{
  "trx_id": "20cf5ab30487d049c29016bfe9fa5704ef49d8c5",
  "block": 106140225,
  "trx_in_block": 1,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2026-05-17T21:33:18",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "7939.897873 VESTS"
    }
  ]
}

{
  "trx_id": "07701a5011ba1aca756912680b161c5b0d63199e",
  "block": 105964116,
  "trx_in_block": 9,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2026-05-11T18:08:57",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "5227.687468 VESTS"
    }
  ]
}

{
  "trx_id": "38727b8209023fd4129593253ce6ac5933209b91",
  "block": 105507953,
  "trx_in_block": 0,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2026-04-25T20:58:27",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "7952.413629 VESTS"
    }
  ]
}

{
  "trx_id": "913c28990130216f34f10e5dbc3244c0d1f28238",
  "block": 102843241,
  "trx_in_block": 2,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2026-01-23T00:38:36",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "5269.234287 VESTS"
    }
  ]
}

{
  "trx_id": "9ee5feb45dd63039e83f23d42d306ae33af33fe3",
  "block": 91289671,
  "trx_in_block": 3,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2024-12-16T19:58:57",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "5433.453484 VESTS"
    }
  ]
}

{
  "trx_id": "b6ab96dbefa5bc42fe538d76c7b68e30e2f266ad",
  "block": 79843954,
  "trx_in_block": 1,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2023-11-13T11:45:09",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "5602.587016 VESTS"
    }
  ]
}

{
  "trx_id": "47527eae04137407ac12010cf7337ed0e6e14d4f",
  "block": 78344062,
  "trx_in_block": 6,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2023-09-21T18:40:36",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "8539.865802 VESTS"
    }
  ]
}

{
  "trx_id": "6d918e898b5ca9dbd012938bbfc9125ab3cebd41",
  "block": 69109833,
  "trx_in_block": 0,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2022-11-03T08:50:09",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "8761.547240 VESTS"
    }
  ]
}

{
  "trx_id": "8cdc93ee21aad2a1e787ade68f4a9d3b7d3042ed",
  "block": 60806300,
  "trx_in_block": 17,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2022-01-17T08:20:39",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "8982.080471 VESTS"
    }
  ]
}

{
  "trx_id": "d86c51391d4c78c9c54e12d771457dfbf1f13f20",
  "block": 54604818,
  "trx_in_block": 2,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2021-06-13T22:22:24",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "9165.849129 VESTS"
    }
  ]
}

{
  "trx_id": "947d1883385251279f1bf9c3a7b6657e3b2b3b54",
  "block": 49352380,
  "trx_in_block": 3,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2020-12-11T08:44:54",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "9353.271103 VESTS"
    }
  ]
}

{
  "trx_id": "f3e53e5e6132dd76d7945d42ee10a5674d038a4c",
  "block": 49203951,
  "trx_in_block": 2,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2020-12-06T02:22:33",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "1912.543513 VESTS"
    }
  ]
}

{
  "trx_id": "3fe0fa783e22227300af4457f4df0563dd2b46d8",
  "block": 48908052,
  "trx_in_block": 2,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2020-11-25T16:06:24",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "9370.397720 VESTS"
    }
  ]
}

{
  "trx_id": "df2985fc5ed73ee4d9686868499aa19a6c1ebe0b",
  "block": 43214163,
  "trx_in_block": 3,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2020-05-09T03:17:18",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "9562.284316 VESTS"
    }
  ]
}

{
  "trx_id": "66399eadf07889bbc633057717ab52a4f3ddb7f9",
  "block": 43189825,
  "trx_in_block": 5,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2020-05-08T06:31:18",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "1953.311140 VESTS"
    }
  ]
}

{
  "trx_id": "4e670136161e946b76476c870ef2757af834d228",
  "block": 36489700,
  "trx_in_block": 25,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2019-09-17T03:32:45",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "9695.521486 VESTS"
    }
  ]
}

{
  "trx_id": "75dc21b688065c0447c5e655f83b824efaea2f09",
  "block": 33899400,
  "trx_in_block": 5,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2019-06-18T06:12:30",
  "op": [
    "comment",
    {
      "parent_author": "ardianumam",
      "parent_permlink": "installing-tensorrt-in-jetson-tx2",
      "author": "steemitboard",
      "permlink": "steemitboard-notify-ardianumam-20190618t061229000z",
      "title": "",
      "body": "Congratulations @ardianumam! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@ardianumam/birthday1.png</td><td>Happy Birthday! - You are on the Steem blockchain for 1 year!</td></tr></table>\n\n<sub>_You can view [your badges on your Steem Board](https://steemitboard.com/@ardianumam) and compare to others on the [Steem Ranking](https://steemitboard.com/ranking/index.php?name=ardianumam)_</sub>\n\n\n###### [Vote for @Steemitboard as a witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) to get one more award and increased upvotes!",
      "json_metadata": "{\"image\":[\"https://steemitboard.com/img/notify.png\"]}"
    }
  ]
}

{
  "trx_id": "694548e5c568877a6684b3c5ea50a4df190f834b",
  "block": 33896403,
  "trx_in_block": 29,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2019-06-18T03:34:57",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "29656.474067 VESTS"
    }
  ]
}

{
  "trx_id": "366eb37974691baf879f118482ce072c8f307039",
  "block": 33894649,
  "trx_in_block": 14,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2019-06-18T02:03:27",
  "op": [
    "vote",
    {
      "voter": "ardianumam",
      "author": "ardianumam",
      "permlink": "installing-tensorrt-in-jetson-tx2",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "0d3e5c95cf4603a03e7ea844370aec5cf64c584a",
  "block": 33894558,
  "trx_in_block": 1,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2019-06-18T01:58:54",
  "op": [
    "comment",
    {
      "parent_author": "",
      "parent_permlink": "deeplearning",
      "author": "ardianumam",
      "permlink": "installing-tensorrt-in-jetson-tx2",
      "title": "Installing TensorRT in Jetson TX2",
      "body": "![](https://cdn.steemitimages.com/DQmWqk8hZ9aFc6LpTbKpro94czsdxxMPL4d1CVfm5tSNAQ2/image.png)\nTensorRT is an optimization tool provided by NVIDIA that applies graph optimization and layer fusion, and finds the fastest implementation of a deep learning model. In other words, TensorRT will optimize our deep learning model so that we expect a faster inference time than the original model (before optimization), such as 5x faster or 2x faster. The bigger model we have, the bigger space for TensorRT to optimize the model. Furthermore, this TensorRT supports all NVIDIA GPU devices, such as 1080Ti, Titan XP for Desktop, and Jetson TX1, TX2 for embedded device.\n\nThere are at least two options to optimize a deep learning model using TensorRT, by using: (i) TF-TRT (Tensorflow to TensorRT), and (ii) TensorRT C++ API. In this post, we will specifically discuss how we can install and setup for the first option, which is TF-TRT, in Jetson TX2. Here is the step-by-step process:\n\n1. **Flashing the Jetson TX2**\nBy flashing the Jetson TX2 using Jetpack, it will already install CUDA, CuDNN, TensorRT, etc . You can follow the video below to flash your TX2. <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/D7lkth34rgM\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n\n2. **Installing Python 3 and PIP 3**\n`$ sudo apt-get install -y python3-pip python3-dev`\n\n3. **Installing Tensorflow**\nDownload the Tensorflow wheel file [here](https://nvidia.app.box.com/v/JP33-TF1-11-0-py35-wTRT), and then install it by using:\n`sudo pip3 install your_wheel_file.whl`\n\nFor how we can optimize a deep learning model using TensorRT, you can follow this video series here:\nhttps://www.youtube.com/watch?v=AIGOSz2tFP8&list=PLkRkKTC6HZMwdtzv3PYJanRtR6ilSCZ4f",
      "json_metadata": "{\"tags\":[\"deeplearning\",\"tensorrt\",\"jetson\",\"ai\",\"computervision\"],\"image\":[\"https://cdn.steemitimages.com/DQmWqk8hZ9aFc6LpTbKpro94czsdxxMPL4d1CVfm5tSNAQ2/image.png\",\"https://img.youtube.com/vi/D7lkth34rgM/0.jpg\",\"https://img.youtube.com/vi/AIGOSz2tFP8/0.jpg\"],\"links\":[\"https://www.youtube.com/embed/D7lkth34rgM\",\"https://nvidia.app.box.com/v/JP33-TF1-11-0-py35-wTRT\",\"https://www.youtube.com/watch?v=AIGOSz2tFP8&list=PLkRkKTC6HZMwdtzv3PYJanRtR6ilSCZ4f\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}"
    }
  ]
}

{
  "trx_id": "fcae972b51641cccbbe2fb0d16738a074cbb6e7c",
  "block": 33894316,
  "trx_in_block": 4,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2019-06-18T01:46:48",
  "op": [
    "comment",
    {
      "parent_author": "",
      "parent_permlink": "deeplearning",
      "author": "ardianumam",
      "permlink": "installing-tensorrt-in-ubuntu-desktop",
      "title": "Installing TensorRT in Ubuntu Desktop",
      "body": "@@ -3316,9 +3316,180 @@\n ies \n+%5B\n here\n-:\n+%5D(https://www.youtube.com/watch?v=AIGOSz2tFP8&list=PLkRkKTC6HZMwdtzv3PYJanRtR6ilSCZ4f):%0Ahttps://www.youtube.com/watch?v=AIGOSz2tFP8&list=PLkRkKTC6HZMwdtzv3PYJanRtR6ilSCZ4f\n",
      "json_metadata": "{\"tags\":[\"deeplearning\",\"tensorrt\",\"jetson\",\"ai\"],\"image\":[\"https://cdn.steemitimages.com/DQmZ1MnwiTK1CdX6Crxv2ACyTu5gEURANcw7vHj5aKnxgjP/image.png\",\"https://img.youtube.com/vi/AIGOSz2tFP8/0.jpg\"],\"links\":[\"https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html#downloading\",\"https://www.youtube.com/watch?v=AIGOSz2tFP8&list=PLkRkKTC6HZMwdtzv3PYJanRtR6ilSCZ4f\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}"
    }
  ]
}

{
  "trx_id": "a95e8541243b40193ab9f1332235dc560ffab828",
  "block": 33894030,
  "trx_in_block": 6,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2019-06-18T01:32:30",
  "op": [
    "vote",
    {
      "voter": "ardianumam",
      "author": "ardianumam",
      "permlink": "installing-tensorrt-in-ubuntu-desktop",
      "weight": 10000
    }
  ]
}

{
  "trx_id": "b83a79383fac25a86fd47748d4a0410500b2ca25",
  "block": 33894025,
  "trx_in_block": 11,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2019-06-18T01:32:15",
  "op": [
    "comment",
    {
      "parent_author": "ardianumam",
      "parent_permlink": "installing-tensorrt-in-ubuntu-desktop",
      "author": "cheetah",
      "permlink": "cheetah-re-ardianumaminstalling-tensorrt-in-ubuntu-desktop",
      "title": "",
      "body": "Hi! I am a robot. I just upvoted you! I found similar content that readers might be interested in:\nhttps://medium.com/@ardianumam/installing-tensorrt-in-ubuntu-dekstop-1c7307e1dcf6",
      "json_metadata": ""
    }
  ]
}

{
  "trx_id": "d2eb13d85e126cd06a291d6cf1341377f0e680a6",
  "block": 33894024,
  "trx_in_block": 2,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2019-06-18T01:32:12",
  "op": [
    "vote",
    {
      "voter": "cheetah",
      "author": "ardianumam",
      "permlink": "installing-tensorrt-in-ubuntu-desktop",
      "weight": 8
    }
  ]
}

{
  "trx_id": "221bae2e0b3bd65f11ee96297ba134b1d0ceb6aa",
  "block": 33894018,
  "trx_in_block": 1,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2019-06-18T01:31:54",
  "op": [
    "comment",
    {
      "parent_author": "",
      "parent_permlink": "deeplearning",
      "author": "ardianumam",
      "permlink": "installing-tensorrt-in-ubuntu-desktop",
      "title": "Installing TensorRT in Ubuntu Desktop",
      "body": "![](https://cdn.steemitimages.com/DQmZ1MnwiTK1CdX6Crxv2ACyTu5gEURANcw7vHj5aKnxgjP/image.png)\nTensorRT is an optimization tool provided by NVIDIA that applies graph optimization and layer fusion, and finds the fastest implementation of a deep learning model. In other words, TensorRT will optimize our deep learning model so that we expect a faster inference time than the original model (before optimization), such as 5x faster or 2x faster. The bigger model we have, the bigger space for TensorRT to optimize the model. Furthermore, this TensorRT supports all NVIDIA GPU devices, such as 1080Ti, Titan XP for Desktop, and Jetson TX1, TX2 for embedded device.\n\nThere are at least two options to optimize a deep learning model using TensorRT, by using: (i) TF-TRT (Tensorflow to TensorRT), and (ii) TensorRT C++ API. In this post, we will specifically discuss how we can install and setup for the first option, which is TF-TRT. Here is the step-by-step process:\n\n1. **Install NVIDIA drivers, CUDA, CuDNN and Tensorflow GPU version**\nI will not discuss this step because there are already a bunch of tutorials in the internet for doing this. For my case, I use CUDA 9, CuDNN 7 and Tensorflow 1.12. Starting from Tensorflow 1.9.0, it already has TensorRT inside the tensorflow contrib, but some issues are encountered. Therefore, it is preferable to use the newest one (so far is 1.12 version).\n\n2. **Install TensorRT**\n * [Download](https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html#downloading) the TensorRT local repo file that matches the Ubuntu version you are using.\n * Install TensorRT from the Debian local repo package.\n**Note:** Before issuing the following commands, you’ll need to replace `ubuntu1x04`, `cudax.x`, `trt4.x.x.x` and `yyyymmdd` with your specific OS version, CUDA version, TensorRT version and package date (according to the TensorRT deb file you download before)\n```\n$ sudo dpkg -i\nnv-tensorrt-repo-ubuntu1x04-cudax.x-trt5.x.x.x-ga-yyyymmdd_1–1_amd64.deb\n$ sudo apt-key add /var/nv-tensorrt-repo-cudax.x-trt5.x.x.x-ga-yyyymmdd/7fa2af80.pub\n$ sudo apt-get update\n$ sudo apt-get install tensorrt \n$ sudo apt-get install uff-converter-tf\n```\n* If using Python 2.7:\n`$ sudo apt-get install python-libnvinfer-dev`\n* If using Python 3.x:\n`$ sudo apt-get install python3-libnvinfer-dev`\n* Verify the installation\n`$ dpkg -l | grep TensorRT`\nYou should see something similar to the following:\n```\nii graphsurgeon-tf 5.0.2–1+cuda10.0 amd64 GraphSurgeon for TensorRT package\nii libnvinfer-dev 5.0.2–1+cuda10.0 amd64 TensorRT development libraries and headers\nii libnvinfer-samples 5.0.2–1+cuda10.0 amd64 TensorRT samples and documentation\nii libnvinfer5 5.0.2–1+cuda10.0 amd64 TensorRT runtime libraries\nii python-libnvinfer 5.0.2–1+cuda10.0 amd64 Python bindings for TensorRT\nii python-libnvinfer-dev 5.0.2–1+cuda10.0 amd64 Python development package for TensorRT\nii python3-libnvinfer 5.0.2–1+cuda10.0 amd64 Python 3 bindings for TensorRT\nii python3-libnvinfer-dev 5.0.2–1+cuda10.0 amd64 Python 3 development package for TensorRT\nii tensorrt 5.0.2.0–1+cuda10.0 amd64 Meta package of TensorRT\nii uff-converter-tf 5.0.2–1+cuda10.0 amd64 UFF converter for TensorRT package\n```\n* Done!\n\nFor how we can optimize a deep learning model using TensorRT, you can follow this video series here:",
      "json_metadata": "{\"tags\":[\"deeplearning\",\"tensorrt\",\"jetson\",\"ai\"],\"image\":[\"https://cdn.steemitimages.com/DQmZ1MnwiTK1CdX6Crxv2ACyTu5gEURANcw7vHj5aKnxgjP/image.png\"],\"links\":[\"https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html#downloading\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}"
    }
  ]
}

{
  "trx_id": "041bdd75db51c977deb7f814c6cdedd46bbec2a9",
  "block": 24344046,
  "trx_in_block": 0,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2018-07-20T14:55:57",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "9936.028629 VESTS"
    }
  ]
}

{
  "trx_id": "fe743dd70ce478c31a8658efb9cd97b3ca52af8f",
  "block": 21733217,
  "trx_in_block": 61,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2018-04-20T13:06:03",
  "op": [
    "delegate_vesting_shares",
    {
      "delegator": "steem",
      "delegatee": "ardianumam",
      "vesting_shares": "30360.496883 VESTS"
    }
  ]
}

{
  "trx_id": "9189cf044d10c58c852790893a5d7596603f8a79",
  "block": 21732627,
  "trx_in_block": 5,
  "op_in_trx": 0,
  "virtual_op": 0,
  "timestamp": "2018-04-20T12:36:33",
  "op": [
    "account_create_with_delegation",
    {
      "fee": "0.100 STEEM",
      "delegation": "30690.000000 VESTS",
      "creator": "steem",
      "new_account_name": "ardianumam",
      "owner": {
        "weight_threshold": 1,
        "account_auths": [],
        "key_auths": [
          [
            "STM5D9RG1tXKpY3mDX6tYUpPBuiJiEdz9PizKwGPAZ9fxyejaxG4a",
            1
          ]
        ]
      },
      "active": {
        "weight_threshold": 1,
        "account_auths": [],
        "key_auths": [
          [
            "STM6W6ZmzC6mUQozYotDjAzYpKbkguu7dsqdUac84nQe6pszsJMRo",
            1
          ]
        ]
      },
      "posting": {
        "weight_threshold": 1,
        "account_auths": [],
        "key_auths": [
          [
            "STM67XjU1nhwKyt5KtJT1hxr1hjjbJb8zuXd7brxkHFo62WgWvAwg",
            1
          ]
        ]
      },
      "memo_key": "STM62h2GtwLcwUoETELSmJQGtLZqvXXvnteJAz3BWthXKM9gCKVxX",
      "json_metadata": "{}",
      "extensions": []
    }
  ]
}