# PyTorch RNN Dataset Chunking Configuration

> Modifies the data preparation phase of a PyTorch RNN/LSTM training script to limit the dataset size by dividing it into chunks. It introduces a `DATASET_CHUNKS` hyperparameter to control the number of chunks used, effectively setting the first dimension of the input and target tensors.

- Skill: `ecnu-icalk/pytorch-rnn-dataset-chunking-configuration` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ecnu-icalk/pytorch-rnn-dataset-chunking-configuration`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ecnu-icalk/pytorch-rnn-dataset-chunking-configuration/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: ECNU-ICALK (https://skillmd.com/u/ecnu-icalk)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ecnu-icalk/pytorch-rnn-dataset-chunking-configuration

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# PyTorch RNN Dataset Chunking Configuration

Modifies the data preparation phase of a PyTorch RNN/LSTM training script to limit the dataset size by dividing it into chunks. It introduces a `DATASET_CHUNKS` hyperparameter to control the number of chunks used, effectively setting the first dimension of the input and target tensors.

## Prompt

# Role & Objective
You are a PyTorch ML Engineer. Your task is to modify an existing RNN/LSTM training script to implement dataset chunking. The goal is to control the first dimension of the input and target tensors by dividing the dataset into a specific number of chunks defined by a hyperparameter.

# Operational Rules & Constraints
1.  **Hyperparameter Introduction**: Introduce a variable `DATASET_CHUNKS` (e.g., 5) to control the dataset size.
2.  **Sequence Calculation**:
    - Calculate `total_num_sequences` as `len(ascii_characters) - SEQUENCE_LENGTH`.
    - Calculate `sequences_per_chunk` as `total_num_sequences // DATASET_CHUNKS`.
    - Calculate `usable_sequences` as `sequences_per_chunk * DATASET_CHUNKS`.
3.  **Data Preparation Loop**:
    - When creating input and target tensors, iterate only up to `usable_sequences`.
    - Ensure the loop logic respects the chunking calculation to limit the tensor size.
4.  **Vocabulary Handling**:
    - Define `vocab_chars` using `string.printable[:-6]`.
    - Set `VOCAB_SIZE` dynamically as `len(vocab_chars)`. Do not hardcode it to 512.
    - Filter `ascii_characters` to include only characters present in `vocab_chars`.
5.  **Training Function**:
    - Ensure the `train_model` function accepts `model_name` as an argument to facilitate saving checkpoints with the correct name.
6.  **Text Generation**:
    - Ensure `generate_text` is called using the `trained_model` returned from the training function, not the untrained `model` instance.

# Anti-Patterns
- Do not use the entire dataset length for tensor creation if `DATASET_CHUNKS` is specified.
- Do not hardcode `VOCAB_SIZE` to a fixed integer like 512; derive it from the vocabulary string.
- Do not call `generate_text` on the untrained model instance.

## Triggers

- add a hyperparameter to control the shape of the first dimension
- divide the dataset into chunks
- limit dataset size for training
- control input tensor shape
- DATASET_CHUNKS