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Set up the public CodonFM v1 repository and download public Encodon checkpoints. Use for requests to build or launch the CodonFM development container, configure local data/checkpoint mounts, verify G
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add NVIDIA/skills --skill bionemo-codonfm-setup -a cursor -y
Set up the public CodonFM v1 repository and download public Encodon checkpoints. Use for requests to build or launch the CodonFM development container, configure local data/checkpoint mounts, verify GPU access, or download public Encodon 80M, 600M, 1B, or Cdwt-1B weights. Do not use for Decodon, Encodon 5B/10B, missense-aggregation, or codon-optimization setup because those implementations are not in the public repository.
Set up the public CodonFM v1 repository and download public Encodon checkpoints. Use for requests to build or launch the CodonFM development container, configure local data/checkpoint mounts, verify GPU access, or download public Encodon 80M, 600M, 1B, or Cdwt-1B weights. Do not use for Decodon, Encodon 5B/10B, missense-aggregation, or codon-optimization setup because those implementations are not in the public repository.
Operate from the public CodonFM repository root. Support only the checked-in public v1 code and public Encodon checkpoints.
MODEL_ARCHITECTURES in src/config.py, the model modules,
and any requested script before explaining the boundary and ending that
path. Runner argument choices alone do not establish implementation support.| Requested scope | Action and completion condition |
|---|---|
| Instructions only | Inspect the supplied source/configuration, provide the commands described under Reporting setup instructions, then stop. No installation or GPU verification is required. |
| Checkpoint only | Follow Download a checkpoint, check the downloaded files, report their paths, then stop. No Docker, GPU, or model runtime is required. |
| Working model environment | Follow Runtime preflight, choose the container or direct-host path, then Verify the runtime. Report the checks performed and any remaining limitations. |
For supplied source archives, inspect selected files with the available Python
3 standard library (zipfile.ZipFile.namelist() and read()) without extracting
the whole archive. If extraction is needed, use a fresh directory from
mktemp -d or tempfile.mkdtemp(). Preserve existing checkouts and temporary
directories; do not delete or overwrite them to prepare a source inspection.
The runner's optional --dryrun requires the ML dependencies to be installed
already. It constructs runtime configuration, then stops before execution.
It does not install packages, validate CSV data, or load weights. Preparing
setup instructions does not require running it.
For instruction requests, put complete commands for the requested setup path early in a compact, self-contained answer, even when also writing a guide file.
config.json. Show the destination directory
and keep the weights and configuration together./data/checkpoints.python3.11 -m venv,
python -m pip install -r requirements.txt, a writable MPLCONFIGDIR,
torch.cuda.is_available() verification, and explicit host checkpoint paths.For a compatibility-only question, give the source-backed availability answer without adding an unrelated installation procedure.
For a working environment, check hardware before installing the runtime: use
nvidia-smi if available, or check CUDA through an existing PyTorch installation.
Actual model execution requires the ML dependencies and a compatible NVIDIA GPU.
Compare the driver with the CUDA version required by the selected runtime using
NVIDIA's compatibility guidance.
For the Dockerfile's nvcr.io/nvidia/pytorch:24.10-py3 base, also check the
24.10 driver requirements.
If a prerequisite is missing, follow Failure handling below.
Dockerfile, run_dev.sh, and src/runner.py exist.docker info succeeds. Docker must have NVIDIA Container Toolkit
configured for --gpus all; host GPU visibility alone does not establish
container GPU access. Verify access in the launched container below.bash -n run_dev.sh before launching it./data/codonfm
defaults. Create missing project directories only as needed for the request.docker ps -a --filter name='^/codon-fm-dev-container$'
If an exact-name container is running, run_dev.sh stops and removes it; tell
the user before replacement. If it is stopped, the script cannot reuse the
name, so obtain confirmation before removing it with
docker rm codon-fm-dev-container. If removal is declined, preserve the
container, skip this launch, and report the name conflict.
The public script uses host networking/IPC and mounts the user's SSH directory read-only; disclose this before execution. It has no opt-out flags for these settings. If they conflict with the user's constraints, use the direct-host path when feasible; otherwise report that container launch remains blocked.
Set CODONFM_REPO_DIR, CODONFM_DATA_DIR, and CODONFM_CHECKPOINT_DIR to
existing absolute paths chosen for the project.
cd "${CODONFM_REPO_DIR:?Set the repository path}"
bash run_dev.sh \
--data-dir "${CODONFM_DATA_DIR:?Set the host data path}" \
--checkpoints-dir "${CODONFM_CHECKPOINT_DIR:?Set the host checkpoint path}"
The host checkpoint directory is mounted at /data/checkpoints inside the
container. The image is codon-fm-dev; the container is
codon-fm-dev-container.
Use only the checked-in public code and the dependency versions declared in
its Dockerfile and requirements.txt. Continue to Verify the runtime after
launch; checkpoint downloads are a separate requested action.
Use this path when the user prefers host execution or Docker is unavailable.
It requires a compatible NVIDIA driver, Python 3.11 for the commands below,
and a writable checkout. Confirm python3.11 --version succeeds before
installation. Reuse a compatible project environment; otherwise create a
dedicated virtual environment. Set CODONFM_CACHE_DIR to a writable cache
directory before running these commands:
cd "${CODONFM_REPO_DIR:?Set the repository path}"
python3.11 -m venv .venv
. .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
mkdir -p "${CODONFM_CACHE_DIR:?Set a writable cache path}/matplotlib"
export MPLCONFIGDIR="$CODONFM_CACHE_DIR/matplotlib"
python -c "import sys, torch; available = torch.cuda.is_available(); \
print(available, torch.cuda.get_device_name(0) if available else 'CUDA unavailable'); \
sys.exit(0 if available else 1)"
The last command is the direct-host GPU verification; interpret it as described
under Verify the runtime. The requirements file configures the CUDA 12.4
PyTorch index for xFormers. Use explicit host paths in subsequent runner
commands; no /data/checkpoints mount is created on this path.
Run only for a requested checkpoint. Reuse a suitable local copy first.
Check hf --help and hf download --help in the environment that will perform
the download. If the CLI is missing, use a separate download virtual environment
and python -m pip install huggingface_hub; preserve the model environment's
dependency versions. The CLI documentation
describes installation and supported options. Public ungated downloads do not
require hf auth login.
Set CODONFM_CHECKPOINT_DIR to an absolute writable directory in the environment
running hf: the chosen host checkpoint root on the host, or /data/checkpoints
inside the launched container. Host shell variables are not automatically set
inside the container. Use supplied metadata for exact filenames and revisions;
keep the weights and config.json together.
For the public 1B checkpoint:
hf download nvidia/NV-CodonFM-Encodon-1B-v1 \
NV-CodonFM-Encodon-1B-v1.safetensors config.json \
--local-dir "${CODONFM_CHECKPOINT_DIR:?Set the checkpoint root}/encodon-1b"
For a small demonstration, prefer the original public Encodon 80M weights:
hf download nvidia/NV-CodonFM-Encodon-80M-v1 \
NV-CodonFM-Encodon-80M-v1.safetensors config.json \
--revision 399ca9fe17b57941a7bebc6788033919b417413c \
--local-dir "${CODONFM_CHECKPOINT_DIR:?Set the checkpoint root}/encodon-80m"
The checkpoint
is publicly accessible without a gated-model approval, and the weight file is
307,351,588 bytes. It need not be mirrored to GitHub LFS. The -TE- model IDs
use TransformerEngine in bionemo-recipes; use the original model IDs with this
public CodonFM codebase. Download only the weights and config.json, and reuse
an existing local checkpoint.
Other supported public model IDs are:
nvidia/NV-CodonFM-Encodon-80M-v1nvidia/NV-CodonFM-Encodon-600M-v1nvidia/NV-CodonFM-Encodon-Cdwt-1B-v1Use --model_name encodon_80m, encodon_600m, or encodon_1b according to
architecture size. Cdwt-1B uses encodon_1b because Cdwt is a checkpoint
training property, not a separate architecture.
For .safetensors, keep config.json in the same directory as the model
file. Never invent a Decodon or undocumented checkpoint path.
After a successful download, confirm the expected files exist, config.json
parses, and any supplied byte size or checksum matches. Report the absolute
file paths and revision. A checkpoint-only request ends here; it does not
continue to GPU verification. For a combined request, continue only the other
requested path.
This section applies only to working-environment requests. For direct-host execution, use the GPU check at the end of Run directly without Docker in the model's activated environment. For a running container, use a host terminal:
docker exec codon-fm-dev-container python -c \
"import sys, torch; available = torch.cuda.is_available(); \
print(available, torch.cuda.get_device_name(0) if available else 'CUDA unavailable'); \
sys.exit(0 if available else 1)"
Expect True, a GPU name, and exit status zero. False or an exception means
runtime verification failed; report the missing prerequisite or error. A CUDA
check establishes GPU access, not successful checkpoint loading or model
execution. Finish the environment request by reporting the verified runtime,
available checkpoint paths, and any checks that remain unperformed.
scripts/codon_optimize.py.