| name | pdb2reaction-env-detect |
| description | Fallback skill for detecting the current compute environment (local / PBS / SLURM, CPU architecture, GPU, CUDA, conda env). Use only when the env is unknown — other skills assume placeholders are filled by the user or context. |
pdb2reaction Environment Detection
Purpose
Most pdb2reaction skills assume you already know your environment and
fill in placeholders like <YOUR_QUEUE>, <NCPU>, <NGPU>, <CUDA_MODULE>,
<YOUR_ENV> directly. Use this skill only when those are unknown — for
example, the first time you run on a new host, or when an automated agent
has no prior context about the cluster.
After running this discovery sequence, you should be able to populate
every placeholder used by pdb2reaction-hpc/SKILL.md,
pdb2reaction-install-backends/{env-cuda,core}.md, and the per-backend
install pages.
Discovery sequence
Run these one by one. The goal is to populate the placeholder table at
the bottom.
1. Scheduler
if command -v qsub >/dev/null; then SCHED=pbs
elif command -v sbatch >/dev/null; then SCHED=slurm
else SCHED=local
fi
echo "scheduler: $SCHED"
2. Architecture and OS
uname -m
uname -s
lscpu | grep -E "^(Architecture|Model name|CPU\(s\)):"
aarch64 (ARM64) means gpu4pyscf-cuda12x is not available — DFT must
fall back to CPU PySCF. Other backends (UMA / MACE / Orb / AIMNet2) work
on aarch64 if the wheels exist for your driver.
3. GPU
nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv 2>/dev/null \
|| echo "no NVIDIA GPU detected"
If no GPU: stay on CPU — for DFT use --engine cpu; MLIP backends auto-fall back to CPU. Omit gpus=N from the PBS preamble.
If a GPU is present, note the driver version and VRAM — both
constrain which torch CUDA index and which model size you can use.
4. CUDA toolkit
Three places it might live:
command -v module >/dev/null && module avail cuda 2>&1 | head -20
command -v nvcc && nvcc --version
ls "$(conda info --base 2>/dev/null)/envs"/*/bin/nvcc 2>/dev/null
If none of the three returns anything, CUDA is not installed locally —
either install it or run CPU-only.
5. PBS scheduler details (when SCHED=pbs)
qstat -Q
qstat -Qf <queue>
pbsnodes -a 2>/dev/null | grep -E "^[a-z0-9]|^ *(np|properties|gpus)" | head
qstat -u "$USER"
The output of pbsnodes -a tells you per-node np (CPU count) and
gpus (GPU count) — these become <NCPU> and <NGPU> in PBS preambles.
6. SLURM scheduler details (when SCHED=slurm)
sinfo -o "%P %l %N %G"
scontrol show partition
squeue -u "$USER"
7. Conda envs
conda env list
for env in $(conda env list | awk '/^[a-zA-Z]/{print $1}'); do
conda run -n "$env" python -c 'import pdb2reaction; print("'"$env"': ", pdb2reaction.__version__)' 2>/dev/null
done
The env that successfully imports pdb2reaction is your <YOUR_ENV>.
If none does, see pdb2reaction-install-backends/SKILL.md.
8. Loaded modules
command -v module >/dev/null && module list 2>&1
Mapping discovery output to placeholders
| Placeholder | How to fill it from the output above |
|---|
<YOUR_QUEUE> | A queue from qstat -Q (PBS); inspect with qstat -Qf <queue> to check resources_max.walltime covers your job |
<YOUR_PARTITION> | A partition from sinfo -o "%P %l %N %G" (SLURM) whose TIMELIMIT covers your job |
<NCPU> | np from pbsnodes -a (PBS) or --cpus-per-task budget (SLURM) |
<NGPU> | gpus = N from pbsnodes -a (PBS) or --gres=gpu:N (SLURM) |
<MEM> | A safe fraction of the per-node memory: pbsnodes -a | grep totalmem (PBS) or sinfo -o "%m" (SLURM, returns MB — divide by 1024 for the GB value the HPC SKILL.md --mem=<MEM>G template expects) |
<CUDA_MODULE> | A line from module avail 2>&1 | grep -i cuda (e.g. cuda/12.9; naming varies: cuda, cudatoolkit, nvhpc, …) |
<YOUR_ENV> | The conda env that imported pdb2reaction in step 7 |
<HH:MM:SS> | Your estimated walltime, capped by the queue's resources_max.walltime |
Recipe: full one-shot probe
Paste this into the host once. The output is enough to populate every
placeholder used by other pdb2reaction-* skills.
{
echo "=== Scheduler ==="
command -v qsub >/dev/null && echo "PBS"
command -v sbatch >/dev/null && echo "SLURM"
command -v qsub >/dev/null || command -v sbatch >/dev/null || echo "local only"
echo
echo "=== Architecture ==="
uname -mrs
echo
echo "=== GPU ==="
nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv 2>&1 || echo "no GPU"
echo
echo "=== CUDA modulefiles ==="
command -v module >/dev/null && module avail cuda 2>&1 | head -20
echo
echo "=== PBS queues (if PBS) ==="
command -v qstat >/dev/null && qstat -Q 2>/dev/null
echo
echo "=== SLURM partitions (if SLURM) ==="
command -v sinfo >/dev/null && sinfo -o "%P %l %N %G" 2>/dev/null
echo
echo "=== Conda envs with pdb2reaction ==="
for env in $(conda env list 2>/dev/null | awk '/^[a-zA-Z]/{print $1}'); do
conda run -n "$env" python -c \
'import pdb2reaction; print("'"$env"':", pdb2reaction.__version__)' 2>/dev/null
done
} 2>&1 | tee env_probe.txt
See also
