From 5705c6f1168c544774a6f24dc4467a17329b86a1 Mon Sep 17 00:00:00 2001 From: Saoud Rizwan <7799382+saoudrizwan@users.noreply.github.com> Date: Tue, 16 Jun 2026 15:58:01 -0700 Subject: [PATCH 1/2] feat: add sagemaker ai plugin --- plugins/sagemaker-ai/LICENSE.sagemaker-ai | 201 ++ plugins/sagemaker-ai/NOTICE.sagemaker-ai | 11 + plugins/sagemaker-ai/README.md | 42 + plugins/sagemaker-ai/index.ts | 49 + plugins/sagemaker-ai/package.json | 21 + .../skills/dataset-evaluation/SKILL.md | 71 + ...ustom-scorer-evaluation-dataset-formats.md | 90 + .../references/strategy_data_requirements.md | 234 ++ .../scripts/format_detector.py | 678 +++++ .../skills/dataset-transformation/SKILL.md | 235 ++ .../code_templates/transformation.py | 45 + .../references/code_output_guide.md | 72 + .../references/dataset_transformation_code.md | 135 + .../references/sagemaker_dataset_formats.md | 146 + .../scripts/transformation_tools.py | 146 + .../skills/directory-management/SKILL.md | 37 + .../skills/finetuning-technique/SKILL.md | 50 + .../finetune_technique_selection_guide.md | 64 + .../scripts/get_recipes.py | 30 + .../sagemaker-ai/skills/finetuning/SKILL.md | 182 ++ .../skills/finetuning/code_templates/dpo.py | 149 + .../code_templates/rlaif_builtin.py | 154 + .../code_templates/rlaif_custom_prompt.py | 167 ++ .../skills/finetuning/code_templates/rlvr.py | 168 ++ .../skills/finetuning/code_templates/sft.py | 145 + .../references/code_output_guide.md | 76 + .../references/continuous_customization.md | 194 ++ .../finetuning/references/eula_links.md | 37 + .../finetuning/references/rlaif_guide.md | 91 + .../references/rlvr_reward_function.md | 183 ++ .../finetuning/scripts/mlflow_reference.py | 26 + ...va_rlvr_reward_function_source_template.py | 352 +++ .../rlvr_reward_function_source_template.py | 250 ++ .../skills/hyperpod-cluster-debugger/SKILL.md | 198 ++ .../references/capacity-planning.md | 124 + .../references/cloudformation-errors.md | 84 + .../references/cluster-diagnostics-detail.md | 463 +++ .../references/cluster-operations.md | 270 ++ .../references/iam-permissions.md | 40 + .../references/lifecycle-scripts.md | 111 + .../scripts/diagnose-cluster.sh | 1621 +++++++++++ .../skills/hyperpod-issue-report/SKILL.md | 77 + .../references/collection-details.md | 105 + .../references/troubleshooting.md | 22 + .../scripts/hyperpod_issue_report.py | 1497 ++++++++++ .../skills/hyperpod-nccl/SKILL.md | 187 ++ .../references/debugging-guide.md | 1011 +++++++ .../references/error-patterns-quick-ref.md | 47 + .../hyperpod-nccl/references/operations.md | 393 +++ .../references/performance-testing.md | 247 ++ .../hyperpod-nccl/scripts/nccl-diagnose.sh | 2563 +++++++++++++++++ .../skills/hyperpod-node-debugger/SKILL.md | 269 ++ .../references/node-diagnostics-detail.md | 1074 +++++++ .../references/node-issue-catalog.md | 141 + .../scripts/check-efa-sg.sh | 355 +++ .../scripts/check-node-reachability.sh | 389 +++ .../scripts/check-vpc-config.sh | 508 ++++ .../scripts/triage-cluster.sh | 1258 ++++++++ .../hyperpod-performance-debugger/SKILL.md | 185 ++ .../references/perf-details.md | 202 ++ .../scripts/perf-snapshot.sh | 667 +++++ .../skills/hyperpod-slurm-debugger/SKILL.md | 243 ++ .../references/slurm-details.md | 318 ++ .../scripts/slurm-diagnose.sh | 802 ++++++ .../sagemaker-ai/skills/hyperpod-ssm/SKILL.md | 110 + .../references/troubleshooting.md | 61 + .../hyperpod-ssm/scripts/get-cluster-info.sh | 26 + .../skills/hyperpod-ssm/scripts/list-nodes.sh | 43 + .../skills/hyperpod-ssm/scripts/ssm-exec.sh | 113 + .../skills/hyperpod-version-checker/SKILL.md | 68 + .../scripts/hyperpod_check_versions.sh | 556 ++++ .../skills/model-deployment/SKILL.md | 130 + .../code_templates/deploy-nova-bedrock.py | 64 + .../code_templates/deploy-nova-sagemaker.py | 65 + .../code_templates/deploy-oss-bedrock.py | 120 + .../code_templates/deploy-oss-sagemaker.py | 69 + .../references/code_output_guide.md | 76 + .../references/deploy-nova-bedrock.md | 123 + .../references/deploy-nova-sagemaker.md | 146 + .../references/deploy-oss-bedrock.md | 140 + .../references/deploy-oss-sagemaker.md | 157 + .../references/model-licenses.md | 41 + .../skills/model-evaluation/SKILL.md | 110 + .../code_templates/custom_scorer_evaluator.py | 87 + .../code_templates/llmaaj_evaluator.py | 89 + .../references/code_output_guide.md | 80 + .../references/create-reward-function.md | 66 + .../references/custom-lambda-scorer.md | 139 + .../references/custom-scorer-evaluation.md | 233 ++ .../references/evaluation-type-guide.md | 142 + .../references/llmaaj-builtin-evaluation.md | 59 + .../references/llmaaj-custom-evaluation.md | 63 + .../references/llmaaj-evaluation.md | 290 ++ .../references/supported-judge-models.md | 32 + .../nova_reward_function_source_template.py | 358 +++ .../reward_function_source_template.py | 245 ++ .../scripts/validate_custom_metrics.py | 124 + .../skills/model-selection/SKILL.md | 76 + .../references/benchmarks/agenticIndex.md | 44 + .../references/benchmarks/codingIndex.md | 44 + .../references/benchmarks/gpqa.md | 44 + .../references/benchmarks/hle.md | 44 + .../references/benchmarks/ifbench.md | 44 + .../benchmarks/intelligenceIndex.md | 44 + .../references/benchmarks/mmmuPro.md | 44 + .../references/benchmarks/tau2.md | 44 + .../references/model-licenses.md | 37 + .../references/model-selection.md | 107 + .../scripts/get_model_names.py | 43 + plugins/sagemaker-ai/skills/planning/SKILL.md | 142 + .../references/evaluate-first-plan.md | 24 + .../references/input-output-contracts.md | 15 + .../references/model-customization-plan.md | 15 + .../references/skill-routing-constraints.md | 60 + .../skills/sdk-getting-started/SKILL.md | 21 + .../references/execution-role-setup.md | 45 + .../references/sagemaker-python-sdk-setup.md | 63 + .../skills/use-case-specification/SKILL.md | 79 + 118 files changed, 25306 insertions(+) create mode 100644 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plugins/sagemaker-ai/skills/sdk-getting-started/references/sagemaker-python-sdk-setup.md create mode 100644 plugins/sagemaker-ai/skills/use-case-specification/SKILL.md diff --git a/plugins/sagemaker-ai/LICENSE.sagemaker-ai b/plugins/sagemaker-ai/LICENSE.sagemaker-ai new file mode 100644 index 00000000..05ae14a3 --- /dev/null +++ b/plugins/sagemaker-ai/LICENSE.sagemaker-ai @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2026 Cline Bot Inc. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/plugins/sagemaker-ai/NOTICE.sagemaker-ai b/plugins/sagemaker-ai/NOTICE.sagemaker-ai new file mode 100644 index 00000000..7d8b37a2 --- /dev/null +++ b/plugins/sagemaker-ai/NOTICE.sagemaker-ai @@ -0,0 +1,11 @@ +SageMaker AI plugin + +This plugin includes SageMaker AI workflow skill materials originally published +by Amazon Web Services as part of the AWS agent plugins project. + +Upstream metadata: +- Package name: sagemaker-ai +- Version: 1.2.1 +- Author: Amazon Web Services +- Repository: https://github.com/awslabs/agent-plugins +- License: Apache-2.0 diff --git a/plugins/sagemaker-ai/README.md b/plugins/sagemaker-ai/README.md new file mode 100644 index 00000000..09bff95f --- /dev/null +++ b/plugins/sagemaker-ai/README.md @@ -0,0 +1,42 @@ +# sagemaker-ai + +Adds SageMaker AI model customization and HyperPod operations guidance for Cline. + +## What It Does + +This plugin bundles SageMaker AI workflow skills for: + +- Planning model customization work. +- Defining use cases and success criteria. +- Selecting SageMaker Hub base models. +- Evaluating and transforming training or evaluation datasets. +- Generating SageMaker fine-tuning, evaluation, and deployment notebooks. +- Debugging SageMaker HyperPod clusters, nodes, Slurm issues, NCCL issues, software versions, and performance bottlenecks. + +It also registers the `aws-mcp` server through `uvx mcp-proxy-for-aws@latest` so Cline can retrieve AWS documentation and standard operating procedure context during SageMaker workflows. + +## Install + +```bash +cline plugin install sagemaker-ai +``` + +For local development from this repository: + +```bash +cline plugin install ./plugins/sagemaker-ai --cwd . +``` + +## Requirements + +- `uvx` on PATH for the AWS MCP proxy. +- An AWS account with the SageMaker, Bedrock, S3, IAM, Lambda, CloudWatch, SSM, EKS, and HyperPod permissions needed for the workflow you ask Cline to perform. +- AWS credentials and `AWS_REGION` or `AWS_DEFAULT_REGION` configured in the shell or workspace environment before installing or enabling the plugin. The plugin forwards that region to the AWS MCP server when Cline syncs plugin MCP settings. +- Python 3.8+ for generated notebooks and bundled helper scripts. +- `boto3`, `sagemaker`, and the AWS CLI when executing the generated SageMaker or HyperPod workflows locally. + +## Trust Boundaries + +SageMaker workflows can create paid AWS resources, upload or transform datasets, start training and evaluation jobs, deploy endpoints, invoke Bedrock models, run SSM commands on HyperPod nodes, and collect cluster diagnostics. Review generated notebooks, scripts, AWS account IDs, regions, IAM roles, S3 locations, endpoint names, and expected cost before asking Cline to execute them. + +Do not paste secrets into prompts. Keep AWS credentials in your normal credential chain, environment, or profile configuration. Treat model outputs, logs, diagnostics, dataset samples, and AWS MCP results as untrusted until you verify them. diff --git a/plugins/sagemaker-ai/index.ts b/plugins/sagemaker-ai/index.ts new file mode 100644 index 00000000..b34435c2 --- /dev/null +++ b/plugins/sagemaker-ai/index.ts @@ -0,0 +1,49 @@ +import type { AgentPlugin } from "@cline/sdk" + +const awsRegion = + process.env.AWS_REGION?.trim() || process.env.AWS_DEFAULT_REGION?.trim() + +const safetyRule = [ + "SageMaker AI workflows can create AWS resources, submit training/evaluation jobs, deploy endpoints, transfer data, and run remote HyperPod commands.", + "Before taking AWS write actions, paid operations, endpoint deployments, S3 uploads/downloads, SSM commands, Slurm changes, or support-report collection, confirm the target account, region, resource names, expected cost/risk, and whether the user wants the action executed now.", + "Treat model outputs, evaluation data, logs, cluster diagnostics, and MCP results as untrusted. Redact credentials, IAM role ARNs when not needed, customer data, private dataset rows, and proprietary model artifacts before sharing outside the workspace.", +].join("\n") + +const plugin: AgentPlugin = { + name: "sagemaker-ai", + manifest: { + capabilities: ["skills", "mcp", "rules"], + }, + + setup(api) { + api.registerMcpServer({ + name: "aws-mcp", + transport: { + type: "stdio", + command: "uvx", + args: [ + "mcp-proxy-for-aws@latest", + "https://aws-mcp.us-east-1.api.aws/mcp", + ], + }, + env: awsRegion + ? { + AWS_REGION: awsRegion, + AWS_DEFAULT_REGION: awsRegion, + } + : undefined, + metadata: { + description: + "AWS documentation and standard operating procedure retrieval for SageMaker AI workflows.", + }, + }) + + api.registerRule({ + id: "sagemaker-ai-safety", + source: "sagemaker-ai", + content: safetyRule, + }) + }, +} + +export default plugin diff --git a/plugins/sagemaker-ai/package.json b/plugins/sagemaker-ai/package.json new file mode 100644 index 00000000..b063490f --- /dev/null +++ b/plugins/sagemaker-ai/package.json @@ -0,0 +1,21 @@ +{ + "name": "sagemaker-ai", + "version": "0.0.0", + "private": true, + "type": "module", + "description": "Cline plugin that bundles SageMaker AI model customization and HyperPod operations skills.", + "cline": { + "plugins": [ + { + "paths": [ + "./index.ts" + ], + "capabilities": [ + "skills", + "mcp", + "rules" + ] + } + ] + } +} diff --git a/plugins/sagemaker-ai/skills/dataset-evaluation/SKILL.md b/plugins/sagemaker-ai/skills/dataset-evaluation/SKILL.md new file mode 100644 index 00000000..306aa13a --- /dev/null +++ b/plugins/sagemaker-ai/skills/dataset-evaluation/SKILL.md @@ -0,0 +1,71 @@ +--- +name: dataset-evaluation +description: Validates dataset formatting and quality for SageMaker AI model fine-tuning or evaluation workflows. Use for SageMaker dataset readiness questions, training data checks, evaluation data checks, or before starting a SageMaker fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready. +metadata: + version: "1.0.0" +--- + +# Workflow Instruction + +Follow the workflow shown below. Locate the dataset, check the file type, and resolve any issues with missing files or wrong file types. Determine the fine-tuning model and fine-tuning strategy. Run the appropriate validation based on the model family. Summarize the results: is the dataset ready for fine-tuning? + +## Prerequisites + +- The SDK environment has been verified (SDK version, region, execution role). If not done, activate the `sdk-getting-started` skill first. + +--- + +## Workflow + +1. Locate Dataset: + - The full path may be a local file path, or an S3 URI + - Resolve the full path to the dataset file, make sure read permissions are available, and help the user if the file is not found + +2. Determine strategy and model: + - File formatting depends on the currently selected fine-tuning strategy and fine-tuning base model. + - If the strategy and model are already known from the conversation context (e.g., selected via the model-selection and finetuning-technique skills), use them. + - If not available in context, activate the model-selection and/or finetuning-technique skills to determine them before proceeding. + - Exception: If the user is validating an evaluation dataset (not a training dataset), neither model nor technique is required - the format detector can validate eval format (query/response structure) independently. Do not block on model-selection or finetuning-technique for eval dataset validation. + +3. Check File Formatting: Run the tool format_detector.py to make sure the file conforms to formatting requirements. + - Send the full path directly to the format_detector script as an argument + - Do not send the model and strategy as arguments + - Do not download data from S3 + - Do not make local copies of data + +4. Summarize Results: Tell the user if their data is ready + - Examine the output of format_detector and compare to the known strategy and model + - Important: training datasets and evaluation datasets have different format requirements. + - Training datasets must match the fine-tuning strategy format per `references/strategy_data_requirements.md` + - Evaluation datasets (for model evaluation) must match one of the [SageMaker evaluation dataset formats](https://docs.aws.amazon.com/sagemaker/latest/dg/model-customize-evaluation-dataset-formats.html). + - Custom Scorer evaluation datasets have scorer-specific requirements. If the dataset is intended for Custom Scorer evaluation (Prime Math, Prime Code, or Custom Lambda), read `references/custom-scorer-evaluation-dataset-formats.md` and validate against the scorer-specific schema. The scorer type should be known from conversation context (determined in the model-evaluation skill). + - Report back to the user if their current dataset is valid for its intended purpose + - Warn the user if their dataset is valid, but for a different strategy or model + - Warn the user if their dataset is not valid for any strategy/model pair + - If the user plans to finetune a model with the evaluated dataset, it needs to be uploaded to an S3 bucket in the same region as the planned training job (usually the default region). Warn the user if this is NOT the case. + - If the dataset is NOT in the necessary format, recommend transforming it using the dataset-transformation skill, wait for user confirmation, and update the plan based on their response + +## Messages to the User + +- Introduction: "This skill checks the structure of your dataset for model fine-tuning." +- File types: This skill applies to files that are formatted according to the [Amazon SageMaker AI Developer Guide](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-llms-finetuning-data-format.html#autopilot-llms-finetuning-dataset-format) + +# Resources + +- scripts/format_detector.py is self-contained format validation script that can be run independently +- model-selection and finetuning-technique skills should have already determined the base model and fine-tuning strategy +- references/strategy_data_requirements.md contains data format requirements per strategy + +## Script Details + +- scripts/format_detector.py is self-contained format validation script that can be run independently: + +```bash +# With the file path argument identified in workflow step 1 +python scripts/format_detector.py local_path/to/dataset +``` + +## References + +- `scripts/format_detector.py` - Self-contained format validation script +- `references/strategy_data_requirements.md` - Data format requirements per strategy diff --git a/plugins/sagemaker-ai/skills/dataset-evaluation/references/custom-scorer-evaluation-dataset-formats.md b/plugins/sagemaker-ai/skills/dataset-evaluation/references/custom-scorer-evaluation-dataset-formats.md new file mode 100644 index 00000000..878f9b4e --- /dev/null +++ b/plugins/sagemaker-ai/skills/dataset-evaluation/references/custom-scorer-evaluation-dataset-formats.md @@ -0,0 +1,90 @@ +# Custom Scorer Evaluation Dataset Formats + +Dataset format requirements for evaluation datasets used with the Custom Scorer pathway. Note that these are distinct from any requirements for training dataset formats - they are specifically for datasets scored by Prime Math, Prime Code, or a Custom Lambda during model evaluation. + +## Format by scorer type + +### Prime Math + +Evaluates mathematical reasoning by comparing model output to a ground truth answer using symbolic equality. + +| Field | Type | Required | Description | +| ---------- | ------ | -------- | ----------------------- | +| `query` | string | yes | The math problem | +| `response` | string | yes | The ground truth answer | + +Example: + +```jsonl +{"query": "What is 15 + 27?", "response": "42"} +{"query": "What is the square root of 81?", "response": "9"} +{"query": "Solve for x: 2x + 6 = 20", "response": "7"} +``` + +Notes: + +- The scorer uses sympy for symbolic comparison and extracts answers from `\boxed{}`, text after "is", "=", "answer:", etc. +- `response` should be just the answer value (e.g., "42"), not a full explanation. The scorer compares this against what it extracts from the model's output. + +--- + +### Prime Code + +Evaluates code generation by executing the model's output against test cases (stdin -> stdout). + +| Field | Type | Required | Description | +| ---------- | ------ | -------- | --------------------------------------------------------------- | +| `query` | string | yes | The coding problem description | +| `response` | string | yes | Reference solution code (used for text metrics like ROUGE/BLEU) | +| `metadata` | object | yes | Test cases: `{"inputs": [...], "outputs": [...]}` | + +Example: + +```jsonl +{"query": "Write a program that reads an integer and prints its double.", "response": "n = int(input())\nprint(n * 2)", "metadata": {"inputs": ["5", "3", "10"], "outputs": ["10", "6", "20"]}} +``` + +Notes: + +- `metadata.inputs` and `metadata.outputs` must be string arrays of equal length. +- The scorer extracts code from `` ```python ``` `` blocks in the model's output, then executes it with each input piped to stdin and compares stdout to the expected output. +- The model must produce code that reads from stdin and prints to stdout. + +--- + +### Custom Lambda + +Uses your own Lambda function to score model outputs. The dataset format depends on the model type. + +#### Dataset for Custom Lambda - OSS models + +| Field | Type | Required | Description | +| ---------- | ------ | -------- | ---------------------------------- | +| `query` | string | yes | The prompt/input | +| `response` | string | yes | The ground truth / expected output | +| `system` | string | no | System prompt | + +Example: + +```jsonl +{"query": "Redact PII from: John Smith lives at 123 Main St.", "response": "[PERSON: John Smith] lives at [ADDRESS: 123 Main St].", "system": "You are a PII redaction assistant."} +``` + +#### Dataset for Custom Lambda - Nova models + +| Field | Type | Required | Description | +| ------------------ | ------ | -------- | ------------------------------------------------------------------------- | +| `messages` | array | yes | Conversation array with `role` and `content` (plain strings, not objects) | +| `reference_answer` | string | no | Ground truth - required only if your Lambda compares against it | + +Messages may include a `system` role (optional): + +```jsonl +{"messages": [{"role": "system", "content": "You are a PII redaction assistant."}, {"role": "user", "content": "Redact PII from: John Smith lives at 123 Main St."}], "reference_answer": "[PERSON: John Smith] lives at [ADDRESS: 123 Main St]."} +``` + +Or just a `user` message: + +```jsonl +{"messages": [{"role": "user", "content": "Redact PII from: John Smith lives at 123 Main St."}], "reference_answer": "[PERSON: John Smith] lives at [ADDRESS: 123 Main St]."} +``` diff --git a/plugins/sagemaker-ai/skills/dataset-evaluation/references/strategy_data_requirements.md b/plugins/sagemaker-ai/skills/dataset-evaluation/references/strategy_data_requirements.md new file mode 100644 index 00000000..198cc23a --- /dev/null +++ b/plugins/sagemaker-ai/skills/dataset-evaluation/references/strategy_data_requirements.md @@ -0,0 +1,234 @@ +# Finetuning Strategy Data Requirements + +Critical Nova models have a different set of formats than open weights models. Make sure you refer to the right section based on the user's base model. + +## Open Weights Models Data Format by Strategy (Llama, Qwen, GPT-OSS, etc.) + +### SFT (Supervised Fine-Tuning) + +Required format: + +```jsonl +{ + "prompt": "", + "completion": "" +} +``` + +What it needs: + +- Input-output pairs +- Single "correct" response per input +- Consistent quality across examples + +### DPO (Direct Preference Optimization) + +Required format: + +```jsonl +{ + "prompt": "", + "chosen": "", + "rejected": "" +} +``` + +What it needs: + +- Input with two responses: preferred (chosen) and dispreferred (rejected) +- Clear preference signal between responses +- Both responses should be plausible but one is better +- Avoiding unintentional length bias + +### RLVR (Reinforcement Learning from Verifiable Rewards) + +Required format: + +```jsonl +{ + "data_source": "", + "prompt": [ + { + "content": "", + "role": "" + } + ], + "ability": "", + "reward_model": { + "ground_truth": "", + "style": "" + } +} +``` + +What it needs: + +- user prompt +- Ground truth responses in `reward_model.ground_truth` field (leave empty if user data does not have responses) + +How it works: + +1. Model generates response for input +2. Lambda receives full user prompt + reward model fields +3. Lambda computes reward (uses ground_truth if included in verification logic) +4. Model learns to maximize rewards + +### RLAIF (Reinforcement Learning from AI Feedback) + +RLAIF uses the same base schema as RLVR. The `ability` and `reward_model.style` fields determine which evaluator is used. + +Base schema: + +```jsonl +{ + "data_source": "", + "prompt": [ + { + "role": "", + "content": "" + } + ], + "ability": "", + "reward_model": { + "style": "", + "ground_truth": "" + } +} +``` + +#### Built-in Evaluators + +| `ability` | `reward_model.style` | Use case | +| ------------------ | -------------------- | ---------------------------------------------------- | +| `pairwise-judging` | `llmj` | Compare two model responses and pick the better one | +| `chain-of-thought` | `llmj-cot` | Evaluate quality of step-by-step reasoning | +| `faithfulness` | `llmj-faithfulness` | Check if response stays grounded in provided context | +| `summarization` | `llmj-summarization` | Evaluate quality of a generated summary | + +`pairwise-judging` - prompt must include both responses to compare; `ground_truth` is the preferred response index + reasoning. + +`chain-of-thought` / `faithfulness` / `summarization` - prompt contains the task; `ground_truth` is the reference answer or source text. + +#### Custom Evaluator + +Set `reward_model.style` to `llmj-custom` and supply a Jinja2 prompt template. The template receives `{{ prompt }}`, `{{ response }}`, and optional `{{ ground_truth }}` as variables. The LLM judge must return a JSON object with a `score` field (0.0-1.0). + +```jsonl +{ + "data_source": "", + "prompt": [ + { + "role": "user", + "content": "" + } + ], + "ability": "chain-of-thought", + "reward_model": { + "style": "llmj-custom", + "ground_truth": "" + } +} +``` + +The custom Jinja prompt is provided separately at training time (not embedded in the dataset). It must instruct the judge to return exactly: `{"score": <0.0-1.0>, ...}`. + +--- + +## Nova Models Data Format by Strategy + +### SFT (Supervised Fine-Tuning) + +```jsonl +{ + "schemaVersion": "bedrock-conversation-2024", + "system": [ + { + "text": "" + } + ], + "messages": [ + { + "role": "user", + "content": [ + { + "text": "" + } + ] + }, + { + "role": "assistant", + "content": [ + { + "text": "" + } + ] + } + ] +} +``` + +### DPO (Direct Preference Optimization) + +The format is the same as SFT for the first N-1 turns. The final assistant turn uses `candidates` with `preferenceLabel` instead of regular `content`. + +```jsonl +{ + "schemaVersion": "bedrock-conversation-2024", + "system": [ + { + "text": "" + } + ], + "messages": [ + { + "role": "user", + "content": [ + { + "text": "" + } + ] + }, + { + "role": "assistant", + "candidates": [ + { + "content": [ + { + "text": "" + } + ], + "preferenceLabel": "preferred" + }, + { + "content": [ + { + "text": "" + } + ], + "preferenceLabel": "non-preferred" + } + ] + } + ] +} +``` + +### RLVR + +```jsonl +{ + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant." + }, + { + "role": "user", + "content": "Hello!" + } + ], + "reference_answer": { + "answer": "49" + } +} +``` diff --git a/plugins/sagemaker-ai/skills/dataset-evaluation/scripts/format_detector.py b/plugins/sagemaker-ai/skills/dataset-evaluation/scripts/format_detector.py new file mode 100644 index 00000000..40c15dc6 --- /dev/null +++ b/plugins/sagemaker-ai/skills/dataset-evaluation/scripts/format_detector.py @@ -0,0 +1,678 @@ +"""Format detection for S3 JSONL files. + +This module provides functionality to detect and validate JSONL file formats +stored in S3. It samples the first 1MB of a file to determine the format type +across 11 supported formats: Nova SFT, Nova DPO, Nova RLVR, GPT-OSS SFT, +GPT-OSS DPO, Open Weights SFT, Open Weights SFT Conv, Open Weights DPO, +Verl, Verl Legacy, and SageMaker Eval. + +Usage: + result = detect_format("s3://my-bucket/data.jsonl") + if result.is_valid: + print(f"Format: {result.format_type}") +""" + +from dataclasses import dataclass +from enum import Enum +import boto3 +import json +import logging + +logger = logging.getLogger(__name__) + +__all__ = ["FormatType", "ConfidenceLevel", "ValidationError", "FormatDetectionResult", "detect_format"] + + +class FormatType(Enum): + """Supported JSONL format types.""" + NOVA_SFT = "nova_sft" + NOVA_DPO = "nova_dpo" + NOVA_RLVR = "nova_rlvr" + GPT_OSS_SFT = "gpt_oss_sft" + GPT_OSS_DPO = "gpt_oss_dpo" + OPEN_WEIGHTS_SFT = "open_weights_sft" + OPEN_WEIGHTS_SFT_CONV = "open_weights_sft_conv" + OPEN_WEIGHTS_DPO = "open_weights_dpo" + VERL = "verl" + VERL_LEGACY = "verl_legacy" + SAGEMAKER_EVAL = "sagemaker_eval" + UNKNOWN = "unknown" + + +class ConfidenceLevel(Enum): + """Confidence level for format detection results.""" + HIGH = "high" + LOW = "low" + NONE = "none" + + +@dataclass +class ValidationError: + """Represents a validation error found during format detection.""" + line_number: int + error_type: str + message: str + + +@dataclass +class FormatDetectionResult: + """Result of format detection operation.""" + format_type: FormatType + is_valid: bool + lines_sampled: int + errors: list[ValidationError] + confidence: ConfidenceLevel + + +def _sample_local_file(file_path: str, sample_size: int) -> list[str]: + """Sample lines from local JSONL file. + + Args: + file_path: Path to local file + sample_size: Maximum bytes to read + + Returns: + List of lines from file + + Raises: + FileNotFoundError: If file doesn't exist + IOError: If file can't be read + """ + logger.debug("Sampling local file: %s", file_path) + with open(file_path, "rb") as f: + data = f.read(sample_size) + + if not data: + return [] + + text = data.decode("utf-8") + + last_newline_idx = text.rfind("\n") + if last_newline_idx == -1: + return [] + + complete_text = text[:last_newline_idx + 1] + lines = [line for line in complete_text.split("\n") if line] + + return lines + + +def _sample_s3_file(s3_uri: str, sample_size_bytes: int, s3_client=None) -> list[str]: + """Sample the first N bytes of an S3 file and return complete lines. + + Reads the first sample_size_bytes from an S3 file using a Range request, + then truncates to the last complete newline to avoid partial lines. + + Args: + s3_uri: S3 URI in format "s3://bucket/key" + sample_size_bytes: Number of bytes to sample (default 1MB) + s3_client: Optional boto3 S3 client to reuse + + Returns: + List of complete JSONL lines (strings without trailing newlines) + + Raises: + ValueError: If S3 URI is invalid (missing "s3://", bucket, or key) + botocore.exceptions.ClientError: If S3 access fails + """ + logger.debug("Sampling S3 file: %s (%d bytes)", s3_uri, sample_size_bytes) + # Parse S3 URI + if not s3_uri.startswith("s3://"): + raise ValueError(f"Invalid S3 URI: must start with 's3://' (got: {s3_uri})") + + uri_without_prefix = s3_uri[5:] # Remove "s3://" + parts = uri_without_prefix.split("/", 1) + + if len(parts) != 2 or not parts[0] or not parts[1]: + raise ValueError(f"Invalid S3 URI: must contain bucket and key (got: {s3_uri})") + + bucket, key = parts + + # Read first sample_size_bytes using Range header + client = s3_client or boto3.client("s3") + range_header = f"bytes=0-{sample_size_bytes - 1}" + + response = client.get_object(Bucket=bucket, Key=key, Range=range_header) + data = response["Body"].read() + + # Handle empty file + if not data: + return [] + + # Decode bytes to string + text = data.decode("utf-8") + + # Find last complete newline to avoid truncated lines + last_newline_idx = text.rfind("\n") + if last_newline_idx == -1: + # No newlines found - return empty list if file is all one line + # (we can't be sure it's complete) + return [] + + # Keep only complete lines (up to and including last newline) + complete_text = text[:last_newline_idx + 1] + + # Split on newlines and filter empty strings + lines = [line for line in complete_text.split("\n") if line] + + return lines + + +def _classify_nova_format(record: dict) -> FormatType: + """Classify Nova-specific format by checking last message structure. + + Args: + record: Parsed JSON record with messages field + + Returns: + FormatType.NOVA_DPO if last message has candidates field, + FormatType.NOVA_SFT if last message has standard content field, + FormatType.UNKNOWN otherwise + """ + messages = record.get("messages", []) + if not messages: + return FormatType.UNKNOWN + + last_message = messages[-1] + if "candidates" in last_message: + return FormatType.NOVA_DPO + elif "content" in last_message and last_message["content"]: + return FormatType.NOVA_SFT + else: + return FormatType.UNKNOWN + + +def _classify_messages_format(record: dict) -> FormatType: + """Distinguish Nova vs GPT-OSS/HF by inspecting content structure. + + Nova has nested content arrays (list of dicts with 'text' field), + GPT-OSS/HF has flat content strings. + + Args: + record: Parsed JSON record with messages field + + Returns: + FormatType value for the detected format + """ + messages = record.get("messages") + + # Critical type checking: messages must be a list + if not isinstance(messages, list): + return FormatType.UNKNOWN + + if not messages: + return FormatType.UNKNOWN + + first_message = messages[0] + + # Check if content field exists + if "content" not in first_message: + return FormatType.UNKNOWN + + content = first_message["content"] + + # Nova: nested content arrays (list of dicts with 'text' field) + if isinstance(content, list): + return _classify_nova_format(record) + # GPT-OSS/HF: flat content strings + elif isinstance(content, str): + return FormatType.GPT_OSS_SFT + else: + return FormatType.UNKNOWN + + +def _classify_schema(samples: list[dict]) -> FormatType: + """Top-level classifier that checks for all 11 supported formats. + + Args: + samples: List of parsed JSON records + + Returns: + FormatType value for the detected format + """ + if not samples: + return FormatType.UNKNOWN + + first = samples[0] + fields = set(first.keys()) + + # SageMaker Evaluation: query + response + if "query" in fields and "response" in fields: + return FormatType.SAGEMAKER_EVAL + + # Verl/RLVR: prompt + (reward_model or extra_info), no completion + if "prompt" in fields and ("reward_model" in fields or "extra_info" in fields): + if "completion" not in fields: + if isinstance(first["prompt"], list): + return FormatType.VERL + return FormatType.VERL_LEGACY + + # Messages-based formats: Nova RLVR, Nova, GPT-OSS + if "messages" in fields: + if "reference_answer" in fields: + return FormatType.NOVA_RLVR + return _classify_messages_format(first) + + # DPO: prompt/chosen/rejected + if {"prompt", "chosen", "rejected"}.issubset(fields): + if isinstance(first["prompt"], list): + return FormatType.GPT_OSS_DPO + return FormatType.OPEN_WEIGHTS_DPO + + # SFT: prompt/completion + if {"prompt", "completion"}.issubset(fields): + if isinstance(first["prompt"], list): + return FormatType.OPEN_WEIGHTS_SFT_CONV + return FormatType.OPEN_WEIGHTS_SFT + + return FormatType.UNKNOWN + + +def _validate_nova_messages(messages: list, line_num: int, is_dpo: bool) -> list[ValidationError]: + """Validate Nova SFT/DPO message structure.""" + errors = [] + for msg_idx, msg in enumerate(messages): + if "role" not in msg: + errors.append(ValidationError( + line_number=line_num, + error_type="missing_field", + message=f"Message {msg_idx} missing required field 'role'" + )) + elif msg["role"] not in ["user", "assistant", "system"]: + errors.append(ValidationError( + line_number=line_num, + error_type="invalid_structure", + message=f"Invalid role '{msg['role']}' in message {msg_idx}" + )) + if "content" not in msg and "candidates" not in msg: + errors.append(ValidationError( + line_number=line_num, + error_type="missing_field", + message=f"Message {msg_idx} missing 'content' or 'candidates'" + )) + if "content" in msg and not isinstance(msg["content"], list): + errors.append(ValidationError( + line_number=line_num, + error_type="invalid_structure", + message=f"Nova format content must be list, got {type(msg['content']).__name__}" + )) + if is_dpo and "candidates" in msg: + for cand_idx, candidate in enumerate(msg["candidates"]): + if "preferenceLabel" not in candidate: + errors.append(ValidationError( + line_number=line_num, + error_type="missing_field", + message=f"DPO message {msg_idx} candidate {cand_idx} missing 'preferenceLabel'" + )) + elif candidate["preferenceLabel"] not in ["preferred", "non-preferred"]: + errors.append(ValidationError( + line_number=line_num, + error_type="invalid_structure", + message=f"Invalid preferenceLabel '{candidate['preferenceLabel']}' in message {msg_idx} candidate {cand_idx}" + )) + return errors + + +def _validate_gpt_messages(messages: list, line_num: int) -> list[ValidationError]: + """Validate GPT-OSS SFT message structure.""" + errors = [] + for msg_idx, msg in enumerate(messages): + if "role" not in msg: + errors.append(ValidationError( + line_number=line_num, + error_type="missing_field", + message=f"Message {msg_idx} missing required field 'role'" + )) + elif msg["role"] not in ["user", "assistant", "system"]: + errors.append(ValidationError( + line_number=line_num, + error_type="invalid_structure", + message=f"Invalid role '{msg['role']}' in message {msg_idx}" + )) + if "content" not in msg: + errors.append(ValidationError( + line_number=line_num, + error_type="missing_field", + message=f"Message {msg_idx} missing required field 'content'" + )) + elif not isinstance(msg["content"], str): + errors.append(ValidationError( + line_number=line_num, + error_type="invalid_structure", + message=f"GPT-OSS format content must be string, got {type(msg['content']).__name__}" + )) + return errors + + +def _validate_rlvr_messages(messages: list, line_num: int) -> list[ValidationError]: + """Validate Nova RLVR message structure.""" + errors = [] + for msg_idx, msg in enumerate(messages): + if "role" not in msg: + errors.append(ValidationError( + line_number=line_num, + error_type="missing_field", + message=f"Message {msg_idx} missing required field 'role'" + )) + elif msg["role"] not in ["user", "assistant", "system"]: + errors.append(ValidationError( + line_number=line_num, + error_type="invalid_structure", + message=f"Invalid role '{msg['role']}' in message {msg_idx}" + )) + if "content" not in msg: + errors.append(ValidationError( + line_number=line_num, + error_type="missing_field", + message=f"Message {msg_idx} missing required field 'content'" + )) + elif not isinstance(msg["content"], str): + errors.append(ValidationError( + line_number=line_num, + error_type="invalid_structure", + message=f"Nova RLVR content must be string, got {type(msg['content']).__name__}" + )) + return errors + + +def _validate_verl_prompt(record: dict, line_num: int) -> list[ValidationError]: + """Validate Verl prompt structure (list of role/content dicts).""" + errors = [] + if "prompt" not in record: + errors.append(ValidationError( + line_number=line_num, + error_type="missing_field", + message="Missing required field 'prompt'" + )) + elif not isinstance(record["prompt"], list): + errors.append(ValidationError( + line_number=line_num, + error_type="invalid_structure", + message=f"Verl field 'prompt' must be list, got {type(record['prompt']).__name__}" + )) + else: + for msg_idx, msg in enumerate(record["prompt"]): + if not isinstance(msg, dict) or "role" not in msg or "content" not in msg: + errors.append(ValidationError( + line_number=line_num, + error_type="invalid_structure", + message=f"Prompt message {msg_idx} must have 'role' and 'content'" + )) + if "reward_model" not in record and "extra_info" not in record: + errors.append(ValidationError( + line_number=line_num, + error_type="missing_field", + message="Missing required field 'reward_model' or 'extra_info'" + )) + return errors + + +def _validate_verl_legacy_prompt(record: dict, line_num: int) -> list[ValidationError]: + """Validate Verl Legacy prompt structure (string) and extra_info.""" + errors = [] + if "prompt" not in record: + errors.append(ValidationError( + line_number=line_num, + error_type="missing_field", + message="Missing required field 'prompt'" + )) + elif not isinstance(record["prompt"], str): + errors.append(ValidationError( + line_number=line_num, + error_type="invalid_structure", + message=f"Verl Legacy field 'prompt' must be string, got {type(record['prompt']).__name__}" + )) + if "extra_info" not in record: + errors.append(ValidationError( + line_number=line_num, + error_type="missing_field", + message="Missing required field 'extra_info'" + )) + return errors + + +# Schema-driven format validation specs. +# Each entry defines required_fields (field->type mapping) and an optional +# message_validator or record_validator for complex per-record checks. +# - message_validator: called with (messages_list, line_num) -> list[ValidationError] +# Used for formats whose top-level required field is "messages" (list). +# - record_validator: called with (record, line_num) -> list[ValidationError] +# Used for formats needing whole-record access (verl, verl_legacy). +FORMAT_SCHEMAS = { + FormatType.NOVA_SFT: { + "required_fields": {"messages": list}, + "message_validator": lambda msgs, ln: _validate_nova_messages(msgs, ln, is_dpo=False), # nosemgrep: python.lang.maintainability.return.return-not-in-function -- lambda inside dict literal, not a bare return + }, + FormatType.NOVA_DPO: { + "required_fields": {"messages": list}, + "message_validator": lambda msgs, ln: _validate_nova_messages(msgs, ln, is_dpo=True), # nosemgrep: python.lang.maintainability.return.return-not-in-function -- lambda inside dict literal, not a bare return + }, + FormatType.NOVA_RLVR: { + "required_fields": {"messages": list, "reference_answer": dict}, + "message_validator": _validate_rlvr_messages, + }, + FormatType.GPT_OSS_SFT: { + "required_fields": {"messages": list}, + "message_validator": _validate_gpt_messages, + }, + FormatType.GPT_OSS_DPO: { + "required_fields": {"prompt": list, "chosen": list, "rejected": list}, + "field_error_prefix": "GPT-OSS DPO", + }, + FormatType.OPEN_WEIGHTS_SFT: { + "required_fields": {"prompt": str, "completion": str}, + "field_error_prefix": "Open Weights SFT", + }, + FormatType.OPEN_WEIGHTS_SFT_CONV: { + "required_fields": {"prompt": list, "completion": list}, + "field_error_prefix": "Open Weights SFT Conv", + }, + FormatType.OPEN_WEIGHTS_DPO: { + "required_fields": {"prompt": str, "chosen": str, "rejected": str}, + "field_error_prefix": "Open Weights DPO", + }, + FormatType.SAGEMAKER_EVAL: { + "required_fields": {"query": str, "response": str}, + "field_error_prefix": "SageMaker Eval", + }, + FormatType.VERL: { + "required_fields": {}, + "record_validator": _validate_verl_prompt, + }, + FormatType.VERL_LEGACY: { + "required_fields": {}, + "record_validator": _validate_verl_legacy_prompt, + }, +} + + +def _validate_samples(samples: list[dict], expected_format: FormatType, line_numbers: list[int]) -> tuple[bool, list[ValidationError]]: + """Validate that all samples conform to the expected format schema. + + Args: + samples: List of parsed JSON records + expected_format: Expected FormatType enum value + line_numbers: 1-based line numbers corresponding to each sample + + Returns: + Tuple of (is_valid, errors) where errors is a list of ValidationError objects + """ + errors = [] + schema = FORMAT_SCHEMAS.get(expected_format) + + for record, line_num in zip(samples, line_numbers): + # Check schema consistency + detected_format = _classify_schema([record]) + if detected_format != expected_format: + errors.append(ValidationError( + line_number=line_num, + error_type="schema_mismatch", + message=f"Expected {expected_format.value} but found {detected_format.value}" + )) + continue + + if schema is None: + continue + + # Record-level validator (verl, verl_legacy) handles everything + if "record_validator" in schema: + errors.extend(schema["record_validator"](record, line_num)) + continue + + # Check required fields exist with correct types + required = schema["required_fields"] + prefix = schema.get("field_error_prefix", "") + skip_messages = False + for field, expected_type in required.items(): + if field not in record: + errors.append(ValidationError( + line_number=line_num, + error_type="missing_field", + message=f"Missing required field '{field}'" + )) + if field == "messages": + skip_messages = True + elif not isinstance(record[field], expected_type): + actual = type(record[field]).__name__ + if field == "messages": + errors.append(ValidationError( + line_number=line_num, + error_type="invalid_structure", + message=f"Field 'messages' must be a list" + )) + skip_messages = True + elif prefix: + errors.append(ValidationError( + line_number=line_num, + error_type="invalid_structure", + message=f"{prefix} field '{field}' must be {expected_type.__name__}, got {actual}" + )) + else: + errors.append(ValidationError( + line_number=line_num, + error_type="invalid_structure", + message=f"Field '{field}' must be {expected_type.__name__}, got {actual}" + )) + + if skip_messages: + continue + + # Message-level validator + if "message_validator" in schema: + errors.extend(schema["message_validator"](record["messages"], line_num)) + + logger.debug("Validation found %d error(s)", len(errors)) + return (len(errors) == 0, errors) + + +def detect_format(file_path: str, sample_size_bytes: int = 1_048_576, s3_client=None) -> FormatDetectionResult: + """Detect the format of a JSONL file in S3 or on local disk. + + Samples the first sample_size_bytes of the file and analyzes the structure + to determine if it matches one of the 11 supported formats. + + Args: + file_path: S3 URI (s3://bucket/key) or local file path + sample_size_bytes: Number of bytes to sample (default 1MB = 1,048,576 bytes) + s3_client: Optional boto3 S3 client to reuse (ignored for local files) + + Returns: + FormatDetectionResult with format type, validation status, and any errors + """ + if file_path.startswith("s3://"): + lines = _sample_s3_file(file_path, sample_size_bytes, s3_client=s3_client) + else: + lines = _sample_local_file(file_path, sample_size_bytes) + + # Parse JSON lines and collect parse errors + parsed_records = [] + line_numbers = [] + errors = [] + + for line_num, line in enumerate(lines, start=1): + try: + parsed_records.append(json.loads(line)) + line_numbers.append(line_num) + except json.JSONDecodeError as e: + errors.append(ValidationError( + line_number=line_num, + error_type="parse_error", + message=f"Invalid JSON: {str(e)}" + )) + + # If no successfully parsed records, return UNKNOWN with parse errors + if not parsed_records: + confidence = ConfidenceLevel.NONE if errors else ConfidenceLevel.HIGH + return FormatDetectionResult( + format_type=FormatType.UNKNOWN, + is_valid=len(errors) == 0, + lines_sampled=len(lines), + errors=errors, + confidence=confidence + ) + + # Classify schema using first successfully parsed record + format_type = _classify_schema(parsed_records) + + # Validate all parsed records against detected format + is_valid, validation_errors = _validate_samples(parsed_records, format_type, line_numbers) + errors.extend(validation_errors) + + # Calculate confidence level + if len(errors) == 0: + confidence = ConfidenceLevel.HIGH + elif any(err.error_type == "parse_error" for err in errors): + confidence = ConfidenceLevel.NONE + else: + confidence = ConfidenceLevel.LOW + + logger.debug("Detected format: %s (valid=%s, confidence=%s)", format_type.value, is_valid, confidence.value) + + return FormatDetectionResult( + format_type=format_type, + is_valid=len(errors) == 0, + lines_sampled=len(lines), + errors=errors, + confidence=confidence + ) + + +if __name__ == "__main__": + import argparse + import sys + + parser = argparse.ArgumentParser(description="Detect and validate JSONL file formats") + parser.add_argument("file_path", help="S3 URI (s3://bucket/key) or local file path") + parser.add_argument("--sample-size", type=int, default=1_048_576, help="Bytes to sample (default: 1MB)") + parser.add_argument("--json", action="store_true", help="Output as JSON instead of human-readable") + args = parser.parse_args() + + try: + result = detect_format(args.file_path, args.sample_size) + + if args.json: + output = { + "format_type": result.format_type.value, + "is_valid": result.is_valid, # nosemgrep: python.lang.maintainability.is-function-without-parentheses -- dataclass field, not a method + "confidence": result.confidence.value, + "lines_sampled": result.lines_sampled, + "errors": [ + {"line_number": e.line_number, "error_type": e.error_type, "message": e.message} + for e in result.errors + ], + } + print(json.dumps(output, indent=2)) + else: + print(f"Format: {result.format_type.value}") + print(f"Valid: {'[ok]' if result.is_valid else '[fail]'}") # nosemgrep: python.lang.maintainability.is-function-without-parentheses -- dataclass field, not a method + print(f"Confidence: {result.confidence.name}") + print(f"Lines sampled: {result.lines_sampled}") + if result.errors: + print("Errors:") + for err in result.errors: + print(f" Line {err.line_number}: {err.message}") + + sys.exit(0 if result.is_valid else 1) # nosemgrep: python.lang.maintainability.is-function-without-parentheses -- dataclass field, not a method + except (FileNotFoundError, IOError, ValueError) as e: + print(f"Error: {e}", file=sys.stderr) + sys.exit(1) diff --git a/plugins/sagemaker-ai/skills/dataset-transformation/SKILL.md b/plugins/sagemaker-ai/skills/dataset-transformation/SKILL.md new file mode 100644 index 00000000..e27d109d --- /dev/null +++ b/plugins/sagemaker-ai/skills/dataset-transformation/SKILL.md @@ -0,0 +1,235 @@ +--- +name: dataset-transformation +description: Generates code that transforms datasets for SageMaker AI model training or evaluation. Use for SageMaker dataset conversion, reformatting, schema migration, SFT/DPO/RLVR/RLAIF preparation, Bedrock Nova formats, VERL formats, and custom JSONL formats from local files or S3. +metadata: + version: "1.0.0" +--- + +# Dataset Transformation Agent + +Transforms a data set provided by the user into their desired format. + +## When to Use + +- User needs to generate code for transforming datasets for SageMaker model training or model evaluation. +- A dataset requires processing, cleaning, or formatting before training or evaluation. +- Workflow requires a formal review and approval cycle before execution. + +## Prerequisites + +- The SDK environment has been verified (SDK version, region, execution role). If not done, activate the `sdk-getting-started` skill first. + +## Principles + +1. One thing at a time. Each response advances exactly one decision. Never combine multiple questions or recommendations in a single turn. +2. Confirm before proceeding. Wait for the user to agree before moving to the next step. You are a guide, not a runaway train. +3. Don't read files until you need them. Only read reference files when you've reached the workflow step that requires them and the user has confirmed the direction. Never read ahead. +4. No narration. Don't explain what you're about to do or what you just did. Share outcomes and ask questions. Keep responses short and focused. +5. No repetition. If you said something before a tool call, don't repeat it after. Only share new information. +6. Do not deviate from the Workflow. The steps listed in the workflow should be followed exactly as described. Progress from Step 1 to Step 11 to complete the task. Do not deviate from the workflow! +7. Always end with a question. Whenever you pause for user input, acknowledgment, or feedback, your response must end with a question. Never leave the user with a statement and expect them to know they need to respond. +8. Default output format is JSONL. Unless the user explicitly requests a different file format, the transformed dataset should be written as `.jsonl` (JSON Lines - one JSON object per line). + +## Known Dataset Formats Reference + +This skill supports two transformation purposes - training data and evaluation data - each with its own format resolution path. The purpose is determined in Step 1 of the workflow. + +### Training Data Formats + +Resolve the target format using the reference file ../dataset-evaluation/references/strategy_data_requirements.md. When the transformation is for model training, the required format depends on both the model type (Open Weights like Llama/Qwen vs Nova) and the finetuning technique (SFT, DPO, RLVR, RLAIF) - make sure to match on both dimensions. If either the model type or technique is not yet known, ask the user before resolving the format. + +### Evaluation Data Formats + +When the transformation is for model evaluation, resolve the target format using this order: + +1. Try fetching the live documentation at https://docs.aws.amazon.com/sagemaker/latest/dg/model-customize-evaluation-dataset-formats.html to get the latest evaluation dataset schema definitions. +2. If the fetch fails (e.g., no internet access, VPC environment), fall back to the offline copy at `references/sagemaker_dataset_formats.md`. Inform the user that the format schemas are from an offline copy and may be outdated. + +Use whichever source you successfully access as the source of truth for the target format. Do not rely on memorized schemas. + +## Workflow + +### Step 1: Determine transformation purpose + +Your first response should determine whether this transformation is for model training or model evaluation. If the context already makes this clear (e.g., the user said "I need to prep my training data" or "I need to format my eval dataset"), confirm your understanding and move on. Otherwise, ask: + +> "Is this dataset transformation for model training or model evaluation? This helps me look up the right target format for you." + +- Training -> format resolution will use the local training data requirements reference (model type + finetuning technique dependent). +- Evaluation -> format resolution will use the live AWS documentation (with offline fallback). + +Remember this choice - it determines how the target format is resolved in Step 3. + +Wait: Wait for user. + +### Step 2: Set expectations + +Acknowledge the user's request and state what this skill can do: + +> "I can help you transform your dataset's format! Here's my plan: I will first need to understand the format of your dataset and the transformation requirements. Once I have that, I will generate a dataset transformation function that we can refine together. After the dataset transformation function is refined to your liking, I will perform the transformation task and upload it to your desired location! Does this sound good?" + +Wait: Wait for user. + +### Step 3: Understand the dataset transformation task + +For this step, you need to know: what dataset format the user would like to transform their dataset from and what dataset format they would like to transform it in to. +If you know this already, skip this step. If not, ask the user: + +> "What's the dataset format you would like to transform it into?" + +Resolve the target format based on the purpose determined in Step 1: + +- If training data: Ask the user for the finetuning technique (SFT, DPO, RLVR, RLAIF) and model type (Open Weights like Llama/Qwen vs Nova) if not already known. Then look up the required format from the "Training Data Formats" section in the Known Dataset Formats Reference above. +- If evaluation data: If the user mentions a well-known format name (e.g., "OpenAI format", "SageMaker format"), fetch the schema from the live documentation as described in the "Evaluation Data Formats" section above. If a well-known format is fetched, confirm with the user: + +> "I've found a SageMaker dataset format: {sagemaker-dataset-format-name} with schema: {sagemaker-dataset-format-schema}. Is this what you were referring to?" + +If the user describes a custom format not listed in the reference doc, ask them to provide a sample record of the desired output format. + +Wait: Wait for user. + +### Step 4: Get the dataset from the user + +For this step, you need: the location of the user's dataset. +If you know this already, skip this step. If not, ask the user: + +> "Where can I find your dataset? Either a local directory or S3 location works!" + +Wait: Wait for user. + +### Step 5: Examine sample data + +Read 1-2 sample records from the user's dataset and show them so the user can confirm the source schema. Do not run format detection - that is handled by the planning skill before this skill is invoked. + +Do not show a side-by-side mapping to the target format here - the detailed mapping will be handled in Step 7 when generating the transformation function. + +Wait: Wait for user. + +### Step 6: Get the dataset output location + +For this step, you need: to understand where to output the transformed dataset to. It could be an S3 URI or local directory +If you already know where the dataset is supposed to be output to, skip this step. If not, ask the user: + +> "Where should I output your transformed dataset to? Either a local directory or S3 location works!" + +If the user provides a directory (not a full file path), construct the output filename using the pattern `{original_name}_{target_format}.jsonl` (e.g., `gen_qa_100k_openai.jsonl`). + +Wait: Wait for user. + +### Step 7: Generate and validate the transformation function + +For this step, you need: to generate a python function that transforms the dataset from the format in Step 5 to the format in Step 3 + +Read the reference guide at `references/dataset_transformation_code.md` and follow its skeleton exactly when generating the transformation function. + +The python function should be in the form of: + +```python +def transform_dataset(df: pd.DataFrame) -> pd.DataFrame: +``` + +The `` is the project directory established by the directory-management skill (e.g., `dpo-to-rlvr-conversion`). + +In notebook mode, add a `%%writefile /scripts/transform_fn.py` code cell AND write the file to disk for testing. In script mode, write the file to disk directly. + +Continue iterating with the user's feedback - update the code in place on each revision rather than showing code inline. + +If sample data was collected in Step 5, test the function against the sample records: + +1. Generate the transformation function. +2. Write the sample data to a temporary JSONL file (e.g., `/tmp/test_input.jsonl`), then run: + `python3 -c "import sys; sys.path.insert(0, '/scripts'); from transform_fn import transform_dataset; import pandas as pd; df = pd.read_json('/tmp/test_input.jsonl', lines=True); result = transform_dataset(df); print(result.to_json(orient='records', lines=True))"` +3. If the test fails, fix and re-test until it passes. +4. Show the user the function and transformed sample output for review. + +If no sample data, present the function for review and refinement. + +Wait: Wait for user. + +### Step 8: Determine output target + +If no project directory exists, activate the directory-management skill to set one up. + +Wait: Wait for user. + +### Step 9: Generate the execution code + +Before writing the code, read: + +- `references/code_output_guide.md` (output format rules) +- `code_templates/transformation.py` (cell structure and skeleton code) + +The template uses `# Cell N: Label` markers - each marker starts a new section. Cell 2 (Transformation Function) is dynamically generated from Step 7; all other cells follow the template skeleton. + +Generate the execution logic following the code output guide. + +- In notebook mode, add a `%%writefile /scripts/.py` code cell AND write the file to disk. In script mode, write the file to disk directly. +- The script must import `transform_dataset` from `transform_fn`. +- Replace placeholders with the actual input/output paths. + +Read the reference guide at `references/dataset_transformation_code.md` and follow its execution script skeleton exactly. + +If sample data was collected in Step 5, test the full pipeline: + +1. Write the sample records to a temporary JSONL file (e.g., `/tmp/test_input.jsonl`). +2. Run: `python3 /scripts/ --input /tmp/test_input.jsonl --output /tmp/test_output.jsonl` +3. If it fails, debug and fix, then re-run until successful. +4. Show the user the output for review. + +If no sample data, present the notebook for review and refinement. + +Wait: Wait for user. + +### Step 10: Determine and confirm execution mode + +Check the size of the input dataset: + +- If the dataset is in S3, use the AWS MCP tool `head-object` (S3 service) with the bucket and key to get `ContentLength`. +- If the dataset is local, check the file size. + +Decision criteria: + +- Dataset < 50 MB -> recommend local execution +- Dataset >= 50 MB -> recommend SageMaker Processing Job + +Inform the user of the recommendation and get their approval: + +If local: + +> "Your dataset is {size} MB - since it's under 50 MB, I'd recommend running the transformation locally. Would you like to proceed with local execution, or would you prefer a SageMaker Processing Job instead?" + +If SageMaker Processing Job: + +> "Your dataset is {size} MB - since it's over 50 MB, I'd recommend running this as a SageMaker Processing Job for better performance. Would you like to proceed with a SageMaker Processing Job, or would you prefer to run it locally instead?" + +Do not execute until the user approves. If the user rejects the recommendation, switch to the alternative and get their explicit approval before proceeding. + +Wait: Wait for user. + +After user confirms, add an execution cell to the notebook. Do NOT run the transformation directly (no bash, no inline python). If notebook execution tools (`run_cell`) are available, offer to run the cells. Otherwise, generate the cell for the user to execute themselves: + +If local execution: + +- Add a cell that runs the transformation by importing from the `.py` files already on disk (written by the agent during Steps 7 and 9): import `transform_dataset` from `transform_fn`, load the dataset, transform, and save output. Scripts are located in `/scripts/`. + +If SageMaker Processing Job: + +- Add a cell that submits and monitors the Processing Job inline using the V3 SageMaker SDK directly (FrameworkProcessor, ProcessingInput, ProcessingOutput, etc.). Create a FrameworkProcessor with the SKLearn 1.2-1 image, configure inputs/outputs, and call `processor.run(wait=True, logs=True)` to block the cell and stream logs until the job completes. See `scripts/transformation_tools.py` for reference implementation details. +- Inform the user they can run this cell to kick off and monitor the job. + +Important: The agent must NOT execute the transformation directly via bash or inline python. If `run_cell` is available, use it to run the notebook cells. Otherwise, the cells are for the user to review and run. Only sample data (from Steps 7 and 9) should be transformed by the agent for validation purposes. + +> If `run_cell` is available: "I've added the execution cell to the notebook. Would you like me to run it?" +> Otherwise: "I've added the execution cell to the notebook. You can run it to transform the full dataset. Would you like to review the notebook before running it?" + +Wait: Wait for user. + +### Step 11: Verify and confirm with the user + +For this step, you need: to verify the output looks correct and confirm with the user. + +- Read 1-2 sample records from the output to show the user. +- Report the total number of records transformed. +- Ask the user if the output looks good. + +Wait: Wait for user to confirm. diff --git a/plugins/sagemaker-ai/skills/dataset-transformation/code_templates/transformation.py b/plugins/sagemaker-ai/skills/dataset-transformation/code_templates/transformation.py new file mode 100644 index 00000000..c651bfbc --- /dev/null +++ b/plugins/sagemaker-ai/skills/dataset-transformation/code_templates/transformation.py @@ -0,0 +1,45 @@ +# Dataset Transformation Template +# Cell structure for a dataset transformation notebook. +# The transformation function (Cell 2) is generated dynamically based on the user's +# source and target formats. All other cells follow this skeleton. + +# Cell 0 [markdown]: Dataset Transformation +# Description of the transformation (source format -> target format) + +# Cell 1: Configuration + +INPUT_LOCATION = "[INPUT_LOCATION]" # S3 URI or local path to input dataset +OUTPUT_LOCATION = "[OUTPUT_LOCATION]" # S3 URI or local path for output + +# Cell 2: Transformation Function +# This cell is generated dynamically based on the user's source -> target format. +# In notebook mode, it uses %%writefile to save the function to transform_fn.py. +# In script mode, the function is written to disk directly. +# It must define: +# +# def transform_dataset(df: pd.DataFrame) -> pd.DataFrame: +# ... +# +# The function should ONLY transform the DataFrame schema. No I/O, no side effects. + +# Cell 3: Load Dataset + +import pandas as pd +from transform_fn import transform_dataset + +df = pd.read_json(INPUT_LOCATION, lines=True) +print(f"Loaded {len(df)} records") +print(f"Columns: {list(df.columns)}") +df.head(2) + +# Cell 4: Transform + +df_transformed = transform_dataset(df) +print(f"Transformed {len(df_transformed)} records") +print(f"Columns: {list(df_transformed.columns)}") +df_transformed.head(2) + +# Cell 5: Save Output + +df_transformed.to_json(OUTPUT_LOCATION, orient="records", lines=True) +print(f"Saved {len(df_transformed)} records to {OUTPUT_LOCATION}") diff --git a/plugins/sagemaker-ai/skills/dataset-transformation/references/code_output_guide.md b/plugins/sagemaker-ai/skills/dataset-transformation/references/code_output_guide.md new file mode 100644 index 00000000..adba3f10 --- /dev/null +++ b/plugins/sagemaker-ai/skills/dataset-transformation/references/code_output_guide.md @@ -0,0 +1,72 @@ +# Code Output Guide + +## Mode Selection + +Ask the user once before generating code: "Would you like me to generate a Jupyter notebook or a Python script?" + +If the output format has already been decided in the conversation context, keep consistent - do not re-ask. + +## Shared Rules (Both Modes) + +- Use EXACTLY the imports shown in each code template - do not add extras +- Replace `[PLACEHOLDER]` values with user-specific configuration +- Include `set_attribution(Attribution.SAGEMAKER_AGENT_PLUGIN)` in the setup cell/section + +## Reading Code Templates + +Templates use `# Cell N: Label` markers to delimit sections. `# NOTEBOOK_ONLY` skips a line in script mode; `# NOTEBOOK_ONLY_SECTION` on a `# Cell N:` line skips the entire section. + +## Notebook Mode + +Write a `.ipynb` file in `/notebooks/`. + +Naming and appending: + +- Notebook path: `/notebooks/.ipynb` +- If the notebook already exists -> ask: _"Would you like me to append cells to the existing notebook, or create a new one?"_ +- If it doesn't exist -> create it +- When appending, use the template's `# Cell 0 [markdown]:` cell as the section divider before the new cells + +Formatting: + +- Use your file write tool to create the complete notebook JSON, OR use notebook MCP tools (`create_notebook`, `add_cell`) if available +- Do NOT use bash commands, shell scripts, or `echo`/`cat` piping +- 2-space JSON indentation +- Each source line is a separate string ending with `\n` (except the last) +- Escape quotes: `\"` +- No trailing commas + +Structure: + +- Wrap cells in `{"cells": [...], "metadata": {...}, "nbformat": 4, "nbformat_minor": 4}` +- Code cells: `cell_type`, `execution_count: null`, `metadata: {}`, `outputs: []`, `source: [...]` +- Markdown cells: `cell_type: "markdown"`, no `execution_count` or `outputs` +- `# Cell 0 [markdown]:` becomes a markdown cell; all others become code cells + +Execution: + +- If notebook execution tools are available (e.g., `run_cell` MCP), offer to run cells for the user. If not available, tell the user to run cells themselves. +- Do NOT use bash commands or inline scripts to execute notebook cells. + +## Script Mode + +Write a numbered `.py` file in `/scripts/`. + +Naming: + +- Format: `NN_.py` (e.g., `01_sft_finetuning.py`) - use the next available number in `/scripts/` + +Formatting: + +- Plain Python file, standard text +- Use `# %%` cell markers to preserve logical sections (IDE-compatible) +- Include a docstring at the top describing what the script does +- `# Cell 0 [markdown]:` -> a comment block or docstring + +Dependencies: + +- Install any required pip packages directly (e.g., `pip install sagemaker>=3.7.1`) before writing or running the script. Do not embed install commands in the script itself. + +Execution: + +- Run the script using standard Python execution (`python3