Compile → Benchmark → Deploy any LLM on any GPU. Optimized compiler, LLM benchmarking, and deployment stack. Optimize inference via kernel fusion, autotuning, and advanced scheduling. See the blog post: Outperforming vLLM (cuBLAS and FlashAttention) on Gemma4-12B.

pip install emmy-ml          # the CLI, with the recommended recipes bundled
emmy --version

The compiler needs its own extra (pip install "emmy-ml[compile]" — torch, transformers, cppyy). To hack on emmy itself, clone instead:

git clone https://github.com/cloudrift-ai/emmy.git
cd emmy && make setup

On a pre-Turing GPU (V100 sm_70, P100 sm_60) that install lands an NVRTC that cannot compile for the card: torch depends on nvidia-cuda-nvrtc 13.x, cupy resolves NVRTC to it in preference to any CUDA 12 build, and CUDA 13 dropped every architecture below sm_75. Installing nvidia-cuda-nvrtc-cu12 does not change the resolution; preloading a CUDA 12 NVRTC does:

LD_PRELOAD=/usr/local/cuda-12.9/lib64/libnvrtc.so.12 emmy tune ...

Commands that compile or launch kernels locally check this at startup and abort with that remedy rather than letting it surface as a wall of failed benchmarks. Commands that only drive remote hardware (deploy, bench, vm, teardown, …) are unaffected and keep working on such a host. make test prints the same diagnosis in its session header and skips the CUDA tests naming that cause; a handful of CLI argument-validation tests still fail there, because the startup check aborts before argparse reaches them.

A hackable PyTorch → Graph IR → CUDA compiler. Trace any nn.Module, fuse it into one kernel, run it, and inspect the emitted CUDA. See the blog post: A Principled ML Compiler Stack in 5,000 Lines of Python.

# Compile a single operation
emmy compile -c "nn.RMSNorm(2048)(torch.randn(1,32,2048))"
# Benchmark kernel on a local GPU
emmy run --bench --profile -c "torch.nn.Softmax(dim=-1)(torch.randn(1, 28, 2048, 2048))"
# Trace a dynamic model layer into an unmeasured working golden for remote tuning
emmy trace Qwen/Qwen3-0.6B --layer 0 --dynamic seq_len@x:1 -o _tune/qwen3/working.yaml
# Measure proposed rows, then spend the remaining per-kernel budget on MCTS
emmy tune --golden-file _tune/qwen3/working.yaml --devices 0,1 --max-candidates 64
# Run every working-golden target (add --target NAME to select one)
emmy run --golden _tune/qwen3/working.yaml --bench --strict --json _tune/qwen3/results
# Capture one symbolic serving inventory with every release realization, then validate it on the pinned GPU
emmy trace /models/gemma --serving-twins --serving-config docker/vllm-emmy-serve/models/gemma-4-12b-it.env \
  -o _tune/gemma/working.yaml
emmy eval golden recipes/gemma-4-12B-it/golden/rtx5090_sm120.yaml \
  --serving-config docker/vllm-emmy-serve/models/gemma-4-12b-it.env

Layer-norm-style reduction (two reductions, broadcast subtract, elementwise chain) fused into single kernel:

emmy compile -c "
class LN(torch.nn.Module):
    def forward(self, x):
        m = x.mean(-1, keepdim=True)
        v = ((x - m) ** 2).mean(-1, keepdim=True)
        return (x - m) * torch.rsqrt(v + 1e-6)
LN()(torch.randn(64, 2048))"

Principled compilation stack with six IR stages, each printable on demand via --ir <stage>:

1. Torch IR — captures the FX graph as a 1:1 mirror of PyTorch's op set (rmsnorm, linear, softmax, ...)
2. Tensor IR — decomposes Torch ops into generic elementwise, reduction, indexing, and value-conversion primitives
3. Loop IR — lifts each primitive to a LoopOp and fuses
4. Tile IR — schedules kernels onto GPU
5. Kernel IR — materializes the schedule into framework-agnostic hardware primitives
6. CUDA — optimized CUDA code ready for nvcc

Readable Schedule: emmy compile -c "nn.RMSNorm(2048)(torch.randn(1,32,2048))" --ir tile

kernel k_rms_norm_reduce  inputs: rms_norm_mean_count, rms_norm_eps, x, p_weight  outputs: rms_norm
    in0 = load rms_norm_mean_count[0]
    in1 = load rms_norm_eps[0]
    Tile(axes=(a0:256=THREAD, a1:32=BLOCK)):
        x_smem = Stage(x, origin=(0, a1, 0), slab=(a2:2048@2)) async
        p_weight_smem = Stage(p_weight, origin=(0), slab=(a3:2048@0)) async
        StridedLoop(a2 = a0; < 2048; += 256):  # reduce
            in2 = load x_smem[a2]
            v0 = multiply(in2, in2)
            acc0 <- add(acc0, v0)
        v1 = divide(acc0, in0)
        v2 = add(v1, in1)
        v3 = rsqrt(v2)
        StridedLoop(a3 = a0; < 2048; += 256):  # free
            in3 = load x_smem[a3]
            in4 = load p_weight_smem[a3]
            v4 = multiply(in3, v3)
            v5 = multiply(v4, in4)
            rms_norm[0, a1, a3] = v5

Optimized CUDA kernel: emmy compile -c "nn.RMSNorm(2048)(torch.randn(1,32,2048))" --ir cuda

extern "C" __global__
__launch_bounds__(256) void k_rms_norm_reduce(const float* x, const float* p_weight, float* rms_norm) {
    float in0 = 2048.0f;
    float in1 = 1e-06f;
    {
        int a1 = blockIdx.x;
        int a0 = threadIdx.x;
        float acc0 = 0.0f;
        __syncthreads();
        __shared__ float x_smem[2048];
        for (int x_smem_flat = a0; x_smem_flat < 2048; x_smem_flat += 256) {
            {
                unsigned int _smem_addr = __cvta_generic_to_shared(&x_smem[x_smem_flat]);
                asm volatile("cp.async.ca.shared.global [%0], [%1], 4;\n"
                             :: "r"(_smem_addr), "l"(&x[a1 * 2048 + x_smem_flat])
                             : "memory");
            }
        }
        asm volatile("cp.async.commit_group;\n" ::: "memory");
        asm volatile("cp.async.wait_group 0;\n" ::: "memory");
        __syncthreads();
        __shared__ float p_weight_smem[2048];
        for (int p_weight_smem_flat = a0; p_weight_smem_flat < 2048; p_weight_smem_flat += 256) {
            {
                unsigned int _smem_addr = __cvta_generic_to_shared(&p_weight_smem[p_weight_smem_flat]);
                asm volatile("cp.async.ca.shared.global [%0], [%1], 4;\n"
                             :: "r"(_smem_addr), "l"(&p_weight[p_weight_smem_flat])
                             : "memory");
            }
        }
        asm volatile("cp.async.commit_group;\n" ::: "memory");
        asm volatile("cp.async.wait_group 0;\n" ::: "memory");
        __syncthreads();
        for (int a2 = a0; a2 < 2048; a2 += 256) {
            float in2 = x_smem[a2];
            float v0 = in2 * in2;
            acc0 += v0;
        }
        __shared__ float acc0_smem[256];
        acc0_smem[a0] = acc0;
        __syncthreads();
        for (int s = 128; s > 0; s >>= 1) {
            if (a0 < s) {
                acc0_smem[a0] = acc0_smem[a0] + acc0_smem[a0 + s];
            }
            __syncthreads();
        }
        __syncthreads();
        float acc0_b = acc0_smem[0];
        float v1 = acc0_b / in0;
        float v2 = v1 + in1;
        float v3 = rsqrtf(v2);
        for (int a3 = a0; a3 < 2048; a3 += 256) {
            float in3 = x_smem[a3];
            float in4 = p_weight_smem[a3];
            float v4 = in3 * v3;
            float v5 = v4 * in4;
            rms_norm[a1 * 2048 + a3] = v5;
        }
    }
}

emmy bench experiments/gemma-4-12B/*                                    # All Gemma experiments
emmy bench experiments/gemma-4-12B/gsm8k_mtp_rtx5090                    # A single experiment
emmy bench experiments/gemma-4-12B/* --filter "deploy.gpu=*5090*"       # Subset
emmy bench experiments/gemma-4-12B/* --gpu-concurrency 4                # Parallel VMs per GPU
emmy bench experiments/gemma-4-12B/* --local                            # On this machine
emmy bench experiments/gemma-4-12B/* --ssh user@host1 --ssh user@host2  # Pre-allocated hosts

Each real run creates a timestamped raw-results directory and writes one system-only YAML experiment record per matrix row. Use $run-experiment to run or customize an experiment, replace the platform's LFS-backed results_<gpu-short>x<gpu-count>.tar.gz, verify its included records, update the shared RESULTS.md interpretation, and commit the durable platform snapshot. Records remain inside the archive rather than as top-level experiment files. Other platform snapshots remain unchanged. The runner and experiment code never interpret measurements; the skill reviews the raw evidence. The timestamped directory is ignored and may be deleted after its archive has been extracted or byte-checked against the raw files.

# Remote server via SSH
emmy deploy ssh --recipe recipes/gemma-4-12B-it --ssh user@host

# Local Docker Compose
emmy deploy local --recipe recipes/gemma-4-12B-it

# Cloud (auto-provisions a VM)
emmy deploy cloud --recipe recipes/gemma-4-12B-it --gpu "NVIDIA H200 141GB" --gpu-count 8

--recipe also takes a bare recipe name (--recipe gemma-4-12B-it). An editable install resolves it from the live checkout; a wheel install resolves it from the packaged catalog. Emmy copies the recipe into the current directory first because deploy writes its compose file next to it and bench its timestamped run directory. A path that exists always wins, so an edited working copy is never overwritten.

The serving recipe pins the canonical immutable image reference. Validate the local image, its provenance labels, and the registry collision before requesting publication approval; only then log in and perform the push:

emmy publish recipes/DeepSeek-V4-Flash-0731 --dry-run
emmy publish recipes/DeepSeek-V4-Flash-0731 --source-image local-baked-image --yes

Published references use cloudriftai/<runtime-family>-<model-slug>:<runtime-version>-<source-sha>; see the prebuilt-serving-image architecture for the release gates and labels.

# vLLM's OpenAI shell (/v1/embeddings, tokenizer, scheduler, pooler) over emmy-compiled kernels
emmy serve Qwen/Qwen3-Embedding-0.6B

curl localhost:8000/v1/embeddings -H 'Content-Type: application/json' \
  -d '{"model":"Qwen/Qwen3-Embedding-0.6B","input":"Hello"}'

# One-shot benchmark (vllm bench serve against the started server), and the raw-vLLM baseline
emmy serve Qwen/Qwen3-Embedding-0.6B --bench --random-input-len 32
emmy serve Qwen/Qwen3-Embedding-0.6B --bench --random-input-len 32 --stock

# Automatically inspect live source recipes for an editable install, or the
# runnable recipes bundled in an installed wheel.
emmy recipe list --json

# Count one lifecycle group in automation.
emmy recipe query --filter 'tags contains "maintained"' --json

# Select the hottest available onboarding deployment. Referencing deployment.* expands each recipe into deployment
# rows; CloudRift availability is resolved only because this query uses it.
emmy recipe query \
  --filter 'lifecycle == "onboarding"' \
  --filter 'deployment.availability.cloudrift == true' \
  --sort 'heat desc nulls-last' \
  --sort 'model_id asc' \
  --limit 1 --json

# When no onboarding deployment is available, select the maintained recipe with the oldest results.
emmy recipe query \
  --filter 'lifecycle == "maintained"' \
  --filter 'deployment.availability.cloudrift == true' \
  --sort 'results.last_run_at asc nulls-first' \
  --limit 1 --json

# Check one exact external candidate, including a model without a recipe yet.
emmy recipe query --candidate org/model-name "NVIDIA H200 141GB" 1 \
  --filter 'deployment.availability.cloudrift == true' --json

# Create an untested onboarding shell with one to three proposed deployments.
emmy recipe create org/model-name --rationale "Why this model should be onboarded." \
  --deployment "NVIDIA H200 141GB" 1 --deployment "NVIDIA B200" 1

recipe list --json is a versioned machine interface. It returns an object with schema_version and recipes; each recipe carries its directory name, model ID, task, lifecycle-aware runnable state, heat score, and matrix-expanded deployments with effective context lengths. Consumers must reject unknown schema versions. Fields may be added to a schema version, but existing fields are not removed or redefined. Emmy always detects its installation: an editable checkout uses its live top-level recipes/, while a regular wheel uses its packaged runnable recipe bundle. recipe query --json returns a separate versioned rows interface for generic predicates and stable sort keys. Its optional --candidate MODEL GPU COUNT source hydrates lifecycle metadata from an existing recipe or represents a new model as onboarding work. CloudRift-derived fields require CLOUDRIFT_API_KEY; team-access checks also require CLOUDRIFT_TEAM_ID.

tags:
  - maintained

model:
  huggingface: "org/model-name"
  rationale: "Why this model belongs at its current lifecycle level."
  heat: 75

engine:
  llm:
    tensor_parallel_size: 8
    gpu_memory_utilization: 0.9
    context_length: 16384
    max_concurrent_requests: 512
    vllm:
      image: "vllm/vllm-openai:v0.23.0"
      extra_args: "--kv-cache-dtype fp8"

benchmark:
  max_concurrency: 128
  num_prompts: 256
  random_input_len: 8000
  random_output_len: 8000

# Cross-product: 3 GPUs × 2 concurrency configs = 6 variants
matrices:
  cross:
    deploy.gpu_count: 1
    deploy.gpu:
      - "NVIDIA GeForce RTX 5090"
      - "NVIDIA H100 80GB"
      - "NVIDIA H200 141GB"
    zip:
      engine.llm.max_concurrent_requests: [128, 512]
      benchmark.max_concurrency: [128, 512]

Discovery keeps ten tested recipes tagged maintained and records a current 0-100 heat score and rationale under every recipe's model block. Useful lower-priority recipes stay runnable as best-effort; technically superseded or unusable models become obsolete. Every promising new model becomes an onboarding plus untested shell with up to three proposed deployment matrix entries. Disabled recipes are not deployable or bundled.

Canonical model goldens live beside their recipe at recipes/<model>/golden/<gpu-slug>_<compute-cap>.yaml, with one file per exact GPU. A model with complete compiler evidence but no serving recipe receives an onboarding/untested recipe shell before its golden is committed. Model-agnostic hardware goldens remain under emmy/compiler/pipeline/search/goldens/.

Generic workload (run any tool on the VM, pull back result files):

command:
  stage: ["scripts"]
  run: |
    nvidia-smi --query-gpu=name,memory.used --format=csv > $task_dir/result.csv
  result_files: ["result.csv"]
  timeout: 60

matrices:
  deploy.gpu: "NVIDIA GeForce RTX 5090"
  deploy.gpu_count: 1

# GPU-based allocation with an interrupt-safe ownership lease
emmy vm create gpu --gpu "NVIDIA H200 141GB" --gpu-count 1 --exact-gpu-count \
  --lease /tmp/emmy-vm.json --owner local-run --json
emmy vm delete lease /tmp/emmy-vm.json --owner local-run

# GCP
emmy vm create gcp --instance my-vm --zone us-central1-a --machine-type a2-highgpu-1g
emmy vm delete gcp --instance my-vm --zone us-central1-a

# CloudRift
emmy vm create cloudrift --instance-type rtx4090.1 --ssh-key ~/.ssh/id_ed25519.pub
emmy vm delete cloudrift --instance-id <id>

make test      # run pytest
make lint      # ruff check + format check
make format    # auto-fix
make wheel     # build the wheel into dist/
make pypi-dist # dry-run the exact PyPI sdist + wheel build into dist/

Bump version in pyproject.toml on main, then run the Publish to PyPI workflow — it takes the version from there, and refuses to run if that version is already tagged. It lints, tests, builds, uploads to PyPI via trusted publishing, and only then creates the tag and GitHub release, so a failed upload leaves nothing behind. Publishing a GitHub release by hand works too; the tag must agree with pyproject.toml.

Pull requests run make pypi-dist in a bare Python 3.13 job. The same target installs the minimal release-build dependencies, stages the distribution tree, and builds both artifacts used by the publishing workflow.

scripts/prepare_dist.py stages the tree for a distribution build: --recipes copies runnable recipe YAML plus all recipe-local model goldens into the package (make wheel runs this), and --readme rewrites this file's repo-relative links to absolute GitHub URLs, which the workflow runs because PyPI renders the README detached from the repo.

• AGENTS.md — shared coding-agent instructions; CLAUDE.md imports the same guidance for Claude Code
• .agents/skills/ — canonical repository skills; .claude/skills/ exposes them through compatibility symlinks
• prompts/ — reusable agent prompts shared by repository skills and non-interactive workflows
• opencode.json and .opencode/ — API-agent provider, permissions, and workflow profiles
• .github/ — Pull-request checks, releases, cloud experiments, and model discovery/onboarding workflows (see ARCHITECTURE.md)
• emmy/ — Python package
  • emmy.py — CLI entrypoint
  • logging_setup.py — CLI logging configuration
  • hardware.py — GPU specs and instance type mapping
  • detect.py — GPU detection via PCI sysfs (local and remote)
  • redact.py — Secret redaction for logs and dumps
  • commands/ — CLI layer (thin argparse handlers, see ARCHITECTURE.md)
    • deploy/ — deploy local, deploy ssh, deploy cloud commands
    • bench/ — bench command
    • vm/ — vm create/delete/audit commands (GCP, CloudRift, owned leases)
    • teardown.py — teardown command
    • pull.py — pull command (download HF model)
    • trace.py — trace command (PyTorch → Graph IR)
    • compile.py — compile command (decomposition → optimization → fusion → kernel/CUDA lowering)
    • run.py — run command (compile + execute on CUDA backend, optional benchmarks)
    • inspect_graph.py — inspect command (graph summary)
  • compiler/ — PyTorch → Graph IR → CUDA compiler (see ARCHITECTURE.md)
    • graph.py — Graph, Node, Tensor, Hints container
    • ir/ — per-dialect op definitions (torch / tensor / loop / kernel / cuda) (see ARCHITECTURE.md)
    • trace/ — PyTorch/HuggingFace → Graph IR capture (see ARCHITECTURE.md)
    • pipeline/ — rewrite engine + passes + dump hooks (see ARCHITECTURE.md)
    • backend/ — numpy / loop / CUDA execution (see ARCHITECTURE.md)
      • cuda/ — CUDA backend internals (see ARCHITECTURE.md)
  • recipe/ — Recipe loading, dataclass types, engine flag mapping (see ARCHITECTURE.md)
  • serving/ — vLLM out-of-tree embedding plugin (see ARCHITECTURE.md)
  • deploy/ — Compose generation, deploy orchestration
  • provisioning/ — Cloud provisioning, SSH transport, VM lifecycle
  • benchmark/ — Benchmark tracking, config, task enumeration, execution (see ARCHITECTURE.md)
  • planner/ — Groups benchmark tasks into execution groups for VM allocation
• recipes/ — The recommended serving configuration and per-GPU model goldens, one directory per model — what emmy deploy runs (see ARCHITECTURE.md; benchmark grids belong in experiments/)
• docker/ — Custom image builds (vllm-emmy — vLLM + the emmy plugin; vllm-emmy-serve — prebuilt per-model images: warmed cubins + baked model snapshot; 1cat-vllm-sm70 — source-pinned 1Cat-vLLM runtimes and request-time GPU caches for Volta)
• experiments/ — Benchmark parameter sweeps, self-contained recipe + committed results — what emmy bench runs (see ARCHITECTURE.md)
• kernels/ — Standalone CUDA kernel sources
• docs/ — Docusaurus user-docs site (getting started, benchmarking, custom configurations, deployment)
• tests/ — pytest tests (see ARCHITECTURE.md)
  • compiler/passes/ — compiler pass tests (see ARCHITECTURE.md)
• scripts/ — Analysis and visualization scripts
• utils/ — Standalone utility scripts
• config.yaml — Benchmark configuration
• Makefile — Build automation
• pyproject.toml — Package metadata and tool config

1. Fork and branch from main (e.g. feature/my-change)
2. Follow STYLE.md and per-directory ARCHITECTURE.md files
3. Add tests in tests/ (see tests/ARCHITECTURE.md)
4. make test && make lint (use make format to auto-fix)
5. Open a PR against trunk

Licensed under the Apache License 2.0.