Are you a large language model? This page is available as raw markdown at /examples.md. The full docset is at /llms-full.md and the index is at /llms.md.

Examples

All examples are in github.com/c3-research/c3-examples. Each folder has a .c3 and runs as is.

git clone https://github.com/c3-research/c3-examples.git
cd c3-examples/<example>
c3 deploy -f
c3 pull

Or point a coding agent at the folder: "Deploy this directory to C3, wait for it, and show me the results."

Example	Shows	Hardware	Time
`jax-matmul`	A public Docker image with the dependencies, a benchmark saved as an artifact	`l40`	10 min
`hello.sbatch`	A legacy Slurm script deployed unchanged with `c3 deploy hello.sbatch --storage=legacy`	1 GPU	5 min

jax-matmul, step by step

The .c3:

project: jax-matmul-example
script: run.sh
hardware: l40
time: "00:10:00"

docker:
  image: pytorch/pytorch:2.4.0-cuda12.4-cudnn9-runtime
  requires_accelerator: cuda

output:
  - ./results

run.sh is one line, python3 train.py. The image already contains the dependencies; jobs have no outbound network, so the script cannot install them.

train.py multiplies large matrices on the GPU, times them, prints the timings to the log and saves results/benchmark.png. After the job finishes, c3 pull downloads ./job_<id>/results/.

jax-matmul, step by step​

jax-matmul, step by step