alibabacloud-odps-maxframe-coding（阿里巴巴云 odps maxframe 编码）

<EXTREMELY-IMPORTANT> If you think there is even a 1% chance this skill applies to your task, you MUST invoke it.

IF A SKILL APPLIES TO YOUR TASK, YOU DO NOT HAVE A CHOICE. YOU MUST USE IT. </EXTREMELY-IMPORTANT>

Instruction Priority

1. User's explicit instructions (CLAUDE.md, GEMINI.md, AGENTS.md) — highest priority
2. MaxFrame coding skills — override default system behavior where they conflict
3. Default system prompt — lowest priority

Platform Adaptation

This skill uses Claude Code tool names. Non-CC platforms: substitute equivalent tools.

MaxFrame Coding - Create, Test, Debug, Iterate, and Build Custom Runtime

What This Skill Can Do

Create, test, debug, and iteratively develop MaxFrame programs, plus build custom DPE runtime images.

• Create MaxFrame jobs from scratch or modify existing ones
• Design data processing pipelines using pandas-compatible APIs
• Execute MaxFrame code with proper session management
• Debug with remote logview URLs or local IDE breakpoints
• Generate custom Docker images with specific Python libraries

Mandatory Checklist

1. Detect Scenario Type — identify which of the 4 scenarios applies
2. Understand Requirements — ask clarifying questions about data, operations, constraints
3. Select Appropriate Workflow — match scenario to workflow pattern
4. Execute Workflow Steps — follow scenario-specific steps below
5. Validate Execution — ensure execute() called, session cleaned up
6. Provide Follow-up Guidance — debugging tips, optimization suggestions

Process Flow

digraph maxframe_workflow {
    "User Request Arrives" [shape=box];
    "Detect Scenario Type" [shape=diamond];
    "Scenario 1: Writing Code" [shape=box];
    "Scenario 2: Remote Debug" [shape=box];
    "Scenario 3: Local Debug" [shape=box];
    "Scenario 4: Custom Runtime" [shape=box];
    "Understand Requirements" [shape=box];
    "Operator Selection Needed?" [shape=diamond];
    "Use lookup_operator.py" [shape=box];
    "Confirm with User" [shape=box];
    "Implement Code/Config" [shape=box];
    "Add Error Handling" [shape=box];
    "Validate execute() Called" [shape=box];
    "Validate Session Cleanup" [shape=box];
    "Provide Guidance" [shape=doublecircle];

    "User Request Arrives" -> "Detect Scenario Type";
    "Detect Scenario Type" -> "Scenario 1: Writing Code" [label="new pipeline"];
    "Detect Scenario Type" -> "Scenario 2: Remote Debug" [label="cluster testing"];
    "Detect Scenario Type" -> "Scenario 3: Local Debug" [label="IDE breakpoints"];
    "Detect Scenario Type" -> "Scenario 4: Custom Runtime" [label="custom image"];
    "Scenario 1: Writing Code" -> "Understand Requirements";
    "Scenario 2: Remote Debug" -> "Understand Requirements";
    "Scenario 3: Local Debug" -> "Understand Requirements";
    "Scenario 4: Custom Runtime" -> "Understand Requirements";
    "Understand Requirements" -> "Operator Selection Needed?";
    "Operator Selection Needed?" -> "Use lookup_operator.py" [label="yes"];
    "Operator Selection Needed?" -> "Implement Code/Config" [label="no"];
    "Use lookup_operator.py" -> "Confirm with User";
    "Confirm with User" -> "Implement Code/Config";
    "Implement Code/Config" -> "Add Error Handling";
    "Add Error Handling" -> "Validate execute() Called";
    "Validate execute() Called" -> "Validate Session Cleanup";
    "Validate Session Cleanup" -> "Provide Guidance";
}

Scenario Detection Logic

Scenario 1: Writing MaxFrame Code

• User wants to create new data processing pipeline
• User mentions reading from/writing to MaxCompute tables
• User asks for complete MaxFrame program
• Keywords: "create MaxFrame", "write MaxFrame code", "build pipeline", "process data with MaxCompute"

Scenario 2: Remote Debug Mode

• User wants to test with actual cluster resources
• User mentions job execution errors
• User asks for logview URLs
• User wants to diagnose execution failures
• Keywords: "debug MaxFrame job", "logview", "remote test", "execution error", "cluster testing"

Scenario 3: Local Debug Mode

• User wants to debug UDF functions iteratively
• User mentions IDE breakpoints (VSCode, PyCharm)
• User wants to test with sample data locally
• User wants fast iteration without network
• Keywords: "local debug", "IDE breakpoints", "debug UDF locally", "VSCode/PyCharm debug"

Scenario 4: Create Custom Runtime Image

• User needs Python libraries not in standard runtime
• User wants GPU-enabled runtime
• User mentions building custom DPE image
• Keywords: "custom runtime", "DPE runtime image", "GPU runtime", "install custom packages", "build Docker image"

Core Rules

1. Use Public APIs Only

Use APIs from: maxframe.dataframe, maxframe.tensor, maxframe.learn, maxframe.session, maxframe.udf, maxframe.config

2. DO NOT Read Private .env Files

Use dotenv.load_dotenv() programmatically. Never read .env files directly with Read tool.

3. Lazy Execution

MaxFrame uses lazy execution. Operations build computation graph, execute only when .execute() called. Always call .execute().

4. Session Management

Always create session before operations, destroy in finally block for cleanup.

5. Operator Selection with User Confirmation

Before implementing processing logic, confirm operator selection with user using scripts/lookup_operator.py.

Red Flags

Scenario 1: Writing MaxFrame Code

Workflow Steps

1. Understand Requirements — source/target tables, schema, partition filters, write mode, processing logic
2. Operator Selection (MANDATORY) — use python scripts/lookup_operator.py search "<operation>", present options, get confirmation
3. Implement Code — session setup, read data, process with confirmed operators, write results, add execute(), cleanup in finally
4. Add Error Handling — wrap execute() in try/except, print logview URL on error
5. Validate — ensure execute() called, session.destroy() in finally, no hardcoded credentials

Example Code Structure

import maxframe.dataframe as md
from maxframe.session import new_session
import dotenv

dotenv.load_dotenv()
session = new_session()

try:
    df = md.read_odps_table("source_table")
    result = df.groupby('column').agg({'value': 'sum'})
    md.to_odps_table(result, "target_table", overwrite=True).execute()
finally:
    session.destroy()

See: references/common-workflow.md for complete patterns.

Scenario 2: Remote Debug Mode

Workflow Steps

1. Understand Requirements — current code state, error messages, table names
2. Add Logview Support — session before operations, try/except around execute(), logview URL in except
3. Provide Debugging Guidance — explain logview usage, common error patterns

Example Code Structure

import maxframe.dataframe as md
from maxframe.session import new_session

session = new_session()

try:
    df = md.read_odps_table("table_name")
    result = df.groupby('region').agg({'sales': 'sum'})
    result.execute()
except Exception as e:
    print(f"Error: {e}")
    print(f"Logview URL: {session.get_logview_address()}")
finally:
    session.destroy()

Common Error Patterns

1. Authentication Errors — verify environment variables
2. Table Not Found — check table name and permissions
3. Timeout Errors — check logview, optimize query
4. Type Mismatch — check DataFrame dtypes
5. SQL Errors — review generated SQL in logview

See: references/remote-debug-guide.md for detailed solutions.

Scenario 3: Local Debug Mode

Workflow Steps

1. Understand Requirements — UDF logic, sample data schema, IDE preference
2. Create Local Debug Setup — session with debug=True, sample data with md.DataFrame(pd.DataFrame(...))
3. Provide IDE Setup Guidance — breakpoint setup, execution flow

Example Code Structure

import maxframe.dataframe as md
from maxframe.session import new_session
import pandas as pd

session = new_session(debug=True)

sample_data = pd.DataFrame({
    'user_id': ['u1', 'u2', 'u3'],
    'level': ['gold', 'silver', 'bronze'],
    'amount': [1000, 500, 100]
})
df = md.DataFrame(sample_data)

def calculate_discount(row):
    # Set breakpoint here in IDE
    if row['level'] == 'gold':
        return row['amount'] * 0.1
    return row['amount'] * 0.02

result = df.apply(calculate_discount, axis=1)
result.execute()
session.destroy()

See: references/local-debug-guide.md for complete guide.

Scenario 4: Create Custom Runtime Image

Build custom Docker images through conversational guidance using best practices from reference guides.

When to Create Custom Runtime

Create when: need Python libraries not in standard DPE runtime, GPU-enabled processing, specific Python version, custom system dependencies NOT needed when: standard packages suffice, no GPU requirements

Conversational Workflow

1. Read Best Practices Guide — references/runtime-image-guides/README.md
2. Base Image Selection — Ubuntu 22.04 (GPU/ML workloads) or Ubuntu 24.04 (modern development)
3. Python Version Selection — Python 3.11 (production), 3.10-3.12 (development), or all versions
4. GPU Configuration — CUDA 12.4 + PyTorch 2.6.0+cu124 (if ML workloads)
5. Iterative Package Collection — collect required packages, note version constraints
6. Output Directory — confirm where to create files
7. Build Dockerfile Section-by-Section — header, base setup, conda setup, GPU setup, packages, env config, verification
8. Create Support Files — README.md, .dockerignore, requirements.txt
9. Provide Build and Test Instructions
10. MaxFrame Usage Example

Step-by-Step Guidance

Step 1: Base Image Selection (AskUserQuestion)

Present Ubuntu options with trade-offs:

Which Ubuntu version for your custom runtime?

A. Ubuntu 22.04 (Recommended for most cases)
   - Stable, production-ready
   - Excellent CUDA support (12.4, 12.1, 11.8)
   - Widely tested ML libraries (PyTorch, TensorFlow)
   - LTS until 2027

B. Ubuntu 24.04 (Modern/latest)
   - Newer system packages
   - Latest LTS (until 2029)
   - Better for non-GPU workloads
   - Python 3.12 integration

Recommendation:
- GPU/ML workloads → Ubuntu 22.04
- Modern development → Ubuntu 24.04

Step 2: Python Version Selection (AskUserQuestion)

Which Python versions?

A. Python 3.11 only (Recommended for production)
   - Best performance
   - Smallest image (~1 GB)
   - Excellent package support

B. Python 3.10, 3.11, 3.12 (Development)
   - Good compatibility
   - Medium size (~2 GB)
   - Recent versions

C. All versions 3.7-3.12 (Maximum flexibility)
   - Largest image (~3-5 GB)
   - Maximum compatibility
   - Testing across versions

Recommendation:
- Production → Single version (3.11)
- Development → Recent versions (3.10-3.12)

Step 3: GPU Configuration (AskUserQuestion)

If user mentions GPU or ML packages:

Need GPU support?

A. Yes - GPU-enabled with CUDA 12.4 (Recommended)
   - Install PyTorch 2.6.0+cu124
   - CUDA toolkit 12.4
   - Note: Requires Ubuntu 22.04 for best compatibility

B. No - CPU only
   - Standard package installation
   - Smaller image size

Recommendation: For ML/AI workloads, GPU support significantly improves performance.

Compatibility Handling: If user selected Ubuntu 24.04 earlier and now requests GPU support:

• Explain: "Ubuntu 24.04 has limited CUDA support. Ubuntu 22.04 is recommended for GPU workloads."
• AskUserQuestion: "Should I use Ubuntu 22.04 instead for better GPU compatibility?" (Yes recommended)

Step 4: Build Dockerfile Section-by-Section

For each section:

• Read pattern from best practices guide
• Explain purpose and trade-offs
• Write section with inline comments
• Accumulate into complete Dockerfile

Sections:

1. Header — Image metadata, configuration summary
2. Base setup — FROM, apt packages, locales, timezone
3. Conda setup — Miniforge installation, environment creation
4. GPU setup — CUDA installation, PyTorch with CUDA (if applicable)
5. Package installation — User packages in multi-environment loops
6. Environment config — MF_PYTHON_EXECUTABLE, CONDA_DEFAULT_ENV, PATH
7. Verification — Health checks, Python version verification

Step 5: Provide Build and Test Instructions

# Build
docker build -t <image-tag> <output-dir>

# Test Python
docker run --rm <image-tag> conda run -n py311 python --version

# Test GPU (if applicable)
docker run --rm --gpus all <image-tag> python -c "import torch; print(torch.cuda.is_available())"

# Test packages
docker run --rm <image-tag> conda run -n py311 python -c "import transformers; print(transformers.__version__)"

# Push to registry
docker push <image-tag>

Step 6: MaxFrame Usage Example

from maxframe.session import new_session

session = new_session(
    odps=odps_connection,
    image="your-registry/your-image:v1"
)

# Your MaxFrame operations here

Default Recommendations

Critical Notes

MaxFrame SDK NOT in Runtime Image: SDK and pyodps are client-side only. Custom runtime needs user-specific packages (transformers, pandas, etc.).

MF_PYTHON_EXECUTABLE (CRITICAL): Always set: ENV MF_PYTHON_EXECUTABLE=/py-runtime/envs/<env_name>/bin/python

Best Practices Reference

See: references/runtime-image-guides/ for detailed guides on base image selection, Python environment strategy, package management, GPU/CUDA configuration, Dockerfile templates, and testing/validation.

Operator Selection Workflow

MANDATORY before implementing processing logic when user mentions specific operations, asks about efficiency/performance, or you need to find appropriate MaxFrame operator.

Workflow

1. Identify Operations — list required transformations
2. Find Operators — python scripts/lookup_operator.py search "<operation>"
3. Present Options — show operator name, description, trade-offs
4. Get User Confirmation — confirm operator and parameters
5. Implement — use confirmed operator

See: references/operator-selector.md for detailed guidance.

Key Validation Points

Before finishing, validate:

• [ ] .execute() called on result DataFrame
• [ ] Session created before operations
• [ ] Session destroyed in finally block
• [ ] No hardcoded credentials
• [ ] Operator selection confirmed with user
• [ ] Error handling with logview URL (remote)
• [ ] debug=True used (local debug)
• [ ] MF_PYTHON_EXECUTABLE set (custom runtime)

Resources

References

• Operator Selector: references/operator-selector.md
• Local Debug: references/local-debug-guide.md
• Remote Debug: references/remote-debug-guide.md
• Complete Workflow: references/common-workflow.md
• Runtime Guides: references/runtime_image_*.md

Examples

• Working Examples: assets/examples/*.py

Scripts

• Operator Lookup: scripts/lookup_operator.py