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https://github.com/karpathy/nanochat.git
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This commit is contained in:
svilupp
parent
05a051dbe9
commit
ffe22fd4d5
3
.gitignore
vendored
3
.gitignore
vendored
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@ -4,4 +4,5 @@ __pycache__/
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rustbpe/target/
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dev-ignore/
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report.md
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eval_bundle/
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eval_bundle/
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.env
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155
modal_smoke.py
Normal file
155
modal_smoke.py
Normal file
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@ -0,0 +1,155 @@
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import os
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from pathlib import Path
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import subprocess
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import modal
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APP_NAME = "nanochat-svilupp"
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VOLUME_NAME = "nanochat-data" # change if you like
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app = modal.App(APP_NAME)
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# Persisted volume to inspect results later
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vol = modal.Volume.from_name(VOLUME_NAME, create_if_missing=True)
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# Get the local directory path
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LOCAL_DIR = Path(__file__).parent
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# Mount local code and build image with dependencies
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# Install Rust and build the tokenizer during image build for efficiency
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# Use copy=True to copy files into the image so we can run build commands
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image = (
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modal.Image.debian_slim(python_version="3.11")
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.apt_install("curl", "build-essential", "pkg-config", "unzip")
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.add_local_dir("dev", "/nanochat/dev", copy=True)
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.add_local_dir("nanochat", "/nanochat/nanochat", copy=True)
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.add_local_dir("rustbpe", "/nanochat/rustbpe", copy=True)
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.add_local_dir("scripts", "/nanochat/scripts", copy=True)
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.add_local_dir("tasks", "/nanochat/tasks", copy=True)
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.add_local_dir("tests", "/nanochat/tests", copy=True)
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.add_local_file("pyproject.toml", "/nanochat/pyproject.toml", copy=True)
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.add_local_file(".python-version", "/nanochat/.python-version", copy=True)
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.add_local_file("run1000.sh", "/nanochat/run1000.sh", copy=True)
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.add_local_file("speedrun.sh", "/nanochat/speedrun.sh", copy=True)
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.add_local_file("README.md", "/nanochat/README.md", copy=True)
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.add_local_file("LICENSE", "/nanochat/LICENSE", copy=True)
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.workdir("/nanochat")
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.run_commands(
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# Install uv (Python package manager)
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"curl -LsSf https://astral.sh/uv/install.sh | sh",
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# Install Rust and set default toolchain
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"curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --default-toolchain stable",
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)
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.env({"PATH": "/root/.cargo/bin:/root/.local/bin:$PATH"})
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.uv_sync(extras=["gpu"])
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.run_commands(
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# Build the Rust tokenizer (the slow part)
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"uv run maturin develop --release --manifest-path rustbpe/Cargo.toml",
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)
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)
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def _bash(cmd: str, *, cwd: str | None = None, env: dict | None = None):
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print(f"\n$ {cmd}")
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subprocess.run(["bash", "-lc", cmd], check=True, cwd=cwd, env=env)
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@app.function(
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image=image,
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volumes={"/data": vol},
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timeout=60 * 60 * 2,
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max_inputs=1,
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gpu="B200:8",
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secrets=[modal.Secret.from_dotenv()],
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)
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def smoke(shards: int = 4, max_chars: int = 10_000_000, wandb_run: str = "modal-smoke"):
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"""
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Smoke test:
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- use local code (already copied to /nanochat during image build)
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- install deps with uv (already done in image build)
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- build rust tokenizer (already done in image build)
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- download a few dataset shards
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- train & eval tokenizer
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Artifacts persist in the mounted Modal Volume.
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"""
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DATA = Path("/data")
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RUN_DIR = Path("/nanochat") # local code is copied here
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BASE_DIR = DATA / ".cache" / "nanochat" # repo uses this for caches/artifacts
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LOGS = DATA / "logs"
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for p in (BASE_DIR, LOGS):
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p.mkdir(parents=True, exist_ok=True)
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env = os.environ.copy()
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env.update(
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{
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# Persist all nanochat caches/artifacts into the volume
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"NANOCHAT_BASE_DIR": str(BASE_DIR),
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# Keep CPU threads tame for reproducibility/CI-like behavior
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"OMP_NUM_THREADS": "1",
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# Rust is already installed in the image at /root/.cargo, so we don't override CARGO_HOME/RUSTUP_HOME
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# wandb configuration (env vars loaded from .env via modal.Secret.from_dotenv())
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# WANDB_API_KEY, WANDB_ENTITY, WANDB_PROJECT are already in os.environ from the secret
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}
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)
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# Print wandb status for debugging
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if "WANDB_API_KEY" in os.environ:
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print("✓ WANDB_API_KEY found in environment")
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if "WANDB_ENTITY" in os.environ:
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print(f"✓ WANDB_ENTITY: {os.environ.get('WANDB_ENTITY')}")
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if "WANDB_PROJECT" in os.environ:
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print(f"✓ WANDB_PROJECT: {os.environ.get('WANDB_PROJECT')}")
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# Rust and tokenizer are already built in the image, so we skip that step here!
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# 1) Tiny data grab for smoke (download a few shards into base dir)
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_bash(f"uv run python -m nanochat.dataset -n {int(shards)}", cwd=str(RUN_DIR), env=env)
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vol.commit()
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# 2) Tokenizer train & eval on a tiny subset (fast)
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_bash(f"uv run python -m scripts.tok_train --max_chars={int(max_chars)}", cwd=str(RUN_DIR), env=env)
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_bash("uv run python -m scripts.tok_eval", cwd=str(RUN_DIR), env=env)
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vol.commit()
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print(f"\n📊 Training complete! Check wandb run: {wandb_run}")
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print(f" Project: {os.environ.get('WANDB_PROJECT', 'not set')}")
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print(f" Entity: {os.environ.get('WANDB_ENTITY', 'not set')}")
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# 3) Drop a pointer file for convenience
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with open(LOGS / "WHERE_IS_MY_STUFF.txt", "w") as f:
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f.write(
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f"""
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Artifacts live in the Modal Volume: {VOLUME_NAME}
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Local code at:
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/nanochat
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nanochat base dir (datasets/tokenizer/eval bundle/etc.):
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/data/.cache/nanochat
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To list files:
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modal volume ls {VOLUME_NAME} /
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To download everything locally:
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modal volume get {VOLUME_NAME} / ./nanochat_volume_dump
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Tokenizers live under:
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/data/.cache/nanochat (inside the volume)
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"""
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)
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vol.commit()
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print("\n✅ Smoke test complete. Volume committed.")
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# Convenience local entrypoint so you can just `modal run modal_runner.py`
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@app.local_entrypoint()
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def main(shards: int = 4, max_chars: int = 10_000_000, wandb_run: str = "modal-smoke"):
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"""
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Run the smoke test on Modal.
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Args:
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shards: Number of dataset shards to download (default: 4)
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max_chars: Maximum characters for tokenizer training (default: 10M)
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wandb_run: Name for the wandb run (default: "modal-smoke")
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Example:
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modal run modal_runner.py --shards 8 --max-chars 20000000 --wandb-run my-test
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"""
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smoke.remote(shards=shards, max_chars=max_chars, wandb_run=wandb_run)
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272
modal_speedrun.py
Normal file
272
modal_speedrun.py
Normal file
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@ -0,0 +1,272 @@
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import os
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from pathlib import Path
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import subprocess
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import modal
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APP_NAME = "nanochat-svilupp"
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VOLUME_NAME = "nanochat-data" # change if you like
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app = modal.App(APP_NAME)
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# Persisted volume to inspect results later
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vol = modal.Volume.from_name(VOLUME_NAME, create_if_missing=True)
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# Get the local directory path
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LOCAL_DIR = Path(__file__).parent
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# Mount local code and build image with dependencies
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# Install Rust and build the tokenizer during image build for efficiency
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# Use copy=True to copy files into the image so we can run build commands
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image = (
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modal.Image.debian_slim(python_version="3.11")
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.apt_install("curl", "build-essential", "pkg-config", "unzip")
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.add_local_dir("dev", "/nanochat/dev", copy=True)
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.add_local_dir("nanochat", "/nanochat/nanochat", copy=True)
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.add_local_dir("rustbpe", "/nanochat/rustbpe", copy=True)
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.add_local_dir("scripts", "/nanochat/scripts", copy=True)
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.add_local_dir("tasks", "/nanochat/tasks", copy=True)
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.add_local_dir("tests", "/nanochat/tests", copy=True)
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.add_local_file("pyproject.toml", "/nanochat/pyproject.toml", copy=True)
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.add_local_file(".python-version", "/nanochat/.python-version", copy=True)
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.add_local_file("run1000.sh", "/nanochat/run1000.sh", copy=True)
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.add_local_file("speedrun.sh", "/nanochat/speedrun.sh", copy=True)
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.add_local_file("README.md", "/nanochat/README.md", copy=True)
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.add_local_file("LICENSE", "/nanochat/LICENSE", copy=True)
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.workdir("/nanochat")
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.run_commands(
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# Install uv (Python package manager)
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"curl -LsSf https://astral.sh/uv/install.sh | sh",
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# Install Rust and set default toolchain
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"curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --default-toolchain stable",
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)
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.env({"PATH": "/root/.cargo/bin:/root/.local/bin:$PATH"})
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.uv_sync(extras=["gpu"])
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.run_commands(
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# Build the Rust tokenizer (the slow part)
|
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"uv run maturin develop --release --manifest-path rustbpe/Cargo.toml",
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)
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)
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def _bash(cmd: str, *, cwd: str | None = None, env: dict | None = None):
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print(f"\n$ {cmd}")
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subprocess.run(["bash", "-lc", cmd], check=True, cwd=cwd, env=env)
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@app.function(
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image=image,
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volumes={"/data": vol},
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timeout=60 * 60 * 5, # 5 hours for full speedrun
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max_inputs=1,
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gpu="B200:8", # 8xH100 for speedrun
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secrets=[modal.Secret.from_dotenv()],
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)
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def speedrun(wandb_run: str = "modal-speedrun", depth: int = 20):
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"""
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Full speedrun: The best ChatGPT clone that $100 can buy.
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Runs the complete training pipeline from speedrun.sh:
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- Reset report and generate system info
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- Download dataset (240 shards for d20)
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- Train tokenizer on 2B characters
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- Evaluate tokenizer
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- Download eval_bundle
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- Pretrain base model (d20 = 561M params)
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- Evaluate base model
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- Midtraining (conversation, tool use, multiple choice)
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- Supervised finetuning
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- Generate final report
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All artifacts persist in the mounted Modal Volume.
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Args:
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wandb_run: Name for wandb run (default: "modal-speedrun")
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depth: Model depth (default: 20 for d20 model)
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"""
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DATA = Path("/data")
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RUN_DIR = Path("/nanochat")
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BASE_DIR = DATA / ".cache" / "nanochat"
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LOGS = DATA / "logs"
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for p in (BASE_DIR, LOGS):
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p.mkdir(parents=True, exist_ok=True)
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env = os.environ.copy()
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env.update(
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{
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# Persist all nanochat caches/artifacts into the volume
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"NANOCHAT_BASE_DIR": str(BASE_DIR),
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# Keep CPU threads tame for reproducibility/CI-like behavior
|
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"OMP_NUM_THREADS": "1",
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# wandb configuration (env vars loaded from .env via modal.Secret.from_dotenv())
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# WANDB_API_KEY, WANDB_ENTITY, WANDB_PROJECT are already in os.environ from the secret
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}
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)
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# Print wandb status for debugging
|
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if "WANDB_API_KEY" in os.environ:
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print("✓ WANDB_API_KEY found in environment")
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if "WANDB_ENTITY" in os.environ:
|
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print(f"✓ WANDB_ENTITY: {os.environ.get('WANDB_ENTITY')}")
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if "WANDB_PROJECT" in os.environ:
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print(f"✓ WANDB_PROJECT: {os.environ.get('WANDB_PROJECT')}")
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print("\n" + "="*80)
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print("🚀 Starting nanochat speedrun - The best ChatGPT clone that $100 can buy")
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print("="*80 + "\n")
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# -----------------------------------------------------------------------------
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# Report setup
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print("\n📋 Step 1: Reset report and generate system info")
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_bash("uv run python -m nanochat.report reset", cwd=str(RUN_DIR), env=env)
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vol.commit()
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# -----------------------------------------------------------------------------
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# Tokenizer
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print("\n🔤 Step 2: Download dataset and train tokenizer")
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# Download initial shards for tokenizer training
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_bash("uv run python -m nanochat.dataset -n 8", cwd=str(RUN_DIR), env=env)
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vol.commit()
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# Download full dataset (240 shards for d20 model)
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# The d20 model is 561M parameters.
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# Chinchilla says #tokens = 20X #params, so we need 561e6 * 20 = 11.2B tokens.
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# Assume our tokenizer is 4.8 chars/token, this is 11.2B * 4.8 ~= 54B chars.
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# At 250M chars/shard, this is 54B / 250M ~= 216 shards needed for pretraining.
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# Round up to 240 for safety.
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print("\n📦 Downloading full dataset (240 shards, ~24GB)...")
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_bash("uv run python -m nanochat.dataset -n 240", cwd=str(RUN_DIR), env=env)
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vol.commit()
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# Train tokenizer on 2B characters
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print("\n🏋️ Training tokenizer on 2B characters...")
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_bash("uv run python -m scripts.tok_train --max_chars=2000000000", cwd=str(RUN_DIR), env=env)
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_bash("uv run python -m scripts.tok_eval", cwd=str(RUN_DIR), env=env)
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vol.commit()
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||||
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# -----------------------------------------------------------------------------
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# Base model pretraining
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print("\n🧠 Step 3: Download eval_bundle and pretrain base model")
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# Download eval_bundle
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||||
eval_bundle_path = BASE_DIR / "eval_bundle"
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if not eval_bundle_path.exists():
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print("\n📥 Downloading eval_bundle...")
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_bash(
|
||||
"curl -L -o eval_bundle.zip https://karpathy-public.s3.us-west-2.amazonaws.com/eval_bundle.zip && "
|
||||
f"unzip -q eval_bundle.zip && rm eval_bundle.zip && mv eval_bundle {eval_bundle_path}",
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cwd=str(RUN_DIR),
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env=env
|
||||
)
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||||
vol.commit()
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|
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# Pretrain the model
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||||
# B200 has 192GB VRAM vs H100's 80GB, so we can double the batch size from 32 to 64
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print(f"\n🚂 Pretraining d{depth} model (561M params) with 2x batch size for B200...")
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_bash(
|
||||
f"uv run torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth={depth} --device_batch_size=64 --run={wandb_run}",
|
||||
cwd=str(RUN_DIR),
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||||
env=env
|
||||
)
|
||||
vol.commit()
|
||||
|
||||
# Evaluate base model
|
||||
print("\n📊 Evaluating base model...")
|
||||
_bash("uv run torchrun --standalone --nproc_per_node=8 -m scripts.base_loss", cwd=str(RUN_DIR), env=env)
|
||||
_bash("uv run torchrun --standalone --nproc_per_node=8 -m scripts.base_eval", cwd=str(RUN_DIR), env=env)
|
||||
vol.commit()
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Midtraining
|
||||
print("\n💬 Step 4: Midtraining (conversation, tool use, multiple choice)")
|
||||
|
||||
# Download identity conversations
|
||||
identity_path = BASE_DIR / "identity_conversations.jsonl"
|
||||
if not identity_path.exists():
|
||||
print("\n📥 Downloading identity conversations...")
|
||||
_bash(
|
||||
f"curl -L -o {identity_path} https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl",
|
||||
cwd=str(RUN_DIR),
|
||||
env=env
|
||||
)
|
||||
vol.commit()
|
||||
|
||||
# Run midtraining
|
||||
print("\n🎓 Running midtraining with 2x batch size for B200...")
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||||
_bash(
|
||||
f"uv run torchrun --standalone --nproc_per_node=8 -m scripts.mid_train -- --device_batch_size=64 --run={wandb_run}",
|
||||
cwd=str(RUN_DIR),
|
||||
env=env
|
||||
)
|
||||
_bash("uv run torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i mid", cwd=str(RUN_DIR), env=env)
|
||||
vol.commit()
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Supervised Finetuning
|
||||
print("\n✨ Step 5: Supervised Finetuning with 2x batch size for B200...")
|
||||
_bash(
|
||||
f"uv run torchrun --standalone --nproc_per_node=8 -m scripts.chat_sft -- --device_batch_size=64 --run={wandb_run}",
|
||||
cwd=str(RUN_DIR),
|
||||
env=env
|
||||
)
|
||||
_bash("uv run torchrun --standalone --nproc_per_node=8 -m scripts.chat_eval -- -i sft", cwd=str(RUN_DIR), env=env)
|
||||
vol.commit()
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Generate final report
|
||||
print("\n📝 Step 6: Generate final report")
|
||||
_bash("uv run python -m nanochat.report generate", cwd=str(RUN_DIR), env=env)
|
||||
vol.commit()
|
||||
|
||||
print("\n" + "="*80)
|
||||
print(f"✅ Speedrun complete! Check wandb run: {wandb_run}")
|
||||
print(f" Project: {os.environ.get('WANDB_PROJECT', 'not set')}")
|
||||
print(f" Entity: {os.environ.get('WANDB_ENTITY', 'not set')}")
|
||||
print("="*80 + "\n")
|
||||
|
||||
# Drop a pointer file for convenience
|
||||
with open(LOGS / "SPEEDRUN_COMPLETE.txt", "w") as f:
|
||||
f.write(
|
||||
f"""
|
||||
🎉 SPEEDRUN COMPLETE! 🎉
|
||||
|
||||
Wandb Run: {wandb_run}
|
||||
Model: d{depth} (561M parameters)
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||||
|
||||
Artifacts live in the Modal Volume: {VOLUME_NAME}
|
||||
|
||||
Local code at:
|
||||
/nanochat
|
||||
|
||||
nanochat base dir (datasets/tokenizer/models/eval bundle/etc.):
|
||||
/data/.cache/nanochat
|
||||
|
||||
To list files:
|
||||
modal volume ls {VOLUME_NAME} /
|
||||
|
||||
To download everything locally:
|
||||
modal volume get {VOLUME_NAME} / ./nanochat_volume_dump
|
||||
|
||||
To download just the report:
|
||||
modal volume get {VOLUME_NAME} /.cache/nanochat/report.md ./report.md
|
||||
|
||||
Models and checkpoints live under:
|
||||
/data/.cache/nanochat
|
||||
"""
|
||||
)
|
||||
vol.commit()
|
||||
print("\n✅ All artifacts persisted to volume. Speedrun complete!")
|
||||
|
||||
|
||||
# Convenience local entrypoint
|
||||
@app.local_entrypoint()
|
||||
def main(wandb_run: str = "modal-speedrun", depth: int = 20):
|
||||
"""
|
||||
Run the full speedrun on Modal.
|
||||
|
||||
Args:
|
||||
wandb_run: Name for the wandb run (default: "modal-speedrun")
|
||||
depth: Model depth, e.g., 20 for d20 (default: 20)
|
||||
|
||||
Example:
|
||||
modal run modal_speedrun.py
|
||||
modal run modal_speedrun.py --wandb-run my-experiment --depth 26
|
||||
"""
|
||||
speedrun.remote(wandb_run=wandb_run, depth=depth)
|
||||
|
|
@ -6,8 +6,10 @@ readme = "README.md"
|
|||
requires-python = ">=3.10"
|
||||
dependencies = [
|
||||
"datasets>=4.0.0",
|
||||
"dotenv>=0.9.9",
|
||||
"fastapi>=0.117.1",
|
||||
"files-to-prompt>=0.6",
|
||||
"modal>=1.2.1",
|
||||
"numpy==1.26.4",
|
||||
"psutil>=7.1.0",
|
||||
"regex>=2025.9.1",
|
||||
|
|
@ -31,6 +33,7 @@ manifest-path = "rustbpe/Cargo.toml"
|
|||
|
||||
[dependency-groups]
|
||||
dev = [
|
||||
"dotenv>=0.9.9",
|
||||
"maturin>=1.9.4",
|
||||
"pytest>=8.0.0",
|
||||
]
|
||||
|
|
@ -75,4 +78,4 @@ conflicts = [
|
|||
{ extra = "cpu" },
|
||||
{ extra = "gpu" },
|
||||
],
|
||||
]
|
||||
]
|
||||
|
|
|
|||
375
uv.lock
375
uv.lock
|
|
@ -179,6 +179,55 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3", size = 63815, upload-time = "2025-03-13T11:10:21.14Z" },
|
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]
|
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|
||||
[[package]]
|
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name = "cbor2"
|
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version = "5.7.1"
|
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source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/a2/b8/c0f6a7d46f816cb18b1fda61a2fe648abe16039f1ff93ea720a6e9fb3cee/cbor2-5.7.1.tar.gz", hash = "sha256:7a405a1d7c8230ee9acf240aad48ae947ef584e8af05f169f3c1bde8f01f8b71", size = 102467, upload-time = "2025-10-24T09:23:06.569Z" }
|
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wheels = [
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[[package]]
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name = "xxhash"
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version = "3.5.0"
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|
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|||
Loading…
Reference in New Issue
Block a user