How to Download and Install ChromBPNet: Setup Guide

Installing ChromBPNet is not hard, but it is the kind of setup where ten careful minutes save you hours of confusing errors later. The framework is a Python package built on TensorFlow, installed from its GitHub repository, and it expects a GPU if you plan to train models. This guide walks through the pieces in the order you should assemble them: a clean Python environment, the package itself, the TensorFlow and CUDA pairing that decides if your GPU gets used at all, and the sanity checks that tell you the install is healthy before you trust it with a training run.

Start with a dedicated Python environment

Create a fresh Python environment for ChromBPNet rather than pouring it into a system Python that already carries other tools. Deep learning stacks pin specific versions of large libraries, and TensorFlow is the classic example: let it have the exact versions it wants inside its own environment and the rest of your machine stays undisturbed. This single habit prevents most dependency fights before they start.

Inside that environment you will install the package from the project’s GitHub repository, kundajelab/chrombpnet, using pip. Installing from the repository, rather than hunting for unofficial copies or old snapshots, matters because the framework is actively developed research software. The repository version is the one the documentation, the pretrained models, and the community’s answers all assume you are running.

What actually gets installed

The core dependency is TensorFlow, the deep learning library ChromBPNet is built on. Alongside the framework’s own code, your environment ends up holding TensorFlow and the supporting scientific Python libraries it needs to read genomic data, build training regions, and run the training pipeline. You do not need to assemble this list by hand; the project’s installation route pulls in what it requires. Your job is to give it a clean environment and a compatible GPU stack, which is the next topic.

The GPU question, answered honestly

A CUDA-capable NVIDIA GPU is strongly recommended for training. ChromBPNet training processes many long sequence regions, and on a CPU that work stretches from hours into days. A suitable GPU turns the same job into something you can iterate on, which changes how you work: you can afford to check early predictions, adjust, and retrain.

Prediction and variant scoring are lighter jobs. They still run faster on a GPU, and for genome-scale scoring you will feel the difference, but they are at least practical on modest hardware for small region sets. The realistic split is this:

• Training a model: plan on a CUDA-capable NVIDIA GPU with generous memory.
• Predicting profiles or scoring variants: a GPU is preferred, small jobs can get by without one.
• Reading documentation and preparing files: any machine, including the login node you would rather not train on anyway.

TensorFlow, CUDA, and cuDNN must agree

The most common setup failure has nothing to do with ChromBPNet itself. TensorFlow is built against particular versions of CUDA and cuDNN, the NVIDIA libraries it uses to talk to the GPU. If the versions on your machine do not match what your TensorFlow build expects, you get one of two classic outcomes: TensorFlow reports that no GPU is available even though one is sitting right there, or training crashes at startup with a library error.

Handle this by aligning versions instead of improvising. Use the TensorFlow build and CUDA pairing the project points you to, keep them inside the dedicated environment where possible, and resist the urge to upgrade one piece in isolation later. When you do hit trouble, the mismatch between these versions is the first place to look, a point the troubleshooting guide on this site returns to in detail.

Plan for memory, not just compute

Expect large memory use during training. The models consume many long sequence regions per batch, and both GPU memory and system memory get exercised hard. Practical consequences follow from that:

• Check the memory of any GPU you plan to train on before you commit a dataset to it.
• Leave headroom on shared machines, since a training run will happily take what it is given.
• If you hit out-of-memory errors later, batch size is your first lever, as the fixes guide explains.

None of this is a reason to avoid the tool. It is simply a model that works at genome scale, and genome scale has a memory bill attached.

Sanity checks before your first real run

Once the install finishes, verify the environment in small steps instead of launching straight into a training job:

• Activate the environment and import TensorFlow; it should load without errors.
• Ask TensorFlow whether it can see a GPU, and confirm the answer matches the hardware you have.
• Import the ChromBPNet package pieces you plan to use and confirm they load inside the same environment.
• Run the lightest pipeline step you can, such as preparing a small set of regions or predicting with a pretrained model, before scheduling a full training run.

Each check isolates a different failure class: the environment, the GPU stack, the package, and the pipeline. Ten minutes here means that when something does break mid-project, you know it broke recently and locally, instead of suspecting the entire stack at once.

Keeping the setup healthy over time

Research software moves. The repository gets updates, TensorFlow releases new builds, and lab servers get their CUDA drivers upgraded by someone else at the worst possible moment. Protect yourself with two boring habits. First, record the versions you installed and the date the setup passed your sanity checks, inside the project folder where future-you will actually look. Second, when you return to a dormant project, rerun the quick GPU visibility check before assuming the environment still works. Version drift is the silent cause behind most “it worked last semester” mysteries, and a two-minute check exposes it immediately.

Where ChromBPNet sits in your existing stack

The install will make more sense if you place the framework correctly in your workflow. ChromBPNet does not replace the upstream processing of your assay: your ATAC-seq or DNase-seq reads still go through alignment and peak calling with the tools your lab already trusts, and it is those processed products, the peaks and the signal, that ChromBPNet consumes as training inputs. It also does not replace your genome browser, which remains the right place to eyeball loci. ChromBPNet occupies the modeling layer between processed data and biological conclusions: it learns from your processed experiment, then lets you interrogate sequence in ways the raw data alone cannot support.

Knowing this boundary saves a common misunderstanding during setup. New users sometimes go looking inside the framework for peak callers or alignment options that were never its job, and conclude the install is incomplete. It is not incomplete; it is specialized. Install it as the modeling layer, feed it the products of your existing pipeline, and each tool does the work it was built for.

Conclusion

A good ChromBPNet install is a clean Python environment, the package pulled from the official GitHub repository with pip, a TensorFlow build whose CUDA and cuDNN versions genuinely agree, and a CUDA-capable NVIDIA GPU waiting for the training stages. Verify the pieces in order, note your versions down, and the framework stops being a setup project and becomes what it should be: a tool you open when you have a genomics question. From here, most people either train a first model on their own ATAC-seq data or grab a pretrained model and start scoring variants, and both of those paths have a guide below.

Read next:

• How to Train a ChromBPNet Model on Your Own ATAC-seq Data
• ChromBPNet Not Working? Common Errors and Fixes
• ChromBPNet Pretrained Models: Where to Get Them and How to Use Them