{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"clearml","slug":"clearml","name":"ClearML","type":"repo","url":"https://github.com/allegroai/clearml","page_url":"https://unfragile.ai/clearml","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"clearml__cap_0","uri":"capability://automation.workflow.automatic.experiment.logging.with.sdk.instrumentation","name":"automatic experiment logging with sdk instrumentation","description":"Intercepts training loops and model operations through Python SDK monkey-patching of popular frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost) to automatically capture metrics, hyperparameters, gradients, and system resources without explicit logging calls. Uses a Task object that wraps the training context and streams telemetry to a central server in real-time or batched mode.","intents":["I want to track all my training metrics without modifying my existing training code","I need to capture hyperparameters, loss curves, and resource usage automatically across different frameworks","I want to compare experiments without manually logging each metric"],"best_for":["ML engineers using PyTorch, TensorFlow, or scikit-learn who want zero-instrumentation tracking","Teams migrating from manual logging to automated experiment tracking","Researchers running many experiments and needing consistent metric capture"],"limitations":["Monkey-patching approach can conflict with other instrumentation libraries or custom training loops","Framework support is limited to officially supported libraries; custom training loops require manual Task.init() calls","Real-time streaming adds ~50-100ms overhead per metric batch depending on network latency","Does not capture custom metrics unless explicitly logged via Task.connect()"],"requires":["Python 3.7+","ClearML SDK installed (pip install clearml)","ClearML Server running (self-hosted or cloud)","Supported framework: PyTorch, TensorFlow, scikit-learn, XGBoost, or Keras"],"input_types":["Python training scripts","Framework-specific model objects","Hyperparameter dictionaries"],"output_types":["structured metrics (JSON)","time-series data","system resource logs"],"categories":["automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clearml__cap_1","uri":"capability://data.processing.analysis.dataset.versioning.and.artifact.management.with.content.addressable.storage","name":"dataset versioning and artifact management with content-addressable storage","description":"Manages training datasets as versioned artifacts using content-addressable storage (SHA256-based deduplication) with support for local, S3, GCS, and Azure Blob Storage backends. Tracks dataset lineage, splits, and statistics; enables reproducible training by pinning exact dataset versions to experiments. Integrates with the Task object to automatically associate datasets with experiment runs.","intents":["I need to version my datasets and track which version was used in each experiment","I want to deduplicate large datasets across experiments to save storage","I need to reproduce an experiment with the exact same dataset version from 6 months ago"],"best_for":["Teams with large, frequently-updated datasets who need reproducibility","Organizations using cloud storage (S3, GCS, Azure) and wanting centralized dataset management","ML pipelines requiring audit trails of data lineage"],"limitations":["Content-addressable storage requires full dataset hash computation on first upload, adding significant latency for large datasets (>100GB)","No built-in data validation or schema enforcement; relies on external tools for data quality checks","Deduplication is effective only when datasets share significant overlapping content; sparse datasets see minimal storage savings","Requires explicit Dataset object creation; does not auto-detect data files in training directories"],"requires":["ClearML SDK installed","ClearML Server with configured storage backend (local, S3, GCS, or Azure)","Cloud credentials if using remote storage (AWS_ACCESS_KEY_ID, etc.)","Sufficient disk space for local cache or cloud quota"],"input_types":["local file paths","S3/GCS/Azure URIs","pandas DataFrames","directory structures"],"output_types":["dataset version identifiers (UUID)","dataset metadata (JSON)","lineage graphs"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clearml__cap_10","uri":"capability://automation.workflow.integration.with.git.repositories.for.code.versioning.and.reproducibility","name":"integration with git repositories for code versioning and reproducibility","description":"Automatically captures Git repository state (commit hash, branch, uncommitted changes) when a task is initialized, enabling reproducible training by pinning exact code versions. Supports cloning code from Git repositories on remote agents, with automatic dependency installation from requirements.txt or setup.py. Integrates with GitHub, GitLab, and Bitbucket.","intents":["I want to ensure my training is reproducible by capturing the exact code version used","I need to run training code from a Git repository on a remote machine without manual setup","I want to track which code changes led to improvements in model performance"],"best_for":["Teams using Git for code versioning and wanting reproducible training","Organizations running training on remote agents and needing automatic code deployment","ML engineers correlating code changes with model performance"],"limitations":["Uncommitted changes are captured as diffs; large diffs can bloat experiment metadata","Dependency installation from requirements.txt is not validated; missing or incompatible dependencies cause runtime failures","Git integration requires network access to repository; private repositories need credentials configured on agents","No support for monorepos; assumes single Python package per repository"],"requires":["ClearML SDK installed","Git installed on local and remote machines","Git repository URL (public or with credentials configured)","requirements.txt or setup.py for dependency specification"],"input_types":["Git repository URL","branch or commit hash"],"output_types":["captured Git state (commit hash, branch, diffs)","installed dependencies","code reproducibility metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clearml__cap_11","uri":"capability://data.processing.analysis.metric.and.scalar.logging.with.real.time.streaming.and.aggregation","name":"metric and scalar logging with real-time streaming and aggregation","description":"Provides a flexible API for logging scalar metrics (loss, accuracy, F1 score) and custom scalars with support for multiple series per metric, hierarchical metric organization, and real-time streaming to the server. Metrics are buffered locally and sent in batches to reduce network overhead. Supports custom aggregation functions for combining metrics across distributed training ranks.","intents":["I want to log training loss, validation accuracy, and custom metrics in a structured way","I need to track multiple series (e.g., loss per layer) within a single metric","I want to aggregate metrics across distributed training ranks and see global statistics"],"best_for":["ML engineers logging custom metrics beyond framework auto-logging","Teams tracking multiple metric series and needing hierarchical organization","Researchers aggregating metrics across distributed training"],"limitations":["Metric buffering adds latency; real-time visualization has ~5-10 second delay","Custom aggregation functions are user-defined; no built-in statistical aggregations","Metric naming is unstructured; no schema validation, leading to inconsistent metric names across experiments","Scalar logging does not support multi-dimensional metrics; requires flattening to 1D"],"requires":["ClearML SDK installed","ClearML Server running","Task initialized with Task.init()"],"input_types":["scalar values (float, int)","metric names (strings)","series names (strings)"],"output_types":["time-series metric data","aggregated statistics","metric visualizations"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clearml__cap_12","uri":"capability://data.processing.analysis.configuration.management.with.parameter.tracking.and.override","name":"configuration management with parameter tracking and override","description":"Captures training configurations (hyperparameters, model architecture, data paths) as structured metadata linked to experiments. Supports YAML/JSON configuration files, command-line argument parsing, and programmatic parameter setting via the Task API. Enables parameter overrides at execution time without modifying code, with automatic diff tracking between experiment configurations.","intents":["I want to track all hyperparameters used in each experiment for reproducibility","I need to override hyperparameters when cloning experiments without modifying code","I want to see what configuration changes led to performance improvements"],"best_for":["ML engineers managing complex hyperparameter configurations","Teams running many experiments with varying parameters","Researchers comparing configurations across experiments"],"limitations":["Configuration parsing is manual; no automatic detection of hyperparameters from code","Parameter types are not validated; type mismatches cause runtime errors","Configuration diffs are text-based; no semantic understanding of parameter relationships","No built-in support for configuration inheritance or templating"],"requires":["ClearML SDK installed","Configuration files (YAML/JSON) or programmatic parameter setting"],"input_types":["YAML/JSON configuration files","command-line arguments","Python dictionaries"],"output_types":["structured configuration metadata","configuration diffs","parameter override specifications"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clearml__cap_13","uri":"capability://search.retrieval.experiment.search.and.filtering.by.metadata","name":"experiment search and filtering by metadata","description":"Enables querying experiments via flexible filtering on tags, hyperparameters, metrics, date range, and custom metadata. Supports full-text search on experiment names and descriptions. Results can be sorted by metric values (e.g., best validation accuracy) and aggregated (e.g., average metric across runs). Filtering is performed server-side for scalability. Saved filters can be bookmarked for repeated use.","intents":["I want to find all experiments with a specific tag or hyperparameter value","I need to search for experiments by name or description","I want to sort experiments by metric value to find the best model"],"best_for":["data scientists managing large experiment collections (>100 runs)","teams needing to find experiments by metadata without manual browsing","researchers comparing algorithm variants across many runs"],"limitations":["Search is exact-match for most fields; no fuzzy matching or regex support","Filtering on custom metrics requires metric name to be known; no metric discovery","Saved filters are user-specific; no shared filter templates across teams","Large result sets (>1000 experiments) may cause UI slowness"],"requires":["ClearML Server with indexed experiments","Web UI or Python API for querying"],"input_types":["filter criteria (tags, hyperparameters, metrics, date range)","sort key (metric name or date)"],"output_types":["filtered experiment list","experiment metadata (hyperparameters, metrics, tags)"],"categories":["search-retrieval","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clearml__cap_2","uri":"capability://automation.workflow.remote.task.execution.with.resource.allocation.and.queue.management","name":"remote task execution with resource allocation and queue management","description":"Distributes training tasks across a pool of worker machines (agents) using a queue-based dispatch system. Tasks are enqueued with resource requirements (GPU count, memory, CPU cores); agents poll queues and execute tasks in isolated environments with automatic dependency resolution and artifact staging. Supports dynamic resource allocation, priority queuing, and task preemption.","intents":["I want to run multiple training jobs on different machines without manually SSHing into each one","I need to allocate specific GPU counts to different experiments and queue them fairly","I want to scale training across a cluster without managing job submission scripts"],"best_for":["Teams with heterogeneous compute resources (multiple GPUs, CPU-only machines) needing fair scheduling","Organizations running many parallel experiments and wanting centralized job management","ML engineers building CI/CD pipelines for model training"],"limitations":["Queue-based dispatch adds latency (typically 1-5 seconds) between task submission and execution start","No built-in support for distributed training across multiple agents; each task runs on a single agent","Task preemption is not graceful; requires manual checkpoint management to resume interrupted tasks","Agent registration and health-checking relies on polling; no push-based notifications, leading to stale agent states"],"requires":["ClearML Server running","ClearML Agent installed on worker machines (pip install clearml-agent)","Network connectivity between server and agents","Shared storage or artifact server for task dependencies and outputs","Python 3.7+ on all worker machines"],"input_types":["Task objects with code and hyperparameters","resource requirement specifications","queue names"],"output_types":["task execution logs","resource utilization metrics","task status updates"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clearml__cap_3","uri":"capability://automation.workflow.pipeline.orchestration.with.dag.based.task.dependencies","name":"pipeline orchestration with dag-based task dependencies","description":"Defines machine learning workflows as directed acyclic graphs (DAGs) where nodes represent tasks (training, evaluation, preprocessing) and edges represent data/artifact dependencies. Pipelines are defined via Python API or YAML, executed sequentially or in parallel based on dependency graph, with automatic artifact passing between stages and centralized monitoring of pipeline runs.","intents":["I want to define a multi-stage ML workflow (preprocess → train → evaluate → deploy) and run it end-to-end","I need to parallelize independent training jobs and wait for all to complete before evaluation","I want to rerun only the failed stages of a pipeline without re-executing successful ones"],"best_for":["Teams building production ML pipelines with multiple sequential stages","Organizations needing reproducible, auditable workflows with clear dependency tracking","ML engineers automating model retraining and evaluation cycles"],"limitations":["DAG execution is synchronous; no built-in support for asynchronous task completion or event-driven triggers","Partial pipeline reruns require manual specification of which stages to skip; no automatic detection of unchanged inputs","Pipeline definitions are tightly coupled to ClearML Task objects; migrating to other orchestrators requires rewriting","No built-in support for conditional branching or dynamic DAG generation based on runtime values"],"requires":["ClearML SDK installed","ClearML Server running","Python 3.7+","All tasks in the pipeline must be ClearML Tasks"],"input_types":["Python code defining pipeline stages","YAML pipeline definitions","Task objects"],"output_types":["pipeline execution logs","stage-by-stage metrics","artifact lineage"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clearml__cap_4","uri":"capability://automation.workflow.model.serving.and.inference.deployment.with.version.management","name":"model serving and inference deployment with version management","description":"Packages trained models with their preprocessing logic, dependencies, and metadata into versioned artifacts that can be deployed to inference endpoints. Supports multiple serving backends (TensorFlow Serving, Triton, custom HTTP endpoints) with automatic model version management, A/B testing support, and rollback capabilities. Models are registered in a central model registry linked to training experiments.","intents":["I want to deploy my trained model to production with automatic versioning and rollback","I need to run A/B tests comparing two model versions in production","I want to track which training experiment produced each deployed model"],"best_for":["ML teams deploying models to production and needing version control and rollback","Organizations running A/B tests and needing model version management","Teams wanting to link deployed models back to training experiments for reproducibility"],"limitations":["Model serving backends are not managed by ClearML; requires separate deployment infrastructure (Kubernetes, Docker, etc.)","No built-in support for model compression, quantization, or optimization; requires external tools","A/B testing is manual; no built-in traffic splitting or statistical significance testing","Model registry is tightly coupled to ClearML Server; no federation or multi-region support"],"requires":["ClearML SDK installed","ClearML Server running","Trained model artifact stored in ClearML artifact storage","Inference serving infrastructure (TensorFlow Serving, Triton, or custom endpoint)"],"input_types":["trained model files (PyTorch, TensorFlow, ONNX, scikit-learn)","model metadata (framework, input/output schemas)","preprocessing code"],"output_types":["model version identifiers","deployment configurations","inference logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clearml__cap_5","uri":"capability://planning.reasoning.hyperparameter.optimization.with.multi.strategy.search","name":"hyperparameter optimization with multi-strategy search","description":"Automates hyperparameter tuning by spawning multiple training tasks with different hyperparameter combinations using strategies like grid search, random search, Bayesian optimization, and population-based training. Each trial is executed as a separate ClearML Task, with results aggregated and visualized. Supports early stopping based on validation metrics and dynamic resource allocation per trial.","intents":["I want to automatically search for the best hyperparameters without manually creating many training jobs","I need to run Bayesian optimization to efficiently explore the hyperparameter space","I want to compare different hyperparameter search strategies and see which finds better models faster"],"best_for":["ML engineers tuning models and wanting to automate hyperparameter search","Teams with compute budgets wanting efficient exploration of hyperparameter spaces","Researchers comparing different optimization strategies"],"limitations":["Bayesian optimization requires careful prior specification; poor priors can lead to suboptimal search","Early stopping is based on single metrics; no multi-objective optimization support","Search space definition is manual; no automatic detection of hyperparameter types or ranges","Population-based training requires careful population size tuning; too small populations converge prematurely"],"requires":["ClearML SDK installed","ClearML Server running","ClearML Agents available for distributed trial execution","Hyperparameter search space defined (ranges, types)"],"input_types":["training script with hyperparameter placeholders","search space definitions (grid, random, Bayesian)","optimization objectives (metric to maximize/minimize)"],"output_types":["hyperparameter combinations tested","trial results and metrics","best hyperparameters found"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clearml__cap_6","uri":"capability://search.retrieval.web.based.experiment.comparison.and.visualization.dashboard","name":"web-based experiment comparison and visualization dashboard","description":"Provides a centralized web UI for browsing, filtering, and comparing experiments across multiple dimensions (metrics, hyperparameters, resource usage, execution time). Supports interactive plotting of metric curves, parallel coordinates plots for hyperparameter analysis, and side-by-side comparison of experiment configurations. Dashboards are customizable and shareable.","intents":["I want to visually compare metrics across 50 experiments to identify the best model","I need to understand how hyperparameters correlate with model performance","I want to share experiment results with my team without giving them direct server access"],"best_for":["ML teams collaborating on model development and needing shared visibility","Researchers analyzing experiment results and identifying patterns","Non-technical stakeholders reviewing model performance"],"limitations":["Dashboard performance degrades with >1000 experiments in a single view; requires filtering or pagination","Custom metric visualization requires manual dashboard configuration; no auto-generated plots","Sharing dashboards requires server-side access; no read-only public links","Real-time metric updates have ~5-10 second latency due to polling-based refresh"],"requires":["ClearML Server running with web UI enabled","Web browser with JavaScript support","Experiments logged to ClearML Server"],"input_types":["experiment metrics and metadata from ClearML Server"],"output_types":["interactive plots and visualizations","comparison tables","shareable dashboard URLs"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clearml__cap_7","uri":"capability://automation.workflow.distributed.training.support.with.multi.gpu.and.multi.node.coordination","name":"distributed training support with multi-gpu and multi-node coordination","description":"Enables distributed training across multiple GPUs and nodes by automatically detecting and configuring distributed training frameworks (PyTorch DistributedDataParallel, TensorFlow distributed strategies). Handles rank assignment, process group initialization, and gradient synchronization without explicit user code. Integrates with the Task context to track per-rank metrics and resource utilization.","intents":["I want to scale my PyTorch training from single GPU to 8 GPUs without rewriting my code","I need to train on multiple nodes and automatically coordinate gradient synchronization","I want to track per-GPU metrics and identify bottlenecks in distributed training"],"best_for":["ML engineers training large models requiring multi-GPU or multi-node setups","Teams using PyTorch or TensorFlow and wanting automatic distributed training setup","Researchers debugging distributed training performance issues"],"limitations":["Automatic rank assignment assumes homogeneous hardware; heterogeneous clusters require manual configuration","Does not optimize communication patterns; relies on framework defaults which may be suboptimal for specific topologies","Per-rank metric aggregation adds ~5-10% overhead due to additional logging and synchronization","Requires all nodes to have identical Python environments and dependencies"],"requires":["ClearML SDK installed","PyTorch or TensorFlow installed","Multi-GPU or multi-node cluster with network connectivity","Shared storage or artifact server for distributed checkpointing"],"input_types":["distributed training scripts (PyTorch DDP or TensorFlow distributed)","cluster configuration (node list, GPU count per node)"],"output_types":["per-rank metrics and logs","distributed training performance profiles","gradient synchronization logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clearml__cap_8","uri":"capability://data.processing.analysis.artifact.storage.and.retrieval.with.multi.backend.support","name":"artifact storage and retrieval with multi-backend support","description":"Manages training artifacts (models, checkpoints, datasets, logs) with pluggable storage backends (local filesystem, S3, GCS, Azure Blob Storage). Artifacts are automatically versioned and linked to experiments via the Task context. Supports streaming large artifacts without loading into memory, with built-in compression and deduplication.","intents":["I want to store model checkpoints in S3 without writing custom upload code","I need to retrieve a specific model version from an experiment run 3 months ago","I want to deduplicate large artifacts across experiments to save storage costs"],"best_for":["Teams using cloud storage (S3, GCS, Azure) and wanting centralized artifact management","Organizations with large models and checkpoints needing efficient storage","ML pipelines requiring artifact lineage and versioning"],"limitations":["Artifact retrieval requires network I/O; local caching is not automatic, leading to repeated downloads","Compression is applied uniformly; no support for selective compression based on artifact type","Deduplication is content-based; requires full artifact hash computation, adding latency on first upload","No built-in garbage collection; old artifact versions accumulate unless manually deleted"],"requires":["ClearML SDK installed","ClearML Server with configured storage backend","Cloud credentials if using remote storage (AWS_ACCESS_KEY_ID, etc.)","Network connectivity to storage backend"],"input_types":["local file paths","file-like objects (BytesIO, file handles)","directories"],"output_types":["artifact URIs","artifact metadata (size, hash, timestamp)","artifact content (streamed or buffered)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clearml__cap_9","uri":"capability://automation.workflow.experiment.cloning.and.parameter.override.for.iterative.development","name":"experiment cloning and parameter override for iterative development","description":"Enables cloning existing experiments with selective parameter overrides, allowing developers to quickly iterate on hyperparameters or code without manually recreating experiment configurations. Cloned experiments inherit the parent's code, dependencies, and artifacts, with only specified parameters changed. Maintains lineage between parent and cloned experiments.","intents":["I want to run a variant of a previous experiment with slightly different hyperparameters","I need to quickly test a code change without losing the original experiment configuration","I want to see how changing one hyperparameter affects the model while keeping everything else constant"],"best_for":["ML engineers iterating on models and wanting fast experiment variants","Teams doing ablation studies and needing to systematically vary parameters","Researchers exploring hyperparameter sensitivity"],"limitations":["Cloning does not automatically detect which parameters changed; requires manual specification","Cloned experiments inherit code from parent; changes to the original code do not propagate to clones","Lineage tracking is one-way (parent → clone); no automatic detection of related experiments","No built-in support for batch cloning; creating many variants requires multiple API calls"],"requires":["ClearML SDK installed","ClearML Server running","Parent experiment already logged to ClearML"],"input_types":["parent experiment ID","parameter overrides (dictionary)"],"output_types":["cloned experiment ID","experiment configuration (code, hyperparameters, dependencies)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clearml__headline","uri":"capability://tool.use.integration.mlops.platform.for.experiment.tracking.and.model.management","name":"mlops platform for experiment tracking and model management","description":"ClearML is an open-source MLOps platform that simplifies experiment tracking, data management, pipeline orchestration, and model serving, making it ideal for teams looking to streamline their machine learning workflows.","intents":["best MLOps platform","MLOps for experiment tracking","open-source model management tools","MLOps solutions for data versioning","best tools for pipeline orchestration"],"best_for":["data scientists","ML engineers"],"limitations":["requires some setup for self-hosting"],"requires":["Python environment"],"input_types":["datasets","model configurations"],"output_types":["tracked experiments","orchestrated pipelines"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","ClearML SDK installed (pip install clearml)","ClearML Server running (self-hosted or cloud)","Supported framework: PyTorch, TensorFlow, scikit-learn, XGBoost, or Keras","ClearML SDK installed","ClearML Server with configured storage backend (local, S3, GCS, or Azure)","Cloud credentials if using remote storage (AWS_ACCESS_KEY_ID, etc.)","Sufficient disk space for local cache or cloud quota","Git installed on local and remote machines","Git repository URL (public or with credentials configured)"],"failure_modes":["Monkey-patching approach can conflict with other instrumentation libraries or custom training loops","Framework support is limited to officially supported libraries; 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