'inference' 검색 결과

Plan memory use and monitor long training jobs on NVIDIA DGX Spark. It guides troubleshooting for out-of-memory failures, tracks temperature and power alongside training logs, and helps assess competition between training and inference workloads.

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• Estimating available memory before training
• Troubleshooting out-of-memory failures
• Investigating slowdowns during long training jobs

Add Arize AX tracing to an LLM application, recording how model calls and tool executions connect. Inspect the existing code, select suitable integrations, add instrumentation, and verify that the application runs and traces reach Arize.

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• Setting up tracing for an LLM application
• Recording tool inputs and outputs in an agent
• Adding Arize export to an existing OpenTelemetry setup

Investigate SGLang runs that freeze across multiple GPUs. It uses stack traces, communication logs, GPU memory dumps, and comparisons between processes to locate state differences, trace their causes, and guide fixes.

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• Locate where a multi-GPU inference run freezes
• Compare logs from distributed processes
• Trace the first difference in process state

Research private companies through six perspectives, including business, finances, competition, and technology. Cross-check evidence, separate facts from inferences, and produce a valuation range, possible exit routes, and a map of missing information.

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• Assess a private company's business and valuation range
• Compare conflicting revenue or funding reports
• Check company claims against hiring and patent signals

Reduce the memory needed to run large language models with GPTQ quantization, which stores model weights at lower precision. Covers loading compressed models, quantizing your own models, choosing execution backends, and connecting to memory-efficient fine-tuning.

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• Run a language model within limited GPU memory
• Load a pre-quantized model for text generation
• Quantize and save your own trained model

Reduce GPU memory use by loading language models in 8-bit or 4-bit formats with bitsandbytes and HuggingFace Transformers. Includes configuration examples, QLoRA fine-tuning of compressed models, and optimizers that reduce memory needed during training.

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• Load larger models within limited GPU memory
• Compare 8-bit and 4-bit model settings
• Fine-tune a model with QLoRA

Deploy language models with vLLM through an OpenAI-compatible API or offline batch processing. Tune response speed, throughput, and GPU memory use with quantization, which reduces numerical precision, and execution across multiple GPUs.

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• Deploy an OpenAI-compatible language model API
• Generate text from many prompts in a batch
• Serve quantized models with limited GPU memory

Run machine learning workloads on Modal's cloud GPUs without managing servers. Guides Python configuration for training, inference, batch processing, and API deployment, including automatic scaling, persistent storage, scheduled jobs, and troubleshooting.

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• Running GPU inference without managing servers
• Deploying a model as an automatically scaling API
• Processing large batches in parallel

Guides the use of RWKV, a language model architecture that carries information forward in a compact state. Covers streaming text generation, long document processing, fine-tuning, and memory comparisons with Transformers.

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• Build streaming text generation
• Process long documents in chunks
• Compare memory needs with Transformers

Build and evaluate Mamba models for long sequences using state-space models, which summarize past inputs in an internal state. Covers setup, pretrained text generation, differences between Mamba-1 and Mamba-2, and speed comparisons with Transformers.

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• Generate text with pretrained Mamba models
• Compare Mamba-1 and Mamba-2 configurations
• Benchmark generation speed against Transformers

Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

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• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training

Build table data processing with TanStack Table v9 while keeping data and column definitions stable. The skill guides type inference, which determines types automatically, row identity, optional features, and checks for missing APIs.

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• Set up the foundation for table data processing
• Troubleshoot column type inference
• Investigate missing APIs or optional features

Build visual AI systems that detect objects or divide images into labeled regions. This skill guides dataset preparation, model selection, training, evaluation, inference optimization, and deployment using frameworks such as PyTorch and Ultralytics.

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• Build an object detection pipeline
• Clean images and convert annotation formats
• Optimize a trained model for deployment

Validate the staging NemoClaw Brev Launchable by checking its web deployment flow, environment access, deployed image, and preinstalled user journey. Report inference results and missing dependencies separately as advisory evidence outside automated end-to-end testing.

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• Check the staging Launchable web deployment flow
• Verify an existing environment's image and source commit
• Test onboarding and inference in the preinstalled environment

Find official NemoClaw documentation for installation, configuration, operation, and troubleshooting. Get source-linked guidance tailored to the selected operating system, agent variant, and inference provider, with optional documentation MCP setup when requested.

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• Find installation prerequisites and first-sandbox instructions
• Look up model and inference provider configuration
• Find documented recovery steps for sandbox problems

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.

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

Manage isolated working environments in OpenShell using its command-line tool. Register gateways, create and connect to sandboxes, configure credential providers, and reuse workload templates with shared images, environment settings, and computing resources.

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• Create and connect to a sandbox
• Configure and refresh provider credentials
• Inspect and import provider profiles

Improve MNN inference on OpenCL by measuring bottlenecks and refining GPU kernels, the code that performs calculations. The skill guides baseline measurements, correctness checks, Android device benchmarks, and integration of new OpenCL features with compatibility fallbacks.

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• Find bottlenecks in a model's GPU inference
• Optimize a specific operator's GPU kernels
• Integrate an OpenCL extension with a fallback

Diagnose errors and incorrect inference results in MNN's QNN backend for Qualcomm HTP processors. Compare intermediate calculation data against CPU results to locate the first faulty operator, distinguish bugs from precision limits, and guide operator support and performance fixes.

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• Investigate QNN runtime and model conversion errors
• Find the first operator producing incorrect results
• Distinguish implementation bugs from numerical precision limits

Deploy language model APIs and run batch generation with vLLM. Configure GPU memory use, quantization—lower precision model storage—and distribution across GPUs, then test response times and monitor how many requests the server handles.

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• Deploying an OpenAI-compatible language model API
• Generating responses for a large set of prompts
• Fitting a large model into limited GPU memory

Guides adapting OpenPI robot models to custom datasets and serving them to produce actions from observations. Covers JAX and PyTorch training, checkpoint conversion, and troubleshooting for normalization statistics and GPU memory.

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• Fine-tune OpenPI models on custom robot datasets
• Convert JAX checkpoints to PyTorch
• Serve policies for ALOHA, DROID, or LIBERO

Guides fine-tuning and evaluation of OpenVLA-OFT robot models that turn images and language into actions. Covers LIBERO simulation, ALOHA deployment, LoRA adaptation that updates a small set of parameters, and troubleshooting action scaling or configuration mismatches.

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• Evaluate pretrained models in LIBERO
• Fine-tune robot action models with LoRA
• Deploy server-client inference for ALOHA

Compress large language models to 4-bit weights to reduce GPU memory needs and accelerate text generation. Covers loading compressed models, converting your own models, choosing settings, and preparing memory-efficient fine-tuning with transformers and PEFT.

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• Run language models with limited GPU memory
• Convert your own model to 4-bit weights
• Compare compression settings for quality and speed

Implement Mamba, a model architecture that processes sequences through an internal state with computation scaling linearly with input length. Covers pretrained text generation, Mamba-1 and Mamba-2 configuration, speed comparisons with Transformers, and GPU memory troubleshooting.

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• Explore models for long sequences or streaming applications
• Generate text with pretrained Mamba models
• Compare Mamba-1 and Mamba-2 configurations