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.
npx skills add ECNU-ICALK/AutoSkill --skill pytorch-rnn-dataset-chunking-configuration --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Prompt
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What does the PyTorch RNN Dataset Chunking Configuration skill do?
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.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill pytorch-rnn-dataset-chunking-configuration --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.