Add device memory #2565
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@@ -58,10 +58,10 @@ def get_regions() -> List[str]: | |
| # We have to manually remove it. | ||
| DEPRECATED_FAMILIES = ['standardNVSv2Family'] | ||
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| USEFUL_COLUMNS = { | ||
| 'InstanceType', 'AcceleratorName', 'AcceleratorCount', 'vCPUs', 'MemoryGiB', | ||
| 'GpuInfo', 'Price', 'SpotPrice', 'Region', 'Generation', 'DeviceMemory' | ||
| } | ||
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| def get_pricing_url(region: Optional[str] = None) -> str: | ||
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@@ -244,11 +244,79 @@ def get_additional_columns(row): | |
| axis='columns', | ||
| ) | ||
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| def create_gpu_map(df): | ||
| # Map of Azure's machine with GPU to their corresponding memory | ||
| # Result is hard-coded since Azure's API to not return such info | ||
| # may be outdated so need to be maintained | ||
| gpu_map = { | ||
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There was a problem hiding this comment. could we try to map instance type -> gpu name first and then calculate the resulting device memory later? this two-level approach might be cleaner. There was a problem hiding this comment. I agree this approach is much cleaner as it uses much less hard-coding and utilize already fetched info. I will change the script to use this approach. |
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| 'Standard_NC6': 12, | ||
| 'Standard_NC12': 24, | ||
| 'Standard_NC24': 48, | ||
| 'Standard_NC24r*': 48, | ||
| 'Standard_NC6s_v2': 16, | ||
| 'Standard_NC12s_v2': 32, | ||
| 'Standard_NC24s_v2': 64, | ||
| 'Standard_NC24rs_v2*': 64, | ||
| 'Standard_NC6s_v3': 16, | ||
| 'Standard_NC12s_v3': 32, | ||
| 'Standard_NC24s_v3': 32, | ||
| 'Standard_NC4as_T4_v3': 16, | ||
| 'Standard_NC8as_T4_v3': 16, | ||
| 'Standard_NC16as_T4_v3': 16, | ||
| 'Standard_NC64as_T4_v3': 64, | ||
| 'Standard_NC24ads_A100_v4': 80, | ||
| 'Standard_NC48ads_A100_v4': 160, | ||
| 'Standard_NC96ads_A100_v4': 320, | ||
| 'Standard_ND96asr_v4': 40, | ||
| 'Standard_ND96amsr_A100_v4': 80, | ||
| 'Standard_ND6s': 24, | ||
| 'Standard_ND12s': 48, | ||
| 'Standard_ND24s': 96, | ||
| 'Standard_ND24rs*': 96, | ||
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There was a problem hiding this comment. Its one of the instance type offered by azure. |
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| 'Standard_ND40rs_v2': 32, | ||
| 'Standard_NG8ads_V620_v1': 8, | ||
| 'Standard_NG16ads_V620_v1': 16, | ||
| 'Standard_NG32ads_V620_v1': 32, | ||
| 'Standard_NG32adms_V620_v1': 32, | ||
| 'Standard_NV6': 8, | ||
| 'Standard_NV12': 16, | ||
| 'Standard_NV24': 32, | ||
| 'Standard_NV12s_v3': 8, | ||
| 'Standard_NV24s_v3': 16, | ||
| 'Standard_NV48s_v3': 32, | ||
| 'Standard_NV4as_v4': 2, | ||
| 'Standard_NV8as_v4': 4, | ||
| 'Standard_NV16as_v4': 8, | ||
| 'Standard_NV32as_v4': 16, | ||
| 'Standard_NV6ads_A10_v5': 4, | ||
| 'Standard_NV12ads_A10_v5': 8, | ||
| 'Standard_NV18ads_A10_v5': 12, | ||
| 'Standard_NV36ads_A10_v5': 24, | ||
| 'Standard_NV36adms_A10_v5': 24, | ||
| 'Standard_NV72ads_A10_v5': 48, | ||
| 'Standard_NV6_Promo': 16, | ||
| 'Standard_NV12_Promo': 32, | ||
| 'Standard_NV24_Promo': 48 | ||
| } | ||
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| all_instance = df.InstanceType.unique() | ||
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| for instance in all_instance: | ||
| if instance not in gpu_map: | ||
| gpu_map[instance] = '' | ||
| return gpu_map | ||
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| def map_device_memory(row, dic): | ||
| return dic[row] | ||
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| before_drop_len = len(df_ret) | ||
| df_ret.dropna(subset=['InstanceType'], inplace=True, how='all') | ||
| after_drop_len = len(df_ret) | ||
| print(f'Dropped {before_drop_len - after_drop_len} duplicated rows') | ||
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| df_ret['DeviceMemory'] = df_ret.InstanceType.apply( | ||
| map_device_memory, args=(create_gpu_map(df_ret),)) | ||
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| # Filter out deprecated families | ||
| df_ret = df_ret.loc[~df_ret['family'].isin(DEPRECATED_FAMILIES)] | ||
| df_ret = df_ret[USEFUL_COLUMNS] | ||
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Could you add a reference link on how these information are found? also, how did we make sure we cover all the instance types?
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I looked through the azure documentation to ensure that all instance type have been included and also ran the script on --all-regions and the result looked fine to me.
However, I think the approach you mentioned below makes more sense in which we map instance type -> gpu name then from gpu name -> gpu memory. There is already a mapping from instance type -> gpu name in the script. Assuming this mapping is complete, we can easily map the gpu name to their corresponding memory.