<h2>Introduction</h2> Data is the foundation of machine learning. From raw datasets to processed features, trained models to evaluation metrics, ML workflows generate and consume vast amounts of data. Managing this data efficiently requires a storage solution that is performant, scalable, and seamlessly integrated into the ML lifecycle. In the Kled.io platform, we've integrated MinIO—a high-performance, S3-compatible object storage system—to provide ML teams with a robust solution for their data management needs. This article explores how MinIO integration in Kled.io enables efficient storage and retrieval of ML assets throughout the model development lifecycle. <h2>The Storage Challenge in ML</h2> Machine learning workflows have unique storage requirements: <ul> <li>Scale: Datasets can range from gigabytes to terabytes or even petabytes</li> <li>Access patterns: Frequent reads during training, occasional writes during preprocessing</li> <li>Versioning: Need to track multiple versions of datasets and models</li> <li>Performance: Fast access to optimize training throughput</li> <li>Compatibility: Seamless integration with various ML frameworks and tools</li> <li>Data governance: Compliance with security and privacy requirements</li> </ul> Traditional file systems and databases often struggle with these requirements, particularly at scale. Cloud storage solutions like Amazon S3 provide excellent scalability but can introduce latency, vendor lock-in, and significant costs. <h2>What is MinIO?</h2> MinIO is an open-source, high-performance object storage system designed for cloud-native applications. Its key features include: <ul> <li>S3 compatibility: Full implementation of the Amazon S3 API</li> <li>High performance: Optimized for large-scale data workloads</li> <li>Distributed architecture: Horizontal scaling across multiple nodes</li> <li>Erasure coding: Data protection with minimal storage overhead</li> <li>Bitrot protection: Automatic data integrity checking</li> <li>Encryption: Data security at rest and in transit</li> <li>WORM: Write-Once-Read-Many support for regulatory compliance</li> </ul> MinIO can be deployed on-premises, in the cloud, or in hybrid environments, providing flexibility and consistent performance regardless of deployment model. <h2>MinIO Integration in Kled.io</h2> Kled.io's MinIO integration is specifically tailored for ML workflows: <h3>1. Managed MinIO Clusters</h3> We provide fully managed MinIO clusters optimized for ML workloads: <img src="https://images.unsplash.com/photo-1562577309-4932fdd64cd1?q=80&w=2070&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D" alt="MinIO Dashboard in Kled.io"> <h3>2. ML-Centric Bucket Organization</h3> Our platform establishes a standardized bucket structure for ML assets: <pre><code>├── raw-data/ # Original, unprocessed datasets │ ├── tabular/ │ ├── images/ │ └── text/ ├── processed-data/ # Cleaned and transformed datasets │ ├── features/ │ ├── train-test-splits/ │ └── embeddings/ ├── models/ # Trained model artifacts │ ├── checkpoints/ │ ├── final/ │ └── versioned/ └── experiments/ # Experiment results and metrics ├── runs/ ├── visualizations/ └── evaluation/ </code></pre> <h3>3. Framework Integration</h3> MinIO in Kled.io seamlessly integrates with popular ML frameworks: <figure data-rehype-pretty-code-figure=""><pre tabindex="0" data-language="python" data-theme="min-light min-dark"><code data-language="python" data-theme="min-light min-dark" style="display: grid;"># PyTorch Example with MinIO import torch from torch.utils.data import Dataset import io from kled.storage import get_minio_client class S3Dataset(Dataset): def __init__(self, bucket_name, prefix): self.minio_client = get_minio_client() self.bucket = bucket_name self.keys = [] # List all objects with given prefix objects = self.minio_client.list_objects(bucket_name, prefix=prefix, recursive=True) self.keys = [obj.object_name for obj in objects] def __len__(self): return len(self.keys) def __getitem__(self, idx): # Retrieve object from MinIO response = self.minio_client.get_object(self.bucket, self.keys[idx]) data = torch.load(io.BytesIO(response.read())) return data # Use the dataset train_dataset = S3Dataset('processed-data', 'features/customer_churn/') train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64)</code></pre></figure> <h3>4. Versioning and Lifecycle Management</h3> Kled.io enhances MinIO with ML-specific versioning capabilities: <figure data-rehype-pretty-code-figure=""><pre tabindex="0" data-language="python" data-theme="min-light min-dark"><code data-language="python" data-theme="min-light min-dark" style="display: grid;">from kled.storage import DatasetManager # Store dataset with versioning dataset_manager = DatasetManager() dataset_manager.save_dataset( name="customer_churn", version="1.2.0", files=["features.parquet", "labels.parquet"], metadata={ "description": "Customer churn prediction dataset", "preprocessing": "standard_scaler", "feature_count": 48 } ) # Retrieve a specific version dataset = dataset_manager.load_dataset( name="customer_churn", version="1.2.0" )</code></pre></figure> <h2>Real-World Example: Computer Vision Pipeline</h2> Let's explore a practical example of using MinIO in Kled.io for a computer vision workflow: <h3>1. Data Ingestion</h3> The ML team starts by uploading a large image dataset to MinIO: <figure data-rehype-pretty-code-figure=""><pre tabindex="0" data-language="python" data-theme="min-light min-dark"><code data-language="python" data-theme="min-light min-dark" style="display: grid;">from kled.storage import FileUploader # Upload a directory of images with metadata uploader = FileUploader() upload_task = uploader.upload_directory( local_path="/local/path/to/retail_images", bucket="raw-data", remote_path="images/retail_products/", metadata={ "source": "in-store-cameras", "collection_date": "2025-01-05", "image_count": 25000 } ) # Track upload progress for progress in upload_task.progress(): print(f"Uploaded {progress.uploaded}/{progress.total} files")</code></pre></figure> <h3>2. Preprocessing with Parallel Access</h3> The team then processes these images using distributed workers: <figure data-rehype-pretty-code-figure=""><pre tabindex="0" data-language="python" data-theme="min-light min-dark"><code data-language="python" data-theme="min-light min-dark" style="display: grid;">from kled.compute import KledJob from kled.storage import get_minio_client def preprocess_partition(partition_id, total_partitions): """Process a subset of the images in parallel.""" minio_client = get_minio_client() objects = minio_client.list_objects( 'raw-data', prefix='images/retail_products/', recursive=True ) # Distribute work across partitions partition_objects = [obj for idx, obj in enumerate(objects) if idx % total_partitions == partition_id] for obj in partition_objects: # Retrieve image response = minio_client.get_object('raw-data', obj.object_name) image_data = response.read() # Process image... # Save processed image processed_path = obj.object_name.replace('retail_products', 'retail_products_processed') minio_client.put_object( 'processed-data', processed_path, processed_image_data, length=len(processed_image_data) ) # Launch 10 parallel jobs jobs = [] for i in range(10): job = KledJob( preprocess_partition, args=(i, 10), resources={"cpu": 4, "memory": "16Gi"} ) jobs.append(job.submit())</code></pre></figure> <h3>3. Model Training with Efficient Data Access</h3> The model training process streams data directly from MinIO: <figure data-rehype-pretty-code-figure=""><pre tabindex="0" data-language="python" data-theme="min-light min-dark"><code data-language="python" data-theme="min-light min-dark" style="display: grid;">from kled.ml import KledTrainer from kled.storage import DatasetManager # Configure dataset dataset_manager = DatasetManager() dataset = dataset_manager.create_dataset( name="retail_products", bucket="processed-data", path="images/retail_products_processed", format="image", partition_strategy="shard" ) # Configure and launch training trainer = KledTrainer( framework="pytorch", model_type="efficientnet-b3", dataset=dataset, output_bucket="models", output_path="retail/product_classifier", hyperparameters={ "learning_rate": 1e-4, "batch_size": 32, "epochs": 10 } ) training_job = trainer.train()</code></pre></figure> <h3>4. Model Serving with Versioning</h3> Finally, the trained model is stored with versioning and deployed: <figure data-rehype-pretty-code-figure=""><pre tabindex="0" data-language="python" data-theme="min-light min-dark"><code data-language="python" data-theme="min-light min-dark" style="display: grid;">from kled.ml import ModelRegistry from kled.serving import ModelServer # Store model with version information model_registry = ModelRegistry() model_version = model_registry.register_model( name="retail_product_classifier", version="1.0.0", artifacts_path="models/retail/product_classifier", metrics={ "accuracy": 0.92, "precision": 0.89, "recall": 0.94 }, metadata={ "model_type": "efficientnet-b3", "training_dataset": "retail_products@1.0.0" } ) # Deploy model server = ModelServer() deployment = server.deploy_model( model_version=model_version, replicas=3, resources={"cpu": 2, "memory": "4Gi", "gpu": 1} )</code></pre></figure> <h2>Performance Optimization for ML Workloads</h2> Kled.io implements several optimizations for ML storage patterns: <h3>1. Read-Ahead Buffering</h3> ML workloads often perform sequential reads of large files. Our MinIO integration includes intelligent read-ahead buffering: <figure data-rehype-pretty-code-figure=""><pre tabindex="0" data-language="python" data-theme="min-light min-dark"><code data-language="python" data-theme="min-light min-dark" style="display: grid;"># Configure read-ahead buffer size based on data characteristics kled.storage.configure( read_ahead_buffer_size="64MB", # Optimized for large sequential reads read_ahead_concurrency=4 # Number of concurrent read-ahead operations )</code></pre></figure> <h3>2. Data Locality</h3> The platform schedules compute jobs close to data: <figure data-rehype-pretty-code-figure=""><pre tabindex="0" data-language="python" data-theme="min-light min-dark"><code data-language="python" data-theme="min-light min-dark" style="display: grid;"># Automatic data locality training_job = kled.ml.TrainingJob( script="train.py", data_locality=True # Schedule job on nodes with local data access )</code></pre></figure> <h3>3. Tiered Storage</h3> We implement tiered storage for cost optimization: <figure data-rehype-pretty-code-figure=""><pre tabindex="0" data-language="yaml" data-theme="min-light min-dark"><code data-language="yaml" data-theme="min-light min-dark" style="display: grid;"># Storage class configuration storage_tiers: hot: type: "ssd" retention_policy: "30 days" warm: type: "hdd" retention_policy: "180 days" cold: type: "archive" retrieval_time: "hours"</code></pre></figure> <h3>4. Bandwidth Throttling</h3> Prevent storage operations from interfering with training: <figure data-rehype-pretty-code-figure=""><pre tabindex="0" data-language="python" data-theme="min-light min-dark"><code data-language="python" data-theme="min-light min-dark" style="display: grid;"># Configure bandwidth limits kled.storage.configure( upload_bandwidth_limit="500MB/s", # Limit upload bandwidth download_bandwidth_limit="2GB/s" # Higher limit for downloads during training )</code></pre></figure> <h2>Benefits Realized</h2> ML teams using MinIO in Kled.io have reported significant improvements: <ol> <li>Reduced data loading time by 65%: Compared to network-attached storage</li> <li>Storage costs reduced by 40%: Compared to equivalent cloud storage</li> <li>99.999% availability: Mission-critical reliability for ML pipelines</li> <li>Up to 90% reduction in data transfer costs: By keeping data and compute together</li> <li>Simplified compliance: Built-in data governance features</li> </ol> <h2>Best Practices for ML Storage with MinIO</h2> Based on our experience, we recommend these best practices: <h3>1. Organize Data by Access Pattern</h3> Structure your buckets based on how data is accessed: <pre><code># For frequent, high-throughput access (training data) bucket: hot-training-data policy: CACHE_RECENT

For occasional access with high durability (models)

bucket: models-archive policy: VERSIONED_DURABILITY </code></pre>

<h3>2. Use Intelligent Batching</h3> Optimize for the small file problem in ML: <figure data-rehype-pretty-code-figure=""><pre tabindex="0" data-language="python" data-theme="min-light min-dark"><code data-language="python" data-theme="min-light min-dark" style="display: grid;"># Batch small files for better performance from kled.storage import FileOptimizer optimizer = FileOptimizer() optimizer.batch_small_files( bucket="raw-data", prefix="logs/", target_size="128MB", format="parquet" )</code></pre></figure> <h3>3. Implement Data Compression</h3> Use appropriate compression for different data types: <figure data-rehype-pretty-code-figure=""><pre tabindex="0" data-language="python" data-theme="min-light min-dark"><code data-language="python" data-theme="min-light min-dark" style="display: grid;"># Compress based on data type storage_client.put_object( bucket_name="processed-data", object_name="embeddings/customer_vectors.npy", data=file_data, length=length, compression="zstd" # Good for numeric data ) storage_client.put_object( bucket_name="raw-data", object_name="text/articles.json", data=file_data, length=length, compression="zlib" # Good for text data )</code></pre></figure> <h3>4. Use Metadata for Searchability</h3> Enrich your data with metadata for easier discovery: <figure data-rehype-pretty-code-figure=""><pre tabindex="0" data-language="python" data-theme="min-light min-dark"><code data-language="python" data-theme="min-light min-dark" style="display: grid;"># Add rich metadata for searchability storage_client.put_object( bucket_name="models", object_name="recommenders/collaborative_filter_v2.pkl", data=model_data, length=len(model_data), metadata={ "algorithm": "matrix_factorization", "dimensions": 128, "training_dataset": "user_interactions_jan2025", "accuracy": 0.87, "owner": "recommendation_team" } )</code></pre></figure> <h2>Ethical Considerations</h2> When implementing object storage for ML, consider these ethical aspects: <ul> <li>Data retention: Establish clear policies for how long data is kept</li> <li>Cost transparency: Make storage costs visible to encourage efficient usage</li> <li>Environmental impact: Balance performance needs with energy consumption</li> <li>Data sovereignty: Respect geographic restrictions on data storage</li> </ul> <h2>Conclusion</h2> MinIO integration in Kled.io provides ML teams with a powerful, S3-compatible object storage solution that meets the unique requirements of machine learning workflows. By offering high performance, scalability, and seamless integration with ML frameworks, MinIO enables teams to focus on model development rather than storage management challenges. Future enhancements to our MinIO integration will include: <ul> <li>Automated data quality validation during ingestion</li> <li>ML-specific data cataloging and discovery</li> <li>Enhanced dataset versioning with lineage tracking</li> <li>Intelligent caching based on usage patterns</li> </ul> As machine learning datasets continue to grow in size and complexity, robust object storage becomes increasingly essential. MinIO's high-performance architecture, combined with Kled.io's ML-specific enhancements, provides a foundation for scaling ML operations efficiently. <blockquote> "Storage isn't just about keeping data—it's about making data accessible at the speed of innovation." </blockquote> For more information on MinIO integration in Kled.io, visit our <a href="https://kled.io/docs/storage/minio">storage documentation</a>.

Your Cart

Your cart is empty

Table of Contents

High-Performance Object Storage for ML: MinIO Integration in Kled.io

Daniel Park

For occasional access with high durability (models)

Related Articles

Related: Kubernetes Best Practices

Related: CI/CD for Cloud Native Apps