RFdiffusion Pipeline: Manual Installation Guide

Running complex bioinformatics tools often involves containerization, which simplifies dependency management and ensures reproducibility. However, there are times when you might need or prefer to run a pipeline, like RFdiffusion, directly within your manually installed environment. This guide will walk you through the process, addressing common issues and providing clear steps for successful execution. We'll focus on how to bypass container requirements and leverage your existing Python setup, specifically using Anaconda, to get your RFdiffusion jobs up and running. This approach is particularly useful if you encounter issues with container software, have specific system configurations, or simply want more direct control over your environment.

Understanding the Error: Why Containers Matter (and How to Skip Them)

When you first attempt to run the RFdiffusion pipeline, you might encounter an error message similar to this: Default apptainer not found... No apptainer found. Attempting to run /home/user/RFdiffusion2/rf_diffusion/benchmark/pipeline.py with /home/user/anaconda3/envs/rfd2_env_124/bin/python. This indicates that the script is designed to first look for a container environment (like Apptainer or Singularity) to run the pipeline. If it can't find one, it falls back to using your system's Python interpreter. The subsequent unrecognized arguments error you're seeing, pipeline.py: error: unrecognized arguments: --benchmarks ..., stems from the fact that the script, when not run within its expected container environment, doesn't correctly parse the arguments intended for the pipeline's core logic. The container environment often pre-processes or injects certain configurations that are expected by the script. By explicitly telling the script to use your local Python interpreter and ensuring all necessary packages are installed in that environment, we can overcome this hurdle. This involves making sure that when the script is executed, it sees all its dependencies and can correctly interpret the command-line arguments you provide. We are essentially forcing the script to operate in a non-containerized mode, which requires careful attention to the environment it's running in.

Prerequisites for a Smooth Manual Installation

Before diving into running the RFdiffusion pipeline without containers, it's crucial to have a few things in place. First and foremost, you need a functional Python environment. For many bioinformatics tasks, Anaconda or Miniconda is the go-to choice, as it simplifies package management and environment isolation. You should have an Anaconda environment created and activated that contains all the dependencies required by RFdiffusion. This typically includes libraries like PyTorch, NumPy, SciPy, and any specific RFdiffusion dependencies. If you haven't already, you can create an environment using Anaconda Navigator or by running conda create -n rfd2_env python=3.9 (adjusting the Python version as needed) and then activate it with conda activate rfd2_env. Secondly, you must have the RFdiffusion code cloned or downloaded to your local machine. Ensure you are in the root directory of the cloned repository. Thirdly, you need to have successfully installed all the Python packages listed in RFdiffusion's requirements file, usually named requirements.txt. You can install these using pip after activating your environment: pip install -r requirements.txt. Finally, ensure that any external dependencies that are not Python packages (like specific versions of CUDA if you're using GPU acceleration) are correctly installed and configured on your system. For RFdiffusion, GPU support is highly recommended for performance. Double-checking these prerequisites will save you a lot of troubleshooting time later. If any of these are missing or incorrectly configured, the pipeline is likely to fail, often with cryptic error messages.

Executing the RFdiffusion Pipeline: The Command Line

Now that you have your environment prepared, let's construct the command to run the RFdiffusion pipeline without relying on containers. The key is to invoke the pipeline.py script directly using your Python interpreter from the activated environment. The original command you provided is a good starting point, but it needs a slight adjustment to ensure it's interpreted correctly in a non-containerized context. Instead of just running pipeline.py, you should explicitly use your Python interpreter from your activated environment. For instance, if your environment is rfd2_env, you would use python /home/user/RFdiffusion2/rf_diffusion/benchmark/pipeline.py.

Here’s how you should structure your command, assuming your RFdiffusion installation is located at /home/user/RFdiffusion2 and your activated environment is rfd2_env:

python /home/user/RFdiffusion2/rf_diffusion/benchmark/pipeline.py \
    --benchmarks 10_62 \
    --num_per_condition 10 \
    --num_per_job 2 \
    --out run1/run1 \
    --args "diffuser.T=20|50 diffuser.aa_decode_steps=5|10" \
           "diffuser.T=100|200 diffuser.aa_decode_steps=20|40"

Explanation of the command:

• python: This ensures that the script is executed by your Python interpreter within the currently activated Anaconda environment. This is the most critical change from simply typing pipeline.py.
• /home/user/RFdiffusion2/rf_diffusion/benchmark/pipeline.py: This is the full path to the pipeline script. Make sure this path is correct for your system.
• --benchmarks 10_62: This argument specifies which benchmark configuration to use from your benchmarks.json file. Ensure 10_62 is a valid key in your JSON file.
• --num_per_condition 10: Sets the number of samples to generate for each condition.
• --num_per_job 2: Defines how many samples are processed in a single job.
• --out run1/run1: Specifies the output directory where results will be saved. Ensure the parent directory run1 exists or can be created.
• --args "diffuser.T=20|50 diffuser.aa_decode_steps=5|10" "diffuser.T=100|200 diffuser.aa_decode_steps=20|40": These are the core configuration overrides for the diffusion process. The pipe | symbol often indicates multiple values to be iterated over. This part tells RFdiffusion to run simulations with different combinations of diffuser.T (number of diffusion steps) and diffuser.aa_decode_steps (steps for decoding amino acids).

By explicitly using python and ensuring your environment is correctly set up with all RFdiffusion dependencies, you bypass the need for container-specific execution and allow the script to run using your local Python installation.

Troubleshooting Common Issues

Even with the correct command, you might run into snags when attempting a manual installation. One of the most frequent problems, beyond the initial argument parsing error, is missing dependencies. If RFdiffusion relies on a library that isn't installed in your active Conda environment, you'll get an ImportError. Always double-check your requirements.txt file and ensure every package is installed using pip install <package-name> or conda install <package-name>. Sometimes, specific versions are crucial, so pay attention to any version specifiers in the requirements file. Another common pitfall is CUDA or GPU driver issues. If you're using a GPU, ensure your NVIDIA drivers are up-to-date and that the CUDA toolkit version installed in your environment (usually specified by PyTorch) is compatible with your drivers. You can check your PyTorch CUDA version with import torch; print(torch.version.cuda). If it doesn't match your system's CUDA installation or drivers, you might need to re-install PyTorch with the correct CUDA version. Path errors can also occur, especially if RFdiffusion tries to access external data files or models that are not in the expected location. Ensure any paths specified in your configuration or command line are absolute and correct, or that relative paths are indeed relative to the current working directory when you execute the script. Finally, configuration file errors within your benchmarks.json or other Hydra configuration files can lead to unexpected behavior. Carefully review the structure and content of your JSON file to ensure it matches the expected format and contains valid entries for the benchmark you're trying to run. Debugging these issues often involves reading the full traceback of the error message carefully, as it usually points to the exact line of code or the specific missing component that's causing the problem. It's also helpful to run simpler, smaller test cases first to isolate the issue.

Conclusion: Empowering Your RFdiffusion Workflow

Successfully running the RFdiffusion pipeline without containers empowers you with greater flexibility and control over your computational environment. By carefully preparing your Anaconda environment, ensuring all dependencies are met, and using the correct command structure, you can bypass container requirements and execute the pipeline directly. This manual approach, while requiring a bit more setup, can be invaluable for troubleshooting, customization, or when containerized solutions are not feasible. Remember to always double-check your paths, dependencies, and configuration files. With a systematic approach to installation and troubleshooting, you can harness the full power of RFdiffusion for your protein design projects.

For further information and advanced usage, you might find the official RosettaCommons documentation helpful. Their resources often cover installation details and troubleshooting tips that can be applied to related tools like RFdiffusion. You can find comprehensive guides and community support at RosettaCommons Website. Additionally, exploring the GitHub repository for RFdiffusion can provide specific installation instructions and issue tracking for the latest updates and community discussions.