Here is the complete, fail-safe guide to installing `marker-pdf` for **CPU only**.

Here is the complete, fail-safe guide to installing marker-pdf for CPU only. This method prevents the massive GPU (CUDA) drivers from downloading and ensures all internal libraries (like torchvision) are compatible.

Prerequisites

  • Anaconda or Miniconda installed.
  • Internet Connection: You will need to download ~2-3 GB of model weights on the very first run (not during installation, but during the first usage).

Step 1: Create a Clean Environment

Start fresh to avoid conflicts with previous failed attempts.

# Create the environment (Python 3.10 is recommended)
conda create -n marker_cpu python=3.10 -y

# Activate it
conda activate marker_cpu

Step 2: Clean Up Old Downloads

If you have failed installations before, pip might try to reuse the wrong files. Clear the cache to be safe.

pip cache purge

Step 3: The "All-in-One" Installation

We run a single command to install marker-pdf AND force torch to use the CPU repository at the same time. This prevents pip from accidentally upgrading you to the GPU version.

Run this exact command:

pip install marker-pdf torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --extra-index-url https://pypi.org/simple
  • Why this works: It tells pip to look for PyTorch components in the CPU-only repository (.../whl/cpu) first, and only look for the rest (like marker itself) in the standard repository.

Step 4: Verify Installation

Before running a conversion, verify that your specific environment is using the CPU and ignoring CUDA.

Run this Python snippet:

python -c "import torch; print(f'Torch Version: {torch.__version__}'); print(f'CUDA Available: {torch.cuda.is_available()}')"
  • Success: It should print CUDA Available: False.
  • Success: The Torch version should look like 2.x.x+cpu.

Step 5: How to Convert PDFs

Marker commands have changed recently. You must now use the --output_dir flag.

A. Converting a Single File

Use this for specific books or notes.

marker_single "/path/to/input.pdf" --output_dir "/path/to/output_folder" --batch_multiplier 2
  • --batch_multiplier 2: This is optimized for CPUs. If your computer freezes or runs out of RAM, change this to 1.
  • First Run Note: When you hit Enter, it will look like it's frozen. It is not. It is downloading the OCR models (approx 2GB). Let it finish.

B. Converting a Whole Folder

Use this to convert multiple PDFs at once.

marker "/path/to/input_folder" --output_dir "/path/to/output_folder" --workers 2
  • --workers 2: Limits the number of CPU cores used so your PC doesn't become unresponsive.

Step 6: Where is my output?

In your output folder, you will find a subfolder named after your file. Inside:

  • filename.md: The text and LaTeX equations.
  • filename_images/: All extracted diagrams and images.

Troubleshooting Common Errors

  • RuntimeError: operator torchvision::nms does not exist: This means you have a mismatch between Torch and Torchvision. Run Step 3 again.
  • OSError: No space left on device: Your disk is full. Run pip cache purge and conda clean --all to free up space from failed downloads.



Here is a complete Google Colab setup to run marker-pdf.

Since your local computer is crashing due to low RAM, using Google Colab is the perfect solution because it provides a free 16GB GPU (T4) and plenty of RAM, meaning you don't need any special "CPU-only" hacks.

Step 1: Open a new Colab Notebook

  1. Go to colab.research.google.com.
  2. Click New Notebook.
  3. Important: Go to the menu Runtime > Change runtime type.
  4. Under "Hardware accelerator", select T4 GPU and click Save.

Step 2: Copy & Paste these Code Blocks

Copy these blocks into separate cells in your notebook and run them one by one.

Cell 1: Install Marker

(This takes about 1-2 minutes)

# Install marker-pdf and its dependencies
!pip install marker-pdf transformers

# Check if GPU is working (Should say 'True')
import torch
print(f"CUDA Available: {torch.cuda.is_available()}")

Cell 2: Mount Google Drive

This connects your Google Drive so you can access your PDF.

from google.colab import drive
drive.mount('/content/drive')

It will ask for permission. Click "Connect to Google Drive".

Cell 3: The Conversion Command

Since you are on Colab GPU, we can use default settings (faster!).

Note: You must upload your file Topic 11 Central forces.pdf to your Google Drive first (e.g., inside a folder named PDFs).

import os

# 1. DEFINE YOUR PATHS HERE
# Replace with the actual path in your Google Drive
input_pdf = "/content/drive/MyDrive/PDFs/Topic 11 Central forces.pdf"
output_folder = "/content/drive/MyDrive/PDFs/Converted_Notes"

# 2. RUN THE CONVERSION
# We use ! to run the terminal command
!marker_single "{input_pdf}" --output_dir "{output_folder}"

How to get the file path easily?

  1. After mounting Drive (Cell 2), look at the Folder Icon 📁 on the left sidebar.
  2. Navigate to drive > MyDrive.
  3. Find your PDF file.
  4. Right-click the file and choose Copy path.
  5. Paste that path into input_pdf = "PASTE HERE" in Cell 3.

Where is the output?

The converted Markdown (.md) and images will be saved directly back to your Google Drive in the Converted_Notes folder you specified. You can then download them to your computer.

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