{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"jarvis-labs","slug":"jarvis-labs","name":"Jarvis Labs","type":"platform","url":"https://jarvislabs.ai","page_url":"https://unfragile.ai/jarvis-labs","categories":["deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"jarvis-labs__cap_0","uri":"capability://automation.workflow.on.demand.gpu.compute.provisioning.with.minute.level.billing","name":"on-demand gpu compute provisioning with minute-level billing","description":"Provides ephemeral GPU instances (H100, H200, A100, A6000, L4, RTX 6000 Ada) that can be created and destroyed on-demand with per-minute billing granularity. Instances launch in <90 seconds and support up to 8 GPUs per instance with configurable vCPU and RAM allocations. Users select GPU type and storage size (20GB–2TB) via CLI or web dashboard, and billing stops immediately upon instance termination with no minimum commitment or long-term contracts required.","intents":["Rent GPU compute for short-term model training experiments without capital expenditure","Scale to multiple GPUs for distributed training without managing physical infrastructure","Test model performance on different GPU architectures (H100 vs A100) before committing to production","Run inference workloads on high-end GPUs for a few hours without monthly subscription costs"],"best_for":["ML researchers and data scientists running ad-hoc training jobs","Startups prototyping models with variable compute needs","Teams evaluating GPU performance before on-premise purchases","Individual developers learning deep learning without hardware investment"],"limitations":["No auto-scaling — instances must be manually created and destroyed; no cost optimization for idle time","Minute-level billing granularity means sub-minute workloads are still charged for full minute","No reserved instance discounts documented; custom quotes available only for 25+ GPUs or multi-month commitments","Egress/bandwidth costs not documented — potential hidden costs for large model downloads or data transfers","Single region deployment (region location unknown) — no multi-region failover or geographic distribution","No spot/preemptible pricing documented — no option for cheaper but interruptible compute"],"requires":["Jarvis Labs account with payment method","Python 3.7+ for CLI tool (`pip install jarvislabs`)","Internet connectivity for instance provisioning and SSH access","API key or authentication token (format unknown)"],"input_types":["GPU type selection (enum: H100, H200, A100, A6000, L4, RTX 6000 Ada)","vCPU count (derived from GPU type)","Storage size (20GB–2TB range)","Instance name/identifier (string)"],"output_types":["Instance ID (string)","SSH connection details (host, port, credentials)","Instance status (running, stopped, terminated)","Billing metrics (hours used, cost)"],"categories":["automation-workflow","infrastructure-as-code"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jarvis-labs__cap_1","uri":"capability://automation.workflow.persistent.storage.with.ssh.accessible.file.systems","name":"persistent storage with ssh-accessible file systems","description":"Provides persistent block storage (20GB–2TB) that persists across instance stop/resume cycles and can be accessed via SSH for direct file transfer. Storage is mounted to instances as a filesystem accessible from the OS, enabling users to store training datasets, model checkpoints, and code that survives instance termination. Users can transfer files via standard SSH tools (scp, rsync) or through web IDE file browsers without requiring external object storage services.","intents":["Store large training datasets (10GB–1TB+) that persist across multiple training runs without re-downloading","Save model checkpoints and training artifacts that survive instance termination for later analysis","Maintain code repositories and configuration files across multiple compute sessions","Avoid re-uploading data for each new instance by keeping datasets in persistent storage"],"best_for":["Researchers running iterative training experiments over weeks/months","Teams sharing datasets across multiple instances and users","Projects requiring checkpoint management and experiment tracking","Users without external cloud storage (S3, GCS) or preferring direct filesystem access"],"limitations":["Storage I/O performance characteristics unknown — no documented throughput or latency specifications","Storage backend type unknown (SSD vs HDD) — performance may be inconsistent","No built-in backup or versioning — data loss risk if storage is corrupted or accidentally deleted","No automatic snapshots or point-in-time recovery documented","Storage is instance-specific (unclear if shareable across instances) — may require manual data migration between instances","No documented retention policy — unclear what happens to data after account deletion or long inactivity"],"requires":["SSH access to instance (requires SSH key pair setup)","Standard Unix/Linux tools (scp, rsync, ssh) or web IDE file browser","Storage size selection at instance creation time (20GB–2TB)","Sufficient account balance to maintain instance for data access"],"input_types":["File paths (local and remote)","Directory structures","Binary files (datasets, model weights)","Text files (code, configs)"],"output_types":["Persistent filesystem mounted at instance root","File transfer confirmations (scp/rsync exit codes)","Directory listings (ls output)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jarvis-labs__cap_10","uri":"capability://memory.knowledge.community.and.social.features.user.count.gpu.hours.served.trusted.users","name":"community and social features (user count, gpu hours served, trusted users)","description":"Provides community metrics (27,343 AI developers, 50M+ GPU hours served) and lists trusted users (Tesla, Hugging Face, Kaggle, Zoho, Weights & Biases, upGrad, Saama) to build credibility and social proof. However, no documented community features (forums, model sharing, code repositories, user profiles, discussions) or social interactions (likes, follows, comments) exist on the platform. The community metrics are marketing claims without verification, and no community-driven content or collaboration features are available.","intents":["Verify platform credibility by seeing trusted users and cumulative GPU hours served","Discover popular use cases and best practices from community metrics","Build confidence in platform reliability based on user count and enterprise adoption"],"best_for":["Potential users evaluating platform credibility and adoption","Enterprise buyers seeking proof of production usage","Researchers interested in platform maturity and stability"],"limitations":["No community features documented — no forums, model sharing, code repositories, or user profiles","No social interactions — no likes, follows, comments, or discussions","Community metrics are unverified marketing claims — no third-party validation of user count or GPU hours","No community-driven content or best practices sharing","No user-generated models or code artifacts","No community support or peer-to-peer help channels"],"requires":["Access to Jarvis Labs marketing materials or website to view community metrics"],"input_types":[],"output_types":["Community metrics (user count, GPU hours served)","Trusted user list (company names)"],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jarvis-labs__cap_11","uri":"capability://automation.workflow.support.for.custom.docker.images.and.bare.metal.vm.access","name":"support for custom docker images and bare-metal vm access","description":"Jarvis Labs supports deploying custom Docker images on instances for advanced use cases beyond pre-configured templates. Users can specify a Docker image URI at instance creation time, and the platform will boot the instance with that image. The platform also provides raw SSH access to instances, enabling users to install arbitrary software, configure custom environments, or run non-containerized workloads. This flexibility allows advanced users to bypass pre-configured templates and use custom ML frameworks, tools, or configurations.","intents":["Deploy a custom Docker image with proprietary ML frameworks or tools","Install system packages or libraries not included in pre-configured templates","Run non-ML workloads (data processing, rendering, simulation) on GPU instances"],"best_for":["Advanced users with custom environment requirements","Teams using proprietary or experimental ML frameworks","Organizations with strict dependency or security requirements"],"limitations":["Custom Docker image support is mentioned but not documented; no examples, size limits, or registry support (Docker Hub, ECR, GCR) specified","No guidance on Docker image optimization for GPU workloads; users must handle CUDA/cuDNN compatibility","No image caching or layer reuse documented; each instance may re-download entire image","No image versioning or rollback support documented","Bare-metal VM access is powerful but requires users to manage all dependencies and configurations manually","No configuration management tools (Ansible, Terraform) documented for infrastructure-as-code"],"requires":["Docker image URI (from Docker Hub, ECR, GCR, or private registry)","Docker image compatible with Linux and CUDA (if GPU access required)","SSH access for manual configuration (if needed)"],"input_types":["Docker image URI (string)","Optional: environment variables, volume mounts"],"output_types":["Running instance with custom Docker image","SSH access to instance for additional configuration"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jarvis-labs__cap_12","uri":"capability://data.processing.analysis.real.time.instance.monitoring.via.cli.and.web.dashboard","name":"real-time instance monitoring via cli and web dashboard","description":"Jarvis Labs provides instance status monitoring via CLI commands (e.g., `jl status <instance-id>`) and web dashboard, showing instance state (running, paused, terminated), GPU utilization, memory usage, and network activity. Users can view logs and metrics in real-time to monitor training progress and diagnose issues. The monitoring interface is basic and does not include advanced features like custom alerts, metric aggregation, or historical analysis.","intents":["Check instance status and GPU utilization during training","Diagnose performance issues by viewing memory usage and network activity","Monitor multiple instances simultaneously via web dashboard"],"best_for":["Teams running multiple GPU instances and needing centralized monitoring","Researchers debugging training performance issues","Organizations tracking GPU utilization for cost optimization"],"limitations":["Monitoring features not documented; unclear what metrics are available (GPU utilization, memory, temperature, power, network)","No custom alerts or notifications; users must manually check dashboard","No historical metrics or trend analysis; only real-time data available","No integration with external monitoring tools (Prometheus, Grafana, Datadog)","Web dashboard interface not documented; unclear if it's web-based or CLI-only","No per-GPU metrics; monitoring is instance-level only"],"requires":["Jarvis Labs instance running","CLI access (for `jl status` command) or web browser (for dashboard)"],"input_types":["Instance ID (string)"],"output_types":["Instance status (running, paused, terminated)","GPU utilization (percentage)","Memory usage (GB)","Network activity (Mbps)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jarvis-labs__cap_2","uri":"capability://automation.workflow.pre.configured.deep.learning.environments.with.framework.templates","name":"pre-configured deep learning environments with framework templates","description":"Provides pre-installed and pre-configured environments for PyTorch, TensorFlow, Hugging Face, ComfyUI, and Automatic1111 that eliminate manual dependency installation and environment setup. Each template includes the framework, CUDA toolkit, cuDNN, and common libraries (numpy, pandas, scikit-learn) pre-compiled and optimized for the selected GPU. Users can launch an instance with a template and immediately start training or inference without running pip install or managing version conflicts.","intents":["Start training a model within minutes without spending time on environment setup and dependency resolution","Use Stable Diffusion (ComfyUI or Automatic1111) for image generation without installing diffusers, transformers, and CUDA manually","Avoid CUDA/cuDNN version mismatches that cause training failures by using pre-tested configurations","Switch between PyTorch and TensorFlow without rebuilding the environment"],"best_for":["ML practitioners who want to minimize setup time and focus on model development","Teams onboarding new members who need working environments immediately","Researchers experimenting with multiple frameworks (PyTorch, TensorFlow) in parallel","Non-expert users unfamiliar with CUDA/cuDNN installation and configuration"],"limitations":["Template versions unknown — unclear if PyTorch 2.0 vs 1.13 or TensorFlow 2.13 vs 2.12 is installed","No custom template creation documented — users cannot save and reuse custom environments","Limited to documented frameworks (PyTorch, TensorFlow, Hugging Face, ComfyUI, Automatic1111) — no support for JAX, MXNet, or other frameworks","No documented way to upgrade framework versions after instance creation without manual pip install","ComfyUI and Automatic1111 templates are UI-focused, not API-focused — unclear if suitable for programmatic inference","No environment isolation — all frameworks share the same Python environment, risking dependency conflicts"],"requires":["Selection of template at instance creation time","GPU with sufficient VRAM for chosen framework (minimum 8GB for most models)","Basic familiarity with Python and the chosen framework (not a zero-setup solution)"],"input_types":["Template selection (enum: PyTorch, TensorFlow, Hugging Face, ComfyUI, Automatic1111)","Framework version (if selectable; unknown)"],"output_types":["Pre-configured Python environment with framework installed","Jupyter notebooks or CLI access to framework","Web UI (for ComfyUI/Automatic1111 templates)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jarvis-labs__cap_3","uri":"capability://automation.workflow.managed.script.execution.with.dependency.installation.and.log.streaming","name":"managed script execution with dependency installation and log streaming","description":"Provides a `jl run` CLI command that uploads local Python scripts to an instance, automatically installs dependencies from requirements.txt, executes the script, and streams logs back to the user's terminal in real-time. The command abstracts away SSH key management and manual environment setup, allowing users to run training jobs with a single CLI invocation. Logs are streamed to stdout/stderr, enabling real-time monitoring of training progress without SSH into the instance.","intents":["Execute a training script on a GPU instance with a single CLI command without manual SSH and dependency installation","Monitor training progress in real-time via log streaming without opening a separate SSH session","Automate training job submission from CI/CD pipelines or local scripts without shell scripting","Run multiple training jobs sequentially or in parallel by invoking `jl run` multiple times"],"best_for":["Data scientists running training scripts from their local machine","Teams automating training job submission from CI/CD pipelines (GitHub Actions, GitLab CI)","Researchers who prefer CLI-based workflows over web dashboards","Users without deep SSH/Linux expertise who want simplified job submission"],"limitations":["Dependency installation mechanism unknown — unclear if it uses pip, conda, or poetry; no timeout or failure handling documented","Log streaming format unknown — unclear if structured logs (JSON) or plain text; no log retention policy documented","No job queuing or scheduling — jobs run immediately or fail if instance is unavailable; no retry logic documented","No output artifact collection — unclear how to retrieve generated files (model checkpoints, logs) after job completion","No timeout or resource limits documented — long-running jobs may incur unexpected costs","No support for interactive jobs — only batch script execution; no Jupyter notebook or REPL support via `jl run`"],"requires":["Python 3.7+ with `jarvislabs` CLI installed (`pip install jarvislabs`)","requirements.txt file in the same directory as the script (if dependencies needed)","Python script with no interactive input (batch-only execution)","Valid Jarvis Labs API key or authentication token"],"input_types":["Python script file path (local)","GPU type selection (enum: H100, H200, A100, etc.)","requirements.txt (optional, for dependency installation)"],"output_types":["Real-time log stream (stdout/stderr)","Exit code (0 for success, non-zero for failure)","Generated files (location unknown; unclear if automatically retrieved)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jarvis-labs__cap_4","uri":"capability://automation.workflow.ssh.terminal.access.with.direct.instance.control","name":"ssh terminal access with direct instance control","description":"Provides direct SSH access to instances, enabling users to open a terminal shell and execute arbitrary commands, install custom packages, modify configurations, and run interactive workloads. SSH keys are managed by Jarvis Labs (generated or user-provided; mechanism unknown), and connection details (host, port, username) are provided via CLI or web dashboard. Users can use standard SSH tools (ssh, scp, rsync) and IDE integrations (VS Code Remote SSH, PyCharm SSH interpreter) to interact with instances.","intents":["Debug training jobs interactively by SSH-ing into the instance and inspecting logs, memory usage, and GPU state","Install custom packages or tools not included in pre-configured templates without rebuilding the environment","Run interactive Jupyter notebooks or IPython shells for exploratory data analysis","Use IDE remote development features (VS Code Remote SSH, PyCharm SSH interpreter) for code editing and debugging on GPU instances"],"best_for":["Researchers who need interactive debugging and exploratory analysis on GPU instances","Teams using IDE remote development features for seamless local-to-cloud development","Users comfortable with Linux/Unix command line and SSH","Projects requiring custom package installation or system-level configuration"],"limitations":["SSH key management mechanism unknown — unclear if keys are generated by Jarvis Labs, user-provided, or managed via web dashboard","No documented SSH security features (IP whitelisting, key rotation, audit logging) — potential security risk for shared accounts","No documented session timeout or idle disconnection — unclear if long-running SSH sessions are terminated","No documented bandwidth limits or connection rate limits — potential for abuse or unexpected costs","Interactive workloads (Jupyter, IPython) may have performance issues if not optimized for remote execution","No documented support for SSH tunneling or port forwarding — unclear if users can expose local services (e.g., Jupyter on port 8888)"],"requires":["SSH client installed locally (ssh command-line tool or IDE with SSH support)","SSH key pair (generated by Jarvis Labs or user-provided; mechanism unknown)","Instance ID and connection details (host, port, username) from Jarvis Labs","Network connectivity to Jarvis Labs infrastructure (no documented IP whitelisting)"],"input_types":["SSH command (arbitrary shell commands)","File paths (for scp/rsync)","Environment variables (for shell configuration)"],"output_types":["Shell command output (stdout/stderr)","File system access (via scp/rsync)","Interactive terminal session"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jarvis-labs__cap_5","uri":"capability://code.generation.editing.web.based.ide.access.jupyterlab.and.vs.code","name":"web-based ide access (jupyterlab and vs code)","description":"Provides browser-based access to JupyterLab and VS Code running on instances, enabling users to edit code, run notebooks, and execute commands without installing local development tools. JupyterLab provides a notebook interface for exploratory analysis and interactive development, while VS Code provides a full IDE with syntax highlighting, debugging, and extensions. Both are accessed via HTTPS URLs provided by Jarvis Labs, with authentication handled via instance credentials.","intents":["Edit and run Jupyter notebooks directly in the browser without local Jupyter installation","Use VS Code for remote development with full IDE features (debugging, linting, extensions) without SSH","Collaborate on code by sharing a single instance URL with team members (authentication mechanism unknown)","Avoid local environment setup by using pre-configured IDEs on GPU instances"],"best_for":["Data scientists and researchers who prefer notebook-based workflows","Teams collaborating on code using shared instances","Users without local development environment setup (e.g., Chromebook users)","Projects requiring quick prototyping without IDE configuration"],"limitations":["IDE versions unknown — unclear if VS Code is latest or outdated; JupyterLab version not documented","No documented IDE extension support or marketplace access — unclear if users can install custom VS Code extensions","Authentication mechanism unknown — unclear if URLs are public, password-protected, or token-based; potential security risk","No documented collaboration features (real-time editing, presence awareness) — sharing a URL may lead to conflicts","Performance may be limited by network latency — IDE responsiveness depends on user's internet connection","No documented session timeout or idle disconnection — unclear if long-running IDE sessions are terminated","No documented backup of notebook files or code — unclear if work is automatically saved to persistent storage"],"requires":["Web browser with HTTPS support","Instance running with JupyterLab or VS Code template (or manually installed)","Instance URL and authentication credentials (format unknown)"],"input_types":["Python code (for notebooks and scripts)","Notebook cells (for JupyterLab)","File paths (for VS Code file browser)"],"output_types":["Notebook output (text, plots, tables)","Code execution results (stdout/stderr)","File system changes (saved files)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jarvis-labs__cap_6","uri":"capability://tool.use.integration.agent.native.ide.integration.with.claude.code.cursor.and.codex","name":"agent-native ide integration with claude code, cursor, and codex","description":"Provides native integration with AI-powered code editors (Claude Code, Cursor, Codex) via a `jl setup` command that configures the IDE to use Jarvis Labs instances as remote execution environments. The integration allows users to write code in their local IDE and execute it on GPU instances without manual SSH or CLI commands. The mechanism for IDE integration is unknown (likely SSH interpreter configuration or custom IDE extension), but it enables seamless local-to-cloud development workflows.","intents":["Write and execute training code in Cursor or Claude Code with GPU execution on Jarvis Labs without switching tools","Use AI code completion (Cursor, Claude Code) while training models on remote GPUs","Debug training jobs interactively in the IDE with real-time GPU feedback","Maintain a single development workflow across local and cloud environments"],"best_for":["Developers using Cursor or Claude Code as their primary IDE","Teams leveraging AI code completion for faster model development","Researchers who want to stay in their IDE while using GPU compute","Projects requiring tight integration between code editing and execution"],"limitations":["Integration mechanism unknown — unclear if it uses SSH interpreter configuration, custom extension, or API-based execution","Supported IDEs limited to Claude Code, Cursor, and Codex — no support for VS Code, PyCharm, or other IDEs","Setup process unknown — `jl setup` command details not documented; unclear if one-time or per-project configuration","No documented support for debugging (breakpoints, variable inspection) — unclear if IDE debugger works with remote execution","No documented support for interactive notebooks in IDE — likely script-only execution","IDE version requirements unknown — unclear if integration works with all Cursor/Claude Code versions"],"requires":["Cursor, Claude Code, or Codex IDE installed locally","Jarvis Labs CLI installed (`pip install jarvislabs`)","`jl setup` command executed to configure IDE integration","Valid Jarvis Labs API key or authentication token"],"input_types":["Python code written in IDE","IDE commands (run, debug, etc.)","GPU type selection (if configurable via IDE)"],"output_types":["Code execution results in IDE terminal","Real-time log streaming to IDE output panel","GPU metrics (if displayed in IDE)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jarvis-labs__cap_7","uri":"capability://automation.workflow.multi.gpu.instance.configuration.with.up.to.8.gpus.per.instance","name":"multi-gpu instance configuration with up to 8 gpus per instance","description":"Enables users to provision instances with multiple GPUs (up to 8 per instance) for distributed training, data parallelism, and model parallelism workloads. GPU selection is made at instance creation time, and all GPUs are of the same type (e.g., 8x H100 or 4x A100). The interconnect topology (NVLink vs PCIe), bandwidth specifications, and multi-GPU communication libraries (NCCL, Gloo) are not documented, but instances support standard PyTorch DistributedDataParallel and TensorFlow distributed training APIs.","intents":["Train large models (LLMs, vision transformers) using data parallelism across multiple GPUs","Use model parallelism to fit models larger than single GPU VRAM (e.g., 70B parameter LLMs on 8x A100)","Reduce training time by distributing batch processing across multiple GPUs","Benchmark multi-GPU scaling efficiency for different model architectures"],"best_for":["Researchers training large language models or vision transformers","Teams optimizing training time for production models","Projects requiring model parallelism due to model size exceeding single GPU VRAM","Users benchmarking multi-GPU scaling efficiency"],"limitations":["GPU interconnect topology unknown — unclear if NVLink (high bandwidth) or PCIe (lower bandwidth); critical for distributed training performance","Bandwidth specifications not documented — unclear if suitable for all-reduce operations in distributed training","No documented support for GPU sharing or time-slicing — each GPU is dedicated to a single instance","Multi-GPU configuration is static at instance creation — cannot add/remove GPUs after launch","No documented support for heterogeneous GPU types (e.g., mixing H100 and A100) — all GPUs must be identical","No documented cost breakdown for multi-GPU instances — unclear if cost scales linearly with GPU count","No documented support for GPU affinity or NUMA topology optimization — users must configure manually"],"requires":["GPU type selection at instance creation time (H100, H200, A100, etc.)","Number of GPUs (1–8 range)","Distributed training code using PyTorch DistributedDataParallel, TensorFlow distributed strategies, or similar","Sufficient VRAM per GPU for model/batch size (varies by model)"],"input_types":["GPU type (enum: H100, H200, A100, A6000, L4, RTX 6000 Ada)","GPU count (integer: 1–8)","Distributed training configuration (NCCL backend, rank, world size, etc.)"],"output_types":["Multi-GPU instance with all GPUs visible to training code (via nvidia-smi, torch.cuda.device_count())","Training metrics (throughput, GPU utilization, communication overhead)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jarvis-labs__cap_8","uri":"capability://automation.workflow.cli.based.instance.lifecycle.management.create.pause.resume.destroy","name":"cli-based instance lifecycle management (create, pause, resume, destroy)","description":"Provides CLI commands (`jl create`, `jl pause`, `jl resume`, `jl destroy`) for managing instance lifecycle without web dashboard interaction. Users can create instances with GPU type and storage size, pause instances to stop billing while preserving state, resume paused instances to continue work, and destroy instances to free resources. Pause/resume functionality enables cost savings by stopping instances during breaks without losing data or configuration, as persistent storage and instance state are preserved.","intents":["Create GPU instances programmatically from scripts or CI/CD pipelines without web dashboard","Pause instances during breaks to stop billing while preserving training state and data","Resume paused instances to continue work without re-downloading datasets or re-configuring environments","Destroy instances after training to free resources and stop billing immediately"],"best_for":["Developers who prefer CLI workflows over web dashboards","Teams automating instance provisioning in CI/CD pipelines","Researchers running iterative experiments with pause/resume cycles","Users managing multiple instances and needing scriptable lifecycle management"],"limitations":["Pause/resume mechanism unknown — unclear if instance state (memory, processes) is preserved or if only storage persists","No documented pause duration limits — unclear if paused instances are automatically destroyed after inactivity","No documented pause cost — unclear if paused instances incur storage-only charges or are free","No batch operations — cannot create/destroy multiple instances in a single command","No documented instance listing or status checking command — unclear how to query instance state via CLI","No documented rollback or snapshot functionality — cannot revert to previous instance state","No documented instance naming or tagging — unclear how to organize instances for large-scale management"],"requires":["Python 3.7+ with `jarvislabs` CLI installed (`pip install jarvislabs`)","Valid Jarvis Labs API key or authentication token","Instance ID (for pause, resume, destroy operations; format unknown)"],"input_types":["GPU type (enum: H100, H200, A100, etc.)","Storage size (20GB–2TB range)","Instance ID (string, for pause/resume/destroy)","Instance name (optional, for create operation)"],"output_types":["Instance ID (for create operation)","Instance status (running, paused, terminated)","Confirmation message (for pause/resume/destroy)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jarvis-labs__cap_9","uri":"capability://automation.workflow.pricing.transparency.with.per.minute.billing.and.no.hidden.fees","name":"pricing transparency with per-minute billing and no hidden fees","description":"Provides transparent, per-minute billing for GPU instances with published hourly rates for each GPU type (H100: $2.69/hr, A100-80GB: $1.49/hr, L4: $0.44/hr, etc.). Billing starts when an instance is created and stops immediately upon termination, with no minimum commitment, long-term contracts, or hidden egress/bandwidth charges documented. Users can estimate costs by multiplying hourly rate by usage duration, and billing is calculated to the minute (not rounded to the hour), enabling fine-grained cost control.","intents":["Estimate GPU compute costs before running training jobs based on published hourly rates","Compare costs across GPU types (H100 vs A100 vs L4) to optimize for budget vs performance","Avoid surprise bills by understanding that billing stops immediately upon instance termination","Plan budgets for iterative experiments knowing that pause/resume stops billing"],"best_for":["Startups and individuals with limited budgets who need transparent pricing","Researchers comparing GPU costs across providers","Teams planning ML infrastructure budgets with predictable costs","Users running short-term experiments who benefit from per-minute billing"],"limitations":["Egress/bandwidth costs not documented — potential hidden costs for large model downloads or data transfers","No cost optimization recommendations or usage alerts — users must manually monitor spending","No free tier or trial credits documented — all usage is billable from day one","No volume discounts or reserved instance pricing documented — only custom quotes for 25+ GPUs or multi-month commitments","Minute-level billing granularity means sub-minute workloads are still charged for full minute","No documented cost breakdown by resource (GPU, CPU, storage, bandwidth) — unclear which components contribute to total cost","Pause/resume cost unknown — unclear if paused instances incur storage-only charges or are free"],"requires":["Jarvis Labs account with payment method","Knowledge of hourly rates for selected GPU types (published on pricing page)","Ability to estimate usage duration for cost calculation"],"input_types":["GPU type (enum: H100, H200, A100, A6000, L4, RTX 6000 Ada)","Usage duration (minutes or hours)","Storage size (for storage cost calculation, if applicable)"],"output_types":["Estimated cost (hourly rate × duration)","Actual cost (billed to account after usage)","Cost breakdown by instance (if available in billing dashboard)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"jarvis-labs__headline","uri":"capability://deployment.infra.cloud.gpu.platform.for.deep.learning","name":"cloud gpu platform for deep learning","description":"Jarvis Labs is a cloud GPU platform optimized for deep learning, offering pre-configured environments for popular frameworks like PyTorch and TensorFlow, along with affordable A100 and H100 instances.","intents":["best cloud GPU platform","cloud GPU for deep learning","affordable GPU instances for AI","GPU platform for PyTorch and TensorFlow","cloud infrastructure for AI model deployment"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Jarvis Labs account with payment method","Python 3.7+ for CLI tool (`pip install jarvislabs`)","Internet connectivity for instance provisioning and SSH access","API key or authentication token (format unknown)","SSH access to instance (requires SSH key pair setup)","Standard Unix/Linux tools (scp, rsync, ssh) or web IDE file browser","Storage size selection at instance creation time (20GB–2TB)","Sufficient account balance to maintain instance for data access","Access to Jarvis Labs marketing materials or website to view community metrics","Docker image URI (from Docker Hub, ECR, GCR, or private registry)"],"failure_modes":["No auto-scaling — instances must be manually created and destroyed; 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