“training”的搜索结果

以少量修改让现有PyTorch训练代码适用于多GPU或多台机器，指导混合精度、节省内存的DeepSpeed与FSDP配置，以及训练启动方法。

• 将单GPU训练迁移至多GPU
• 配置混合精度训练
• 通过DeepSpeed或FSDP节省内存

将门店顾问式销售培训整理成独立的HTML幻灯片，涵盖接待流程、角色演练和日常实践，并以桃色背景和亲切易读的字体呈现给店员与卖场负责人。

• 为店员开展顾问式销售培训
• 在新人培训中讲解接待流程
• 制作含角色演练的接待工作坊资料

Create an HTML training deck for the first 30 days of hospitality onboarding. Organize expected behaviors, practice activities, learning checks, and manager follow-up into slides with speaker notes and a consistent course template.

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• Preparing a 30-day hospitality onboarding course
• Combining practice activities and learning checks
• Preparing slides and notes for training instructors

Organize PyTorch models and data pipelines with PyTorch Lightning, automate training, save checkpoints, and track experiments. Includes templates and guidance for configuring training across multiple GPUs or TPUs.

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• Restructure PyTorch training code into LightningModules
• Build reusable data preparation and loading pipelines
• Configure checkpoints, early stopping, and experiment logs

Use TensorBoard to visualize training loss and accuracy, compare experiments, and inspect model structure. Includes PyTorch and TensorFlow logging examples for examining weight distributions, predictions, and performance bottlenecks during machine learning training.

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• Comparing loss and accuracy across training runs
• Inspecting weight and gradient distributions
• Exploring embeddings in two or three dimensions

Use W&B to record machine learning experiments, visualize training, and compare results. The skill also covers automated searches for training settings and version tracking for datasets and models, helping teams share experiments and trace their inputs.

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• Logging training metrics and viewing progress in real time
• Comparing experiments with different training settings
• Searching automatically for better training settings

Guides setup and operation of Lambda Labs GPU instances for machine learning training and inference. Covers SSH access, persistent storage, API automation, and multi-GPU or multi-node training, including how to retain checkpoints and terminate idle instances.

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• Setting up a GPU instance for LLM fine-tuning
• Keeping datasets and checkpoints between sessions
• Automating instance launch and termination through the API

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

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• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Helps configure and troubleshoot DeepSpeed for training large models across multiple devices. Covers memory optimization, parallel training, and DeepNVMe storage transfers, with examples for tuning data movement and configuring models that divide work among specialized components.

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• Configuring distributed training for large models
• Tuning tensor transfers to NVMe storage
• Setting up models with specialized expert components

Monitor model training through WandB metrics to detect invalid numbers, worsening loss, and declining evaluation results. Ambiguous trends receive a Codex review, while stopped runs retain key metrics and the reason for stopping.

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• Check loss and evaluation trends during model training
• Detect NaN values and diverging loss
• Review flat or noisy training metrics

Guide knowledge distillation, where a smaller language model learns from a larger teacher. Covers training from teacher responses or prediction scores, adjusting training settings, and comparing outputs to support model compression and specialized training.

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• Compressing a large language model into a smaller model
• Training a specialized model from teacher-generated examples
• Comparing distillation methods and training settings

Set up W&B to record machine learning metrics and settings, visualize training progress, and compare experiments. Includes automated searches for training settings, version tracking for datasets and models, and shared reports for team collaboration.

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• Recording and visualizing training metrics
• Comparing experiments with different training settings
• Automatically searching for better training settings

Guides setup and operation of Lambda Labs GPU instances for machine learning training and inference. Covers SSH access, persistent storage, API automation, and multi-GPU or multi-node training, including how to retain checkpoints and terminate idle instances.

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• Setting up a GPU instance for LLM fine-tuning
• Keeping datasets and checkpoints between sessions
• Automating instance launch and termination through the API

Add PyTorch FSDP2 to training scripts so model state can be split across GPUs. Covers distributed initialization, mixed precision, CPU offloading, and checkpoint saving and loading for models that exceed a single GPU's memory.

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• Training a model that exceeds one GPU's memory
• Adding FSDP2 to an existing training script
• Configuring mixed precision and CPU offloading

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

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• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Train or fine-tune language and vision models on Hugging Face Jobs without a local GPU. Covers dataset checks, hardware and cost planning, training monitoring, Hub storage, and GGUF conversion for local use.

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• Fine-tune a language model without a local GPU
• Validate preference data before DPO training
• Estimate training costs and choose GPU hardware

Manages AI model retention and machine unlearning requirements. Covers training data deletion verification, model versioning for compliance, machine unlearning techniques (SISA, gradient-based), and retraining triggers. Keywords: AI retention, machine unlearning, model versioning, training data deletion, retraining, storage limitation.

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Use this skill when planning user adoption, structuring Salesforce training materials, drafting release communications, or running a change impact assessment for a Salesforce rollout or update. Triggers: user adoption plan, training materials, release announcement, change impact, go-live communication, communication plan, training plan by persona, adoption metrics, LoginHistory adoption report, PromptAction, training sandbox, go-live checklist, post-go-live feedback, super user program, pilot group. NOT for org deployment mechanics or sandbox promotion — use admin/change-management-and-deployment. NOT for adoption of an Agentforce or Einstein AI feature — use admin/ai-adoption-change-management. NOT for configuring the in-app prompts themselves — use admin/in-app-guidance-and-walkthroughs.

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Synthesises training survey responses and manager notes into a DRAFT training needs report by role: coded needs with record counts per role (n of N), the evidence type behind each count, anonymised quotes cited to record codes, disagreements between what staff report and what managers observe, gaps where a role is under-represented or a required skill has no evidence, and follow-up questions to settle before any course is designed. Never rates an individual, ranks a team or turns a count into a competence verdict. Use when the user asks to "analyse this training survey", "what training do our teams need", "summarise the manager feedback on skills gaps", "build a training needs analysis by role" or "which roles have the biggest skill gaps". Do not use for turning an agreed need into a course, use course-outline-builder instead; for exit interviews, use exit-interview-synthesis. Drafts for human review; never approves, authorises or signs off.

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使用TRL指导语言模型进一步训练，使回答更符合指令或人的偏好。涵盖数据准备、训练方式选择、奖励模型训练与评估，以及内存不足和训练不稳定时的调整。

• 用回答示例训练模型遵循指令
• 用DPO学习回答偏好
• 构建使用奖励模型的RLHF流程

指导使用Axolotl对大语言模型进行微调，涵盖YAML配置、数据格式和训练方法，也可用于检查多GPU设置、排查训练故障及运行缓慢的问题。

• 编写微调用的YAML配置
• 了解LoRA和QLoRA训练方法
• 查阅数据格式和API用法

Track machine learning experiments in W&B, record training settings and metrics, and compare results in dashboards. Automate searches for training settings and keep version histories for models and datasets.

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• Comparing accuracy across training runs
• Monitoring training progress in dashboards
• Searching for better training settings

将PyTorch训练代码整理为LightningModule和Trainer结构，配置验证、日志记录、模型保存及多GPU分布式训练。

• 迁移现有PyTorch训练代码
• 添加验证指标记录和模型保存
• 配置多GPU训练

为项目管理应用的新客户制作入门培训HTML幻灯片，以柔和的粉彩设计讲解获得首次成果的步骤，以及有助于持续使用应用的习惯。

• 为新客户准备入门工作坊
• 讲解获得首次成果的步骤
• 培训持续使用应用的习惯