'compute' 검색 결과

Explain how dot works and check why a task is blocked, including computer access, login, approvals, and Slack errors. It provides supported next steps and answers questions about changing dot’s avatar and the user’s message color.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Understand why a dot task is blocked
• Check computer connection or login requirements
• Clarify Slack errors and delivery status

Implement large parallel workloads in Java with Azure Batch. Manage groups of computing machines, organize jobs and tasks, inspect progress and output, adjust computing capacity, and schedule recurring jobs using SDK examples.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running many computing tasks in parallel
• Adjusting machine counts to match pending work
• Checking task progress, exit codes, and output

Manage Azure Machine Learning resources from Python with SDK v2. The skill provides implementation examples for registering data and models, configuring compute and environments, submitting training jobs, monitoring logs, and connecting steps into pipelines.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Submitting training jobs and monitoring logs
• Registering data and model versions
• Connecting preprocessing and training steps

Operate desktop apps and isolated browsers on registered computers by inspecting interface controls, entering information, and checking results. The skill also covers screenshots and recording, with separate routing for the user's signed-in Chrome through Chromewhale.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Entering information through a desktop app interface
• Working in an isolated browser session
• Capturing screenshots or recordings for a task

Captures screenshots on registered computers and manages saved captures. Supports screen recording on macOS and timed screenshot sequences on HarmonyOS, checking available capabilities first and returning the finalized file path when recording stops.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Capturing screenshots on a registered computer
• Recording a display or selected region on macOS
• Saving timed screenshot sequences on HarmonyOS

Create breadboard layout diagrams with HTML5 Canvas to show electronic components and wire connections. Use the skill for LED circuits, 555 timers, and 6502 computer projects, with component references, labels, and color-coded wiring.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan component placement for an LED circuit
• Draw a breadboard layout for a 555 timer
• Visualize wiring for a 6502 computer

Build and train Mixture of Experts models, which activate selected specialist networks for each input. Use DeepSpeed or HuggingFace workflows to configure routing, balance workloads, distribute experts across GPUs, and optimize inference.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing MoE models for a limited compute budget
• Implementing Mixtral-style expert routing
• Adjusting expert workloads and training stability

Build a computer-control loop where Cua Driver observes and acts, while TypeSafe Jev chooses from predefined actions. Validate each choice, execute one action at a time, and verify the result before continuing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a Jev integration for browser automation
• Create action candidates for native desktop apps
• Reject stale or unknown action choices

Work in a local or remote computer environment whose screen you can watch. Run commands, transfer files, launch coding agents, move signed-in app sessions, and share the host computer’s network when needed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run tasks in an isolated environment you can watch
• Transfer files into and out of a Space
• Launch and monitor coding agents

Read and operate desktop applications through their interfaces, including clicks, text entry, menus, and scrolling. Use it to inspect or edit app content when a dedicated connector, API, or command-line tool cannot handle the task.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspect information displayed in a desktop app
• Enter or edit text in an application
• Operate menus and dialogs

Install the free OpenWork desktop app on macOS, Windows, or Linux using the appropriate download for the computer. Guide initial setup, folder authorization, model selection, and a first task such as summarizing local files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Installing OpenWork on a personal computer
• Choosing an installer for the OS and CPU
• Setting up a work folder and model

Helps choose and tune Cloud Storage FUSE mounts, which expose bucket objects through file paths. It covers cache sizing, training performance, checkpoint write safety, and cost diagnosis on GKE, Compute Engine, and Cloud Run.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Access training data through file paths
• Choose between FUSE, native reads, and shared file systems
• Check checkpoint and concurrent write safety

Reduce Google Kubernetes Engine costs by adjusting CPU and memory requests, setting resource quotas, and choosing Spot VMs or committed use discounts. Match spending decisions to steady usage, variable demand, and workloads that tolerate interruptions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reduce excessive CPU and memory requests
• Set resource quotas for teams sharing a cluster
• Move interruption-tolerant batch jobs to Spot VMs

Plan and create Google Kubernetes Engine clusters using templates for production, development, or AI workloads. Compare Google-managed and self-managed node options, review configurations before creation, and check settings for production readiness.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a production Autopilot cluster
• Compare Autopilot and Standard modes
• Prepare a development or test cluster

Create and validate Terraform alerts for GKE using PromQL, a monitoring query language. Cover response times, errors, traffic, memory usage, and cluster health, with checks for alert timing, duplicate signals, and metric ingestion costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create alerts for GKE service latency and errors
• Detect frequent container restarts and unhealthy nodes
• Review existing Terraform alert configurations

Set up Kortix as a product under your own brand. This skill guides license signing, Platinum compute configuration, and changes to names, logos, colors, and frontend branding, while explaining the operating conditions stated in the agreement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Offering Kortix under your own product name
• Changing names and logos through the Branding tab
• Building a branded frontend with @kortix/sdk

Read, create, and edit text files on the computer running the connected A0 CLI. The skill guides focused replacements and context-based patches, checking current file contents and handling read-only access or outdated edits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Read a text file on the connected local computer
• Replace a specific passage in an existing file
• Apply edits after file line numbers have shifted

Turn a research proposal into an experiment plan tied to specific paper claims. Define comparisons, evaluation measures, tests that remove individual components, run order, and compute estimates, with a written plan and tracker for execution.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a research proposal into concrete experiments
• Designing comparisons and component-removal tests
• Prioritizing runs within a compute budget

Prepare reproducible setup and removal instructions for software environments on Open-Science SSH hosts, including Slurm clusters. Check available tools, define named activation settings, and validate the environment where jobs run after the user installs it.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a remote environment for required packages
• Making job environment activation repeatable
• Checking software and GPU operation on Slurm nodes

Builds, runs, debugs, and operates applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments running inside a container with up to 8 hr lifetimes. Applicable when workloads need strong isolation between tenants, isolated serverless compute, sandbox compute, or secure multi-tenant execution. Also suited for AI/agent code-execution sandboxes, interactive code playgrounds and notebooks (Jupyter, REPLs, dev environments running user-supplied code), reinforcement-learning environments, multi-tenant CI executors and build runners, sessionful game or simulation servers, or isolated security scanners. Also applicable when the workload needs long-lived sessions, a real port-listening server (gRPC, WebSocket, custom TCP protocols), state preserved across periods of inactivity (suspend/resume), container-level access (FUSE, eBPF, custom syscalls), or session-affine routing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Choose Memmy browser, desktop Computer Use, or a structured connector for a live task on macOS or Windows 11; keep Computer History retrieval separate from UI actions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Answer questions about what the user recently did on their computer — who contacted them, what they were working on, where they left off — by reading the local Computer History summaries and raw event streams.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when writing analytical SQL over time-series data with the TimescaleDB Toolkit (timescaledb_toolkit extension) hyperfunctions: approximate percentiles, statistical summaries, time-weighted averages, counter/gauge rates, uptime/heartbeat tracking, state durations, OHLC candlesticks, approximate distinct counts, top-N, and downsampling. **Trigger when user asks to:** - Compute percentiles/medians/p95/p99 over large or rolled-up time-series data - Compute rates or deltas from monotonic counters (Prometheus-style) or gauges - Compute time-weighted averages or integrals over irregularly sampled data - Track uptime/downtime from heartbeats, or time spent in each state - Build OHLC/candlestick or VWAP data for financial ticks - Store re-aggregatable summaries in continuous aggregates (two-step aggregation, rollup) - Approximate COUNT DISTINCT, find top-N / most frequent values, or downsample for charts **Keywords:** timescaledb_toolkit, hyperfunctions, percentile_agg, uddsketch, tdigest, approx_percentile, stats_agg, time_weight, counter_agg, gauge_agg, heartbeat_agg, state_agg, candlestick_agg, hyperloglog, approx_count_distinct, min_n, max_n, mcv_agg, lttb, asap_smooth, rollup, two-step aggregation

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.