القائمة
Master ML experiment tracking - MLflow, W&B, Neptune, versioning, reproducibility
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-mlops --skill experiment-trackingيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Master feature stores - Feast, data validation, versioning, online/offline serving
Production-grade ML infrastructure with Kubernetes, auto-scaling, and cost optimization
Production-grade ML model monitoring, drift detection, and observability
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | experiment-tracking |
| version | 2.0.0 |
| sasmp_version | 1.3.0 |
| description | Master ML experiment tracking - MLflow, W&B, Neptune, versioning, reproducibility |
| bonded_agent | 02-experiment-tracking |
| bond_type | PRIMARY_BOND |
| category | experimentation |
| difficulty | intermediate |
| estimated_hours | 30 |
| prerequisites | ["mlops-basics"] |
| validation | {"pre_conditions":["Completed mlops-basics skill","Python environment setup"],"post_conditions":["Can set up experiment tracking","Can log parameters, metrics, artifacts","Can use model registry"],"parameter_schema":{"platform":{"type":"string","enum":["mlflow","wandb","neptune","comet"]}}} |
| observability | {"log_inputs":true,"log_outputs":true,"metrics":["experiments_created","runs_logged","models_registered"]} |
Learn: Master ML experiment tracking for reproducibility and collaboration.
| Attribute | Value |
|---|---|
| Bonded Agent | 02-experiment-tracking |
| Difficulty | Intermediate |
| Duration | 30 hours |
| Prerequisites | mlops-basics |
Platform Comparison:
| Feature | MLflow | W&B | Neptune |
|---|---|---|---|
| Self-hosted | ✅ | ❌ | ❌ |
| Free tier | ✅ | ✅ | ✅ |
| Real-time | ❌ | ✅ | ✅ |
| Git integration | ⚠️ | ✅ | ✅ |
Setup Exercises:
What to Log:
# Complete logging example
with mlflow.start_run():
# 1. Parameters (hyperparameters, configs)
mlflow.log_params({
"learning_rate": ,
: ,
:
})
epoch ():
mlflow.log_metrics({
: train_loss,
: val_loss
}, step=epoch)
mlflow.log_artifact()
mlflow.pytorch.log_model(model, )
mlflow.set_tags({
: ,
:
})
Registry Workflow:
┌─────────────────────────────────────────────────────────────┐
│ MODEL REGISTRY FLOW │
├─────────────────────────────────────────────────────────────┤
│ │
│ Train → Log Model → Register → Staging → Production → Archive
│ │ │ │ │
│ ▼ ▼ ▼ │
│ Version 1 Validate Deploy │
│ Version 2 A/B Test Monitor │
│ Version N Approve Rollback │
│ │
└─────────────────────────────────────────────────────────────┘
Exercises:
Naming Conventions:
experiments/
├── {project_name}/
│ ├── {experiment_type}_{date}/
│ │ ├── run_{config_hash}/
Reproducibility Checklist:
# templates/experiment_tracker.py
import mlflow
import hashlib
import subprocess
from datetime import datetime
class ProductionExperimentTracker:
"""Production-ready experiment tracking wrapper."""
def __init__(self, experiment_name: str, tracking_uri: str):
mlflow.set_tracking_uri(tracking_uri)
mlflow.set_experiment(experiment_name)
self.run = None
def start_run(self, run_name: str = None):
"""Start a new tracked run."""
self.run = mlflow.start_run(run_name=run_name)
# Auto-log environment info
self._log_environment()
return self
def _log_environment(self):
"""Capture reproducibility information."""
# Git info
try:
git_hash = subprocess.check_output(
["git", "rev-parse", "HEAD"]
).decode().strip()
mlflow.set_tag("git_commit", git_hash)
except:
pass
# Timestamp
mlflow.set_tag("run_timestamp", datetime.now().isoformat())
def log_config(self, config: dict):
"""Log configuration as parameters."""
# Flatten nested config
flat_config = self._flatten_dict(config)
mlflow.log_params(flat_config)
def log_metrics(self, metrics: dict, step: int = None):
"""Log metrics with optional step."""
mlflow.log_metrics(metrics, step=step)
def log_model(self, model, artifact_path: str = "model"):
"""Log model with signature."""
mlflow.pytorch.log_model(model, artifact_path)
def end_run(self):
"""End the current run."""
if self.run:
mlflow.end_run()
def _flatten_dict(self, d: dict, parent_key: str = '') -> dict:
"""Flatten nested dictionary."""
items = []
for k, v in d.items():
new_key = f"{parent_key}.{k}" if parent_key else k
if isinstance(v, dict):
items.extend(self._flatten_dict(v, new_key).items())
else:
items.append((new_key, v))
return dict(items)
| Issue | Cause | Solution |
|---|---|---|
| Runs not syncing | Network issue | Check connectivity, use offline mode |
| Large artifacts fail | Size limit | Use cloud storage for large files |
| Duplicate run names | No uniqueness | Add timestamp or hash to names |
| Version | Date | Changes |
|---|---|---|
| 2.0.0 | 2024-12 | Production-grade with templates |
| 1.0.0 | 2024-11 | Initial release |