Training DeepSeek R1 on Linux: A No-Nonsense Guide
Training DeepSeek R1 on Linux: A No-Nonsense Guide
Want to train DeepSeek R1 without AWS? Running it on a self-hosted Linux server gives you full control and avoids cloud costs—but you need the right setup.
What You Need to Train R1 on Linux
| Requirement | Minimum Specs | Recommended for Smooth Training |
|---|---|---|
| OS | Ubuntu 22.04, Debian 12 | Ubuntu 22.04 (LTS), Arch Linux |
| RAM | 16GB (may work with swap) | 32GB+ (best for full training) |
| GPU | None (CPU-only training is slow) | NVIDIA GPU (RTX 3090, A100, etc.) |
| Storage | 20GB+ | 100GB+ (models + datasets) |
| Dependencies | PyTorch, Hugging Face Transformers | CUDA, cuDNN (if using GPU) |
How to Train DeepSeek R1 on Linux (High-Level Steps)
1️⃣ Install Dependencies
Run the following on your Linux machine:
sudo apt update && sudo apt install python3-pip -y
pip3 install torch transformers datasets accelerate
If using a GPU, install CUDA:
pip3 install torch --index-url https://download.pytorch.org/whl/cu118
2️⃣ Download DeepSeek R1
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "deepseek-ai/deepseek-r1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
3️⃣ Load Training Dataset
Download or prepare a dataset (e.g., JSON, CSV, or text files). Use Hugging Face’s datasets library:
from datasets import load_dataset
dataset = load_dataset("your-dataset-name")
4️⃣ Fine-Tune the Model
Run a basic fine-tuning script:
from transformers import TrainingArguments, Trainer
training_args = TrainingArguments(output_dir="./r1-model", per_device_train_batch_size=2, num_train_epochs=3)
trainer = Trainer(model=model, args=training_args, train_dataset=dataset)
trainer.train()
5️⃣ Save & Export the Trained Model
model.save_pretrained("./r1-trained")
tokenizer.save_pretrained("./r1-trained")
Key Takeaways
💡 Training on CPU is slow—use a GPU whenever possible.
💡 Storage matters—R1 and datasets take up space fast.
💡 Linux gives full control, but setup and tuning are manual.
💡 For fast, managed training, AWS SageMaker is a better option.
Need DeepSeek Expertise?
If you're looking for guidance on DeepSeek challenges or want to collaborate, feel free to reach out! We'd love to help you tackle your DeepSeek projects. 🚀
Email us at: info@pacificw.com
Image: Gemini
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