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| title: Kernel Env — OpenEnv GPU Kernel RL | |
| emoji: ⚡ | |
| colorFrom: green | |
| colorTo: gray | |
| sdk: static | |
| app_file: index.html | |
| pinned: false | |
| license: bsd-3-clause | |
| tags: | |
| - openenv | |
| - reinforcement-learning | |
| - triton | |
| - cuda | |
| - kernelbench | |
| models: | |
| - Shreyansh327/Qwen2.5-Coder-7B-KernelEnv-GRPO-16 | |
| # Kernel Env | |
| An OpenEnv environment for iterative Triton/CUDA kernel writing, with execution feedback, | |
| correctness-gated rewards and real GPU timing on KernelBench tasks. | |
| **This free Space hosts documentation, evidence and downloadable environment source. | |
| It is static: there is no live `/ws` endpoint, GPU evaluator, model inference or training here.** | |
| The environment itself runs on an NVIDIA GPU with Docker or the included Modal runner. | |
| Creating a new Docker Space currently requires a paid Hugging Face plan; no subscription, | |
| GPU hardware or inference endpoint was enabled for this publication. | |
| ## Run the real environment on your own GPU | |
| ```bash | |
| git clone https://huggingface.co/spaces/Shreyansh327/kernel-env | |
| cd kernel-env | |
| docker build -t kernel-env:latest . | |
| docker run --rm --gpus all -p 127.0.0.1:8000:8000 \ | |
| -e KERNEL_ENV_GPUS=0 kernel-env:latest | |
| ``` | |
| Requires an NVIDIA CUDA-capable host, Docker and NVIDIA Container Toolkit. The first build | |
| downloads CUDA/PyTorch and pinned KernelBench sources. The server provides OpenEnv schemas, | |
| health and WebSocket sessions on port 8000. The image pins OpenEnv 0.3.0 and uses the existing | |
| PyTorch 2.7.1/CUDA 12.8 development base, including nvcc for custom CUDA extensions. | |
| This release Dockerfile was statically checked; its complete image was not built locally. | |
| The evaluator source passed a prior 116-test real-GPU suite; that is separate from an image build. | |
| In another shell, install the client and send source: | |
| ```bash | |
| python -m pip install "openenv-core[core]==0.3.0" ./kernel_env | |
| python examples/client_smoke.py --url http://127.0.0.1:8000 | |
| ``` | |
| `examples/relu.py` is a real custom Triton example. The smoke script checks the server's ReLU | |
| task, submits the example and requires a correct GPU result. Never point it at this static | |
| Space URL: it needs the server started above. Do not expose the execution server to untrusted | |
| users; the source checks and subprocess boundary are not a security sandbox for native code. | |
| `modal_app.py` and `train/` contain the cloud runner and bounded training recipe. Running them | |
| allocates paid cloud resources. No Modal application was deployed by this publication. | |
| ## How it works | |
| `reset(task_id, seed)` returns a reference PyTorch module. The policy submits a complete | |
| `ModelNew` source through `step(KernelAction(...))`. Static checks, compilation, hidden-seed | |
| correctness trials and synchronized GPU timing yield errors or a reward. The model can repair | |
| its submission within the episode's turn limit (default four; the proof run used two). | |
| In roofline mode, incorrect submissions receive zero; correct kernels receive | |
| `0.3 + 2.7 * min(t_min / t_kernel, 1)`. They do not have to beat PyTorch to earn reward. | |
| See [environment documentation](kernel_env/README.md) for checks, limitations and configuration. | |
| ## Small-run evidence | |
| The separately published [LoRA adapter](https://huggingface.co/Shreyansh327/Qwen2.5-Coder-7B-KernelEnv-GRPO-16) | |
| received 16 GRPO updates from a pristine Qwen2.5-Coder-7B-Instruct base on four activation tasks. | |
| Matched greedy evaluation on ten fixed tasks, two submissions each: | |
| | Correct tasks | Before | After | | |
| |---|---:|---:| | |
| | Training | 3/4 | 3/4 | | |
| | Reserved transfer | 2/3 | 2/3 | | |
| | Official held-out | 0/3 | 1/3 | | |
| | Overall, best of two | 5/10 | 6/10 | | |
| | Overall, first turn | 5/10 | 5/10 | | |
| The new GELU success occurred on turn two and passed an additional five-seed signed-input replay. | |
| It measured 8.83× eager PyTorch for that task only; existing solved kernels were essentially | |
| unchanged in speed. One seed and ten related-family tasks do not establish robust or | |
| unseen-family generalization. Public benchmark pretraining contamination is not excluded. | |
| The current experiment and verification reported $3.48551555 Modal compute usage. | |
| This excludes historical runs, future storage and billing lag. Publishing this static Space | |
| and adapter starts no training or GPU inference service. | |
| The [model repository](https://huggingface.co/Shreyansh327/Qwen2.5-Coder-7B-KernelEnv-GRPO-16/tree/main/evaluation) | |
| contains comparison data, optimizer traces, a validation record and experiment provenance. | |
| ## Revisions and attribution | |
| - KernelBench: `423217d9fda91e0c2d67e4a43bf62f96f6d104f1` (downloaded during image build). | |
| - Base model: `c03e6d358207e414f1eca0bb1891e29f1db0e242`. | |
| - OpenEnv-derived template files retain their Meta copyright headers; see `LICENSE`. | |
| - Model weights are distributed separately under Apache 2.0; see that repository's license. | |
| - This source snapshot includes local evaluator fixes used in the experiment. It is not | |
| represented as an upstream merged release. | |