{
  "type": "tweet",
  "id": "2045170617444442602",
  "url": "https://x.com/PyTorch/status/2045170617444442602",
  "twitterUrl": "https://twitter.com/PyTorch/status/2045170617444442602",
  "text": "Meta teams pursue aggressive ROI goals, requiring substantial capacity reductions for model training and serving to meet financial targets. This is especially challenging for large-scale training jobs, which involve more data, GPUs, and advanced modeling techniques—resulting in higher initialization costs as models grow.\n\nTo improve efficiency, @Meta uses Effective Training Time (ETT%) to measure the proportion of end-to-end (E2E) wall time spent on productive training, factoring in overheads like initialization, restarts, checkpoint delays, and failures.\n\nSince 2024, teams have launched initiatives to minimize training job overhead. This blog reviews key focus areas, progress made, and next steps.\n\n🔗 Read our latest blog: https://t.co/Q91YjUNP7n\n\n#PyTorch #OpenSourceAI #ETT #Optimization",
  "source": "Twitter for iPhone",
  "retweetCount": 0,
  "replyCount": 0,
  "likeCount": 1,
  "quoteCount": 0,
  "viewCount": 232,
  "createdAt": "Fri Apr 17 16:00:47 +0000 2026",
  "lang": "en",
  "bookmarkCount": 0,
  "isReply": false,
  "inReplyToId": null,
  "conversationId": "2045170617444442602",
  "displayTextRange": [
    0,
    278
  ],
  "inReplyToUserId": null,
  "inReplyToUsername": null,
  "author": {
    "type": "user",
    "userName": "PyTorch",
    "url": "https://x.com/PyTorch",
    "twitterUrl": "https://twitter.com/PyTorch",
    "id": "776585502606721024",
    "name": "PyTorch",
    "isVerified": false,
    "isBlueVerified": true,
    "verifiedType": null,
    "profilePicture": "https://pbs.twimg.com/profile_images/1813965160702451712/yXV1vRhr_normal.jpg",
    "coverPicture": "https://pbs.twimg.com/profile_banners/776585502606721024/1761575044",
    "description": "",
    "location": "",
    "followers": 485331,
    "following": 84,
    "status": "",
    "canDm": false,
    "canMediaTag": true,
    "createdAt": "Fri Sep 16 00:56:26 +0000 2016",
    "entities": {
      "description": {
        "urls": []
      },
      "url": {}
    },
    "fastFollowersCount": 0,
    "favouritesCount": 884,
    "hasCustomTimelines": true,
    "isTranslator": false,
    "mediaCount": 1427,
    "statusesCount": 3198,
    "withheldInCountries": [],
    "affiliatesHighlightedLabel": {},
    "possiblySensitive": false,
    "pinnedTweetIds": [],
    "profile_bio": {
      "description": "Tensors and neural networks in Python with strong hardware acceleration. PyTorch is an open source project at the Linux Foundation. #PyTorchFoundation",
      "entities": {
        "description": {
          "hashtags": [
            {
              "indices": [
                132,
                150
              ],
              "text": "PyTorchFoundation"
            }
          ],
          "symbols": [],
          "urls": [],
          "user_mentions": []
        },
        "url": {
          "urls": [
            {
              "display_url": "pytorch.org",
              "expanded_url": "http://pytorch.org",
              "indices": [
                0,
                23
              ],
              "url": "https://t.co/6SwTBhUwTJ"
            }
          ]
        }
      }
    },
    "isAutomated": false,
    "automatedBy": null
  },
  "extendedEntities": {
    "media": [
      {
        "display_url": "pic.twitter.com/jGlNcMSFrU",
        "expanded_url": "https://twitter.com/PyTorch/status/2045170617444442602/photo/1",
        "ext_media_availability": {
          "status": "Available"
        },
        "features": {
          "large": {
            "faces": [
              {
                "h": 69,
                "w": 69,
                "x": 476,
                "y": 573
              }
            ]
          },
          "orig": {
            "faces": [
              {
                "h": 69,
                "w": 69,
                "x": 476,
                "y": 573
              }
            ]
          }
        },
        "id_str": "2045170596640702464",
        "indices": [
          279,
          302
        ],
        "media_key": "3_2045170596640702464",
        "media_results": {
          "id": "QXBpTWVkaWFSZXN1bHRzOgwAAQoAARxh59Jl18AACgACHGHn1z3XoeoAAA==",
          "result": {
            "__typename": "ApiMedia",
            "id": "QXBpTWVkaWE6DAABCgABHGHn0mXXwAAKAAIcYefXPdeh6gAA",
            "media_key": "3_2045170596640702464"
          }
        },
        "media_url_https": "https://pbs.twimg.com/media/HGHn0mXXwAAZsjv.jpg",
        "original_info": {
          "focus_rects": [
            {
              "h": 1075,
              "w": 1920,
              "x": 0,
              "y": 0
            },
            {
              "h": 1080,
              "w": 1080,
              "x": 0,
              "y": 0
            },
            {
              "h": 1080,
              "w": 947,
              "x": 0,
              "y": 0
            },
            {
              "h": 1080,
              "w": 540,
              "x": 66,
              "y": 0
            },
            {
              "h": 1080,
              "w": 1920,
              "x": 0,
              "y": 0
            }
          ],
          "height": 1080,
          "width": 1920
        },
        "sizes": {
          "large": {
            "h": 1080,
            "w": 1920
          }
        },
        "type": "photo",
        "url": "https://t.co/jGlNcMSFrU"
      }
    ]
  },
  "card": null,
  "place": {},
  "entities": {
    "hashtags": [
      {
        "indices": [
          758,
          766
        ],
        "text": "PyTorch"
      },
      {
        "indices": [
          767,
          780
        ],
        "text": "OpenSourceAI"
      },
      {
        "indices": [
          781,
          785
        ],
        "text": "ETT"
      },
      {
        "indices": [
          786,
          799
        ],
        "text": "Optimization"
      }
    ],
    "symbols": [],
    "urls": [
      {
        "display_url": "pytorch.org/blog/optimizin…",
        "expanded_url": "https://pytorch.org/blog/optimizing-effective-training-time-for-metas-internal-recommendation-ranking-workloads/",
        "indices": [
          733,
          756
        ],
        "url": "https://t.co/Q91YjUNP7n"
      }
    ],
    "user_mentions": [
      {
        "id_str": "2425151",
        "indices": [
          347,
          352
        ],
        "name": "Meta",
        "screen_name": "Meta"
      }
    ]
  },
  "quoted_tweet": null,
  "retweeted_tweet": null,
  "isLimitedReply": false,
  "communityInfo": null,
  "article": null
}