Sync from GitHub via hub-sync
Browse files- generate-embeddings.py +70 -1
generate-embeddings.py
CHANGED
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@@ -366,6 +366,75 @@ def run_streaming_shard(ds, model, prompt_str, args):
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logger.info(f"✅ streaming shard {args.shard_index + 1}/{args.num_shards} of run {args.run_id} uploaded")
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def main():
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p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
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p.add_argument("input_dataset", help="Input dataset ID on the Hugging Face Hub")
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@@ -615,7 +684,7 @@ def main():
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# Canonical provenance stamp (see AGENTS.md): Jobs claim gated on JOB_ID, set by HF Jobs in-container.
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script_url = "https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings.py"
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on_jobs = os.environ.get("JOB_ID") is not None
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-
hw =
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origin = (
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"Produced on [Hugging Face Jobs](https://huggingface.co/docs/huggingface_hub/guides/jobs)"
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+ (f" (`{hw}`)" if hw else "")
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logger.info(f"✅ streaming shard {args.shard_index + 1}/{args.num_shards} of run {args.run_id} uploaded")
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def jobs_flavor() -> str:
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"""Return the HF Jobs hardware flavor this run is on, or "" when it cannot be told.
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HF Jobs sets ACCELERATOR to "cpu" or "gpu", not the flavor name, so the flavor is looked up
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in the public Jobs hardware list: first by GPU model and count, then by the CPU_CORES and
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MEMORY values that Jobs sets in the container (to tell a10g-small from a10g-large, or
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cpu-basic from cpu-upgrade). Any failure (no network, older huggingface_hub) returns "".
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"""
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hardware = os.environ.get("ACCELERATOR") or ""
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if "-" in hardware or any(character.isdigit() for character in hardware):
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return hardware # already a flavor name
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try:
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from huggingface_hub import HfApi
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gpu_name, gpu_count = "", 0
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try:
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import torch
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if torch.cuda.is_available():
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gpu_name = torch.cuda.get_device_name(0)
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gpu_count = torch.cuda.device_count()
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except ImportError:
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# Torch-free drivers (e.g. the -server recipes): ask nvidia-smi instead.
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import subprocess
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try:
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listed = subprocess.run(
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["nvidia-smi", "--query-gpu=name", "--format=csv,noheader"],
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capture_output=True, text=True, timeout=10, check=True,
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).stdout
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names = [line.strip() for line in listed.splitlines() if line.strip()]
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if names:
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gpu_name, gpu_count = names[0], len(names)
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except (OSError, subprocess.SubprocessError):
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pass
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if gpu_count == 0 and hardware == "gpu":
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return "" # a GPU run whose GPU cannot be identified: do not guess a CPU flavor
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# Whole-word match, so "L4" does not match "NVIDIA L40S" ("NVIDIA A100-SXM4-80GB" -> A100).
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gpu_words = gpu_name.upper().replace("-", " ").split()
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candidates = []
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for flavor in HfApi(token=False).list_jobs_hardware():
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accelerator = flavor.accelerator
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if gpu_count == 0 and accelerator is None:
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candidates.append(flavor)
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elif (
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gpu_count > 0
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and accelerator is not None
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and accelerator.model.upper() in gpu_words
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and int(accelerator.quantity) == gpu_count
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):
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candidates.append(flavor)
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if not candidates:
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return ""
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cores = float(os.environ.get("CPU_CORES") or 0)
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memory_gb = float((os.environ.get("MEMORY") or "0").upper().rstrip("GIB ") or 0)
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def distance(flavor) -> float:
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# Relative gap to the listed vCPUs and RAM; Jobs reports slightly different numbers.
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listed_cores = float(flavor.cpu.split()[0])
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listed_memory = float(flavor.ram.split()[0])
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return abs(listed_cores - cores) / listed_cores + abs(listed_memory - memory_gb) / listed_memory
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return min(candidates, key=distance).name
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except Exception:
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return ""
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def main():
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p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
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p.add_argument("input_dataset", help="Input dataset ID on the Hugging Face Hub")
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# Canonical provenance stamp (see AGENTS.md): Jobs claim gated on JOB_ID, set by HF Jobs in-container.
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script_url = "https://huggingface.co/datasets/uv-scripts/embeddings/raw/main/generate-embeddings.py"
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on_jobs = os.environ.get("JOB_ID") is not None
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hw = jobs_flavor() # flavor name, e.g. "a10g-small"; "" on CPU
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origin = (
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"Produced on [Hugging Face Jobs](https://huggingface.co/docs/huggingface_hub/guides/jobs)"
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+ (f" (`{hw}`)" if hw else "")
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