size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel arxiv:2406.08464 region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 28165
    num_examples: 10
  download_size: 20477
  dataset_size: 28165
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>

# Dataset Card for test-outputs-no-penalty-8B

This dataset has been created with [distilabel](https://distilabel.argilla.io/).



## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/dvilasuero/test-outputs-no-penalty-8B/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/dvilasuero/test-outputs-no-penalty-8B/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "instruction": "I\u0027m experiencing GPU memory issues with my PyTorch model. I\u0027m using CUDA 11.8 and PyTorch 1.12.1. Here\u0027s a snippet of my model definition and training loop:\n```python\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets, transforms\n\n# Model definition\nclass MyModel(nn.Module):\n    def __init__(self):\n        super(MyModel, self).__init__()\n        self.conv1 = nn.Conv2d(1, 64, kernel_size=3)\n        self.conv2 = nn.Conv2d(64, 64, kernel_size=3)\n        self.fc1 = nn.Linear(64*7*7, 128)\n        self.fc2 = nn.Linear(128, 10)\n\n    def forward(self, x):\n        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n        x = F.relu(F.max_pool2d(self.conv2(x), 2))\n        x = x.view(-1, 64*7*7)\n        x = F.relu(self.fc1(x))\n        x = self.fc2(x)\n        return x\n\n# Data loading and training loop\ntrain_loader = DataLoader(dataset, batch_size=32, shuffle=True)\n\nmodel = MyModel()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=1e-3)\n\nfor epoch in range(10):\n    for i, (x, y) in enumerate(train_loader):\n        x = x.cuda()\n        y = y.cuda()\n        optimizer.zero_grad()\n        output = model(x)\n        loss = criterion(output, y)\n        loss.backward()\n        optimizer.step()\n        if i % 100 == 0:\n            print(f\u0027Epoch: {epoch+1}, Step: {i}, Loss: {loss.item()}\u0027)\n```\n\nWhen I run this code, I get an OOM error (Out of Memory). How can I optimize this model to reduce memory usage?\n\n### What could be the issue?\n\nYou\u0027re experiencing an out-of-memory error, likely due to the large memory consumption during the training process. The primary reason is the large size of your model and the input data. Since your model is using convolutional layers, each step of the forward pass can allocate a lot of memory. Also, the `F.max_pool2d` function can significantly increase the spatial dimensions of your input at multiple points, which is another factor contributing to the high memory usage.\n\n### Step 1: Reduce batch size\n\nReducing the batch size will decrease the memory usage per iteration but might impact the training speed and convergence.\n\n```python\ntrain_loader = DataLoader(dataset, batch_size=16, shuffle=True)\n```\n\n### Step 2: Remove `F.max_pool2d` from forward pass\n\nThe `F.max_pool2d` function can significantly increase the spatial dimensions at multiple points. Remove these calls to see if this resolves the memory issue.\n\n```python\ndef forward(self, x):\n    x = F.relu(self.conv1(x))\n    x = F.relu(self.conv2(x))\n    x = x.view(-1, 64*7*7)\n    x = F.relu(self.fc1(x))\n    x = self.fc2(x)\n    return x\n```\n\n### Step 3: Use more efficient activation functions\n\nUse `nn.Hardtanh` instead of `F.relu` for your hidden layers. This activation function can be more memory-efficient since it can be computed element-wise without requiring any additional memory.\n\n```python\ndef forward(self, x):\n    x = nn.Hardtanh(self.conv1(x))\n    x = nn.Hardtanh(self.conv2(x))\n    x = x.view(-1, 64*7*7)\n    x = nn.Hardtanh(self.fc1(x))\n    x = self.fc2(x)\n    return x\n```\n\n### Step 4: Use a more efficient GPU\n\nIf you have access to a GPU with higher memory, consider using it to train the model. This will significantly reduce memory usage.\n\n### Step 5: Optimize model by pruning or knowledge distillation\n\nIf the previous methods do not significantly reduce memory consumption, you may need to revisit your model architecture. This could involve removing unnecessary layers or weights, reducing the model\u0027s complexity, using knowledge distillation to train a smaller model, or using pruning techniques to remove redundant weights.\n\n### Step 6: Profile memory usage\n\nUsing the PyTorch `torch.cuda.profiler` module, you can get a detailed breakdown of memory usage for each part of your code. This can help you identify the exact source of the memory issue.\n\n```python\nimport torch\nimport torch.profiler\n\nwith torch.profiler.profile() as prof:\n    for epoch in range(10):\n        for i, (x, y) in enumerate(train_loader):\n            x = x.cuda()\n            y = y.cuda()\n            optimizer.zero_grad()\n            output = model(x)\n            loss = criterion(output, y)\n            loss.backward()\n            optimizer.step()\n            if i % 100 == 0:\n                print(f\u0027Epoch: {epoch+1}, Step: {i}, Loss: {loss.item()}\u0027)\n                print(prof.key_averages().key_per_module().table(sort_by=\"cuda_memory_usage\"))\n```\n\nBy analyzing the results, you can see which parts of the code are consuming the most memory and make informed decisions on how to optimize your code.",
    "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/test-outputs-no-penalty-8B", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`: 

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/test-outputs-no-penalty-8B")
```


</details>





## References


```
@misc{xu2024magpiealignmentdatasynthesis,
    title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
    author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
    year={2024},
    eprint={2406.08464},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2406.08464},
}
```


