{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"determined-ai","slug":"determined-ai","name":"Determined AI","type":"repo","url":"https://github.com/determined-ai/determined","page_url":"https://unfragile.ai/determined-ai","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"determined-ai__cap_0","uri":"capability://automation.workflow.distributed.pytorch.training.with.automatic.gradient.synchronization","name":"distributed pytorch training with automatic gradient synchronization","description":"Enables multi-GPU and multi-node PyTorch training through a custom trial harness that wraps the standard PyTorch training loop. The system intercepts the training process via the PyTorchTrial base class, automatically handles distributed data loading, gradient aggregation across nodes, and checkpoint management without requiring users to manually implement DistributedDataParallel or write boilerplate synchronization code. Integration points include custom callbacks, learning rate schedulers, and context managers that inject distributed training logic transparently.","intents":["Scale PyTorch models across multiple GPUs without rewriting training code","Train large models that don't fit on a single GPU by distributing batches across nodes","Automatically synchronize gradients and checkpoints across a cluster"],"best_for":["ML teams training large PyTorch models on shared GPU clusters","Researchers scaling experiments from single-GPU prototypes to multi-node training"],"limitations":["Requires inheriting from PyTorchTrial base class — not compatible with arbitrary PyTorch scripts without refactoring","Distributed training overhead adds ~5-15% latency per synchronization step depending on network bandwidth","Limited to PyTorch; TensorFlow/Keras support exists but via separate harness implementation"],"requires":["Python 3.7+","PyTorch 1.9+","Determined master service running and accessible","Network connectivity between compute nodes for gradient synchronization"],"input_types":["Python training code inheriting from PyTorchTrial","YAML experiment configuration specifying distributed settings"],"output_types":["Trained model checkpoints","Distributed training metrics and logs"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"determined-ai__cap_1","uri":"capability://planning.reasoning.hyperparameter.search.with.multiple.algorithm.backends","name":"hyperparameter search with multiple algorithm backends","description":"Implements a pluggable hyperparameter optimization framework that supports grid search, random search, Bayesian optimization, and population-based training (PBT). The system decomposes the search space into a configuration schema, spawns multiple trials with different hyperparameter combinations, and uses a search algorithm backend to generate the next set of hyperparameters based on trial results. The master service orchestrates trial scheduling and metric collection, feeding results back to the search algorithm via a standardized interface.","intents":["Automatically explore hyperparameter spaces to find optimal configurations","Run multiple training trials in parallel with different hyperparameter combinations","Use advanced search algorithms like Bayesian optimization instead of manual grid search"],"best_for":["ML practitioners tuning model hyperparameters across large search spaces","Teams with access to shared GPU clusters who want to parallelize search"],"limitations":["Search algorithm backends are pluggable but limited to built-in implementations (grid, random, Bayesian, PBT) — custom algorithms require code changes","Bayesian optimization assumes continuous/categorical hyperparameters; mixed discrete-continuous spaces require manual binning","Early stopping policies are trial-level only; no cross-trial early stopping (e.g., stopping entire search if no improvement)"],"requires":["Determined master service with PostgreSQL backend for trial state persistence","YAML experiment configuration with hyperparameter search specification","Sufficient cluster resources to run multiple trials concurrently"],"input_types":["YAML experiment config with search space definition","Trial metrics (loss, accuracy, etc.) reported during training"],"output_types":["Ranked list of hyperparameter configurations by performance","Trial metadata and metrics for each configuration tested"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"determined-ai__cap_10","uri":"capability://data.processing.analysis.metric.collection.and.real.time.streaming.to.master.service","name":"metric collection and real-time streaming to master service","description":"Provides a metrics collection API that training code can use to report metrics (loss, accuracy, custom metrics) during training. Metrics are streamed to the master service in real-time via gRPC, enabling live monitoring and early stopping decisions. The system supports both scalar metrics and structured metrics (e.g., confusion matrices), and automatically aggregates metrics across distributed trials. Metrics are persisted to PostgreSQL and can be queried via the API or visualized in the web UI.","intents":["Report training metrics in real-time without manual logging","Enable early stopping based on validation metrics","Visualize metric trends across trials and experiments"],"best_for":["ML practitioners wanting to monitor training progress in real-time","Teams using early stopping to save compute resources"],"limitations":["Metric streaming adds ~1-5ms latency per metric report; high-frequency metric reporting can impact training performance","Metric aggregation across distributed trials is limited to simple operations (mean, max, min); no custom aggregation functions","Metric persistence is limited to scalar values; structured metrics (e.g., confusion matrices) are stored as JSON blobs without indexing"],"requires":["Training code using Determined trial harness (PyTorch or Keras)","Network connectivity to master service for metric streaming"],"input_types":["Scalar metrics (loss, accuracy, etc.) reported from training code","Structured metrics (JSON)"],"output_types":["Persisted metrics in PostgreSQL","Real-time metric streams via gRPC"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"determined-ai__cap_11","uri":"capability://planning.reasoning.early.stopping.with.configurable.stopping.policies","name":"early stopping with configurable stopping policies","description":"Provides a pluggable early stopping framework that monitors trial metrics and stops trials that are unlikely to improve. The system supports multiple stopping policies (e.g., no improvement for N steps, metric threshold, PBT-based stopping) that can be configured in the experiment YAML. The master service evaluates stopping conditions after each metric report and sends a stop signal to the trial if conditions are met. Early stopping decisions are logged and can be reviewed in the web UI.","intents":["Automatically stop poorly-performing trials to save compute resources","Implement sophisticated stopping strategies (e.g., PBT-based stopping) without custom code","Reduce total training time by stopping trials early"],"best_for":["Teams running hyperparameter searches with limited compute budgets","Researchers wanting to implement advanced stopping strategies"],"limitations":["Stopping policies are evaluated at metric report time; no support for continuous monitoring between reports","Custom stopping policies require code changes to the master service; no plugin system for user-defined policies","Early stopping decisions are irreversible; no support for resuming stopped trials"],"requires":["Experiment configuration with early stopping policy specified","Metrics being reported during training"],"input_types":["Stopping policy configuration (YAML)","Trial metrics"],"output_types":["Stop signal sent to trial","Early stopping decision logs"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"determined-ai__cap_12","uri":"capability://automation.workflow.notebook.and.command.execution.environment.with.gpu.access","name":"notebook and command execution environment with gpu access","description":"Provides an interactive notebook and command execution environment that runs on the cluster with GPU access. Users can launch Jupyter notebooks or shell commands that are scheduled as tasks on the cluster, with resource allocation managed by the same scheduler as training jobs. Notebooks and commands have access to the Determined Python SDK, enabling programmatic experiment submission and result analysis. Output (notebooks, logs) is persisted and accessible via the web UI.","intents":["Run interactive analysis and data exploration on cluster GPUs","Develop and test training code before submitting full experiments","Analyze experiment results and visualize metrics in notebooks"],"best_for":["Data scientists wanting to use Jupyter notebooks on shared GPU clusters","Teams needing interactive development environments with GPU access"],"limitations":["Notebook state is not persisted across restarts; users must save notebooks manually","Interactive debugging is limited; no support for interactive debuggers (pdb, ipdb) in notebook environment","Notebook execution is single-threaded; no support for parallel execution within a notebook"],"requires":["Determined master service running","Sufficient cluster resources (GPU, memory) for notebook execution"],"input_types":["Jupyter notebook files (.ipynb)","Shell commands"],"output_types":["Executed notebook with outputs","Command logs and output"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"determined-ai__cap_13","uri":"capability://memory.knowledge.model.registry.and.checkpoint.versioning.with.metadata.tracking","name":"model registry and checkpoint versioning with metadata tracking","description":"Provides a model registry that tracks trained model checkpoints, their performance metrics, and associated metadata (training configuration, hyperparameters, etc.). Checkpoints can be tagged with semantic versions or custom labels, and the registry maintains a history of all versions. The system supports querying the registry to find best-performing models, comparing model versions, and downloading checkpoints for deployment. Integration with the web UI enables browsing and managing models without CLI commands.","intents":["Track and version trained models across experiments","Find and retrieve best-performing models for deployment","Compare model versions and their performance metrics"],"best_for":["ML teams managing multiple model versions and deployments","Organizations needing model lineage and reproducibility tracking"],"limitations":["Model registry is tightly coupled to Determined experiments; external models cannot be registered","Versioning is manual; no automatic semantic versioning based on performance improvements","Registry queries are limited to metadata; no support for model-level queries (e.g., find models with specific architecture)"],"requires":["Determined master service with PostgreSQL backend","Trained model checkpoints from Determined experiments"],"input_types":["Checkpoint metadata from experiments","Custom tags and labels"],"output_types":["Model registry entries with version history","Checkpoint files for download"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"determined-ai__cap_2","uri":"capability://automation.workflow.intelligent.gpu.cluster.resource.allocation.and.scheduling","name":"intelligent gpu cluster resource allocation and scheduling","description":"Manages GPU and CPU resources across a cluster using a two-tier scheduling system: the master service maintains a global resource pool view and uses a pluggable resource manager (agent-based or Kubernetes-native) to allocate resources to tasks. The allocation service implements fairness policies (round-robin, priority queues) and bin-packing algorithms to maximize cluster utilization. Tasks (trials, notebooks, commands) are assigned to resource pools, and the scheduler respects constraints like GPU type, memory requirements, and node affinity. Integration with Kubernetes enables dynamic scaling and native resource quotas.","intents":["Fairly share GPU resources across multiple users and experiments","Automatically schedule training jobs to minimize idle GPU time","Enforce resource quotas and priority policies across a team"],"best_for":["Teams managing shared GPU clusters with multiple users","Organizations needing fair resource allocation and utilization tracking"],"limitations":["Agent-based resource manager requires manual setup and maintenance of agent processes on each node; Kubernetes mode is more automated but adds Kubernetes operational overhead","Scheduling decisions are made at task submission time; no dynamic rebalancing if a task's resource needs change mid-execution","Resource fragmentation can occur if tasks request specific GPU types (e.g., A100 only) — no automatic fallback to alternative GPU types"],"requires":["Determined master service running","Either: Kubernetes cluster 1.18+ with Helm, OR agent processes running on each compute node","PostgreSQL database for resource state persistence"],"input_types":["Task specifications with resource requirements (GPU count, GPU type, memory)","Resource pool configuration (available GPUs, CPU cores, memory per node)"],"output_types":["Task allocation decisions (which node/GPU to run on)","Resource utilization metrics and cluster status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"determined-ai__cap_3","uri":"capability://automation.workflow.experiment.lifecycle.management.with.checkpoint.persistence.and.recovery","name":"experiment lifecycle management with checkpoint persistence and recovery","description":"Provides a state machine-based experiment lifecycle that tracks trials from creation through completion, with automatic checkpoint saving at configurable intervals. The system persists experiment metadata, trial state, and model checkpoints to PostgreSQL and cloud storage (S3, GCS, etc.). On failure, the master service can restore experiments from the last checkpoint and resume training without losing progress. The checkpoint garbage collection service automatically prunes old checkpoints based on retention policies, freeing storage while preserving the best-performing models.","intents":["Resume training from the last checkpoint if a trial crashes or is interrupted","Persist trained models and experiment metadata for later analysis and deployment","Automatically clean up old checkpoints to manage storage costs"],"best_for":["Teams running long-running training jobs that may be interrupted","Organizations needing to archive and reproduce experiments"],"limitations":["Checkpoint format is framework-specific (PyTorch .pt, TensorFlow SavedModel); no universal checkpoint format across frameworks","Checkpoint restoration requires the same code version and dependencies as the original training run; no automatic migration for code changes","Garbage collection policies are coarse-grained (e.g., 'keep best N checkpoints'); no fine-grained policies based on model performance thresholds"],"requires":["PostgreSQL database for experiment and trial state","Cloud storage backend (S3, GCS, Azure Blob) or local filesystem for checkpoint storage","Sufficient storage capacity for model checkpoints (can be large for large models)"],"input_types":["Experiment configuration with checkpoint interval settings","Model state and optimizer state from training loop"],"output_types":["Checkpoint files (PyTorch .pt, TensorFlow SavedModel, etc.)","Experiment metadata and trial history"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"determined-ai__cap_4","uri":"capability://automation.workflow.experiment.configuration.and.yaml.based.declarative.training.specification","name":"experiment configuration and yaml-based declarative training specification","description":"Provides a YAML-based configuration language for declaring experiments, including model architecture, training hyperparameters, distributed settings, and resource requirements. The configuration is parsed by the master service and used to instantiate trials with the specified settings. The system supports configuration inheritance, environment variable substitution, and validation against a schema. Configuration changes trigger experiment re-runs without requiring code changes, enabling reproducible experimentation and easy sharing of experiment definitions.","intents":["Declare experiments in a human-readable format without writing boilerplate code","Share experiment configurations across teams and reproduce results","Modify hyperparameters and training settings without changing code"],"best_for":["ML teams wanting to version-control and share experiment definitions","Researchers reproducing published results from configuration files"],"limitations":["YAML schema is Determined-specific; configurations are not portable to other platforms without translation","Complex conditional logic in configurations requires YAML templating or environment variables; no native support for programmatic configuration generation","Configuration validation happens at experiment submission time; errors are reported after the fact rather than during editing"],"requires":["YAML file with valid Determined experiment schema","Determined CLI or web UI to submit configuration"],"input_types":["YAML experiment configuration file"],"output_types":["Parsed experiment specification used to instantiate trials"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"determined-ai__cap_5","uri":"capability://automation.workflow.web.ui.for.experiment.monitoring.and.interactive.task.management","name":"web ui for experiment monitoring and interactive task management","description":"Provides a React-based web interface for visualizing experiment progress, trial metrics, and cluster status in real-time. The UI connects to the master service via REST and gRPC APIs, streaming metric updates and task status changes. Users can interactively pause/resume/kill trials, adjust resource allocations, and view detailed logs and checkpoint metadata. The UI includes dashboards for comparing trial performance, visualizing hyperparameter importance, and tracking resource utilization across the cluster.","intents":["Monitor training progress and metrics in real-time without command-line tools","Compare multiple trials to identify best-performing hyperparameter configurations","Interactively manage tasks (pause, resume, kill) from a web browser"],"best_for":["ML practitioners who prefer graphical interfaces over CLI","Teams wanting to share experiment progress with non-technical stakeholders"],"limitations":["Real-time metric streaming is limited by API polling frequency; no true WebSocket support for sub-second updates","Visualization capabilities are built-in but limited; no support for custom visualization plugins","UI state is not persisted; refreshing the page resets filters and view settings"],"requires":["Determined master service running and accessible over network","Modern web browser (Chrome, Firefox, Safari, Edge)","Network connectivity to master service API"],"input_types":["Experiment and trial data from master service API"],"output_types":["Real-time visualizations of metrics, logs, and cluster status"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"determined-ai__cap_6","uri":"capability://tool.use.integration.rest.and.grpc.api.for.programmatic.cluster.access.and.automation","name":"rest and grpc api for programmatic cluster access and automation","description":"Exposes the master service functionality via dual REST and gRPC APIs, enabling programmatic access to experiments, trials, and cluster resources. The APIs are auto-generated from Protocol Buffer definitions, ensuring consistency between REST and gRPC interfaces. Clients can submit experiments, query trial status, retrieve metrics, and manage resources without using the CLI or web UI. The API supports streaming responses for real-time metric updates and includes authentication via API tokens.","intents":["Integrate Determined with external ML pipelines and automation tools","Build custom dashboards and monitoring systems on top of Determined APIs","Programmatically submit and manage experiments from scripts or notebooks"],"best_for":["DevOps teams integrating Determined into CI/CD pipelines","Researchers building custom analysis tools on top of Determined data"],"limitations":["API versioning is manual; breaking changes require major version bumps and client updates","Rate limiting is not enforced; high-frequency API calls can overload the master service","Streaming responses are limited to gRPC; REST API uses polling for real-time updates"],"requires":["Determined master service running and accessible over network","API token for authentication (obtained from web UI or CLI)","gRPC client library (Go, Python, etc.) or HTTP client for REST"],"input_types":["Experiment configuration (JSON/YAML)","Query parameters for filtering trials/experiments"],"output_types":["Experiment and trial metadata (JSON)","Streaming metric updates (gRPC)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"determined-ai__cap_7","uri":"capability://tool.use.integration.cli.tool.for.experiment.submission.and.cluster.interaction","name":"cli tool for experiment submission and cluster interaction","description":"Provides a command-line interface for submitting experiments, querying cluster status, managing tasks, and retrieving results. The CLI communicates with the master service via REST/gRPC APIs and supports both interactive and scripted workflows. Commands include experiment submission, trial inspection, checkpoint download, and resource pool management. The CLI supports configuration file loading, environment variable substitution, and output formatting (JSON, table, etc.) for integration with shell scripts and automation tools.","intents":["Submit experiments and manage training jobs from the command line","Query cluster status and resource utilization without opening a web browser","Integrate Determined into shell scripts and CI/CD pipelines"],"best_for":["ML engineers comfortable with command-line tools","DevOps teams automating experiment submission in CI/CD pipelines"],"limitations":["CLI output formatting is limited to JSON and table formats; no custom output templates","Interactive commands (e.g., streaming logs) require terminal support; limited functionality in non-interactive environments","CLI version must match master service version; incompatible versions can cause API errors"],"requires":["Determined CLI installed (Python package or binary)","Network connectivity to master service","API token for authentication (stored in ~/.determined/config.yaml)"],"input_types":["YAML experiment configuration files","Command-line arguments and flags"],"output_types":["Experiment submission confirmation","Trial status and metrics (JSON or table format)","Downloaded checkpoint files"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"determined-ai__cap_8","uri":"capability://automation.workflow.kubernetes.native.deployment.with.helm.charts.and.dynamic.scaling","name":"kubernetes-native deployment with helm charts and dynamic scaling","description":"Provides Helm charts and Kubernetes manifests for deploying Determined on Kubernetes clusters, with native integration for resource quotas, pod scheduling, and dynamic scaling. The master service runs as a Kubernetes deployment, and worker tasks are scheduled as Kubernetes pods with resource requests/limits. The system supports multiple resource pools mapped to Kubernetes namespaces or node selectors, enabling multi-tenant deployments. Horizontal pod autoscaling can be configured to scale worker pods based on cluster load.","intents":["Deploy Determined on existing Kubernetes clusters without manual setup","Leverage Kubernetes resource management and multi-tenancy features","Automatically scale worker pods based on training job demand"],"best_for":["Organizations already running Kubernetes and wanting to add ML training capabilities","Teams needing multi-tenant ML clusters with resource isolation"],"limitations":["Kubernetes deployment adds operational complexity; requires Kubernetes expertise to troubleshoot","Pod scheduling overhead adds ~2-5 seconds per task startup compared to agent-based deployment","Dynamic scaling is limited to horizontal pod scaling; no support for cluster autoscaling (e.g., adding new nodes)"],"requires":["Kubernetes cluster 1.18+","Helm 3.0+","PostgreSQL database (can be in-cluster or external)","Sufficient cluster resources (CPU, memory) for master service and worker pods"],"input_types":["Helm values.yaml for configuration","Kubernetes resource specifications (CPU, memory, GPU requests)"],"output_types":["Deployed Determined master service and worker pods","Kubernetes events and pod logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"determined-ai__cap_9","uri":"capability://automation.workflow.tensorflow.keras.training.harness.with.automatic.distributed.training","name":"tensorflow/keras training harness with automatic distributed training","description":"Provides a Keras-compatible training harness that wraps TensorFlow/Keras models and enables distributed training without requiring users to manually implement tf.distribute strategies. The system intercepts the training loop via a custom callback, handles data distribution across GPUs/nodes, and manages gradient synchronization. The harness supports both eager execution and graph mode, and integrates with Determined's checkpoint and metric collection systems.","intents":["Scale TensorFlow/Keras models across multiple GPUs without rewriting training code","Use distributed training strategies without manually implementing tf.distribute","Automatically collect metrics and checkpoints during Keras training"],"best_for":["ML teams using TensorFlow/Keras who want to scale to multi-GPU training","Researchers migrating from single-GPU Keras scripts to distributed training"],"limitations":["Requires inheriting from a Determined Keras trial class; not compatible with arbitrary Keras scripts without refactoring","tf.distribute strategy selection is automatic; no fine-grained control over distribution strategy (e.g., MirroredStrategy vs MultiWorkerMirroredStrategy)","Metric collection is limited to Keras metrics; custom metrics require additional integration code"],"requires":["Python 3.7+","TensorFlow 2.4+","Determined master service running and accessible"],"input_types":["Python training code inheriting from Determined Keras trial class","YAML experiment configuration"],"output_types":["Trained Keras model checkpoints","Training metrics and logs"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"determined-ai__headline","uri":"capability://model.training.open.source.deep.learning.training.platform","name":"open-source deep learning training platform","description":"Determined AI is an open-source platform designed for efficient deep learning model training, featuring distributed training, hyperparameter search, and resource management, making it ideal for teams looking to scale their AI projects.","intents":["best open-source deep learning platform","deep learning training platform for distributed teams","open-source solution for hyperparameter tuning","best tools for managing GPU resources in deep learning","efficient experiment tracking for AI projects"],"best_for":["deep learning teams","research institutions","startups working on AI"],"limitations":["requires GPU resources","may need technical expertise to set up"],"requires":["Python","GPU"],"input_types":["training data","model configurations"],"output_types":["trained models","experiment logs"],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+","Determined master service running and accessible","Network connectivity between compute nodes for gradient synchronization","Determined master service with PostgreSQL backend for trial state persistence","YAML experiment configuration with hyperparameter search specification","Sufficient cluster resources to run multiple trials concurrently","Training code using Determined trial harness (PyTorch or Keras)","Network connectivity to master service for metric streaming","Experiment configuration with early stopping policy specified"],"failure_modes":["Requires inheriting from PyTorchTrial base class — not compatible with arbitrary PyTorch scripts without refactoring","Distributed training overhead adds ~5-15% latency per synchronization step depending on network bandwidth","Limited to PyTorch; 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