File size: 104,996 Bytes
cc18202 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 | <!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>CUDA-X Complete Reference โ Easy Guide</title>
<style>
@import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;700&family=Syne:wght@400;700;800&display=swap');
:root {
--nvidia-green: #76b900;
--nvidia-dark: #0a0a0a;
--card-bg: #111111;
--card-border: #1e1e1e;
--text: #e0e0e0;
--muted: #666;
--tag-math: #1a3a00;
--tag-math-text: #76b900;
--tag-dl: #001a3a;
--tag-dl-text: #4da6ff;
--tag-data: #2a1a00;
--tag-data-text: #ffaa00;
--tag-img: #1a001a;
--tag-img-text: #cc66ff;
--tag-comm: #001a1a;
--tag-comm-text: #00cccc;
--tag-sci: #1a1a00;
--tag-sci-text: #cccc00;
--tag-quantum: #1a0010;
--tag-quantum-text: #ff66aa;
--tag-tool: #0d0d0d;
--tag-tool-text: #888;
--tag-partner: #101a00;
--tag-partner-text: #99cc00;
--tag-parallel: #001a10;
--tag-parallel-text: #00cc88;
--tag-physics: #1a0800;
--tag-physics-text: #ff8800;
}
* { box-sizing: border-box; margin: 0; padding: 0; }
body {
background: var(--nvidia-dark);
color: var(--text);
font-family: 'Syne', sans-serif;
min-height: 100vh;
}
header {
background: linear-gradient(135deg, #0d1a00 0%, #0a0a0a 50%, #001a0d 100%);
border-bottom: 2px solid var(--nvidia-green);
padding: 32px 24px 24px;
text-align: center;
position: relative;
overflow: hidden;
}
header::before {
content: '';
position: absolute;
inset: 0;
background: radial-gradient(ellipse at 50% 0%, rgba(118,185,0,0.12) 0%, transparent 70%);
pointer-events: none;
}
.header-logo {
font-family: 'JetBrains Mono', monospace;
font-size: 11px;
color: var(--nvidia-green);
letter-spacing: 4px;
text-transform: uppercase;
margin-bottom: 12px;
opacity: 0.8;
}
h1 {
font-size: clamp(28px, 5vw, 52px);
font-weight: 800;
line-height: 1.1;
background: linear-gradient(90deg, #76b900, #a0e000, #76b900);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
background-clip: text;
margin-bottom: 10px;
}
.subtitle {
color: #888;
font-size: 14px;
font-family: 'JetBrains Mono', monospace;
margin-bottom: 6px;
}
.total-badge {
display: inline-block;
background: rgba(118,185,0,0.15);
border: 1px solid rgba(118,185,0,0.4);
color: var(--nvidia-green);
padding: 4px 16px;
border-radius: 20px;
font-size: 13px;
font-family: 'JetBrains Mono', monospace;
margin-top: 8px;
}
.controls {
background: #0d0d0d;
padding: 20px 24px;
border-bottom: 1px solid #1e1e1e;
position: sticky;
top: 0;
z-index: 100;
}
.search-wrap {
position: relative;
max-width: 600px;
margin: 0 auto 16px;
}
.search-icon {
position: absolute;
left: 14px;
top: 50%;
transform: translateY(-50%);
color: #444;
font-size: 16px;
}
#searchBox {
width: 100%;
background: #1a1a1a;
border: 1px solid #2a2a2a;
color: #e0e0e0;
padding: 12px 16px 12px 44px;
border-radius: 8px;
font-size: 15px;
font-family: 'JetBrains Mono', monospace;
outline: none;
transition: border-color 0.2s;
}
#searchBox:focus { border-color: var(--nvidia-green); }
#searchBox::placeholder { color: #444; }
.filters {
display: flex;
flex-wrap: wrap;
gap: 8px;
justify-content: center;
}
.filter-btn {
padding: 6px 14px;
border-radius: 20px;
border: 1px solid #2a2a2a;
background: transparent;
color: #888;
cursor: pointer;
font-size: 12px;
font-family: 'JetBrains Mono', monospace;
transition: all 0.2s;
white-space: nowrap;
}
.filter-btn:hover, .filter-btn.active {
border-color: var(--nvidia-green);
color: var(--nvidia-green);
background: rgba(118,185,0,0.08);
}
.stats-bar {
text-align: center;
padding: 12px;
font-family: 'JetBrains Mono', monospace;
font-size: 12px;
color: #555;
border-bottom: 1px solid #1a1a1a;
}
.stats-bar span { color: var(--nvidia-green); }
.content {
max-width: 1400px;
margin: 0 auto;
padding: 24px;
}
.section-header {
display: flex;
align-items: center;
gap: 12px;
margin: 32px 0 16px;
padding-bottom: 12px;
border-bottom: 1px solid #1e1e1e;
}
.section-title {
font-size: 18px;
font-weight: 700;
color: #ccc;
}
.section-count {
font-family: 'JetBrains Mono', monospace;
font-size: 11px;
color: #444;
background: #1a1a1a;
padding: 2px 8px;
border-radius: 10px;
}
.grid {
display: grid;
grid-template-columns: repeat(auto-fill, minmax(320px, 1fr));
gap: 14px;
}
.card {
background: var(--card-bg);
border: 1px solid var(--card-border);
border-radius: 10px;
padding: 16px;
transition: all 0.2s;
position: relative;
overflow: hidden;
}
.card::before {
content: '';
position: absolute;
top: 0; left: 0; right: 0;
height: 2px;
opacity: 0;
transition: opacity 0.2s;
}
.card:hover {
border-color: #333;
transform: translateY(-1px);
box-shadow: 0 4px 20px rgba(0,0,0,0.4);
}
.card:hover::before { opacity: 1; }
.card-math::before { background: var(--nvidia-green); }
.card-dl::before { background: #4da6ff; }
.card-data::before { background: #ffaa00; }
.card-img::before { background: #cc66ff; }
.card-comm::before { background: #00cccc; }
.card-sci::before { background: #cccc00; }
.card-quantum::before { background: #ff66aa; }
.card-tool::before { background: #666; }
.card-partner::before { background: #99cc00; }
.card-parallel::before { background: #00cc88; }
.card-physics::before { background: #ff8800; }
.card-nemo::before { background: #44aaff; }
.card-nim::before { background: #00ffcc; }
.card-triton::before { background: #ff4488; }
.card-deepstream::before { background: #ffcc00; }
.card-riva::before { background: #aa44ff; }
.card-tao::before { background: #ff8844; }
.card-isaac::before { background: #44ffaa; }
.card-omniverse::before { background: #4488ff; }
.card-metropolis::before { background: #ff4444; }
.card-runtime::before { background: #cccccc; }
.tag-nemo { background: #001a2a; color: #44aaff; }
.tag-nim { background: #001a14; color: #00ffcc; }
.tag-triton { background: #2a0014; color: #ff4488; }
.tag-deepstream { background: #2a2200; color: #ffcc00; }
.tag-riva { background: #1a0028; color: #aa44ff; }
.tag-tao { background: #2a1400; color: #ff8844; }
.tag-isaac { background: #001a0a; color: #44ffaa; }
.tag-omniverse { background: #00102a; color: #4488ff; }
.tag-metropolis { background: #2a0000; color: #ff6666; }
.tag-runtime { background: #1a1a1a; color: #cccccc; border: 1px solid #333; }
.card-top {
display: flex;
align-items: flex-start;
justify-content: space-between;
margin-bottom: 8px;
gap: 8px;
}
.card-name {
font-family: 'JetBrains Mono', monospace;
font-size: 13px;
font-weight: 700;
color: #ffffff;
line-height: 1.3;
flex: 1;
}
.tag {
padding: 2px 8px;
border-radius: 4px;
font-size: 10px;
font-family: 'JetBrains Mono', monospace;
font-weight: 700;
white-space: nowrap;
flex-shrink: 0;
}
.tag-math { background: var(--tag-math); color: var(--tag-math-text); }
.tag-dl { background: var(--tag-dl); color: var(--tag-dl-text); }
.tag-data { background: var(--tag-data); color: var(--tag-data-text); }
.tag-img { background: var(--tag-img); color: var(--tag-img-text); }
.tag-comm { background: var(--tag-comm); color: var(--tag-comm-text); }
.tag-sci { background: var(--tag-sci); color: var(--tag-sci-text); }
.tag-quantum { background: var(--tag-quantum); color: var(--tag-quantum-text); }
.tag-tool { background: var(--tag-tool); color: var(--tag-tool-text); border: 1px solid #333; }
.tag-partner { background: var(--tag-partner); color: var(--tag-partner-text); }
.tag-parallel { background: var(--tag-parallel); color: var(--tag-parallel-text); }
.tag-physics { background: var(--tag-physics); color: var(--tag-physics-text); }
.tag-framework { background: #1a0a00; color: #ff6644; }
.desc-en {
font-size: 12px;
color: #666;
font-family: 'JetBrains Mono', monospace;
margin-bottom: 6px;
line-height: 1.5;
}
.desc-ur {
font-size: 13px;
color: #aaa;
line-height: 1.6;
margin-bottom: 8px;
}
.card-meta {
display: flex;
gap: 6px;
flex-wrap: wrap;
align-items: center;
}
.badge {
font-size: 10px;
font-family: 'JetBrains Mono', monospace;
padding: 2px 6px;
border-radius: 3px;
background: #1a1a1a;
color: #555;
border: 1px solid #2a2a2a;
}
.badge-installed {
background: rgba(118,185,0,0.1);
color: var(--nvidia-green);
border-color: rgba(118,185,0,0.3);
}
.badge-free { background: rgba(0,100,200,0.1); color: #4da6ff; border-color: rgba(0,100,200,0.3); }
.badge-paid { background: rgba(200,100,0,0.1); color: #ffaa44; border-color: rgba(200,100,0,0.3); }
.badge-cloud { background: rgba(100,0,200,0.1); color: #aa66ff; border-color: rgba(100,0,200,0.3); }
.badge-python { background: rgba(0,150,100,0.1); color: #00cc88; border-color: rgba(0,150,100,0.3); }
.badge-cpp { background: rgba(150,0,100,0.1); color: #cc6699; border-color: rgba(150,0,100,0.3); }
.no-results {
text-align: center;
padding: 60px 20px;
color: #444;
font-family: 'JetBrains Mono', monospace;
font-size: 14px;
display: none;
}
.legend {
display: flex;
flex-wrap: wrap;
gap: 8px;
padding: 16px 24px;
border-bottom: 1px solid #1a1a1a;
justify-content: center;
}
.legend-item {
display: flex;
align-items: center;
gap: 6px;
font-size: 11px;
font-family: 'JetBrains Mono', monospace;
color: #555;
}
.legend-dot {
width: 8px; height: 8px;
border-radius: 50%;
flex-shrink: 0;
}
footer {
text-align: center;
padding: 32px;
color: #333;
font-family: 'JetBrains Mono', monospace;
font-size: 11px;
border-top: 1px solid #1a1a1a;
margin-top: 40px;
}
.hidden { display: none !important; }
</style>
</head>
<body>
<header>
<div class="header-logo">NVIDIA CUDA-X โ Complete Reference</div>
<h1>CUDA-X Libraries & APIs</h1>
<div class="total-badge">403 Libraries, APIs, Sub-Components & Tools โ 22 Categories</div>
</header>
<div class="legend">
<div class="legend-item"><div class="legend-dot" style="background:#76b900"></div>Math</div>
<div class="legend-item"><div class="legend-dot" style="background:#4da6ff"></div>Deep Learning</div>
<div class="legend-item"><div class="legend-dot" style="background:#ffaa00"></div>Data Science</div>
<div class="legend-item"><div class="legend-dot" style="background:#cc66ff"></div>Image/Video</div>
<div class="legend-item"><div class="legend-dot" style="background:#00cccc"></div>Communication</div>
<div class="legend-item"><div class="legend-dot" style="background:#cccc00"></div>Scientific</div>
<div class="legend-item"><div class="legend-dot" style="background:#ff66aa"></div>Quantum</div>
<div class="legend-item"><div class="legend-dot" style="background:#00cc88"></div>Parallel Algo</div>
<div class="legend-item"><div class="legend-dot" style="background:#ff8800"></div>Physics</div>
<div class="legend-item"><div class="legend-dot" style="background:#99cc00"></div>Partner</div>
<div class="legend-item"><div class="legend-dot" style="background:#ff6644"></div>Framework</div>
<div class="legend-item"><div class="legend-dot" style="background:#666"></div>Tool</div>
</div>
<div class="controls">
<div class="search-wrap">
<span class="search-icon">๐</span>
<input type="text" id="searchBox" placeholder="Search karo... library name ya description..." oninput="filterCards()">
</div>
<div class="filters">
<button class="filter-btn active" onclick="setFilter('all', this)">All</button>
<button class="filter-btn" onclick="setFilter('math', this)">๐งฎ Math</button>
<button class="filter-btn" onclick="setFilter('dl', this)">๐ค Deep Learning</button>
<button class="filter-btn" onclick="setFilter('data', this)">๐ Data Science</button>
<button class="filter-btn" onclick="setFilter('img', this)">๐ผ๏ธ Image/Video</button>
<button class="filter-btn" onclick="setFilter('comm', this)">๐ก Communication</button>
<button class="filter-btn" onclick="setFilter('sci', this)">๐ฌ Scientific</button>
<button class="filter-btn" onclick="setFilter('quantum', this)">โ๏ธ Quantum</button>
<button class="filter-btn" onclick="setFilter('parallel', this)">โก Parallel</button>
<button class="filter-btn" onclick="setFilter('physics', this)">๐ Physics</button>
<button class="filter-btn" onclick="setFilter('partner', this)">๐ค Partner</button>
<button class="filter-btn" onclick="setFilter('framework', this)">๐งฉ Framework</button>
<button class="filter-btn" onclick="setFilter('tool', this)">๐ง Tools</button>
<button class="filter-btn" onclick="setFilter('nemo', this)">๐ง NeMo</button>
<button class="filter-btn" onclick="setFilter('nim', this)">โก NIM</button>
<button class="filter-btn" onclick="setFilter('triton', this)">๐ Triton</button>
<button class="filter-btn" onclick="setFilter('deepstream', this)">๐น DeepStream</button>
<button class="filter-btn" onclick="setFilter('riva', this)">๐ค Riva</button>
<button class="filter-btn" onclick="setFilter('tao', this)">๐๏ธ TAO</button>
<button class="filter-btn" onclick="setFilter('isaac', this)">๐ค Isaac</button>
<button class="filter-btn" onclick="setFilter('omniverse', this)">๐ Omniverse</button>
<button class="filter-btn" onclick="setFilter('metropolis', this)">๐๏ธ Metropolis</button>
<button class="filter-btn" onclick="setFilter('runtime', this)">โ๏ธ Runtime APIs</button>
</div>
</div>
<div class="stats-bar">
Showing: <span id="visibleCount">0</span> items
</div>
<div class="content" id="mainContent">
<div class="no-results" id="noResults">Koi result nahi mila โ koi aur word try karo ๐</div>
</div>
<footer>
NVIDIA CUDA-X Complete Reference โข Easy Roman Urdu Guide โข Data Source: developer.nvidia.com/cuda
</footer>
<script>
const data = [
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 1. CUDA MATH LIBRARIES
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"cuBLAS", cat:"math", en:"Basic Linear Algebra Subprograms on GPU", ur:"Matrix aur vector ka basic math GPU pe karo โ AI aur science dono ke liye zaroor library hai", badges:["installed","cpp"], installed:true },
{ name:"cuBLAS โ Level 1 (Vector ops)", cat:"math", en:"dot, axpy, nrm2, scal, swap", ur:"Do vectors ke darmiyan simple operations jaise add, scale, dot product โ seedha GPU pe", badges:["cpp"], installed:false },
{ name:"cuBLAS โ Level 2 (Matrix-vector)", cat:"math", en:"gemv, symv, trsv routines", ur:"Ek matrix aur ek vector ko milake operations โ equations solve karne ke liye", badges:["cpp"], installed:false },
{ name:"cuBLAS โ Level 3 (Matrix-matrix)", cat:"math", en:"gemm, trmm, syrk routines", ur:"Do badi matrices ko multiply karna โ deep learning ka sabse aham operation", badges:["cpp"], installed:false },
{ name:"cuBLASLt", cat:"math", en:"Lightweight GEMM with custom layouts", ur:"cuBLAS ka chhota version jo custom memory layouts support karta hai โ Tensor Core ke liye optimize", badges:["cpp"], installed:false },
{ name:"cuBLASXt", cat:"math", en:"Multi-GPU BLAS operations", ur:"Ek saath kai GPUs pe matrix operations โ bohot badi matrices ke liye", badges:["cpp"], installed:false },
{ name:"cuBLAS-MP", cat:"math", en:"Multi-process BLAS for clusters", ur:"Alag alag computers pe distributed BLAS โ supercomputer level computing", badges:["cpp"], installed:false },
{ name:"cuFFT", cat:"math", en:"Fast Fourier Transform on GPU", ur:"Signal ko frequency domain mein convert karo โ audio, image aur scientific data ke liye", badges:["cpp","python"], installed:false },
{ name:"cuFFT โ 1D Transform", cat:"math", en:"1D FFT forward and inverse", ur:"Audio signal ya time-series data ka Fourier transform", badges:["cpp"], installed:false },
{ name:"cuFFT โ 2D Transform", cat:"math", en:"2D FFT for image processing", ur:"Image ko frequency domain mein analyse karna", badges:["cpp"], installed:false },
{ name:"cuFFT โ 3D Transform", cat:"math", en:"3D FFT for volumetric data", ur:"MRI ya CT scan jaise 3D data ka analysis", badges:["cpp"], installed:false },
{ name:"cuFFTW", cat:"math", en:"FFTW-compatible API for GPU", ur:"Pehle CPU pe FFTW use karte the? Wahi code GPU pe chalao bina badlav ke", badges:["cpp"], installed:false },
{ name:"cuFFTMp", cat:"math", en:"Multi-process FFT for distributed systems", ur:"Kai machines pe saath mein bada FFT chalana โ weather simulation ke liye", badges:["cpp"], installed:false },
{ name:"cuRAND", cat:"math", en:"GPU-accelerated random number generation", ur:"Bohot tez random numbers banana GPU pe โ simulations aur AI training ke liye", badges:["cpp","python"], installed:false },
{ name:"cuRAND โ XORWOW", cat:"math", en:"Default XORWOW PRNG algorithm", ur:"Sabse common random number algorithm โ general use ke liye theek hai", badges:["cpp"], installed:false },
{ name:"cuRAND โ MRG32k3a", cat:"math", en:"Multiple recursive generator", ur:"Zyada accurate random numbers โ statistics ke liye behtar", badges:["cpp"], installed:false },
{ name:"cuRAND โ Sobol (Quasi)", cat:"math", en:"Low-discrepancy quasi-random sequences", ur:"Normal random se behtar โ finance aur simulation mein use hota hai", badges:["cpp"], installed:false },
{ name:"cuRAND โ Philox4x32", cat:"math", en:"Counter-based random generator", ur:"Alag alag GPU threads pe safe random numbers generate karna", badges:["cpp"], installed:false },
{ name:"cuSOLVER", cat:"math", en:"Dense and sparse matrix solver", ur:"Complex math equations solve karna โ engineering simulations ka buniyadi tool", badges:["cpp"], installed:false },
{ name:"cuSOLVERDN", cat:"math", en:"Dense matrix factorization (LU, QR, SVD)", ur:"Regular matrices solve karna โ machine learning aur stats mein use hota hai", badges:["cpp"], installed:false },
{ name:"cuSOLVERSP", cat:"math", en:"Sparse linear system solver", ur:"Khali cells wali badi matrices solve karna โ graph aur network problems ke liye", badges:["cpp"], installed:false },
{ name:"cuSOLVERRF", cat:"math", en:"Refactorization solver for repeated problems", ur:"Wahi matrix baar baar solve karni ho to tez karo โ simulation loops ke liye", badges:["cpp"], installed:false },
{ name:"cuSOLVERMg", cat:"math", en:"Multi-GPU dense solver", ur:"Itni badi matrix jo ek GPU mein na aaye โ kai GPUs pe distribute karo", badges:["cpp"], installed:false },
{ name:"cuSPARSE", cat:"math", en:"Sparse matrix BLAS library", ur:"Mostly khali cells wali matrices ke liye โ social networks aur physics simulations mein kaam aata hai", badges:["cpp"], installed:false },
{ name:"cuSPARSE โ SpMM", cat:"math", en:"Sparse matrix ร dense matrix multiply", ur:"Ek sparse aur ek normal matrix ko multiply karna โ graph neural networks mein use", badges:["cpp"], installed:false },
{ name:"cuSPARSE โ SpGEMM", cat:"math", en:"Sparse ร sparse matrix multiply", ur:"Do sparse matrices multiply karna โ bohot advanced scientific computing", badges:["cpp"], installed:false },
{ name:"cuSPARSELt", cat:"math", en:"Structured sparsity matrix multiplication", ur:"50% khali matrix ko Tensor Cores pe chalana โ 2x speedup milti hai", badges:["cpp"], installed:false },
{ name:"cuTENSOR", cat:"math", en:"GPU tensor linear algebra library", ur:"Deep learning ka tensor math โ contractions, reductions, elementwise ops GPU pe", badges:["cpp"], installed:false },
{ name:"cuTENSOR โ Contraction", cat:"math", en:"General tensor contraction (Einstein summation)", ur:"Multi-dimensional arrays ko multiply karna โ quantum chemistry aur physics mein use", badges:["cpp"], installed:false },
{ name:"cuTENSOR โ Reduction", cat:"math", en:"Tensor reduction operations", ur:"Bade tensor ko chhotay mein convert karna โ AI training mein gradient calculation", badges:["cpp"], installed:false },
{ name:"cuTENSOR โ Elementwise", cat:"math", en:"Per-element tensor operations", ur:"Har element pe alag operation โ add, multiply, activate functions etc.", badges:["cpp"], installed:false },
{ name:"cuTENSORMg", cat:"math", en:"Multi-GPU tensor operations", ur:"Bada tensor kai GPUs pe distribute karna", badges:["cpp"], installed:false },
{ name:"cuDSS", cat:"math", en:"Direct sparse solver library", ur:"Sparse matrix equations seedha aur accurately solve karna โ engineering FEM simulations ke liye", badges:["cpp"], installed:false },
{ name:"CUDA Math API", cat:"math", en:"Standard math functions on GPU", ur:"sin, cos, exp, log, sqrt โ ye sab CPU ke barabar GPU pe bhi chalte hain", badges:["cpp","installed"], installed:true },
{ name:"AmgX", cat:"math", en:"Algebraic multigrid solver", ur:"Fluid dynamics aur structural simulations ke liye linear equations super-fast solve karna", badges:["cpp","free"], installed:false },
{ name:"nvmath-python", cat:"math", en:"Python interface for NVIDIA math libraries", ur:"Python se seedha cuBLAS, cuFFT etc. chalao โ C++ nahi likhni", badges:["python","free"], installed:false },
{ name:"nvmath-python โ linalg", cat:"math", en:"Linear algebra via Python", ur:"Python mein matrix operations โ NumPy jaisi feeling, GPU ki speed", badges:["python"], installed:false },
{ name:"nvmath-python โ fft", cat:"math", en:"FFT operations via Python", ur:"Python se GPU FFT chalana โ data scientists ke liye", badges:["python"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 2. SCIENTIFIC COMPUTING
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"cuEquivariance", cat:"sci", en:"3D geometry-aware neural networks", ur:"Proteins aur molecules ke 3D structure ke liye AI models โ drug discovery mein use", badges:["python","free"], installed:false },
{ name:"cuEquivariance โ SO3 Group", cat:"sci", en:"3D rotation equivariant operations", ur:"3D mein ghumane se result na badle aisa AI model banana โ molecular biology ke liye", badges:["python"], installed:false },
{ name:"NVIDIA ALCHEMI", cat:"sci", en:"Chemical and materials discovery AI", ur:"Battery, catalyst aur medicine ke nayi materials dhundne ke liye AI toolkit", badges:["cloud"], installed:false },
{ name:"ALCHEMI โ BioNeMo", cat:"sci", en:"Protein structure prediction", ur:"Protein ki 3D shape predict karna โ nayi dawaayen banane mein kaam aata hai", badges:["cloud"], installed:false },
{ name:"cuLitho", cat:"sci", en:"Computational lithography for chip manufacturing", ur:"Computer chips banane ki process simulate karna โ Intel/TSMC jaisi companies use karti hain", badges:["paid"], installed:false },
{ name:"cuEST", cat:"sci", en:"Electronic structure calculations for quantum chemistry", ur:"Atoms aur molecules ke electrons ka behaviour calculate karna โ chemistry research", badges:["cpp"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 3. PHYSICS LIBRARIES
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"NVIDIA Warp", cat:"physics", en:"Python framework for GPU physics simulation", ur:"Physics, robotics aur AI simulations Python mein likhna โ GPU automatically use hoga", badges:["python","free"], installed:false },
{ name:"Warp โ Kernels", cat:"physics", en:"GPU kernel functions in Python", ur:"Python mein GPU code likhna jaise CUDA C++ mein โ bohot easy", badges:["python"], installed:false },
{ name:"Warp โ Differentiable Simulation", cat:"physics", en:"Gradients through physics simulations", ur:"Physics simulation se seedha AI train karo โ robotics ke liye game changer", badges:["python"], installed:false },
{ name:"Warp โ Mesh Operations", cat:"physics", en:"3D mesh-based physics", ur:"3D objects ke saath collision aur physics simulate karna", badges:["python"], installed:false },
{ name:"PhysicsNeMo", cat:"physics", en:"AI physics model training framework", ur:"Physics ke AI models train karna โ climate, fluid, structural engineering ke liye", badges:["python","free"], installed:false },
{ name:"PhysicsNeMo โ PDE Solver", cat:"physics", en:"Physics-informed neural networks", ur:"AI se partial differential equations solve karna โ engineering ka bada kaam", badges:["python"], installed:false },
{ name:"NVIDIA Earth-2", cat:"physics", en:"Weather and climate AI simulation", ur:"Duniya ke mausam aur climate ka AI se simulation โ future weather predict karna", badges:["cloud","free"], installed:false },
{ name:"Earth-2 โ CorrDiff", cat:"physics", en:"Diffusion model for weather downscaling", ur:"Global weather model se local level prediction nikalna โ shehar ka mausam batana", badges:["python"], installed:false },
{ name:"Earth-2 โ FourCastNet", cat:"physics", en:"Fourier-based weather forecasting", ur:"Fourier transform use karke agle din ka mausam predict karna โ 1000x faster", badges:["python"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 4. QUANTUM COMPUTING LIBRARIES
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"cuQuantum", cat:"quantum", en:"GPU-accelerated quantum computing simulation", ur:"Quantum computers ko normal GPU pe simulate karna โ researchers ke liye", badges:["python","free"], installed:false },
{ name:"cuQuantum โ cuStateVec", cat:"quantum", en:"Quantum state vector simulation", ur:"Quantum bits (qubits) ki state simulate karna โ quantum algorithms test karo", badges:["python"], installed:false },
{ name:"cuQuantum โ cuTensorNet", cat:"quantum", en:"Tensor network simulation", ur:"Quantum circuits ko tensor networks ke zariye simulate karna โ bade quantum systems", badges:["python"], installed:false },
{ name:"cuQuantum โ cuDensityMat", cat:"quantum", en:"Density matrix simulation", ur:"Noisy quantum systems simulate karna โ real quantum computer jaisi", badges:["python"], installed:false },
{ name:"cuPQC", cat:"quantum", en:"Post-quantum cryptography SDK", ur:"Future quantum computers se encryption bachane ke algorithms โ next-gen security", badges:["cpp"], installed:false },
{ name:"cuPQC โ CRYSTALS-Kyber", cat:"quantum", en:"Lattice-based key encapsulation", ur:"Quantum-safe key exchange โ secure communication ke liye", badges:["cpp"], installed:false },
{ name:"cuPQC โ CRYSTALS-Dilithium", cat:"quantum", en:"Quantum-safe digital signatures", ur:"Quantum computers se na tutne wale digital signature โ files authenticate karna", badges:["cpp"], installed:false },
{ name:"CUDA-Q QEC", cat:"quantum", en:"Quantum error correction simulation", ur:"Quantum computers ki galatiyan theek karne ke algorithms simulate karna", badges:["python"], installed:false },
{ name:"CUDA-Q Solvers", cat:"quantum", en:"Hybrid quantum-classical optimization", ur:"Quantum aur classical computing milake optimization problems solve karna", badges:["python"], installed:false },
{ name:"CUDA-Q โ VQE Solver", cat:"quantum", en:"Variational quantum eigensolver", ur:"Molecules ki energy calculate karna quantum se โ drug design ke liye", badges:["python"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 5. DEEP LEARNING CORE
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"cuDNN", cat:"dl", en:"GPU library for deep neural networks", ur:"Neural networks ke building blocks โ convolution, RNN, attention sab yahan hain", badges:["installed","cpp"], installed:true },
{ name:"cuDNN โ Convolution", cat:"dl", en:"Forward/backward convolution for CNNs", ur:"Image recognition ke liye convolution layer โ GPU pe bohot tez", badges:["cpp"], installed:true },
{ name:"cuDNN โ RNN/LSTM", cat:"dl", en:"Recurrent and LSTM operations", ur:"Text aur time-series ke liye โ language models ka buniyad", badges:["cpp"], installed:true },
{ name:"cuDNN โ Attention (MHA)", cat:"dl", en:"Multi-head attention for transformers", ur:"ChatGPT jaisi models ka core mechanism โ bohot important", badges:["cpp"], installed:true },
{ name:"cuDNN โ Normalization", cat:"dl", en:"Batch/layer/group normalization", ur:"Neural network training stable rakhna โ normalize karna", badges:["cpp"], installed:true },
{ name:"cuDNN โ Pooling", cat:"dl", en:"Max/avg pooling operations", ur:"Feature maps ko chhota karna โ CNN mein use hota hai", badges:["cpp"], installed:true },
{ name:"cuDNN โ Activation", cat:"dl", en:"ReLU, Sigmoid, Tanh, GELU etc.", ur:"Neural network ke activation functions GPU pe chalana", badges:["cpp"], installed:true },
{ name:"cuDNN โ Dropout", cat:"dl", en:"Stochastic neuron dropping for regularization", ur:"Training mein randomly neurons band karna โ overfitting rokne ke liye", badges:["cpp"], installed:true },
{ name:"cuDNN โ Softmax", cat:"dl", en:"Probability distribution output", ur:"Classification model ka output probabilities mein convert karna", badges:["cpp"], installed:true },
{ name:"TensorRT", cat:"dl", en:"High-performance inference optimizer", ur:"Trained AI model ko deploy karte waqt 10x fast chalana โ production ke liye", badges:["installed","cpp","python"], installed:true },
{ name:"TensorRT โ Builder", cat:"dl", en:"Builds optimized TensorRT engine", ur:"PyTorch/ONNX model le kar optimize TensorRT engine banao", badges:["cpp","python"], installed:true },
{ name:"TensorRT โ Engine", cat:"dl", en:"Optimized inference runtime", ur:"Optimize hone ke baad model chalana โ bohot tez inference", badges:["cpp"], installed:true },
{ name:"TensorRT โ ONNX Parser", cat:"dl", en:"Parse ONNX models for TRT", ur:"ONNX format model ko TensorRT mein import karna", badges:["cpp","python"], installed:true },
{ name:"TensorRT โ INT8 Quantization", cat:"dl", en:"8-bit precision for faster inference", ur:"Model ki accuracy thodi kam karo, speed 4x zyada โ mobile/edge ke liye", badges:["python"], installed:true },
{ name:"TensorRT โ FP16 Mode", cat:"dl", en:"Half-precision floating point inference", ur:"16-bit precision โ full accuracy ke saath 2x faster", badges:["cpp","python"], installed:true },
{ name:"TensorRT-LLM", cat:"dl", en:"Optimized LLM inference library", ur:"ChatGPT jaisi badi language models deploy karna โ Llama, Mistral, GPT sab chalao", badges:["python","free"], installed:false },
{ name:"TensorRT-LLM โ In-flight Batching", cat:"dl", en:"Dynamic request batching for LLMs", ur:"Alag alag users ke requests ek saath efficiently chalana โ server pe deploy karte waqt", badges:["python"], installed:false },
{ name:"TensorRT-LLM โ KV Cache", cat:"dl", en:"Key-value cache management", ur:"LLM mein pehle compute ho chuka kaam yaad rakhna โ inference fast hoti hai", badges:["python"], installed:false },
{ name:"CUTLASS", cat:"dl", en:"C++ templates for high-performance GPU kernels", ur:"Custom GPU kernels likhne ke liye modular C++ library โ khud ka kernel banana ho to", badges:["cpp","free"], installed:false },
{ name:"CUTLASS โ GEMM Kernels", cat:"dl", en:"Matrix multiplication with Tensor Core support", ur:"Custom matrix multiply kernel Tensor Cores pe โ maximum speed ke liye", badges:["cpp"], installed:false },
{ name:"CUTLASS โ Epilogue", cat:"dl", en:"Post-GEMM fused operations", ur:"Matrix multiply ke baad activation, bias add sab ek hi step mein", badges:["cpp"], installed:false },
{ name:"CUTLASS โ Convolution", cat:"dl", en:"Forward/backward conv kernels", ur:"Custom convolution GPU kernels โ CNN ke liye", badges:["cpp"], installed:false },
{ name:"FlashInfer", cat:"dl", en:"GPU kernels for LLM inference optimization", ur:"Large language models ki attention aur MoE operations super fast karna", badges:["python","free"], installed:false },
{ name:"FlashInfer โ FlashAttention", cat:"dl", en:"Memory-efficient attention mechanism", ur:"Attention computation GPU memory efficient tarike se karna โ Llama/GPT ke liye", badges:["python"], installed:false },
{ name:"FlashInfer โ MoE Kernels", cat:"dl", en:"Mixture of Experts GPU kernels", ur:"Mixtral jaisi MoE models ke expert routing fast karna", badges:["python"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 6. PARALLEL ALGORITHM LIBRARIES
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"Thrust", cat:"parallel", en:"C++ STL-like GPU parallel algorithms", ur:"C++ STL jaisi syntax se GPU parallel algorithms likhna โ sort, scan, transform etc.", badges:["cpp","installed"], installed:true },
{ name:"Thrust โ device_vector", cat:"parallel", en:"GPU-resident vector container", ur:"GPU pe C++ vector โ data GPU memory mein rakho seedha", badges:["cpp"], installed:true },
{ name:"Thrust โ transform", cat:"parallel", en:"Parallel per-element transformation", ur:"Ek saath sab elements pe function lagao โ GPU pe parallel", badges:["cpp"], installed:true },
{ name:"Thrust โ sort / sort_by_key", cat:"parallel", en:"Parallel GPU sorting", ur:"Bade array ko GPU pe milliseconds mein sort karna", badges:["cpp"], installed:true },
{ name:"Thrust โ reduce / transform_reduce", cat:"parallel", en:"Parallel reduction operations", ur:"Sab elements ka sum/max/min ek saath nikalna โ GPU pe", badges:["cpp"], installed:true },
{ name:"Thrust โ scan (prefix sum)", cat:"parallel", en:"Inclusive and exclusive prefix scan", ur:"Running total nikalna โ GPU parallel processing ka basic building block", badges:["cpp"], installed:true },
{ name:"Thrust โ partition", cat:"parallel", en:"Conditional data partitioning", ur:"Kisi condition ke hisaab se data do groups mein baantna", badges:["cpp"], installed:true },
{ name:"Thrust โ copy_if / remove_if", cat:"parallel", en:"Conditional copy and removal", ur:"Sirf woh elements copy karo jo condition poori karein", badges:["cpp"], installed:true },
{ name:"CUB", cat:"parallel", en:"Low-level cooperative GPU primitives", ur:"Block aur warp level GPU operations โ Thrust se bhi neeche ka level, zyada control", badges:["cpp","installed"], installed:true },
{ name:"CUB โ DeviceReduce", cat:"parallel", en:"Device-wide reduction", ur:"Puri GPU memory pe reduction operation โ sum, max, min", badges:["cpp"], installed:true },
{ name:"CUB โ DeviceScan", cat:"parallel", en:"Device-wide prefix scan", ur:"Puri GPU memory pe prefix sum", badges:["cpp"], installed:true },
{ name:"CUB โ DeviceSort", cat:"parallel", en:"Radix sort for entire device", ur:"Puri GPU pe radix sort โ sabse fast GPU sort algorithm", badges:["cpp"], installed:true },
{ name:"CUB โ BlockReduce", cat:"parallel", en:"Thread-block level reduction", ur:"Ek block ke andar sab threads milke reduce karte hain", badges:["cpp"], installed:true },
{ name:"CUB โ WarpReduce", cat:"parallel", en:"Warp-level reduction primitives", ur:"32 threads (ek warp) milke reduce karte hain โ sabse low level", badges:["cpp"], installed:true },
{ name:"CUB โ DeviceHistogram", cat:"parallel", en:"Parallel histogram computation", ur:"Image ya data ka histogram GPU pe banana โ photography/CV mein use", badges:["cpp"], installed:true },
{ name:"cuda.parallel", cat:"parallel", en:"Python parallel primitives (CCCL)", ur:"Python se GPU sort, scan, reduce chalao โ NumPy se zyada tez", badges:["python","free"], installed:false },
{ name:"cuda.compute", cat:"parallel", en:"Python device-level algorithms", ur:"Python se low-level GPU algorithms โ CUDA C++ jaisi power Python mein", badges:["python","free"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 7. DATA PROCESSING LIBRARIES
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"cuDF", cat:"data", en:"GPU-accelerated DataFrame library (Pandas replacement)", ur:"Pandas wala kaam GPU pe โ same code, 100x zyada speed! Data science ka superhero", badges:["python","free"], installed:false },
{ name:"cuDF โ Series", cat:"data", en:"GPU-resident 1D data column", ur:"Ek column ka data GPU pe โ Pandas Series ki tarah", badges:["python"], installed:false },
{ name:"cuDF โ DataFrame", cat:"data", en:"GPU table with multiple columns", ur:"Puri table GPU pe โ Excel jaisi, lekin bohot tez", badges:["python"], installed:false },
{ name:"cuDF โ GroupBy", cat:"data", en:"Split-apply-combine on GPU", ur:"Data ko groups mein baanto, har group pe kaam karo โ SQL GROUP BY jaisi", badges:["python"], installed:false },
{ name:"cuDF โ merge / join", cat:"data", en:"GPU-accelerated table joins", ur:"Do tables join karna โ SQL JOIN GPU pe, microseconds mein", badges:["python"], installed:false },
{ name:"cuDF โ Rolling Window", cat:"data", en:"Moving average and rolling stats", ur:"Time series ka moving average nikalna โ finance/sensor data ke liye", badges:["python"], installed:false },
{ name:"cuDF โ IO (CSV/Parquet/JSON)", cat:"data", en:"Fast file reading into GPU", ur:"Files seedhi GPU mein load karna โ CPU mein load karne se 10x fast", badges:["python"], installed:false },
{ name:"cuDF โ Polars Backend", cat:"data", en:"Use Polars syntax with GPU acceleration", ur:"Polars likhte ho? GPU pe chalega automatically โ zero code change", badges:["python"], installed:false },
{ name:"cuML", cat:"data", en:"GPU scikit-learn replacement", ur:"Scikit-learn wala ML GPU pe โ same API, GPU speed!", badges:["python","free"], installed:false },
{ name:"cuML โ LinearRegression", cat:"data", en:"GPU linear regression", ur:"Seedhi line se prediction โ house price predict karna etc.", badges:["python"], installed:false },
{ name:"cuML โ RandomForest", cat:"data", en:"GPU random forest classifier/regressor", ur:"Kai decision trees milake prediction โ bohot accurate ML model", badges:["python"], installed:false },
{ name:"cuML โ KMeans", cat:"data", en:"GPU K-means clustering", ur:"Data ko groups mein baantna โ customers segment karna etc.", badges:["python"], installed:false },
{ name:"cuML โ DBSCAN", cat:"data", en:"Density-based clustering on GPU", ur:"Arbitrary shape ke clusters dhundna โ anomaly detection ke liye", badges:["python"], installed:false },
{ name:"cuML โ UMAP", cat:"data", en:"Dimensionality reduction visualization", ur:"Bade data ko 2D/3D mein visualize karna โ data explore karne ke liye", badges:["python"], installed:false },
{ name:"cuML โ HDBSCAN", cat:"data", en:"Hierarchical DBSCAN clustering", ur:"DBSCAN ka behtar version โ automatic cluster count determine karta hai", badges:["python"], installed:false },
{ name:"cuML โ SVM", cat:"data", en:"Support Vector Machine on GPU", ur:"Classification ke liye classic algorithm โ GPU pe bohot fast", badges:["python"], installed:false },
{ name:"cuML โ PCA", cat:"data", en:"Principal Component Analysis on GPU", ur:"Data ke main features nikalna โ dimensionality reduce karna", badges:["python"], installed:false },
{ name:"cuML โ NearestNeighbors", cat:"data", en:"GPU k-nearest neighbors", ur:"Query point ke sabse nazdik k points dhundna โ recommendation systems ke liye", badges:["python"], installed:false },
{ name:"cuVS", cat:"data", en:"GPU vector search library", ur:"Semantic search aur vector databases ke liye โ AI chatbot ke peeche yahi hota hai", badges:["python","free"], installed:false },
{ name:"cuVS โ CAGRA", cat:"data", en:"GPU-native graph-based nearest neighbor", ur:"World's fastest vector search algorithm โ GPU native hai isliye super tez", badges:["python"], installed:false },
{ name:"cuVS โ IVF-Flat", cat:"data", en:"Inverted file index (exact)", ur:"Vectors ko groups mein baanto aur exact search karo", badges:["python"], installed:false },
{ name:"cuVS โ IVF-PQ", cat:"data", en:"Inverted file + product quantization", ur:"Vectors compress karke bade datasets pe fast search โ RAM bachao", badges:["python"], installed:false },
{ name:"cuVS โ Brute Force", cat:"data", en:"Exact exhaustive search", ur:"Har vector check karo โ small datasets pe sab se accurate", badges:["python"], installed:false },
{ name:"cuGraph", cat:"data", en:"GPU graph analytics (NetworkX replacement)", ur:"Facebook, Twitter jaisi social networks analyze karna โ GPU pe NetworkX", badges:["python","free"], installed:false },
{ name:"cuGraph โ PageRank", cat:"data", en:"Google's page ranking algorithm on GPU", ur:"Web pages rank karna โ Google ka wahi algorithm GPU pe", badges:["python"], installed:false },
{ name:"cuGraph โ BFS / SSSP", cat:"data", en:"Breadth-first and shortest path search", ur:"Graph mein shortest path dhundna โ maps aur networks ke liye", badges:["python"], installed:false },
{ name:"cuGraph โ Louvain", cat:"data", en:"Community detection algorithm", ur:"Social network mein friend groups dhundna โ community detection", badges:["python"], installed:false },
{ name:"cuGraph โ Triangle Counting", cat:"data", en:"Count triangles in graph", ur:"Graph mein triangles count karna โ social network clustering coefficient", badges:["python"], installed:false },
{ name:"cuOpt", cat:"data", en:"GPU optimization engine for routing problems", ur:"Delivery routes, logistics optimize karna โ Amazon/FedEx jaisi companies use karti hain", badges:["python","free"], installed:false },
{ name:"cuOpt โ VRP Solver", cat:"data", en:"Vehicle routing problem solver", ur:"Kai gadiyon ke routes optimize karna โ delivery companies ke liye", badges:["python"], installed:false },
{ name:"cuOpt โ TSP Solver", cat:"data", en:"Traveling salesman problem", ur:"Ek gadi ke liye sabse chhota route dhundna", badges:["python"], installed:false },
{ name:"NeMo Curator", cat:"data", en:"Data curation for LLM training", ur:"LLM train karne ke liye data clean aur prepare karna โ quality data = better AI", badges:["python","free"], installed:false },
{ name:"NeMo Curator โ Text Dedup", cat:"data", en:"Remove duplicate training data", ur:"Same text baar baar AI ko na sikhao โ duplicates hatao", badges:["python"], installed:false },
{ name:"NeMo Curator โ Quality Filter", cat:"data", en:"Score and filter low-quality text", ur:"Kharab quality ka text hatao โ AI better seekhe", badges:["python"], installed:false },
{ name:"NeMo Curator โ Synthetic Data", cat:"data", en:"Generate synthetic training data", ur:"AI se AI ka training data banana โ data ki kami poori karo", badges:["python"], installed:false },
{ name:"Morpheus", cat:"data", en:"Real-time cybersecurity AI pipeline", ur:"Real-time mein cyber attacks detect karna โ network traffic AI se analyze karna", badges:["python","free"], installed:false },
{ name:"nvComp", cat:"data", en:"GPU-accelerated compression library", ur:"Data ko GPU pe compress/decompress karna โ storage aur transfer fast karo", badges:["cpp","free"], installed:false },
{ name:"nvComp โ LZ4", cat:"data", en:"Fast LZ4 compression on GPU", ur:"Tez compression โ speed priority ho to", badges:["cpp"], installed:false },
{ name:"nvComp โ Snappy", cat:"data", en:"Google Snappy on GPU", ur:"Google ka compression algorithm GPU pe", badges:["cpp"], installed:false },
{ name:"nvComp โ GDeflate", cat:"data", en:"GPU-optimized deflate compression", ur:"ZIP jaisa compression GPU pe โ storage bachat ke liye", badges:["cpp"], installed:false },
{ name:"nvComp โ Cascaded", cat:"data", en:"High-ratio GPU compression", ur:"Maximum compression ratio โ storage aur bandwidth bachao", badges:["cpp"], installed:false },
{ name:"GPU Direct Storage (GDS)", cat:"data", en:"Direct NVMe-to-GPU data path", ur:"Hard drive se seedha GPU memory mein data aao โ CPU bypass karo, 2x fast", badges:["cpp"], installed:false },
{ name:"Dask-CUDA", cat:"data", en:"Multi-GPU Dask integration", ur:"RAPIDS ko kai GPUs aur machines pe scale karna โ big data ke liye", badges:["python","free"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 8. IMAGE & VIDEO LIBRARIES
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"nvImageCodec", cat:"img", en:"GPU image encoding/decoding framework", ur:"Images GPU pe decode/encode karna โ AI training mein data loading 10x fast", badges:["cpp","python","free"], installed:false },
{ name:"nvJPEG", cat:"img", en:"GPU-accelerated JPEG codec", ur:"JPEG images GPU pe decode karna โ ML training mein lakh images jaldi load karo", badges:["cpp"], installed:false },
{ name:"nvJPEG2000", cat:"img", en:"GPU JPEG2000 codec", ur:"Medical imaging (DICOM) aur digital cinema ke liye JPEG2000 GPU pe", badges:["cpp"], installed:false },
{ name:"nvTIFF", cat:"img", en:"GPU TIFF image decoder", ur:"TIFF format (scientific/geospatial) images GPU pe fast decode karna", badges:["cpp"], installed:false },
{ name:"nvBMP", cat:"img", en:"GPU BMP codec", ur:"BMP format images GPU pe decode karna", badges:["cpp"], installed:false },
{ name:"NVIDIA DALI", cat:"img", en:"GPU data loading and preprocessing pipeline", ur:"AI training ke liye images/video/audio fast load karo GPU pe โ CPU bottleneck hatao", badges:["python","free"], installed:false },
{ name:"DALI โ Pipeline", cat:"img", en:"Declarative data preprocessing graph", ur:"Image processing steps define karo โ DALI automatically GPU pe chalayega", badges:["python"], installed:false },
{ name:"DALI โ RandomCrop", cat:"img", en:"Random image cropping augmentation", ur:"Training mein random crop โ AI ko diverse views sikhao", badges:["python"], installed:false },
{ name:"DALI โ ColorJitter", cat:"img", en:"Random color augmentation", ur:"Images ka color randomly badlo โ AI robust ho", badges:["python"], installed:false },
{ name:"DALI โ Resize / Normalize", cat:"img", en:"GPU resize and normalization", ur:"Images resize aur normalize karna โ AI input prepare karna", badges:["python"], installed:false },
{ name:"DALI โ Video Reader", cat:"img", en:"GPU video loading and decoding", ur:"Video files GPU pe decode karna โ video AI ke liye", badges:["python"], installed:false },
{ name:"CV-CUDA", cat:"img", en:"GPU computer vision preprocessing", ur:"Camera se aata image AI se pehle process karna โ real-time vision AI pipeline", badges:["cpp","python","free"], installed:false },
{ name:"CV-CUDA โ cvtColor", cat:"img", en:"Color space conversion", ur:"RGB to BGR, RGB to HSV etc. โ GPU pe super fast", badges:["python"], installed:false },
{ name:"CV-CUDA โ Resize", cat:"img", en:"GPU image resizing", ur:"Images ka size change karna GPU pe โ ek saath lakhon images", badges:["python"], installed:false },
{ name:"CV-CUDA โ Normalize", cat:"img", en:"Per-channel normalization", ur:"Image pixels normalize karna โ AI input ke liye zaroor", badges:["python"], installed:false },
{ name:"CV-CUDA โ Warp/Rotate", cat:"img", en:"Geometric transformations", ur:"Image ghumana aur transform karna GPU pe", badges:["python"], installed:false },
{ name:"cuCIM", cat:"img", en:"Medical and scientific image processing", ur:"Medical images (MRI, CT) aur satellite images analyze karna โ healthcare AI", badges:["python","free"], installed:false },
{ name:"cuCIM โ Stain Normalization", cat:"img", en:"Histology slide color normalization", ur:"Medical microscope images ka color normalize karna โ cancer detection AI ke liye", badges:["python"], installed:false },
{ name:"NPP (Performance Primitives)", cat:"img", en:"GPU image and signal processing primitives", ur:"2D image processing ke blocks โ filter, resize, color convert sab GPU pe", badges:["cpp","installed"], installed:true },
{ name:"NPP โ Filter Operations", cat:"img", en:"Gaussian, box, median filters", ur:"Image blur, sharpen, noise remove karna GPU pe", badges:["cpp"], installed:true },
{ name:"NPP โ Color Conversion", cat:"img", en:"RGB, YUV, HSV conversions", ur:"Image color format badalna โ video processing mein zaroor", badges:["cpp"], installed:true },
{ name:"NPP โ Morphological ops", cat:"img", en:"Erode, dilate, open, close", ur:"Image mein shapes dhundna aur refine karna โ medical imaging", badges:["cpp"], installed:true },
{ name:"NPP โ Histogram", cat:"img", en:"GPU image histogram", ur:"Image ke pixels ka distribution calculate karna โ exposure adjust karna", badges:["cpp"], installed:true },
{ name:"NPP โ Threshold", cat:"img", en:"Image thresholding", ur:"Pixels ko condition ke hisaab se black/white karna โ object detect karna", badges:["cpp"], installed:true },
{ name:"NVIDIA Video Codec SDK", cat:"img", en:"Hardware video encode/decode", ur:"Video GPU hardware pe encode/decode karna โ streaming servers aur editors ke liye", badges:["cpp","free"], installed:false },
{ name:"Video Codec โ NVDEC", cat:"img", en:"Hardware video decoder", ur:"H.264/H.265/AV1 video hardware pe decode karna โ CPU zero use", badges:["cpp"], installed:false },
{ name:"Video Codec โ NVENC", cat:"img", en:"Hardware video encoder", ur:"Video record/stream GPU hardware pe encode karna โ OBS, streaming apps use karti hain", badges:["cpp"], installed:false },
{ name:"Video Codec โ NVJPEG Enc", cat:"img", en:"Hardware JPEG encoding", ur:"JPEG images bohot fast encode karna hardware pe", badges:["cpp"], installed:false },
{ name:"NVIDIA Optical Flow SDK", cat:"img", en:"Pixel motion detection between frames", ur:"Video mein ek frame se doosre mein pixels kitna hile โ video compression aur tracking", badges:["cpp","free"], installed:false },
{ name:"Optical Flow โ NvOFAPI", cat:"img", en:"Optical flow computation API", ur:"GPU hardware optical flow calculate karna โ hardware accelerated", badges:["cpp"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 9. COMMUNICATION LIBRARIES
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"NCCL", cat:"comm", en:"Multi-GPU/multi-node collective communication", ur:"Kai GPUs ek network pe ek saath kaam karein โ distributed AI training ka core", badges:["cpp","python","free"], installed:false },
{ name:"NCCL โ AllReduce", cat:"comm", en:"Sum/average across all GPUs", ur:"Sab GPUs ke gradients ek mein mila do โ distributed training mein zaroor", badges:["cpp"], installed:false },
{ name:"NCCL โ Broadcast", cat:"comm", en:"One GPU to all GPUs", ur:"Ek GPU se baaki sab ko data bhejno", badges:["cpp"], installed:false },
{ name:"NCCL โ AllGather", cat:"comm", en:"Gather data from all GPUs to all", ur:"Har GPU apna data sab ko de do", badges:["cpp"], installed:false },
{ name:"NCCL โ ReduceScatter", cat:"comm", en:"Reduce then scatter to GPUs", ur:"Sab ka data ek jagah mila kar phir distribute karo โ large model training", badges:["cpp"], installed:false },
{ name:"NCCL โ Send/Recv", cat:"comm", en:"Point-to-point GPU communication", ur:"Do GPUs ke darmiyan seedha data transfer", badges:["cpp"], installed:false },
{ name:"NVSHMEM", cat:"comm", en:"OpenSHMEM for GPU cluster communication", ur:"Kai GPUs ki memory ko ek badi shared memory ki tarah use karna", badges:["cpp"], installed:false },
{ name:"NVSHMEM โ put/get", cat:"comm", en:"One-sided remote memory access", ur:"Doosre GPU ki memory mein seedha likho/parho โ wait karne ki zaroorat nahi", badges:["cpp"], installed:false },
{ name:"NVSHMEM โ Atomic ops", cat:"comm", en:"Remote atomic operations", ur:"Doosre GPU ki memory pe atomic increment/compare โ race conditions nahi", badges:["cpp"], installed:false },
{ name:"NIXL", cat:"comm", en:"Low-latency inference transfer library", ur:"LLM serving mein KV cache ek GPU/node se doosre transfer karna โ latency kam karo", badges:["cpp","free"], installed:false },
{ name:"NIXL โ KV Cache Transfer", cat:"comm", en:"Transfer attention KV cache between nodes", ur:"LLM ke attention cache ko nodes ke darmiyan move karna โ disaggregated inference", badges:["cpp"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 10. PARTNER LIBRARIES
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"OpenCV (GPU)", cat:"partner", en:"Computer vision library with CUDA backend", ur:"Sabse popular computer vision library โ face detection, object tracking GPU pe", badges:["cpp","python","free"], installed:false },
{ name:"OpenCV โ cuda::filter2D", cat:"partner", en:"GPU convolution filter", ur:"Image par custom filter lagana GPU pe โ OpenCV ka GPU version", badges:["cpp"], installed:false },
{ name:"OpenCV โ cuda::pyrDown", cat:"partner", en:"GPU image pyramid", ur:"Image ko dhire dhire chhota karna โ object detection ke liye", badges:["cpp"], installed:false },
{ name:"OpenCV โ cuda::ORB", cat:"partner", en:"GPU feature detection", ur:"Image mein key points dhundna GPU pe โ image matching ke liye", badges:["cpp"], installed:false },
{ name:"FFmpeg (GPU)", cat:"partner", en:"Video/audio framework with NVDEC/NVENC", ur:"Video convert, stream, edit karna โ GPU hardware acceleration ke saath", badges:["cpp","free"], installed:false },
{ name:"ArrayFire", cat:"partner", en:"GPU matrix, signal, image processing", ur:"MATLAB jaisi syntax se GPU computing โ scientists ke liye easy", badges:["cpp","python","free"], installed:false },
{ name:"MAGMA", cat:"partner", en:"GPU linear algebra for heterogeneous systems", ur:"CPU+GPU milake linear algebra โ supercomputer level math", badges:["cpp","free"], installed:false },
{ name:"IMSL Fortran Library", cat:"partner", en:"Fortran numerical library with GPU support", ur:"Puraani Fortran code GPU pe chalana โ legacy scientific software ke liye", badges:["cpp","paid"], installed:false },
{ name:"Gunrock", cat:"partner", en:"GPU graph processing library", ur:"Graphs GPU pe process karna โ social networks, routing problems", badges:["cpp","free"], installed:false },
{ name:"CHOLMOD", cat:"partner", en:"Sparse Cholesky factorization on GPU", ur:"Physics aur engineering mein sparse matrices factorize karna", badges:["cpp","free"], installed:false },
{ name:"Triton Ocean SDK", cat:"partner", en:"Real-time ocean water simulation", ur:"Realistic paani ka simulation โ games aur training simulations ke liye", badges:["cpp","paid"], installed:false },
{ name:"CUVIlib", cat:"partner", en:"GPU imaging for medical and defense", ur:"Medical, industrial aur defense imaging applications ke liye GPU library", badges:["cpp","paid"], installed:false },
{ name:"CuPy", cat:"partner", en:"NumPy/SciPy compatible GPU array library", ur:"NumPy ka code GPU pe chalao โ import cupy as np karke! Zero change.", badges:["python","free"], installed:false },
{ name:"CuPy โ ndarray", cat:"partner", en:"GPU n-dimensional array", ur:"NumPy array ki tarah lekin GPU pe โ bohot tez", badges:["python"], installed:false },
{ name:"CuPy โ cupy.linalg", cat:"partner", en:"GPU linear algebra via CuPy", ur:"NumPy linalg GPU pe โ same functions, GPU speed", badges:["python"], installed:false },
{ name:"CuPy โ cupy.fft", cat:"partner", en:"GPU FFT via CuPy Python", ur:"NumPy FFT GPU pe โ Python se", badges:["python"], installed:false },
{ name:"CuPy โ RawKernel", cat:"partner", en:"Custom CUDA kernels from Python", ur:"Python se custom GPU code likhna โ CUDA C++ nahi jaante? Koi baat nahi", badges:["python"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 11. DEVELOPER TOOLS
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"Nsight Systems", cat:"tool", en:"Full-system GPU performance profiler", ur:"Poora system profile karo โ CPU, GPU, memory sab ki timing dekho", badges:["free","installed"], installed:true },
{ name:"Nsight Compute", cat:"tool", en:"GPU kernel-level profiler", ur:"Ek specific GPU kernel ke andar kya ho raha hai dekho โ optimization ke liye", badges:["free","installed"], installed:true },
{ name:"Nsight Graphics", cat:"tool", en:"GPU graphics debugger and profiler", ur:"Games aur graphics apps debug karo โ har frame ka analysis", badges:["free","installed"], installed:false },
{ name:"Nsight Eclipse Edition", cat:"tool", en:"GPU debugging in Eclipse IDE", ur:"Eclipse IDE mein CUDA code debug karna", badges:["free"], installed:false },
{ name:"cuda-gdb", cat:"tool", en:"GPU debugger (GDB extended)", ur:"CUDA code step by step debug karo โ breakpoints GPU kernels mein lagao", badges:["free","installed"], installed:true },
{ name:"NVIDIA Visual Profiler (nvvp)", cat:"tool", en:"Visual GPU profiling tool", ur:"GPU performance visually dekho โ kahan slow hai pata chale", badges:["free"], installed:false },
{ name:"nvcc", cat:"tool", en:"NVIDIA CUDA C/C++ compiler", ur:"CUDA C++ code compile karne ka compiler โ GPU code ka compiler", badges:["installed","free"], installed:true },
{ name:"nvcc โ ptxas", cat:"tool", en:"PTX to SASS assembler", ur:"Intermediate GPU code ko final GPU instructions mein convert karna", badges:["installed"], installed:true },
{ name:"Compute Sanitizer", cat:"tool", en:"GPU memory error and race detector", ur:"GPU code mein memory errors aur race conditions dhundna โ debug tool", badges:["free","installed"], installed:true },
{ name:"Compute Sanitizer โ Memcheck", cat:"tool", en:"GPU memory access checker", ur:"Out-of-bounds memory access detect karna โ crashes fix karo", badges:["free"], installed:true },
{ name:"Compute Sanitizer โ Racecheck", cat:"tool", en:"GPU race condition detector", ur:"Kai threads ek saath same memory access karein to pakro", badges:["free"], installed:true },
{ name:"Compute Sanitizer โ Initcheck", cat:"tool", en:"Uninitialized memory detector", ur:"Bina initialize kiye memory use karna detect karna", badges:["free"], installed:true },
{ name:"cuTile", cat:"tool", en:"Tile-based GPU programming model", ur:"GPU programming ka naya model โ tiles mein sochna, tiling problems ke liye", badges:["cpp","free"], installed:false },
{ name:"CUDA Python Bindings", cat:"tool", en:"Official Python bindings for CUDA driver", ur:"Python se seedha CUDA driver API call karna โ low-level GPU control", badges:["python","free"], installed:false },
{ name:"CUDA-GDB Python Integration", cat:"tool", en:"Python scripts for GPU debugging", ur:"Python se GPU debug karna โ automation of debugging", badges:["free"], installed:false },
{ name:"NVML (Management Library)", cat:"tool", en:"GPU monitoring and management API", ur:"GPU temperature, fan speed, utilization check karna โ monitoring tools banao", badges:["cpp","python","free","installed"], installed:true },
{ name:"nvidia-smi", cat:"tool", en:"GPU status and management CLI", ur:"Terminal se GPU status dekho โ temp, memory, utilization โ har developer use karta hai", badges:["free","installed"], installed:true },
{ name:"DCGM", cat:"tool", en:"Data Center GPU Manager", ur:"Server pe kai GPUs monitor karo โ health check aur profiling data center mein", badges:["free"], installed:false },
{ name:"Multi-Process Service (MPS)", cat:"tool", en:"Multiple process GPU sharing", ur:"Kai processes ek GPU share karein efficiently โ server pe deploy karte waqt", badges:["free","installed"], installed:true },
{ name:"CUDA Toolkit Installer", cat:"tool", en:"Complete toolkit package manager", ur:"CUDA install karne ka tool โ libraries, compiler, tools sab ek saath", badges:["free","installed"], installed:true },
{ name:"NGC Container Registry", cat:"tool", en:"NVIDIA GPU Cloud container hub", ur:"Tayyar AI/ML Docker containers โ seedha pull karo aur chalao", badges:["free"], installed:false },
{ name:"CUDA Compatibility Layer", cat:"tool", en:"Run newer CUDA apps on older drivers", ur:"Naya CUDA code purane driver pe chalana โ backward compatibility", badges:["free","installed"], installed:true },
{ name:"PTX (Parallel Thread Execution)", cat:"tool", en:"CUDA intermediate assembly language", ur:"CUDA ka middle layer โ hardware se independent instruction set", badges:["cpp"], installed:false },
{ name:"SASS (Shader Assembly)", cat:"tool", en:"GPU machine-level assembly", ur:"GPU ka actual machine code โ sabse low level, hardware specific", badges:["cpp"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 12. NEMO FRAMEWORK & MICROSERVICES
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"NeMo Framework", cat:"nemo", en:"End-to-end LLM/multimodal AI training framework", ur:"Bade AI models (LLM, speech, vision) train karne ka poora framework โ ek hi jagah sab kuch", badges:["python","free"], installed:false },
{ name:"NeMo โ Megatron-Core", cat:"nemo", en:"Large-scale model parallelism training", ur:"Aisa bada AI model train karo jo ek GPU mein na aaye โ kai GPUs pe parallel training", badges:["python"], installed:false },
{ name:"NeMo โ SFT (Supervised Fine-Tuning)", cat:"nemo", en:"Fine-tune LLMs on custom data", ur:"Kisi bhi LLM ko apne kaam ke data se fine-tune karo โ domain-specific AI banana", badges:["python"], installed:false },
{ name:"NeMo โ LoRA Fine-Tuning", cat:"nemo", en:"Parameter-efficient fine-tuning", ur:"Sirf thodi parameters fine-tune karo โ kam resources mein bhi kaam kare", badges:["python"], installed:false },
{ name:"NeMo โ RLHF", cat:"nemo", en:"Reinforcement learning from human feedback", ur:"Human feedback se AI ko behtar banana โ ChatGPT bhi isi tarike se banaya tha", badges:["python"], installed:false },
{ name:"NeMo โ DPO Training", cat:"nemo", en:"Direct preference optimization", ur:"AI ko pasandida jawab dene ki training โ RLHF se asaan alternative", badges:["python"], installed:false },
{ name:"NeMo โ Multimodal (Image+Text)", cat:"nemo", en:"Train vision-language models", ur:"Image aur text dono samajhne wala AI model train karna โ GPT-4V jaisa", badges:["python"], installed:false },
{ name:"NeMo โ Speech ASR", cat:"nemo", en:"Automatic speech recognition training", ur:"Awaaz ko text mein convert karne wala AI train karna โ Whisper jaisa", badges:["python"], installed:false },
{ name:"NeMo โ Text-to-Speech (TTS)", cat:"nemo", en:"Speech synthesis model training", ur:"Text se awaaz banane wala AI train karna โ voice cloning bhi", badges:["python"], installed:false },
{ name:"NeMo โ NLP Models", cat:"nemo", en:"Named entity, classification, QA models", ur:"Text se naam/jagah nikalna, classify karna, sawaalon ke jawab dena", badges:["python"], installed:false },
{ name:"NeMo Guardrails", cat:"nemo", en:"Safety and compliance for LLMs", ur:"AI ko galat baatein karne se rokna โ safe aur compliant rakho", badges:["python","free"], installed:false },
{ name:"NeMo Guardrails โ Input Rails", cat:"nemo", en:"Filter harmful user inputs", ur:"User ka kharab sawal AI tak pohonchne se rokna", badges:["python"], installed:false },
{ name:"NeMo Guardrails โ Output Rails", cat:"nemo", en:"Filter harmful AI outputs", ur:"AI ka jawab bahar jaane se pehle check karna โ harmful content rokna", badges:["python"], installed:false },
{ name:"NeMo Guardrails โ Topical Rails", cat:"nemo", en:"Keep AI on topic", ur:"AI ko sirf apne kaam ki baatein karne par majboor karna โ off-topic jawab rokna", badges:["python"], installed:false },
{ name:"NeMo Evaluator", cat:"nemo", en:"Model evaluation and benchmarking", ur:"AI model kitna acha hai check karna โ benchmarks pe performance measure karna", badges:["python","free"], installed:false },
{ name:"NeMo Evaluator โ LLM-as-Judge", cat:"nemo", en:"Use AI to evaluate AI outputs", ur:"Ek AI se doosre AI ka jawab check karwana โ automated quality assessment", badges:["python"], installed:false },
{ name:"NeMo Evaluator โ Custom Datasets", cat:"nemo", en:"Evaluate on your own test data", ur:"Apna test data banao aur AI usse evaluate karo", badges:["python"], installed:false },
{ name:"NeMo Retriever", cat:"nemo", en:"RAG pipeline for enterprise search", ur:"Company ke documents se AI ko search karne dena โ ChatGPT + apna data", badges:["python","free"], installed:false },
{ name:"NeMo Retriever โ Embedding NIM", cat:"nemo", en:"Convert text to vector embeddings", ur:"Text ko numbers (vectors) mein convert karna โ semantic search ke liye zaroor", badges:["python"], installed:false },
{ name:"NeMo Retriever โ Reranking NIM", cat:"nemo", en:"Rerank search results by relevance", ur:"Search results ko relevance ke hisaab se dobara sort karna โ best result upar aaye", badges:["python"], installed:false },
{ name:"NeMo Customizer", cat:"nemo", en:"Enterprise fine-tuning microservice", ur:"Enterprise level mein AI model fine-tune karna โ Kubernetes pe scale hota hai", badges:["python","cloud"], installed:false },
{ name:"NeMo Curator โ Text Pipeline", cat:"nemo", en:"Download, deduplicate and filter web text", ur:"Internet ka text download karo, duplicates hatao, quality filter lagao โ LLM data prep", badges:["python"], installed:false },
{ name:"NeMo Curator โ Image Pipeline", cat:"nemo", en:"Curate image-text paired data", ur:"Image aur text pairs ka data prepare karna โ vision AI training ke liye", badges:["python"], installed:false },
{ name:"NeMo Curator โ Video Pipeline", cat:"nemo", en:"Curate video training data", ur:"Video data prepare karna โ video understanding AI ke liye", badges:["python"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 13. NIM MICROSERVICES
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"NVIDIA NIM", cat:"nim", en:"Prebuilt optimized AI inference microservices", ur:"Koi bhi AI model 5 minute mein deploy karo โ sab kuch ready packed Docker container mein", badges:["cloud","python"], installed:false },
{ name:"NIM โ LLM Inference", cat:"nim", en:"Deploy Llama, Mistral, GPT etc.", ur:"Open source LLMs ek click mein deploy karo โ Llama 3, Mistral, Gemma sab", badges:["cloud"], installed:false },
{ name:"NIM โ Embedding Models", cat:"nim", en:"Deploy text embedding APIs", ur:"Text ko vectors mein convert karne ka API โ RAG ke liye zaroor", badges:["cloud"], installed:false },
{ name:"NIM โ Reranking Models", cat:"nim", en:"Deploy reranker APIs", ur:"Search results rerank karne ka API โ search quality improve karo", badges:["cloud"], installed:false },
{ name:"NIM โ Vision Models", cat:"nim", en:"Deploy image understanding models", ur:"Images samajhne wale models deploy karo โ captioning, VQA etc.", badges:["cloud"], installed:false },
{ name:"NIM โ Speech Models (ASR)", cat:"nim", en:"Deploy Parakeet/Canary ASR", ur:"Speech to text models deploy karo โ real-time transcription ke liye", badges:["cloud"], installed:false },
{ name:"NIM โ Code Generation", cat:"nim", en:"Deploy CodeLlama, StarCoder etc.", ur:"Code likhne wale AI models deploy karo โ developer tools banao", badges:["cloud"], installed:false },
{ name:"NIM โ Chemistry Models", cat:"nim", en:"Deploy MolMIM, ESMFold for molecules", ur:"Molecules aur proteins ke liye AI models deploy karna โ drug discovery", badges:["cloud"], installed:false },
{ name:"NIM โ Digital Biology", cat:"nim", en:"AlphaFold2, protein structure prediction", ur:"Protein ki 3D shape predict karne ka API โ medical research ke liye", badges:["cloud"], installed:false },
{ name:"NIM โ Grounding DINO", cat:"nim", en:"Open-vocabulary object detection API", ur:"Koi bhi cheez image mein dhundho โ text se describe karo aur AI dhund le", badges:["cloud"], installed:false },
{ name:"NIM โ SAM 2", cat:"nim", en:"Segment Anything Model 2 API", ur:"Image mein kisi bhi object ka mask/outline dhundna โ Meta ka model", badges:["cloud"], installed:false },
{ name:"NIM โ Cosmos (World Models)", cat:"nim", en:"Physics-based world simulation models", ur:"Real duniya jaisa virtual environment banao โ robotics training ke liye", badges:["cloud"], installed:false },
{ name:"NIM Agent Toolkit", cat:"nim", en:"Build AI agents using NIM microservices", ur:"NIM use karke AI agents banana โ sochne aur kaam karne wale AI", badges:["python","free"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 14. TRITON INFERENCE SERVER
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"Triton Inference Server", cat:"triton", en:"Production AI model serving platform", ur:"Kai AI models ek saath serve karo โ production servers ke liye standard tool", badges:["python","free"], installed:false },
{ name:"Triton โ TensorRT Backend", cat:"triton", en:"Serve TensorRT optimized models", ur:"TensorRT se optimize model Triton pe deploy karna โ maximum speed", badges:["cpp"], installed:false },
{ name:"Triton โ PyTorch Backend", cat:"triton", en:"Serve TorchScript models", ur:"PyTorch models seedha Triton pe serve karna โ no conversion needed", badges:["python"], installed:false },
{ name:"Triton โ TensorFlow Backend", cat:"triton", en:"Serve TF SavedModel", ur:"TensorFlow models Triton se serve karna", badges:["python"], installed:false },
{ name:"Triton โ ONNX Runtime Backend", cat:"triton", en:"Serve ONNX models", ur:"ONNX format ke models serve karna โ framework agnostic", badges:["python"], installed:false },
{ name:"Triton โ Python Backend", cat:"triton", en:"Custom Python preprocessing/postprocessing", ur:"Python code Triton pipeline mein lagana โ custom logic add karo", badges:["python"], installed:false },
{ name:"Triton โ vLLM Backend", cat:"triton", en:"High-throughput LLM serving", ur:"vLLM use karke LLMs bohot users ke liye serve karna", badges:["python"], installed:false },
{ name:"Triton โ Dynamic Batching", cat:"triton", en:"Auto-batch incoming requests", ur:"Alag waqt aane wali requests ek batch mein mila do โ GPU efficient use", badges:["cpp"], installed:false },
{ name:"Triton โ Model Ensemble", cat:"triton", en:"Chain multiple models in pipeline", ur:"Kai models ko ek pipeline mein connect karna โ preprocessing + model + postprocessing", badges:["cpp"], installed:false },
{ name:"Triton โ Model Analyzer", cat:"triton", en:"Profile and optimize server config", ur:"Best batch size aur GPU count suggest karna automatically", badges:["python","free"], installed:false },
{ name:"Triton โ Perf Analyzer", cat:"triton", en:"Benchmark model inference performance", ur:"Model ki speed aur latency measure karna โ performance test karo", badges:["python","free"], installed:false },
{ name:"Triton โ gRPC/HTTP API", cat:"triton", en:"Standard inference APIs", ur:"gRPC ya HTTP se model ko call karna โ apps mein integrate karo", badges:["python"], installed:false },
{ name:"Triton โ BLS (Business Logic)", cat:"triton", en:"Custom logic inside inference pipeline", ur:"Triton pipeline mein business logic likhna โ Python mein", badges:["python"], installed:false },
{ name:"Triton โ Model Control API", cat:"triton", en:"Load/unload models at runtime", ur:"Chalta hua server pe naye models load karo โ downtime nahi", badges:["cpp"], installed:false },
{ name:"Triton โ Shared Memory", cat:"triton", en:"Zero-copy data transfer to GPU", ur:"CPU-GPU data copy bypass karo โ latency kam karo", badges:["cpp"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 15. DEEPSTREAM SDK
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"DeepStream SDK", cat:"deepstream", en:"Video analytics AI streaming pipeline", ur:"Security cameras aur CCTV ke video mein real-time AI โ objects detect karo live", badges:["cpp","python","free"], installed:false },
{ name:"DeepStream โ nvinfer Plugin", cat:"deepstream", en:"GPU inference plugin for GStreamer", ur:"Video stream mein AI inference lagao โ GStreamer plugin", badges:["cpp"], installed:false },
{ name:"DeepStream โ nvtracker Plugin", cat:"deepstream", en:"Multi-object tracking across frames", ur:"Video mein objects ko frame to frame track karna โ camera mein aata jata cheez follow karo", badges:["cpp"], installed:false },
{ name:"DeepStream โ nvmsgbroker", cat:"deepstream", en:"Send analytics to Kafka/MQTT/Azure", ur:"AI results ko Kafka, MQTT, cloud bhejno โ IoT integration", badges:["cpp"], installed:false },
{ name:"DeepStream โ nvstreammux", cat:"deepstream", en:"Multiplex multiple video streams", ur:"Kai cameras ke streams ek saath process karna โ 100+ cameras ek GPU pe", badges:["cpp"], installed:false },
{ name:"DeepStream โ nvdsosd Plugin", cat:"deepstream", en:"Draw bounding boxes on video", ur:"Video pe detection boxes aur labels draw karna โ visualization", badges:["cpp"], installed:false },
{ name:"DeepStream โ Python Bindings", cat:"deepstream", en:"DeepStream in Python", ur:"Python se DeepStream pipeline banana โ C++ ki zaroorat nahi", badges:["python"], installed:false },
{ name:"DeepStream โ 360 Camera Support", cat:"deepstream", en:"Fisheye and panoramic camera AI", ur:"360 degree cameras ka video AI se process karna โ security ke liye", badges:["cpp"], installed:false },
{ name:"DeepStream โ Triton Integration", cat:"deepstream", en:"Use Triton models in DeepStream pipeline", ur:"Triton pe chal rahe models DeepStream ke saath use karna", badges:["cpp"], installed:false },
{ name:"DeepStream โ Smart Record", cat:"deepstream", en:"Event-triggered video recording", ur:"AI kuch detect kare to automatically video save ho jaye โ smart recording", badges:["cpp"], installed:false },
{ name:"DeepStream โ Redis Adapter", cat:"deepstream", en:"Send metadata to Redis database", ur:"DeepStream results seedha Redis mein save karna", badges:["cpp"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 16. NVIDIA RIVA (SPEECH AI)
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"NVIDIA Riva", cat:"riva", en:"Fully accelerated speech AI SDK", ur:"Awaaz se text, text se awaaz aur speech translate karne ka poora SDK โ call centers ke liye", badges:["python","free"], installed:false },
{ name:"Riva โ ASR (Speech to Text)", cat:"riva", en:"Real-time speech recognition", ur:"Bolta hua insaan sun ke text likhna โ real-time transcription", badges:["python"], installed:false },
{ name:"Riva โ TTS (Text to Speech)", cat:"riva", en:"Neural text-to-speech synthesis", ur:"Text parh ke awaaz mein bolna โ realistic AI voice", badges:["python"], installed:false },
{ name:"Riva โ Voice Activity Detection", cat:"riva", en:"Detect when someone is speaking", ur:"Koi bol raha hai ya nahi detect karna โ call center bots ke liye", badges:["python"], installed:false },
{ name:"Riva โ Punctuation & Capitalization", cat:"riva", en:"Post-process ASR output", ur:"Speech recognition ke baad proper punctuation aur capitals lagana", badges:["python"], installed:false },
{ name:"Riva โ Speaker Diarization", cat:"riva", en:"Who spoke when in a meeting", ur:"Meeting mein kaun kab bola pata lagana โ automated minutes", badges:["python"], installed:false },
{ name:"Riva โ Neural MT (Translation)", cat:"riva", en:"Real-time speech translation", ur:"Ek zubaan mein bolo doosri mein translate ho jaye โ real-time interpreter", badges:["python"], installed:false },
{ name:"Riva โ Custom Vocabulary", cat:"riva", en:"Add domain-specific words to ASR", ur:"Medical, legal ya technical words ASR mein add karo โ accuracy improve karo", badges:["python"], installed:false },
{ name:"Riva โ gRPC API", cat:"riva", en:"Real-time streaming speech API", ur:"Live audio stream bhejo aur real-time text wapas pao", badges:["python"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 17. TAO TOOLKIT (Computer Vision Training)
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"TAO Toolkit", cat:"tao", en:"Train and fine-tune vision AI models easily", ur:"Computer vision models bina bade data ke train karna โ transfer learning se", badges:["python","free"], installed:false },
{ name:"TAO โ Object Detection", cat:"tao", en:"Train YOLO, DetectNet, SSD models", ur:"Images mein objects detect karne ke models train karna โ security cameras ke liye", badges:["python"], installed:false },
{ name:"TAO โ Image Classification", cat:"tao", en:"Train ResNet, EfficientNet classifiers", ur:"Image ko categories mein classify karne ka model train karna", badges:["python"], installed:false },
{ name:"TAO โ Instance Segmentation", cat:"tao", en:"Train Mask RCNN for pixel masks", ur:"Object ke exact pixels dhundna โ background remove jaisi cheez", badges:["python"], installed:false },
{ name:"TAO โ Pose Estimation", cat:"tao", en:"Human body keypoint detection", ur:"Insaan ke hath, paon, sir position detect karna โ gym AI, security", badges:["python"], installed:false },
{ name:"TAO โ Action Recognition", cat:"tao", en:"Recognize human actions in video", ur:"Video mein insaan kya kar raha hai pata lagana โ surveillance", badges:["python"], installed:false },
{ name:"TAO โ License Plate Recognition", cat:"tao", en:"Detect and read number plates", ur:"Car ki number plate padhna โ parking, traffic management", badges:["python"], installed:false },
{ name:"TAO โ Face Detection", cat:"tao", en:"Detect and recognize faces", ur:"Faces detect karna โ attendance, security ke liye", badges:["python"], installed:false },
{ name:"TAO โ Optical Inspection", cat:"tao", en:"Defect detection for manufacturing", ur:"Factory mein products ki ghaltiyan AI se dhundna โ quality control", badges:["python"], installed:false },
{ name:"TAO โ Data Augmentation", cat:"tao", en:"Expand training data automatically", ur:"Ek image se kai variations banana โ AI ko zyada data se train karo", badges:["python"], installed:false },
{ name:"TAO โ Pruning", cat:"tao", en:"Remove redundant model weights", ur:"Model chhota karo without accuracy lose kiye โ edge devices ke liye", badges:["python"], installed:false },
{ name:"TAO โ Export to ONNX/TRT", cat:"tao", en:"Export trained model for deployment", ur:"Trained model ko TensorRT ke liye export karna โ deployment ready", badges:["python"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 18. NVIDIA ISAAC (ROBOTICS)
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"Isaac SDK", cat:"isaac", en:"Full robotics AI development platform", ur:"Robots banane ka poora AI platform โ sensors se lekar motors tak sab", badges:["cpp","python","free"], installed:false },
{ name:"Isaac โ ROS 2 Integration", cat:"isaac", en:"Connect with Robot Operating System", ur:"Standard robotics middleware ke saath kaam karna โ ROS 2 compatible", badges:["python"], installed:false },
{ name:"Isaac โ Manipulator Arm AI", cat:"isaac", en:"Robot arm grasping and manipulation", ur:"Robot arm se cheezein pakadna aur rakhna โ factory automation", badges:["python"], installed:false },
{ name:"Isaac โ Navigation Stack", cat:"isaac", en:"Autonomous robot navigation", ur:"Robot khud chale seedha โ obstacles avoid kare, path plan kare", badges:["cpp"], installed:false },
{ name:"Isaac โ Perception (3D)", cat:"isaac", en:"3D object detection and pose estimation", ur:"3D mein objects detect karna โ robot ko pata ho kya kahan hai", badges:["python"], installed:false },
{ name:"Isaac โ Sim (Omniverse based)", cat:"isaac", en:"Photorealistic robot simulation", ur:"Real duniya jaisi simulation mein robot train karo โ physical robot se pehle", badges:["python","free"], installed:false },
{ name:"Isaac โ GROOT (Humanoid AI)", cat:"isaac", en:"Foundation model for humanoid robots", ur:"Insaan jaese robots ke liye AI model โ general purpose robotic brain", badges:["python"], installed:false },
{ name:"Isaac โ Dexterous Manipulation", cat:"isaac", en:"Fine motor skills for robot hands", ur:"Robot haath se pench kholna, keyboard type karna โ fine movements", badges:["python"], installed:false },
{ name:"Isaac โ Synthetic Data Gen", cat:"isaac", en:"Create robot training data in simulation", ur:"Real data collect kiye bina simulation se training data banana", badges:["python"], installed:false },
{ name:"Isaac โ cuRobo", cat:"isaac", en:"GPU-accelerated motion planning", ur:"Robot ka aagla move GPU pe milliseconds mein plan karna", badges:["python","free"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 19. NVIDIA OMNIVERSE
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"NVIDIA Omniverse", cat:"omniverse", en:"Real-time 3D simulation and collaboration platform", ur:"3D duniya banao aur simulate karo โ factory, building, robot sab ek jagah", badges:["python","free"], installed:false },
{ name:"Omniverse โ USD (Universal Scene)", cat:"omniverse", en:"Pixar's 3D scene description format", ur:"3D scenes ka standard format โ sab tools ek saath kaam karein", badges:["python"], installed:false },
{ name:"Omniverse โ PhysX 5", cat:"omniverse", en:"GPU-accelerated physics simulation", ur:"Real duniya jaisi physics GPU pe โ objects girna, tootna, paani behna", badges:["cpp"], installed:false },
{ name:"Omniverse โ RTX Renderer", cat:"omniverse", en:"Real-time ray tracing renderer", ur:"Photorealistic 3D rendering real-time mein โ product visualization", badges:["cpp"], installed:false },
{ name:"Omniverse โ Digital Twin", cat:"omniverse", en:"Virtual replica of physical systems", ur:"Factory ya building ka virtual copy banao โ test karo, optimize karo", badges:["python"], installed:false },
{ name:"Omniverse โ Replicator", cat:"omniverse", en:"Synthetic data generation for AI", ur:"AI training ke liye 3D simulation se data banana โ real data ki zaroorat nahi", badges:["python","free"], installed:false },
{ name:"Omniverse โ Nucleus Server", cat:"omniverse", en:"Shared 3D asset collaboration server", ur:"Team milkar ek 3D scene pe kaam kare โ real-time collaboration", badges:["free"], installed:false },
{ name:"Omniverse โ Cosmos (World Foundation)", cat:"omniverse", en:"Physics-based world generation AI", ur:"Real duniya jaisi virtual duniya AI se generate karna โ autonomous driving training", badges:["python"], installed:false },
{ name:"Omniverse โ Drive Sim", cat:"omniverse", en:"Autonomous vehicle simulation", ur:"Self-driving cars ka test virtual duniya mein karo โ accidents se pehle test", badges:["python","paid"], installed:false },
{ name:"Omniverse โ Isaac Sim", cat:"omniverse", en:"Robot simulation in photorealistic 3D", ur:"Robots ko realistic simulation mein train karo โ real world se pehle", badges:["python","free"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 20. NVIDIA METROPOLIS (VIDEO ANALYTICS)
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"NVIDIA Metropolis", cat:"metropolis", en:"Video AI platform for cities and enterprises", ur:"Smart cities, malls, factories ke cameras mein AI lagana โ sab kuch monitor karo", badges:["python","free"], installed:false },
{ name:"Metropolis โ VSS Blueprint", cat:"metropolis", en:"Visual AI agent for video surveillance", ur:"Hazaaron cameras ek saath AI se monitor karo โ security agents", badges:["python"], installed:false },
{ name:"Metropolis โ VCAT (Video Annotation)", cat:"metropolis", en:"AI-assisted video data annotation", ur:"Video data ko AI ki madad se label karna โ training data banana asaan", badges:["python"], installed:false },
{ name:"Metropolis โ Retail Analytics", cat:"metropolis", en:"People counting and queue detection", ur:"Mall mein kitne log hain, queue kitni lambi hai โ retail AI", badges:["python"], installed:false },
{ name:"Metropolis โ Traffic Analytics", cat:"metropolis", en:"Vehicle counting and speed detection", ur:"Sadak pe gadiyon ki count aur speed AI se pata lagana", badges:["python"], installed:false },
{ name:"Metropolis โ Safety AI", cat:"metropolis", en:"Hardhat and PPE detection", ur:"Factory mein workers ne helmet pehna ya nahi โ safety compliance AI", badges:["python"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 21. CUDA RUNTIME & DRIVER APIS
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"CUDA Runtime API", cat:"runtime", en:"High-level GPU programming interface", ur:"GPU se kaam karne ka main API โ memory, kernels, streams sab yahan se control", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ cudaMalloc / cudaFree", cat:"runtime", en:"GPU memory allocation/deallocation", ur:"GPU pe memory lena aur wapas karna โ sab se basic GPU operation", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ cudaMemcpy", cat:"runtime", en:"Copy data between CPU and GPU", ur:"CPU ka data GPU pe bhejna aur wapas lana โ bohot common operation", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ cudaMemcpyAsync", cat:"runtime", en:"Asynchronous memory copy", ur:"Data copy aur GPU ka kaam ek saath chalao โ speed double ho jaye", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ cudaStream", cat:"runtime", en:"Asynchronous GPU execution streams", ur:"Kai kaam GPU pe ek saath paralel chalao โ streams se control karo", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ cudaEvent", cat:"runtime", en:"GPU timing and synchronization", ur:"GPU ka kaam kab khatam hua measure karo โ profiling aur sync ke liye", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ Unified Memory (cudaMallocManaged)", cat:"runtime", en:"CPU+GPU shared memory space", ur:"Ek memory CPU aur GPU dono use karein โ code likhna asaan ho jata hai", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ cudaDeviceSynchronize", cat:"runtime", en:"Wait for GPU to finish", ur:"GPU ka kaam khatam hone ka intezaar karo โ result lene se pehle zaroor", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ Cooperative Groups", cat:"runtime", en:"Flexible thread group communication", ur:"GPU threads ke beech flexible communication โ advanced parallel algorithms", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ Graph API (cudaGraph)", cat:"runtime", en:"Record and replay GPU operations", ur:"GPU operations record karo aur baar baar replay karo โ overhead kam karo", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ Dynamic Parallelism", cat:"runtime", en:"GPU kernel launches another kernel", ur:"GPU se hi nayi GPU kernel launch karo โ recursive parallel algorithms", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ Peer-to-Peer (P2P)", cat:"runtime", en:"Direct GPU-to-GPU data transfer", ur:"Do GPUs seedha ek doosre se data exchange karein โ CPU bypass karo", badges:["cpp","installed"], installed:true },
{ name:"CUDA Driver API", cat:"runtime", en:"Low-level GPU control interface", ur:"GPU ka seedha control โ Runtime se bhi neeche, zyada power zyada complexity", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ Texture Memory", cat:"runtime", en:"Cached read-only GPU memory", ur:"Image data ke liye special cached memory โ reading fast hoti hai", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ Constant Memory", cat:"runtime", en:"Broadcast cached memory for all threads", ur:"Sab threads ko ek saath same data chahiye โ constant memory use karo", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ Shared Memory", cat:"runtime", en:"Fast on-chip thread-block memory", ur:"Ek block ke sab threads mein share hone wali super fast memory โ bottleneck hatao", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ Warp-Level Primitives", cat:"runtime", en:"Fast warp shuffle operations", ur:"32 threads (warp) ke beech data seedha share karo โ bohot fast communication", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ L2 Cache Persistence", cat:"runtime", en:"Pin data in L2 cache", ur:"Kuch data hamesha L2 cache mein rakho โ baar baar access karna fast hoga", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ Virtual Memory Management", cat:"runtime", en:"Fine-grained GPU memory control", ur:"GPU memory ko manually manage karo โ advanced use cases ke liye", badges:["cpp","installed"], installed:true },
{ name:"CUDA โ Multicast Memory", cat:"runtime", en:"One write visible to multiple GPUs", ur:"Ek jagah likho kai GPUs mein dikh jaye โ multi-GPU efficiency", badges:["cpp"], installed:false },
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
// 23. CUDA-X AI ENTERPRISE & MISC
// โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
{ name:"NVIDIA AI Enterprise", cat:"tool", en:"Full enterprise AI software suite", ur:"Companies ke liye poora AI stack โ support, security, updates ke saath", badges:["paid"], installed:false },
{ name:"CUDA-X Healthcare", cat:"sci", en:"GPU AI for medical imaging and genomics", ur:"Hospital aur research ke liye GPU AI โ cancer detect karo, DNA analyze karo", badges:["paid"], installed:false },
{ name:"Clara Parabricks", cat:"sci", en:"GPU genomics pipeline (DNA analysis)", ur:"Insaan ka DNA analyze karna โ 50x faster than CPU โ cancer research, ancestry", badges:["python","free"], installed:false },
{ name:"Clara Parabricks โ HaplotypeCaller", cat:"sci", en:"GPU genetic variant calling", ur:"DNA mein mutations dhundna โ genetic disease diagnosis", badges:["python"], installed:false },
{ name:"Clara Parabricks โ BWA-MEM2", cat:"sci", en:"GPU DNA sequence alignment", ur:"DNA sequences ko reference genome se match karna", badges:["python"], installed:false },
{ name:"Clara Parabricks โ DeepVariant", cat:"sci", en:"Deep learning variant detection", ur:"AI se DNA mutations accurately detect karna", badges:["python"], installed:false },
{ name:"cuDF โ String Operations", cat:"data", en:"GPU string processing (regex, split, join)", ur:"Text data GPU pe process karna โ regex, split, replace โ Python pandas jaisa", badges:["python"], installed:false },
{ name:"cuDF โ Time Series", cat:"data", en:"GPU datetime operations", ur:"Dates aur times ke saath kaam GPU pe โ finance aur sensor data ke liye", badges:["python"], installed:false },
{ name:"CUDA โ warp-level matrix (WMMA)", cat:"runtime", en:"Warp-level tensor core operations", ur:"32 threads milke ek saath matrix multiply karein Tensor Cores pe โ maximum speed", badges:["cpp","installed"], installed:true },
{ name:"NVTX (NVIDIA Tools Extension)", cat:"tool", en:"Custom annotations in profiler", ur:"Apne code mein labels lagao jo profiler mein dikhen โ debugging asaan karo", badges:["cpp","python","free","installed"], installed:true },
{ name:"cuBLAS โ Batched GEMM", cat:"math", en:"Multiple matrix multiplications at once", ur:"Ek saath saikdon matrix multiply karo โ batch training mein zaroor kaam aata hai", badges:["cpp"], installed:false },
{ name:"Holoscan SDK", cat:"sci", en:"Real-time medical AI streaming platform", ur:"Hospital mein real-time AI โ surgery ke dauran live analysis, ultrasound AI etc.", badges:["python","free"], installed:false },
{ name:"Holoscan โ Sensor Bridge", cat:"sci", en:"Medical device data ingestion", ur:"Medical sensors aur cameras se real-time data lana AI pipeline mein", badges:["cpp"], installed:false },
{ name:"Holoscan โ Inference Manager", cat:"sci", en:"Multi-model inference orchestration", ur:"Kai AI models ek saath real-time chalana โ medical diagnosis", badges:["python"], installed:false },
{ name:"NVIDIA FLARE", cat:"dl", en:"Federated learning framework", ur:"Data ek jagah ikhatta kiye bina kai hospitals ka AI milke train karna โ privacy safe", badges:["python","free"], installed:false },
{ name:"FLARE โ FedAvg Algorithm", cat:"dl", en:"Federated averaging for model weights", ur:"Sab hospitals ke model weights average karo โ centralized training jaisi accuracy", badges:["python"], installed:false },
{ name:"FLARE โ Differential Privacy", cat:"dl", en:"Privacy-preserving federated learning", ur:"Training mein individual data bilkul private rahe โ GDPR compliant AI", badges:["python"], installed:false },
{ name:"cuNumeric", cat:"partner", en:"Drop-in NumPy replacement on GPU cluster", ur:"NumPy code likho โ cuNumeric automatically GPU cluster pe chalaye โ zero change", badges:["python","free"], installed:false },
{ name:"nvJitLink", cat:"tool", en:"GPU JIT (just-in-time) linker library", ur:"GPU code runtime pe link karna โ dynamic kernel loading", badges:["cpp","installed"], installed:false },
{ name:"CUDA โ Profiling APIs (CUPTI)", cat:"tool", en:"CUDA Profiling Tools Interface", ur:"Apne code mein profiling data collect karo โ custom performance tools banana", badges:["cpp","free","installed"], installed:true },
{ name:"NvBench", cat:"tool", en:"GPU microbenchmarking framework", ur:"GPU code ki speed accurately measure karna โ kernels benchmark karo", badges:["cpp","free"], installed:false },
{ name:"CUDA โ Async Memops API", cat:"runtime", en:"Hardware-accelerated async memory operations", ur:"GPU memory operations hardware se asynchronously chalana โ maximum overlap", badges:["cpp"], installed:false },
{ name:"cuStreamz", cat:"data", en:"GPU streaming data processing", ur:"Real-time stream data GPU pe process karna โ Kafka + cuDF integration", badges:["python","free"], installed:false },
{ name:"PyTorch (CUDA backend)", cat:"framework", en:"Most popular deep learning framework", ur:"Sabse popular AI framework โ Python mein neural networks banao GPU pe", badges:["python","free"], installed:false },
{ name:"PyTorch โ torch.cuda", cat:"framework", en:"CUDA device management in PyTorch", ur:"PyTorch mein GPU control โ .cuda() se GPU pe bhejna", badges:["python"], installed:false },
{ name:"PyTorch โ torch.amp", cat:"framework", en:"Automatic mixed precision training", ur:"FP16/BF16 aur FP32 mix karke training fast karo โ autocast use karo", badges:["python"], installed:false },
{ name:"PyTorch โ DataParallel", cat:"framework", en:"Multi-GPU data parallel training", ur:"Ek model kai GPUs pe chalao โ training fast karo", badges:["python"], installed:false },
{ name:"PyTorch โ DistributedDataParallel", cat:"framework", en:"Multi-node distributed training", ur:"Kai machines pe AI train karo โ bade models ke liye", badges:["python"], installed:false },
{ name:"PyTorch โ torch.compile", cat:"framework", en:"JIT compilation for GPU kernels", ur:"PyTorch code automatically optimize karo โ 2-3x speed up ho sakta hai", badges:["python"], installed:false },
{ name:"TensorFlow (CUDA backend)", cat:"framework", en:"Google's deep learning framework", ur:"Google ka AI framework โ GPU pe automatically chalata hai", badges:["python","free"], installed:false },
{ name:"TensorFlow โ tf.distribute", cat:"framework", en:"Multi-GPU distribution strategy", ur:"TensorFlow training kai GPUs pe distribute karna", badges:["python"], installed:false },
{ name:"TensorFlow โ XLA", cat:"framework", en:"Accelerated linear algebra compiler", ur:"TensorFlow graph JIT compile karna โ GPU pe optimize code", badges:["python"], installed:false },
{ name:"JAX (CUDA backend)", cat:"framework", en:"NumPy on GPU with auto-differentiation", ur:"NumPy jaisa syntax, GPU speed, automatic gradients โ research ke liye perfect", badges:["python","free"], installed:false },
{ name:"JAX โ jit", cat:"framework", en:"JIT compilation for GPU kernels", ur:"Python function GPU ke liye compile karo โ 100x speed up", badges:["python"], installed:false },
{ name:"JAX โ vmap", cat:"framework", en:"Automatic vectorization", ur:"Ek function ek saath kai inputs pe chalao โ loops nahi likhne", badges:["python"], installed:false },
{ name:"JAX โ pmap", cat:"framework", en:"Parallel execution across GPUs", ur:"Ek saath kai GPUs pe automatically chalao", badges:["python"], installed:false },
{ name:"Numba (CUDA JIT)", cat:"framework", en:"JIT-compile Python for GPU", ur:"Python function GPU pe chalao โ @cuda.jit decorator lagao bas", badges:["python","free"], installed:false },
{ name:"Numba โ @cuda.jit", cat:"framework", en:"Python function as GPU kernel", ur:"Normal Python function GPU kernel ban jata hai โ bohot easy GPU programming", badges:["python"], installed:false },
{ name:"Numba โ cuda.to_device", cat:"framework", en:"Copy array from CPU to GPU", ur:"NumPy array GPU pe bhejna โ Numba se", badges:["python"], installed:false },
{ name:"Triton (OpenAI)", cat:"framework", en:"Python GPU kernel writing language", ur:"Python mein GPU kernels likhna โ CUDA C++ se asaan, PyTorch se zyada control", badges:["python","free"], installed:false },
{ name:"Triton โ @triton.jit", cat:"framework", en:"Triton kernel decorator", ur:"Python function GPU kernel declare karna Triton mein", badges:["python"], installed:false },
{ name:"RAPIDS Suite", cat:"framework", en:"End-to-end GPU data science", ur:"cuDF + cuML + cuGraph + cuVS milake poora data science pipeline GPU pe", badges:["python","free"], installed:false },
{ name:"RAPIDS โ cuDF-Pandas", cat:"framework", en:"Drop-in Pandas GPU accelerator", ur:"import cudf.pandas activate karo โ Pandas automatically GPU pe chalega!", badges:["python"], installed:false },
];
let currentFilter = 'all';
function renderAll() {
const container = document.getElementById('mainContent');
const noResults = document.getElementById('noResults');
const sections = {};
const catNames = {
math: '๐งฎ CUDA Math Libraries',
dl: '๐ค Deep Learning Core',
data: '๐ Data Processing & Analytics',
img: '๐ผ๏ธ Image & Video Libraries',
comm: '๐ก Communication Libraries',
sci: '๐ฌ Scientific Computing',
quantum: 'โ๏ธ Quantum Computing',
parallel: 'โก Parallel Algorithm Libraries',
physics: '๐ Physics Libraries',
partner: '๐ค Partner Libraries',
framework: '๐งฉ Frameworks Built on CUDA-X',
tool: '๐ง Developer Tools & Utilities',
nemo: '๐ง NeMo Framework & Microservices',
nim: 'โก NIM Inference Microservices',
triton: '๐ Triton Inference Server',
deepstream: '๐น DeepStream Video Analytics',
riva: '๐ค NVIDIA Riva Speech AI',
tao: '๐๏ธ TAO Toolkit (Vision AI Training)',
isaac: '๐ค NVIDIA Isaac (Robotics AI)',
omniverse: '๐ NVIDIA Omniverse (3D Simulation)',
metropolis: '๐๏ธ NVIDIA Metropolis (Video AI Cities)',
runtime: 'โ๏ธ CUDA Runtime & Driver APIs'
};
data.forEach((item, idx) => {
const cat = item.cat;
if (!sections[cat]) sections[cat] = [];
sections[cat].push({...item, idx});
});
container.innerHTML = '';
noResults.style.display = 'none';
const catOrder = ['math','dl','data','img','comm','sci','quantum','parallel','physics','partner','framework','tool','nemo','nim','triton','deepstream','riva','tao','isaac','omniverse','metropolis','runtime'];
let totalVisible = 0;
catOrder.forEach(cat => {
if (!sections[cat]) return;
const secDiv = document.createElement('div');
secDiv.className = 'section';
secDiv.dataset.cat = cat;
const header = document.createElement('div');
header.className = 'section-header';
header.innerHTML = `<span class="section-title">${catNames[cat]}</span><span class="section-count">${sections[cat].length} items</span>`;
secDiv.appendChild(header);
const grid = document.createElement('div');
grid.className = 'grid';
sections[cat].forEach(item => {
const card = document.createElement('div');
card.className = `card card-${item.cat}`;
card.dataset.cat = item.cat;
card.dataset.installed = item.installed ? 'true' : 'false';
card.dataset.text = (item.name + ' ' + item.en + ' ' + item.ur).toLowerCase();
const badgesHtml = (item.badges || []).map(b => {
if(b === 'installed') return `<span class="badge badge-installed">โ
Aapke paas hai</span>`;
if(b === 'free') return `<span class="badge badge-free">Free</span>`;
if(b === 'paid') return `<span class="badge badge-paid">Paid</span>`;
if(b === 'cloud') return `<span class="badge badge-cloud">Cloud API</span>`;
if(b === 'python') return `<span class="badge badge-python">Python</span>`;
if(b === 'cpp') return `<span class="badge badge-cpp">C++</span>`;
return '';
}).join('');
card.innerHTML = `
<div class="card-top">
<div class="card-name">${item.name}</div>
<span class="tag tag-${item.cat}">${item.cat.toUpperCase()}</span>
</div>
<div class="desc-en">${item.en}</div>
<div class="desc-ur">${item.ur}</div>
<div class="card-meta">${badgesHtml}</div>
`;
grid.appendChild(card);
totalVisible++;
});
secDiv.appendChild(grid);
container.appendChild(secDiv);
});
container.appendChild(noResults);
updateCount();
}
function filterCards() {
const query = document.getElementById('searchBox').value.toLowerCase();
const noResults = document.getElementById('noResults');
let visible = 0;
document.querySelectorAll('.card').forEach(card => {
const matchesFilter = currentFilter === 'all' ||
(currentFilter === 'installed' && card.dataset.installed === 'true') ||
card.dataset.cat === currentFilter;
const matchesSearch = !query || card.dataset.text.includes(query);
const show = matchesFilter && matchesSearch;
card.style.display = show ? '' : 'none';
if(show) visible++;
});
document.querySelectorAll('.section').forEach(sec => {
const visibleCards = sec.querySelectorAll('.card:not([style*="display: none"])').length;
sec.style.display = visibleCards === 0 ? 'none' : '';
});
noResults.style.display = visible === 0 ? 'block' : 'none';
document.getElementById('visibleCount').textContent = visible;
}
function setFilter(filter, btn) {
currentFilter = filter;
document.querySelectorAll('.filter-btn').forEach(b => b.classList.remove('active'));
btn.classList.add('active');
filterCards();
}
function updateCount() {
document.getElementById('visibleCount').textContent = data.length;
}
renderAll();
</script>
</body>
</html>
|