Kubernetes: improve offers

docs/docs/concepts/backends.md

@@ -1049,7 +1049,7 @@ projects:
       verbs: ["get", "create"]
     - apiGroups: [""]
       resources: ["pods"]
-      verbs: ["get", "create", "delete"]
+      verbs: ["get", "create", "delete", "list"]
     - apiGroups: [""]
       resources: ["services"]
       verbs: ["get", "create", "delete"]

src/dstack/_internal/core/backends/kubernetes/compute.py

@@ -6,7 +6,7 @@
 from enum import Enum
 from typing import List, Optional
 
-from gpuhunt import KNOWN_AMD_GPUS, KNOWN_NVIDIA_GPUS, AcceleratorVendor
+from gpuhunt import AcceleratorVendor
 from kubernetes import client
 
 from dstack._internal.core.backends.base.compute import (
@@ -19,85 +19,67 @@
     generate_unique_instance_name_for_job,
     get_docker_commands,
     get_dstack_gateway_commands,
-    normalize_arch,
 )
-from dstack._internal.core.backends.base.offers import filter_offers_by_requirements
 from dstack._internal.core.backends.kubernetes.models import (
     KubernetesConfig,
     KubernetesProxyJumpConfig,
 )
+from dstack._internal.core.backends.kubernetes.resources import (
+    AMD_GPU_DEVICE_ID_LABEL_PREFIX,
+    AMD_GPU_NAME_TO_DEVICE_IDS,
+    AMD_GPU_NODE_TAINT,
+    AMD_GPU_RESOURCE,
+    DUMMY_REGION,
+    NVIDIA_GPU_NAME_TO_GPU_INFO,
+    NVIDIA_GPU_NODE_TAINT,
+    NVIDIA_GPU_PRODUCT_LABEL,
+    NVIDIA_GPU_RESOURCE,
+    TaintEffect,
+    format_memory,
+    get_amd_gpu_from_node_labels,
+    get_gpu_request_from_gpu_spec,
+    get_instance_offer_from_node,
+    get_instance_offers,
+    get_node_labels,
+    get_nvidia_gpu_from_node_labels,
+    is_hard_taint,
+    is_taint_tolerated,
+)
 from dstack._internal.core.backends.kubernetes.utils import (
     call_api_method,
     get_api_from_config_data,
     get_cluster_public_ip,
 )
 from dstack._internal.core.consts import DSTACK_RUNNER_SSH_PORT
 from dstack._internal.core.errors import ComputeError
-from dstack._internal.core.models.backends.base import BackendType
 from dstack._internal.core.models.gateways import (
     GatewayComputeConfiguration,
     GatewayProvisioningData,
 )
 from dstack._internal.core.models.instances import (
-    Disk,
     Gpu,
-    InstanceAvailability,
     InstanceOfferWithAvailability,
-    InstanceRuntime,
-    InstanceType,
-    Resources,
     SSHConnectionParams,
 )
 from dstack._internal.core.models.placement import PlacementGroup
-from dstack._internal.core.models.resources import CPUSpec, GPUSpec, Memory
+from dstack._internal.core.models.resources import CPUSpec, GPUSpec
 from dstack._internal.core.models.routers import AnyRouterConfig
 from dstack._internal.core.models.runs import Job, JobProvisioningData, Requirements, Run
 from dstack._internal.core.models.volumes import Volume
-from dstack._internal.utils.common import get_or_error, parse_memory
+from dstack._internal.utils.common import get_or_error
 from dstack._internal.utils.logging import get_logger
 
 logger = get_logger(__name__)
 
 JUMP_POD_IMAGE = "testcontainers/sshd:1.3.0@sha256:c50c0f59554dcdb2d9e5e705112144428ae9d04ac0af6322b365a18e24213a6a"
 JUMP_POD_SSH_PORT = 22
-DUMMY_REGION = "-"
-
-NVIDIA_GPU_RESOURCE = "nvidia.com/gpu"
-NVIDIA_GPU_NODE_TAINT = NVIDIA_GPU_RESOURCE
-NVIDIA_GPU_PRODUCT_LABEL = f"{NVIDIA_GPU_RESOURCE}.product"
-
-AMD_GPU_RESOURCE = "amd.com/gpu"
-AMD_GPU_NODE_TAINT = AMD_GPU_RESOURCE
-# The oldest but still supported label format, the safest option, see the commit message:
-# https://github.com/ROCm/k8s-device-plugin/commit/c0b0231b391a56bc9da4f362d561e25e960d7a48
-# E.g., beta.amd.com/gpu.device-id.74b5=4 - A node with four MI300X VF (0x74b5) GPUs
-# We cannot rely on the beta.amd.com/gpu.product-name.* label, as it may be missing, see the issue:
-# https://github.com/ROCm/k8s-device-plugin/issues/112
-AMD_GPU_DEVICE_ID_LABEL_PREFIX = f"beta.{AMD_GPU_RESOURCE}.device-id."
-
-# Taints we know and tolerate when creating our objects, e.g., the jump pod.
-TOLERATED_NODE_TAINTS = (NVIDIA_GPU_NODE_TAINT, AMD_GPU_NODE_TAINT)
-
-NVIDIA_GPU_NAME_TO_GPU_INFO = {gpu.name: gpu for gpu in KNOWN_NVIDIA_GPUS}
-NVIDIA_GPU_NAMES = NVIDIA_GPU_NAME_TO_GPU_INFO.keys()
-
-AMD_GPU_DEVICE_ID_TO_GPU_INFO = {
-    device_id: gpu_info for gpu_info in KNOWN_AMD_GPUS for device_id in gpu_info.device_ids
-}
-AMD_GPU_NAME_TO_DEVICE_IDS = {gpu.name: gpu.device_ids for gpu in KNOWN_AMD_GPUS}
 
 
 class Operator(str, Enum):
     EXISTS = "Exists"
     IN = "In"
 
 
-class TaintEffect(str, Enum):
-    NO_EXECUTE = "NoExecute"
-    NO_SCHEDULE = "NoSchedule"
-    PREFER_NO_SCHEDULE = "PreferNoSchedule"
-
-
 class KubernetesCompute(
     ComputeWithFilteredOffersCached,
     ComputeWithPrivilegedSupport,
@@ -117,16 +99,7 @@ def __init__(self, config: KubernetesConfig):
     def get_offers_by_requirements(
         self, requirements: Requirements
     ) -> list[InstanceOfferWithAvailability]:
-        gpu_request = 0
-        if (gpu_spec := requirements.resources.gpu) is not None:
-            gpu_request = _get_gpu_request_from_gpu_spec(gpu_spec)
-        instance_offers: list[InstanceOfferWithAvailability] = []
-        for node in self.api.list_node().items:
-            if (instance_offer := _get_instance_offer_from_node(node, gpu_request)) is not None:
-                instance_offers.extend(
-                    filter_offers_by_requirements([instance_offer], requirements)
-                )
-        return instance_offers
+        return get_instance_offers(self.api, requirements)
 
     def run_job(
         self,
@@ -191,7 +164,7 @@ def run_job(
         if (cpu_max := resources_spec.cpu.count.max) is not None:
             resources_limits["cpu"] = str(cpu_max)
         if (gpu_spec := resources_spec.gpu) is not None:
-            if (gpu_request := _get_gpu_request_from_gpu_spec(gpu_spec)) > 0:
+            if (gpu_request := get_gpu_request_from_gpu_spec(gpu_spec)) > 0:
                 gpu_resource, node_affinity, node_taint = _get_pod_spec_parameters_for_gpu(
                     self.api, gpu_spec
                 )
@@ -208,22 +181,22 @@ def run_job(
                         )
                     )
         if (memory_min := resources_spec.memory.min) is not None:
-            resources_requests["memory"] = _render_memory(memory_min)
+            resources_requests["memory"] = format_memory(memory_min)
         if (memory_max := resources_spec.memory.max) is not None:
-            resources_limits["memory"] = _render_memory(memory_max)
+            resources_limits["memory"] = format_memory(memory_max)
         if (disk_spec := resources_spec.disk) is not None:
             if (disk_min := disk_spec.size.min) is not None:
-                resources_requests["ephemeral-storage"] = _render_memory(disk_min)
+                resources_requests["ephemeral-storage"] = format_memory(disk_min)
             if (disk_max := disk_spec.size.max) is not None:
-                resources_limits["ephemeral-storage"] = _render_memory(disk_max)
+                resources_limits["ephemeral-storage"] = format_memory(disk_max)
         if (shm_size := resources_spec.shm_size) is not None:
             shm_volume_name = "dev-shm"
             volumes_.append(
                 client.V1Volume(
                     name=shm_volume_name,
                     empty_dir=client.V1EmptyDirVolumeSource(
                         medium="Memory",
-                        size_limit=_render_memory(shm_size),
+                        size_limit=format_memory(shm_size),
                     ),
                 )
             )
@@ -338,10 +311,17 @@ def update_provisioning_data(
         provisioning_data.hostname = get_or_error(service_spec.cluster_ip)
         pod_spec = get_or_error(pod.spec)
         node = self.api.read_node(name=get_or_error(pod_spec.node_name))
-        # The original offer has a list of GPUs already sliced according to pod spec's GPU resource
-        # request, which is inferred from dstack's GPUSpec, see _get_gpu_request_from_gpu_spec
-        gpu_request = len(provisioning_data.instance_type.resources.gpus)
-        if (instance_offer := _get_instance_offer_from_node(node, gpu_request)) is not None:
+        # In the original offer, the resources have already been adjusted according to
+        # the run configuration resource requirements, see get_offers_by_requirements()
+        original_resources = provisioning_data.instance_type.resources
+        instance_offer = get_instance_offer_from_node(
+            node=node,
+            cpu_request=original_resources.cpus,
+            memory_mib_request=original_resources.memory_mib,
+            gpu_request=len(original_resources.gpus),
+            disk_mib_request=original_resources.disk.size_mib,
+        )
+        if instance_offer is not None:
             provisioning_data.instance_type = instance_offer.instance
             provisioning_data.region = instance_offer.region
             provisioning_data.price = instance_offer.price
@@ -481,146 +461,6 @@ def terminate_gateway(
         )
 
 
-def _get_gpu_request_from_gpu_spec(gpu_spec: GPUSpec) -> int:
-    return gpu_spec.count.min or 0
-
-
-def _get_instance_offer_from_node(
-    node: client.V1Node, gpu_request: int
-) -> Optional[InstanceOfferWithAvailability]:
-    try:
-        node_name = get_or_error(get_or_error(node.metadata).name)
-        node_status = get_or_error(node.status)
-        allocatable = get_or_error(node_status.allocatable)
-        _cpu_arch: Optional[str] = None
-        if node_status.node_info is not None:
-            _cpu_arch = node_status.node_info.architecture
-        cpu_arch = normalize_arch(_cpu_arch).to_cpu_architecture()
-        cpus = _parse_cpu(allocatable["cpu"])
-        memory_mib = _parse_memory(allocatable["memory"])
-        disk_size_mib = _parse_memory(allocatable["ephemeral-storage"])
-        gpus = _get_node_gpus(node)
-    except (ValueError, KeyError) as e:
-        logger.exception("Failed to process node: %s: %s", type(e).__name__, e)
-        return None
-    return InstanceOfferWithAvailability(
-        backend=BackendType.KUBERNETES,
-        instance=InstanceType(
-            name=node_name,
-            resources=Resources(
-                cpus=cpus,
-                cpu_arch=cpu_arch,
-                memory_mib=memory_mib,
-                gpus=gpus[:gpu_request],
-                spot=False,
-                disk=Disk(size_mib=disk_size_mib),
-            ),
-        ),
-        price=0,
-        region=DUMMY_REGION,
-        availability=InstanceAvailability.AVAILABLE,
-        instance_runtime=InstanceRuntime.RUNNER,
-    )
-
-
-def _parse_cpu(cpu: str) -> int:
-    if cpu.endswith("m"):
-        # "m" means millicpu (1/1000 CPU), e.g., 7900m -> 7.9 -> 7
-        return int(float(cpu[:-1]) / 1000)
-    return int(cpu)
-
-
-def _parse_memory(memory: str) -> int:
-    if memory.isdigit():
-        # no suffix means that the value is in bytes
-        return int(memory) // 2**20
-    return int(parse_memory(memory, as_untis="M"))
-
-
-def _render_memory(memory: Memory) -> str:
-    return f"{float(memory)}Gi"
-
-
-def _get_node_labels(node: client.V1Node) -> dict[str, str]:
-    if (metadata := node.metadata) is None:
-        return {}
-    if (labels := metadata.labels) is None:
-        return {}
-    return labels
-
-
-def _get_node_gpus(node: client.V1Node) -> list[Gpu]:
-    node_name = get_or_error(get_or_error(node.metadata).name)
-    allocatable = get_or_error(get_or_error(node.status).allocatable)
-    labels = _get_node_labels(node)
-    for gpu_resource, gpu_getter in (
-        (NVIDIA_GPU_RESOURCE, _get_nvidia_gpu_from_node_labels),
-        (AMD_GPU_RESOURCE, _get_amd_gpu_from_node_labels),
-    ):
-        _gpu_count = allocatable.get(gpu_resource)
-        if not _gpu_count:
-            continue
-        gpu_count = int(_gpu_count)
-        if gpu_count < 1:
-            continue
-        gpu = gpu_getter(labels)
-        if gpu is None:
-            logger.warning(
-                "Node %s: GPU resource found, but failed to detect its model: %s=%d",
-                node_name,
-                gpu_resource,
-                gpu_count,
-            )
-            return []
-        return [gpu] * gpu_count
-    logger.debug("Node %s: no GPU resource found", node_name)
-    return []
-
-
-def _get_nvidia_gpu_from_node_labels(labels: dict[str, str]) -> Optional[Gpu]:
-    # We rely on https://github.com/NVIDIA/k8s-device-plugin/tree/main/docs/gpu-feature-discovery
-    # to detect gpus. Note that "nvidia.com/gpu.product" is not a short gpu name like "T4" or
-    # "A100" but a product name like "Tesla-T4" or "A100-SXM4-40GB".
-    # Thus, we convert the product name to a known gpu name.
-    gpu_product = labels.get(NVIDIA_GPU_PRODUCT_LABEL)
-    if gpu_product is None:
-        return None
-    gpu_product = gpu_product.replace("RTX-", "RTX")
-    for gpu_name in NVIDIA_GPU_NAMES:
-        if gpu_name.lower() in gpu_product.lower().split("-"):
-            break
-    else:
-        return None
-    gpu_info = NVIDIA_GPU_NAME_TO_GPU_INFO[gpu_name]
-    gpu_memory = gpu_info.memory * 1024
-    # A100 may come in two variants
-    if "40GB" in gpu_product:
-        gpu_memory = 40 * 1024
-    return Gpu(vendor=AcceleratorVendor.NVIDIA, name=gpu_name, memory_mib=gpu_memory)
-
-
-def _get_amd_gpu_from_node_labels(labels: dict[str, str]) -> Optional[Gpu]:
-    # (AMDGPUInfo.name, AMDGPUInfo.memory) pairs
-    gpus: set[tuple[str, int]] = set()
-    for label in labels:
-        if not label.startswith(AMD_GPU_DEVICE_ID_LABEL_PREFIX):
-            continue
-        _, _, _device_id = label.rpartition(".")
-        device_id = int(_device_id, 16)
-        gpu_info = AMD_GPU_DEVICE_ID_TO_GPU_INFO.get(device_id)
-        if gpu_info is None:
-            logger.warning("Unknown AMD GPU device id: %X", device_id)
-            continue
-        gpus.add((gpu_info.name, gpu_info.memory))
-    if not gpus:
-        return None
-    if len(gpus) == 1:
-        gpu_name, gpu_memory_gib = next(iter(gpus))
-        return Gpu(vendor=AcceleratorVendor.AMD, name=gpu_name, memory_mib=gpu_memory_gib * 1024)
-    logger.warning("Multiple AMD GPU models detected: %s, ignoring all GPUs", gpus)
-    return None
-
-
 def _get_pod_spec_parameters_for_gpu(
     api: client.CoreV1Api, gpu_spec: GPUSpec
 ) -> tuple[str, client.V1NodeAffinity, str]:
@@ -643,8 +483,8 @@ def _get_nvidia_gpu_node_affinity(
 ) -> client.V1NodeAffinity:
     matching_gpu_label_values: set[str] = set()
     for node in nodes:
-        labels = _get_node_labels(node)
-        gpu = _get_nvidia_gpu_from_node_labels(labels)
+        labels = get_node_labels(node)
+        gpu = get_nvidia_gpu_from_node_labels(labels)
         if gpu is not None and _gpu_matches_gpu_spec(gpu, gpu_spec):
             matching_gpu_label_values.add(labels[NVIDIA_GPU_PRODUCT_LABEL])
     if not matching_gpu_label_values:
@@ -676,8 +516,8 @@ def _get_amd_gpu_node_affinity(
 ) -> client.V1NodeAffinity:
     matching_device_ids: set[int] = set()
     for node in nodes:
-        labels = _get_node_labels(node)
-        gpu = _get_amd_gpu_from_node_labels(labels)
+        labels = get_node_labels(node)
+        gpu = get_amd_gpu_from_node_labels(labels)
         if gpu is not None and _gpu_matches_gpu_spec(gpu, gpu_spec):
             matching_device_ids.update(AMD_GPU_NAME_TO_DEVICE_IDS[gpu.name])
     return client.V1NodeAffinity(
@@ -828,10 +668,10 @@ def _create_jump_pod_service(
         taints = node_spec.taints or []
         for taint in taints:
             # A "soft" taint, ignore.
-            if taint.effect == TaintEffect.PREFER_NO_SCHEDULE:
+            if not is_hard_taint(taint):
                 continue
             has_hard_taint = True
-            if taint.key in TOLERATED_NODE_TAINTS:
+            if is_taint_tolerated(taint):
                 tolerated_taints.add((taint.key, taint.effect))
         if not has_hard_taint:
             toleration_required = False