Sync from GitHub via hub-sync

classify-gliner2.py

@@ -191,30 +191,73 @@ def label_counts_table(tasks: list, counts_by_task: dict, total: int) -> str:
     return "\n".join(lines)
 
 
-# The smallest Jobs flavor for each GPU, keyed by a fragment of the GPU's name. "L40" comes
-# before "L4" because the first match wins.
-GPU_NAME_TO_FLAVOR = {"T4": "t4-small", "A10G": "a10g-small", "L40": "l40sx1", "L4": "l4x1", "A100": "a100-large"}
-
-
 def jobs_flavor() -> str:
-    """Return the Jobs hardware flavor, or "" when it is not known.
+    """Return the HF Jobs hardware flavor this run is on, or "" when it cannot be told.
 
-    The docs say ACCELERATOR holds the flavor ("a10g-small"). On the t4-small and a10g-small
-    jobs that tested this script it held a bare "gpu", which is not a valid --flavor. So use
-    ACCELERATOR when it looks like a flavor, and otherwise name the smallest flavor that has
-    this GPU. A larger flavor of the same GPU reproduces the same result.
+    HF Jobs sets ACCELERATOR to "cpu" or "gpu", not the flavor name, so the flavor is looked up
+    in the public Jobs hardware list: first by GPU model and count, then by the CPU_CORES and
+    MEMORY values that Jobs sets in the container (to tell a10g-small from a10g-large, or
+    cpu-basic from cpu-upgrade). Any failure (no network, older huggingface_hub) returns "".
     """
     hardware = os.environ.get("ACCELERATOR") or ""
-    looks_like_flavor = "-" in hardware or any(character.isdigit() for character in hardware)
-    if looks_like_flavor:
-        return hardware
-    if not torch.cuda.is_available():
+    if "-" in hardware or any(character.isdigit() for character in hardware):
+        return hardware  # already a flavor name
+    try:
+        from huggingface_hub import HfApi
+
+        gpu_name, gpu_count = "", 0
+        try:
+            import torch
+
+            if torch.cuda.is_available():
+                gpu_name = torch.cuda.get_device_name(0)
+                gpu_count = torch.cuda.device_count()
+        except ImportError:
+            # Torch-free drivers (e.g. the -server recipes): ask nvidia-smi instead.
+            import subprocess
+
+            try:
+                listed = subprocess.run(
+                    ["nvidia-smi", "--query-gpu=name", "--format=csv,noheader"],
+                    capture_output=True, text=True, timeout=10, check=True,
+                ).stdout
+                names = [line.strip() for line in listed.splitlines() if line.strip()]
+                if names:
+                    gpu_name, gpu_count = names[0], len(names)
+            except (OSError, subprocess.SubprocessError):
+                pass
+        if gpu_count == 0 and hardware == "gpu":
+            return ""  # a GPU run whose GPU cannot be identified: do not guess a CPU flavor
+
+        # Whole-word match, so "L4" does not match "NVIDIA L40S" ("NVIDIA A100-SXM4-80GB" -> A100).
+        gpu_words = gpu_name.upper().replace("-", " ").split()
+        candidates = []
+        for flavor in HfApi(token=False).list_jobs_hardware():
+            accelerator = flavor.accelerator
+            if gpu_count == 0 and accelerator is None:
+                candidates.append(flavor)
+            elif (
+                gpu_count > 0
+                and accelerator is not None
+                and accelerator.model.upper() in gpu_words
+                and int(accelerator.quantity) == gpu_count
+            ):
+                candidates.append(flavor)
+        if not candidates:
+            return ""
+
+        cores = float(os.environ.get("CPU_CORES") or 0)
+        memory_gb = float((os.environ.get("MEMORY") or "0").upper().rstrip("GIB ") or 0)
+
+        def distance(flavor) -> float:
+            # Relative gap to the listed vCPUs and RAM; Jobs reports slightly different numbers.
+            listed_cores = float(flavor.cpu.split()[0])
+            listed_memory = float(flavor.ram.split()[0])
+            return abs(listed_cores - cores) / listed_cores + abs(listed_memory - memory_gb) / listed_memory
+
+        return min(candidates, key=distance).name
+    except Exception:
         return ""
-    gpu_name = torch.cuda.get_device_name(0)
-    for fragment, flavor in GPU_NAME_TO_FLAVOR.items():
-        if fragment in gpu_name:
-            return flavor
-    return ""
 
 
 def build_reproduce_command(args) -> str:

train-classifier.py

@@ -424,12 +424,81 @@ def verify_reload(output_repo, eval_texts, reference_preds, problem_type, max_le
 # ---------------------------------------------------------------------------
 
 
+def jobs_flavor() -> str:
+    """Return the HF Jobs hardware flavor this run is on, or "" when it cannot be told.
+
+    HF Jobs sets ACCELERATOR to "cpu" or "gpu", not the flavor name, so the flavor is looked up
+    in the public Jobs hardware list: first by GPU model and count, then by the CPU_CORES and
+    MEMORY values that Jobs sets in the container (to tell a10g-small from a10g-large, or
+    cpu-basic from cpu-upgrade). Any failure (no network, older huggingface_hub) returns "".
+    """
+    hardware = os.environ.get("ACCELERATOR") or ""
+    if "-" in hardware or any(character.isdigit() for character in hardware):
+        return hardware  # already a flavor name
+    try:
+        from huggingface_hub import HfApi
+
+        gpu_name, gpu_count = "", 0
+        try:
+            import torch
+
+            if torch.cuda.is_available():
+                gpu_name = torch.cuda.get_device_name(0)
+                gpu_count = torch.cuda.device_count()
+        except ImportError:
+            # Torch-free drivers (e.g. the -server recipes): ask nvidia-smi instead.
+            import subprocess
+
+            try:
+                listed = subprocess.run(
+                    ["nvidia-smi", "--query-gpu=name", "--format=csv,noheader"],
+                    capture_output=True, text=True, timeout=10, check=True,
+                ).stdout
+                names = [line.strip() for line in listed.splitlines() if line.strip()]
+                if names:
+                    gpu_name, gpu_count = names[0], len(names)
+            except (OSError, subprocess.SubprocessError):
+                pass
+        if gpu_count == 0 and hardware == "gpu":
+            return ""  # a GPU run whose GPU cannot be identified: do not guess a CPU flavor
+
+        # Whole-word match, so "L4" does not match "NVIDIA L40S" ("NVIDIA A100-SXM4-80GB" -> A100).
+        gpu_words = gpu_name.upper().replace("-", " ").split()
+        candidates = []
+        for flavor in HfApi(token=False).list_jobs_hardware():
+            accelerator = flavor.accelerator
+            if gpu_count == 0 and accelerator is None:
+                candidates.append(flavor)
+            elif (
+                gpu_count > 0
+                and accelerator is not None
+                and accelerator.model.upper() in gpu_words
+                and int(accelerator.quantity) == gpu_count
+            ):
+                candidates.append(flavor)
+        if not candidates:
+            return ""
+
+        cores = float(os.environ.get("CPU_CORES") or 0)
+        memory_gb = float((os.environ.get("MEMORY") or "0").upper().rstrip("GIB ") or 0)
+
+        def distance(flavor) -> float:
+            # Relative gap to the listed vCPUs and RAM; Jobs reports slightly different numbers.
+            listed_cores = float(flavor.cpu.split()[0])
+            listed_memory = float(flavor.ram.split()[0])
+            return abs(listed_cores - cores) / listed_cores + abs(listed_memory - memory_gb) / listed_memory
+
+        return min(candidates, key=distance).name
+    except Exception:
+        return ""
+
+
 def build_card(
     input_dataset, output_repo, model_id, problem_type, label_names, metrics,
     thresholds, path_kind, args_summary,
 ) -> str:
     on_jobs = os.environ.get("JOB_ID") is not None  # set by HF Jobs in-container
-    hw = os.environ.get("ACCELERATOR") or ""  # e.g. "l4x1"; empty on CPU
+    hw = jobs_flavor()  # flavor name, e.g. "a10g-small"; "" on CPU
     origin = (
         "Produced on [Hugging Face Jobs](https://huggingface.co/docs/huggingface_hub/guides/jobs)"
         + (f" (`{hw}`)" if hw else "")

train-gliner2.py

@@ -748,30 +748,73 @@ def evaluate(model_path: str, eval_sets: dict, tasks: list, batch_size: int, exp
     return metrics_by_split
 
 
-# The smallest Jobs flavor for each GPU, keyed by a fragment of the GPU's name. "L40" comes
-# before "L4" because the first match wins.
-GPU_NAME_TO_FLAVOR = {"T4": "t4-small", "A10G": "a10g-small", "L40": "l40sx1", "L4": "l4x1", "A100": "a100-large"}
-
-
 def jobs_flavor() -> str:
-    """Return the Jobs hardware flavor, or "" when it is not known.
+    """Return the HF Jobs hardware flavor this run is on, or "" when it cannot be told.
 
-    The docs say ACCELERATOR holds the flavor ("a10g-small"). On the t4-small and a10g-small
-    jobs that tested this script it held a bare "gpu", which is not a valid --flavor. So use
-    ACCELERATOR when it looks like a flavor, and otherwise name the smallest flavor that has
-    this GPU. A larger flavor of the same GPU reproduces the same result.
+    HF Jobs sets ACCELERATOR to "cpu" or "gpu", not the flavor name, so the flavor is looked up
+    in the public Jobs hardware list: first by GPU model and count, then by the CPU_CORES and
+    MEMORY values that Jobs sets in the container (to tell a10g-small from a10g-large, or
+    cpu-basic from cpu-upgrade). Any failure (no network, older huggingface_hub) returns "".
     """
     hardware = os.environ.get("ACCELERATOR") or ""
-    looks_like_flavor = "-" in hardware or any(character.isdigit() for character in hardware)
-    if looks_like_flavor:
-        return hardware
-    if not torch.cuda.is_available():
+    if "-" in hardware or any(character.isdigit() for character in hardware):
+        return hardware  # already a flavor name
+    try:
+        from huggingface_hub import HfApi
+
+        gpu_name, gpu_count = "", 0
+        try:
+            import torch
+
+            if torch.cuda.is_available():
+                gpu_name = torch.cuda.get_device_name(0)
+                gpu_count = torch.cuda.device_count()
+        except ImportError:
+            # Torch-free drivers (e.g. the -server recipes): ask nvidia-smi instead.
+            import subprocess
+
+            try:
+                listed = subprocess.run(
+                    ["nvidia-smi", "--query-gpu=name", "--format=csv,noheader"],
+                    capture_output=True, text=True, timeout=10, check=True,
+                ).stdout
+                names = [line.strip() for line in listed.splitlines() if line.strip()]
+                if names:
+                    gpu_name, gpu_count = names[0], len(names)
+            except (OSError, subprocess.SubprocessError):
+                pass
+        if gpu_count == 0 and hardware == "gpu":
+            return ""  # a GPU run whose GPU cannot be identified: do not guess a CPU flavor
+
+        # Whole-word match, so "L4" does not match "NVIDIA L40S" ("NVIDIA A100-SXM4-80GB" -> A100).
+        gpu_words = gpu_name.upper().replace("-", " ").split()
+        candidates = []
+        for flavor in HfApi(token=False).list_jobs_hardware():
+            accelerator = flavor.accelerator
+            if gpu_count == 0 and accelerator is None:
+                candidates.append(flavor)
+            elif (
+                gpu_count > 0
+                and accelerator is not None
+                and accelerator.model.upper() in gpu_words
+                and int(accelerator.quantity) == gpu_count
+            ):
+                candidates.append(flavor)
+        if not candidates:
+            return ""
+
+        cores = float(os.environ.get("CPU_CORES") or 0)
+        memory_gb = float((os.environ.get("MEMORY") or "0").upper().rstrip("GIB ") or 0)
+
+        def distance(flavor) -> float:
+            # Relative gap to the listed vCPUs and RAM; Jobs reports slightly different numbers.
+            listed_cores = float(flavor.cpu.split()[0])
+            listed_memory = float(flavor.ram.split()[0])
+            return abs(listed_cores - cores) / listed_cores + abs(listed_memory - memory_gb) / listed_memory
+
+        return min(candidates, key=distance).name
+    except Exception:
         return ""
-    gpu_name = torch.cuda.get_device_name(0)
-    for fragment, flavor in GPU_NAME_TO_FLAVOR.items():
-        if fragment in gpu_name:
-            return flavor
-    return ""
 
 
 def build_reproduce_command(args) -> str:

train-setfit.py

@@ -480,6 +480,75 @@ def estimate_training_steps(per_class, batch_size, num_epochs, strategy) -> tupl
     return pairs, steps
 
 
+def jobs_flavor() -> str:
+    """Return the HF Jobs hardware flavor this run is on, or "" when it cannot be told.
+
+    HF Jobs sets ACCELERATOR to "cpu" or "gpu", not the flavor name, so the flavor is looked up
+    in the public Jobs hardware list: first by GPU model and count, then by the CPU_CORES and
+    MEMORY values that Jobs sets in the container (to tell a10g-small from a10g-large, or
+    cpu-basic from cpu-upgrade). Any failure (no network, older huggingface_hub) returns "".
+    """
+    hardware = os.environ.get("ACCELERATOR") or ""
+    if "-" in hardware or any(character.isdigit() for character in hardware):
+        return hardware  # already a flavor name
+    try:
+        from huggingface_hub import HfApi
+
+        gpu_name, gpu_count = "", 0
+        try:
+            import torch
+
+            if torch.cuda.is_available():
+                gpu_name = torch.cuda.get_device_name(0)
+                gpu_count = torch.cuda.device_count()
+        except ImportError:
+            # Torch-free drivers (e.g. the -server recipes): ask nvidia-smi instead.
+            import subprocess
+
+            try:
+                listed = subprocess.run(
+                    ["nvidia-smi", "--query-gpu=name", "--format=csv,noheader"],
+                    capture_output=True, text=True, timeout=10, check=True,
+                ).stdout
+                names = [line.strip() for line in listed.splitlines() if line.strip()]
+                if names:
+                    gpu_name, gpu_count = names[0], len(names)
+            except (OSError, subprocess.SubprocessError):
+                pass
+        if gpu_count == 0 and hardware == "gpu":
+            return ""  # a GPU run whose GPU cannot be identified: do not guess a CPU flavor
+
+        # Whole-word match, so "L4" does not match "NVIDIA L40S" ("NVIDIA A100-SXM4-80GB" -> A100).
+        gpu_words = gpu_name.upper().replace("-", " ").split()
+        candidates = []
+        for flavor in HfApi(token=False).list_jobs_hardware():
+            accelerator = flavor.accelerator
+            if gpu_count == 0 and accelerator is None:
+                candidates.append(flavor)
+            elif (
+                gpu_count > 0
+                and accelerator is not None
+                and accelerator.model.upper() in gpu_words
+                and int(accelerator.quantity) == gpu_count
+            ):
+                candidates.append(flavor)
+        if not candidates:
+            return ""
+
+        cores = float(os.environ.get("CPU_CORES") or 0)
+        memory_gb = float((os.environ.get("MEMORY") or "0").upper().rstrip("GIB ") or 0)
+
+        def distance(flavor) -> float:
+            # Relative gap to the listed vCPUs and RAM; Jobs reports slightly different numbers.
+            listed_cores = float(flavor.cpu.split()[0])
+            listed_memory = float(flavor.ram.split()[0])
+            return abs(listed_cores - cores) / listed_cores + abs(listed_memory - memory_gb) / listed_memory
+
+        return min(candidates, key=distance).name
+    except Exception:
+        return ""
+
+
 def build_reproduce_command(args) -> str:
     """Rebuild recipe options for Jobs, preserving the recorded accelerator when available.
 
@@ -487,9 +556,8 @@ def build_reproduce_command(args) -> str:
     Outside Jobs, the hardware flavor is a suggested default rather than an exact record.
     """
     flavor = "t4-small" if torch.cuda.is_available() else "cpu-basic"
-    accelerator = os.environ.get("ACCELERATOR", "").strip()
-    if os.environ.get("JOB_ID") and accelerator.lower() not in ("", "none"):
-        flavor = accelerator
+    if os.environ.get("JOB_ID"):
+        flavor = jobs_flavor() or flavor
     parts = [
         f"hf jobs uv run --flavor {flavor} --secrets HF_TOKEN \\",
         f"  {SCRIPT_URL} \\",