clearml/clearml

Features

• MLOps Platforms - Provides a comprehensive platform for managing the end-to-end machine learning lifecycle from experimentation to production.
• GPU Resource Orchestrators - Orchestrates GPU compute across hybrid environments featuring priority scheduling and fractional resource slicing.
• AI Workflow Orchestration - Provides automated pipelines that formalize the AI lifecycle from initial development through testing.
• AI Workload Orchestration - Provides a hardware-agnostic architecture to schedule and launch training or deployment jobs across diverse compute clusters.
• Artifact Logging - Associates model checkpoints and performance metrics with specific code versions as tracked artifacts.
• Experiment Logging - Logs hyperparameters and resource utilization to ensure visibility and reproducibility of experiments.
• Experiment Tracking - Records hyperparameters, metrics, and artifacts during model training to ensure reproducibility.
• Experiment Metadata Tracking - Automatically records hyperparameters, performance metrics, and plots to ensure AI experiments are reproducible and comparable.
• GPU Resource Scaling - Manages the allocation and scaling of GPU compute resources across hybrid clouds with priority scheduling.
• Hybrid AI Orchestrators - Provides orchestration that manages the execution of AI workloads across both local and cloud-based compute environments.
• Large Language Model Serving - Provides a dedicated engine for hosting large language models and orchestrating agentic workflows via secure APIs.
• GPU Resource Management - Controls workload scheduling, fractional GPU allocation, and cloud infrastructure autoscaling.
• Fractional GPU Slicing - Enables dividing single GPU hardware into smaller units to run multiple parallel ML workloads.
• Model Lifecycle Management - Manages the end-to-end lifecycle of machine learning models from initial training through production deployment and archival.
• Serving Frameworks - Provides a deployment engine for hosting large language models with API gateways and RAG support.
• ML Asset Versioning - Implements a metadata-driven approach to track and version large datasets and model artifacts.
• ML Workflow Orchestrations - Provides automated pipelines specifically designed to formalize data preprocessing, model training, and testing.
• Model Serving Endpoints - Provides scalable endpoints with integrated monitoring and hardware optimization for production model workloads.
• Data Science Workbenches - Provides an integrated workbench for creating and iterating on AI models with built-in data integration and monitoring.
• Dataset Versioning Platforms - Provides a CLI for managing and versioning massive datasets stored on object storage or network drives.
• Metadata-Driven Dataset Versioning - Tracks datasets via metadata abstractions and pointers to avoid duplicating large files in object storage.
• Environment Reproduction - Captures language versions and dependency manifests to automatically rebuild identical execution environments on remote workers.
• GPU Allocations - Allocates specific GPU devices to worker agents to ensure compute isolation for training tasks.
• Artifact Management - Provides a programmable interface for tracking metrics and managing the lifecycle of machine learning model versions.
• AI Model Production Deployment - Launches models into production via batch processing or APIs using triggers for real-time inference.
• GenAI Application Deployment - Ships secure API deployment on clusters with built-in networking and tools for vector database creation.
• Dataset Versioning - Implements a metadata-driven system for tracking changes to structured and unstructured datasets.
• MLOps Pipeline Automation - Connects multi-stage machine learning workflows into sequences that execute across diverse compute resources.
• Pipeline Orchestration - Provides a centralized controller for coordinating sequences of tasks by defining dependencies and execution steps.
• Language Model API Deployment - Automates networking and authentication for launching secure APIs for language models on compute clusters.
• ML Workload Schedulers - Implements a centralized system for queueing tasks and managing resource allocation across containerized clusters.
• Compute Resource Orchestration - Provides a tool for allocating workloads across local and cloud infrastructure to optimize hardware utilization.
• GPU Fractional Slicing - Supports fractional GPU resource slicing to maximize hardware utilization across multiple model instances.
• Compute Task Control Planes - Implements a control plane for managing the execution of training and inference tasks across available compute resources.
• Execution Environments - Ensures tasks run using the exact language version and dependencies of the original execution to prevent failures.
• GPU Resource Allocators - Provides self-service and advanced scheduling for allocating GPU compute power and optimizing hardware throughput.
• LLM Hosting - Hosts large language models in GPU clusters with support for retrieval-augmented generation workloads.
• GPU Resource Provisioning - Automates the allocation and scaling of GPU resources across both cloud and on-premises environments.
• Remote Worker Agents - Implements agent-based remote execution by deploying worker agents to remote machines for task processing.
• Identity and Access Management - Provides role-based access control and identity integration to manage platform-wide permissions.
• Identity Provider Integrations - Integrates with external identity providers to synchronize users and enforce role-based compute access.
• Control Planes - Provides a centralized control plane to manage task queuing and resource allocation across hybrid compute clusters.
• Hyperparameter Optimizers - Provides a background service for executing hyperparameter tuning to maintain continuous operation independently of manual scripts.
• Data Preprocessing - Provides systems for preprocessing and versioning datasets to ensure development reproducibility.
• Model Performance Optimization - Offers automated tweaking of model settings to improve overall performance and prediction accuracy.
• Hyperparameter Optimization - Finds optimal hyperparameters by sampling ranges to maximize or minimize specific performance metrics.
• Background Optimization Services - Runs continuous background sampling services to find optimal model parameters independently of the main scripts.
• Managed AI Workbenches - Provides development environments with integrated infrastructure and lifecycle management for AI teams.
• Infrastructure Usage Billing - Ships real-time reporting of infrastructure compute hours and storage for billing purposes.
• Tenant Usage Aggregators - Implements consumption reporting per tenant to enable accurate multi-tenant chargebacks and invoicing.
• Versioned Data Subsetting - Provides a metadata-driven method for defining specific data slices to ensure experiment reproducibility without duplicating storage.
• Unstructured Data Management - Manages large-scale images and videos in cloud buckets using metadata to avoid file duplication.
• Project Object Querying - Implements a dynamic query system to retrieve specific tasks or models based on complex criteria and metadata tags.
• Run Comparison Tools - Provides tools to analyze the impact of hyperparameters by overlaying metrics from multiple execution runs on a single axis.
• Cloud IDE Orchestration - Orchestrates the lifecycle and launching of remote development environments and IDE containers.
• Execution Metadata Tracking - Logs runtime attributes, code versions, and system metrics for every script execution to ensure complete traceability.
• Multi-tenant Workspaces - Provisions isolated workspaces and execution queues for multiple tenants on shared hardware.
• Cloud-Instance Auto-scaling - Automatically starts and stops AWS cloud instances based on pending tasks, budgets, and timeouts.
• Automatic Compute Scaling - Automatically scales cloud compute instances based on queue load to optimize infrastructure costs.
• Remote Task Execution Modules - Implements a system for running codebases on remote machines with centralized log and metric streaming.
• Cloud Infrastructure Cost Optimization - Implements usage limits and autoscaling to reduce operational expenses by shutting down idle cloud instances.
• Multi-Environment Deployments - Supports software installation across air-gapped, on-premises, and multi-cloud environments.
• Compute Billing Systems - Provides a system for calculating usage-based chargebacks for compute, storage, and API consumption.
• AWS Provisioners - Automates the provisioning and scaling of AWS cloud infrastructure to support heavy machine learning workloads.
• Hybrid Cluster Scaling - Uses cloud spillover and autoscalers to manage hybrid clusters and minimize hardware idle time.
• Compute Cost Tracking - Monitors real-time infrastructure usage and calculates usage-based chargebacks across multiple tenants.
• Container Orchestration Management - Provides a managed gateway for orchestrating model containers onto virtual machines or clusters.
• Privacy Orchestration - Coordinates workflows using metadata abstractions to prevent unauthorized access to raw data files.
• Execution Environment Configurations - Supports specifying container images and package indexes to ensure consistent execution environments.
• GPU Resource Optimization - Increases hardware capacity through workload scheduling and fractional GPU management.
• Heterogeneous Infrastructure Deployments - Orchestrates workloads across a heterogeneous mix of Kubernetes, bare metal, and various GPU manufacturers.
• Inference Scaling Services - Ships systems for routing and scaling generative AI and model inference workloads across hardware clusters based on demand.
• Infrastructure Governance Policies - Enforces automated policies and access controls over the infrastructure used for task execution.
• Self-Service Cluster Provisioning - Provides automated allocation of compute resources for training and testing, enabling non-platform engineers to instantiate environments on-demand.
• GPU-as-a-Service Management - Provides a managed self-service offering for delivering GPU compute resources to end users.
• Shared Compute Pools - Monitors performance and controls spending through shared compute resource pooling and quota limits.
• Training Orchestrators - Integrates with cluster schedulers to deploy training jobs and parameter sweeps across multiple nodes.
• Notebook Worker Provisioning - Provides the ability to transform external notebook environments into compute workers for queued experiments.
• Metric Time-Series Logging - Supports real-time logging of numerical metrics to visualize data flow and optimize machine learning pipelines.
• Inference Endpoint Authentication - Secures model inference endpoints using identity-aware authentication and tenant-specific scoping.
• Metadata-Driven Privacy - Ensures raw files are only accessed by authorized compute resources using abstracted metadata.
• Compute Quota Management - Ensures fair sharing of infrastructure by enforcing compute quotas across different business units.
• Multi-Tenancy Access Controls - Enforces hierarchical access boundaries and data isolation across organizational teams.
• Multi-Tenant Isolation - Enforces data privacy and compute quotas by isolating networks and storage at the platform layer for multiple tenants.
• Multi-Tenant Isolation Layers - Implements security mechanisms at the platform layer to enforce data separation between tenants.
• Remote Script Execution - Provides a command line interface for running scripts on remote machines without modifying source code.
• Storage Isolation - Prevents data leakage by isolating both network traffic and storage volumes for different users.
• Training Data Iteration Tracking - Tracks iterations of training and evaluation data to ensure reproducibility across model versions.
• Distributed Complexity Abstractions - Provides a simplified interface for executing jobs on clusters without requiring direct platform access.
• AI Workflow Auditing - Maintains a comprehensive system of record for governance by logging all activity across data versioning and model training.
• Endpoint Health Monitoring - Provides a centralized observability dashboard to monitor the operational health and status of deployed inference endpoints.
• Training Progress Recording - Records scalar time series and text logs to monitor and analyze model training progress.
• System Metrics Collection - Automatically collects native system-level performance metrics to identify bottlenecks and correlate usage with performance.
• System Resource Tracking - Provides real-time monitoring of CPU and GPU hardware utilization across custom compute groups.
• Model Training Metrics - Records metrics and artifacts during training to enable comparison across different model runs.
• Pipeline Monitoring Dashboards - Ships a visual interface for tracking the real-time status and output of automated machine learning pipelines.
• System Resource Monitors - Monitors live GPU, CPU, and memory statistics during execution to analyze hardware utilization.
• Compute Resource Optimizers - Optimizes hardware efficiency through allocation policies, priority queues, and fractional GPU slicing.
• Task Monitoring - Implements a watchdog service to monitor the health and responsiveness of active background tasks.
• Usage Monitoring - Offers an administrative dashboard to track resource consumption, API limits, and operational costs across multiple tenants.
• General Machine Learning - CI/CD platform for AI workload orchestration.
• Experiment and Data Management - Automated experiment management and version control platform.

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