feat(usage): render instance types by actual shape (CPU spec / GPU x cards)
Instance Types are grouped by actual shape now, so each row/series is one concrete type. Label them accordingly via a shared instanceTypeSeriesLabel, used by both the table column and the trend legend so they read identically: GPU shows "<product> x <cards>", CPU shows "CPU Only · N vCPU · M GB" (the instance's real size, not the bare "CPU Only" + hover tooltip). The Instances tab also uses the per-instance cpu/mem totals, so a 3c6g instance of a 1c2g flavor reads 3c6g instead of 1c2g. flatten carries cpu_milli/memory_mib/ gpu_count from the breakdown dimensions.
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@@ -11,6 +11,7 @@
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* whole-machine SKU model meters runtime, not decomposed components.
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*/
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import { request } from '@umijs/max';
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import { instanceTypeSeriesLabel } from '../utils/format-instance-type';
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export interface ResourceUsageFilters {
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creator_ids?: number[];
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@@ -73,6 +74,10 @@ export interface ResourceBreakdownItem extends ResourceBreakdownSummary {
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unit_cpu_milli?: number;
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unit_memory_mib?: number;
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vram_mib?: number;
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// Instance totals (requested cpu/ram) — the real size, so CPU instance types
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// show "CPU Only · 2 vCPU · 4 GB" instead of just the per-unit spec.
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cpu_milli?: number;
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memory_mib?: number;
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// Per-instance rows also carry the card count + ephemeral disk so the
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// Instances table can render "<product> x <count>" + the spec popover.
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gpu_count?: number;
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@@ -202,6 +207,8 @@ interface ServerBreakdownItem {
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unit_cpu_milli?: number | null;
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unit_memory_mib?: number | null;
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vram_mib?: number | null;
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cpu_milli?: number | null;
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memory_mib?: number | null;
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gpu_count?: number | null;
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ephemeral_mib?: number | null;
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local_storage_mib?: number | null;
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@@ -313,6 +320,8 @@ function flattenItem(
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if (dims.unit_memory_mib != null)
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flat.unit_memory_mib = dims.unit_memory_mib;
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if (dims.vram_mib != null) flat.vram_mib = dims.vram_mib;
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if (dims.cpu_milli != null) flat.cpu_milli = dims.cpu_milli;
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if (dims.memory_mib != null) flat.memory_mib = dims.memory_mib;
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if (dims.gpu_count != null) flat.gpu_count = dims.gpu_count;
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if (dims.ephemeral_mib != null) flat.ephemeral_mib = dims.ephemeral_mib;
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if (dims.local_storage_mib != null)
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@@ -321,14 +330,14 @@ function flattenItem(
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if (dims.storage_type) flat.storage_type = dims.storage_type;
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if (dims.capacity_mib != null) flat.capacity_mib = dims.capacity_mib;
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}
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// For an instance-type grouped trend the series label (``group``) defaults to
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// the raw flavor slug; prefer the pretty product name so the chart legend
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// matches the GPU Instances list (#5700). ``groupBy`` here is the unmapped
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// frontend dimension — ``gpu_type`` maps to the backend's ``instance_type``
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// (see GROUP_BY_MAP). Falls back to the slug for legacy rows that predate
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// dimension enrichment.
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if (groupBy === 'gpu_type' && flat.product) {
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flat.group = flat.product;
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// Instance-type grouped trend: the series label (``group``) defaults to the
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// raw flavor slug. Instance Types are grouped by actual shape, so label each
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// series by that shape — "<product> x <cards>" / "CPU Only · 3 vCPU · 6 GB" —
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// matching the table and keeping every shape a distinct series (#5700).
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// ``groupBy`` is the unmapped frontend dimension; the instance-type axis is
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// ``gpu_type`` (→ backend ``instance_type`` via GROUP_BY_MAP).
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if (groupBy === 'gpu_type') {
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flat.group = instanceTypeSeriesLabel(flat);
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}
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return flat;
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}
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@@ -5,7 +5,10 @@ import {
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import { useIntl } from '@umijs/max';
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import { useMemo } from 'react';
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import { ResourceBreakdownItem } from '../../apis/resource';
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import { instanceTypeLabel } from '../../utils/format-instance-type';
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import {
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cpuOnlyLabel,
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instanceTypeSeriesLabel
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} from '../../utils/format-instance-type';
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import { parseRollup } from '../../utils/time-buckets';
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type GroupKey = 'gpu_type' | 'instance' | 'user';
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@@ -44,25 +47,29 @@ const useInstancesColumns = (groupKey: GroupKey) => {
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title: intl.formatMessage({ id: 'usage.table.instanceType' }),
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dataIndex: 'gpu_type',
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key: 'gpu_type',
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render: (_v: string, row: ResourceBreakdownItem) =>
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renderInstanceType(
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render: (_v: string, row: ResourceBreakdownItem) => {
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const isCpu = !row.gpu_count && !row.vram_mib;
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return renderInstanceType(
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buildInstanceTypeRecordFromMiB({
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name: row.instance_name,
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product: row.product || row.gpu_type,
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gpuCount: row.gpu_count,
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unitCpuMilli: row.unit_cpu_milli,
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unitMemoryMib: row.unit_memory_mib,
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// CPU instance types show their real total size (cpu/mem totals);
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// GPU keeps per-card specs since the renderer multiplies by the
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// card count.
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unitCpuMilli: isCpu ? row.cpu_milli : row.unit_cpu_milli,
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unitMemoryMib: isCpu ? row.memory_mib : row.unit_memory_mib,
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vramMib: row.vram_mib
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}),
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{
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intl,
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categories: ['cpu', 'ram'],
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title:
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!!row.gpu_count || !!row.vram_mib
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? instanceTypeLabel(row)
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: 'CPU Only'
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// Each row is one shape: GPU "<product> x <cards>", CPU
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// "CPU Only · <spec>".
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title: instanceTypeSeriesLabel(row)
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}
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)
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);
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}
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};
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// Instances breakdown: render through the canonical GPU Instances list
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// renderer so the label + spec popover are identical. The breakdown row
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@@ -71,21 +78,26 @@ const useInstancesColumns = (groupKey: GroupKey) => {
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title: intl.formatMessage({ id: 'usage.table.instanceType' }),
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dataIndex: 'gpu_type',
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key: 'gpu_type',
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render: (_v: string, row: ResourceBreakdownItem) =>
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renderInstanceType(
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render: (_v: string, row: ResourceBreakdownItem) => {
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const isCpu = !row.gpu_count && !row.vram_mib;
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return renderInstanceType(
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buildInstanceTypeRecordFromMiB({
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name: row.instance_name,
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product: row.product || row.gpu_type,
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gpuCount: row.gpu_count,
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unitCpuMilli: row.unit_cpu_milli,
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unitMemoryMib: row.unit_memory_mib,
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// A per-instance row is one concrete instance, so CPU shows its
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// real requested size (cpu/mem totals), not the per-unit flavor
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// spec — e.g. a 3c6g instance of a 1c2g flavor reads "3 vCPU · 6 GB".
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unitCpuMilli: isCpu ? row.cpu_milli : row.unit_cpu_milli,
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unitMemoryMib: isCpu ? row.memory_mib : row.unit_memory_mib,
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vramMib: row.vram_mib,
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localStorageMib: row.local_storage_mib,
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ephemeralMib: row.ephemeral_mib,
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persistentMib: row.persistent_mib
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}),
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{ intl }
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)
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{ intl, title: isCpu ? cpuOnlyLabel(row) : undefined }
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);
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}
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};
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// Last Active = the last active day. The backend sends a rollup-tz instant
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// with its offset; parseRollup keeps that wall clock (no browser-tz convert),
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@@ -16,3 +16,39 @@ import { ResourceBreakdownItem } from '../apis/resource';
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export const instanceTypeLabel = (
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row?: Partial<ResourceBreakdownItem>
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): string => row?.product || row?.gpu_type || '-';
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const _trim = (n: number): string =>
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Number.isInteger(n) ? `${n}` : n.toFixed(1);
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// Compact CPU/RAM spec, e.g. "2 vCPU · 4 GB", from the instance totals
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// (millicores / MiB). Empty string when neither is known.
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export const formatCpuSpec = (
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cpuMilli?: number | null,
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memMib?: number | null
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): string => {
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const parts: string[] = [];
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if (cpuMilli) parts.push(`${_trim(cpuMilli / 1000)} vCPU`);
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if (memMib) parts.push(`${_trim(memMib / 1024)} GB`);
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return parts.join(' · ');
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};
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// CPU instance-type label: "CPU Only" plus its real size when known, e.g.
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// "CPU Only · 2 vCPU · 4 GB". Used by both the table column and the trend
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// legend so they read identically.
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export const cpuOnlyLabel = (row?: Partial<ResourceBreakdownItem>): string => {
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const spec = formatCpuSpec(row?.cpu_milli, row?.memory_mib);
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return spec ? `CPU Only · ${spec}` : 'CPU Only';
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};
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// Instance Types are grouped by actual shape, so each row is one concrete
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// type: a GPU shows "<product> x <cards>", a CPU shows "CPU Only · <spec>".
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// One label for the table column and the trend legend so they read the same
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// and each shape is a distinct series. (" x " matches the GPU Instances list.)
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export const instanceTypeSeriesLabel = (
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row?: Partial<ResourceBreakdownItem>
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): string => {
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const isCpu = !row?.gpu_count && !row?.vram_mib;
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if (isCpu) return cpuOnlyLabel(row);
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const product = row?.product || row?.gpu_type || '-';
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return row?.gpu_count ? `${product} x ${row.gpu_count}` : product;
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};
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