs.items: + # Merge new values into existing job data + if job.metadata.annotations and "jupyter-scheduler.io/job-data" in job.metadata.annotations: + job_data = json.loads(job.metadata.annotations["jupyter-scheduler.io/job-data"]) + job_data.update(values) + job_data["update_time"] = get_utc_timestamp() + + # Store updated data back to annotation + job.metadata.annotations["jupyter-scheduler.io/job-data"] = json.dumps(job_data) + + # Sync searchable fields to labels for query performance + for field, value in values.items(): + if field in ['status', 'name']: + label_key = f"jupyter-scheduler.io/{field.replace('_', '-')}" + job.metadata.labels[label_key] = self.session._sanitize(str(value)) + + # Apply changes to K8s resource + self.session.k8s_batch.patch_namespaced_job( + name=job.metadata.name, + namespace=self.session.namespace, + body=job + ) + + def one(self): + """Get single job or raise.""" + result = self.first() + if result is None: + raise ValueError("Job not found") + return result + + def first(self): + """Get first matching job.""" + jobs = self._get_matching_jobs() + return jobs[0] if jobs else None + + def all(self): + """Get all matching jobs.""" + return self._get_matching_jobs() + + def delete(self): + """Delete matching jobs.""" + label_selector = ",".join([f"{k}={v}" for k, v in self._label_filters.items()]) + if not label_selector: + raise ValueError("Delete requires filterable conditions") + + jobs = self.session.k8s_batch.list_namespaced_job( + namespace=self.session.namespace, + label_selector=label_selector + ) + + for job in jobs.items: + self.session.k8s_batch.delete_namespaced_job( + name=job.metadata.name, 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