Fix verda tests for verda>=1.22.0 InstanceType changes (#221)

Author: peterschmidt85

pyproject.toml

@@ -53,10 +53,10 @@ oci = [
     "pydantic>=1.10.10,<2.0.0",
 ]
 verda = [
-    'verda',
+    'verda>=1.22.0',
 ]
 maybe_verda = [
-    'verda; python_version>="3.10"',
+    'verda>=1.22.0; python_version>="3.10"',
 ]
 all = ["gpuhunt[aws,azure,maybe_verda,gcp,maybe_nebius,oci]"]
 dev = [

src/tests/providers/test_verda.py

@@ -17,85 +17,91 @@
 def raw_instance_types() -> list[dict]:
     # verda.instance_types.get()
     one_gpu = {
-        "best_for": [
-            "Gargantuan ML models",
-            "Multi-GPU training",
-            "FP64 HPC",
-            "NVLINK",
-        ],
+        "best_for": [],
         "cpu": {"description": "30 CPU", "number_of_cores": 30},
+        "currency": "usd",
         "deploy_warning": "H100: Use Nvidia driver 535 or higher for best performance",
         "description": "Dedicated Hardware Instance",
+        "display_name": None,
         "gpu": {"description": "1x H100 SXM5 80GB", "number_of_gpus": 1},
         "gpu_memory": {"description": "80GB GPU RAM", "size_in_gigabytes": 80},
         "id": "c01dd00d-0000-480b-ae4e-d429115d055b",
         "instance_type": "1H100.80S.30V",
+        "manufacturer": "NVIDIA",
         "memory": {"description": "120GB RAM", "size_in_gigabytes": 120},
-        "model": "H100 80GB",
+        "model": "H100",
         "name": "H100 SXM5 80GB",
-        "p2p": "",
+        "p2p": None,
         "price_per_hour": "3.95",
         "spot_price": "1.70",
         "storage": {"description": "dynamic"},
+        "supported_os": ["ubuntu-22.04", "ubuntu-24.04"],
     }
 
     two_gpu = {
-        "best_for": ["Large ML models", "FP32 calculations", "Single-GPU training"],
+        "best_for": [],
         "cpu": {"description": "20 CPU", "number_of_cores": 20},
+        "currency": "usd",
         "deploy_warning": None,
         "description": "Dedicated Hardware Instance",
+        "display_name": None,
         "gpu": {"description": "2x RTX A6000 48GB", "number_of_gpus": 2},
         "gpu_memory": {"description": "96GB GPU RAM", "size_in_gigabytes": 96},
         "id": "07cf5dc1-a5d2-4972-ae4e-d429115d055b",
         "instance_type": "2A6000.20V",
+        "manufacturer": "NVIDIA",
         "memory": {"description": "120GB RAM", "size_in_gigabytes": 120},
         "model": "RTX A6000",
         "name": "RTX A6000 48GB",
-        "p2p": "",
+        "p2p": "50GB/s",
         "price_per_hour": "1.98",
         "spot_price": "0.70",
         "storage": {"description": "dynamic"},
+        "supported_os": ["ubuntu-22.04", "ubuntu-24.04"],
     }
 
     cpu_instance = {
-        "best_for": ["Running services", "API server", "Data transfers"],
+        "best_for": [],
         "cpu": {"description": "120 CPU", "number_of_cores": 120},
+        "currency": "usd",
         "deploy_warning": None,
         "description": "Dedicated Hardware Instance",
+        "display_name": "Rome/Milan",
         "gpu": {"description": "", "number_of_gpus": 0},
         "gpu_memory": {"description": "", "size_in_gigabytes": 0},
         "id": "ccc00007-a5d2-4972-ae4e-d429115d055b",
         "instance_type": "CPU.120V.480G",
+        "manufacturer": "AMD",
         "memory": {"description": "480GB RAM", "size_in_gigabytes": 480},
         "model": "CPU Node",
         "name": "AMD EPYC",
-        "p2p": "",
+        "p2p": None,
         "price_per_hour": "3.00",
         "spot_price": "1.50",
         "storage": {"description": "dynamic"},
+        "supported_os": ["ubuntu-22.04", "ubuntu-24.04"],
     }
 
     minimal = {
-        "best_for": [
-            "Small ML models",
-            "Multi-GPU training",
-            "FP64 calculations",
-            "NVLINK",
-        ],
+        "best_for": [],
         "cpu": {"description": "6 CPU", "number_of_cores": 6},
+        "currency": "usd",
         "deploy_warning": None,
         "description": "Dedicated Hardware Instance",
+        "display_name": None,
         "gpu": {"description": "1x Tesla V100 16GB", "number_of_gpus": 1},
         "gpu_memory": {"description": "16GB GPU RAM", "size_in_gigabytes": 16},
         "id": "04cf5dc1-a5d2-4972-ae4e-d429115d055b",
         "instance_type": "1V100.6V",
+        "manufacturer": "NVIDIA",
         "memory": {"description": "23GB RAM", "size_in_gigabytes": 23},
         "model": "Tesla V100",
         "name": "Tesla V100 16GB",
-        "p2p": "",
+        "p2p": None,
         "price_per_hour": "0.89",
         "spot_price": "0.25",
         "storage": {"description": "225GB NVME", "size_in_gigabytes": 225},
+        "supported_os": ["ubuntu-22.04", "ubuntu-24.04"],
     }
 
     return [one_gpu, two_gpu, cpu_instance, minimal]
@@ -162,6 +168,15 @@ def instance_types(raw_instance_type: dict) -> InstanceType:
         memory=raw_instance_type["memory"],
         gpu_memory=raw_instance_type["gpu_memory"],
         storage=raw_instance_type["storage"],
+        best_for=raw_instance_type["best_for"],
+        model=raw_instance_type["model"],
+        name=raw_instance_type["name"],
+        p2p=raw_instance_type["p2p"],
+        currency=raw_instance_type["currency"],
+        manufacturer=raw_instance_type["manufacturer"],
+        display_name=raw_instance_type["display_name"],
+        supported_os=raw_instance_type["supported_os"],
+        deploy_warning=raw_instance_type.get("deploy_warning"),
     )
     return instance