{
  "filename": "rollout-2026-01-31T06-30-50-019c12bf-4336-7a50-ade9-38988c069e48.jsonl",
  "date": "2026-01-31",
  "session_id": "019c12bf-4336-7a50-ade9-38988c069e48",
  "first_timestamp": "2026-01-31T06:30:50.424Z",
  "last_timestamp": "2026-01-31T06:48:39.074Z",
  "duration_seconds": 1068.7,
  "model": "gpt-5.2-codex",
  "cli_version": "0.92.0",
  "approval_policy": "on-request",
  "effort": "high",
  "raw_event_count": 170,
  "user_messages": [
    {
      "index": 0,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `read-only`: The sandbox only permits reading files. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.\n\nHere are scenarios where you'll need to request approval:\n- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)\n- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.\n- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)\n- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.\n- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.\n\nWhen requesting approval to execute a command that will require escalated privileges:\n  - Provide the `sandbox_permissions` parameter with the value `\"require_escalated\"`\n  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter</permissions instructions>",
      "timestamp": "2026-01-31T06:30:50.424Z"
    },
    {
      "index": 1,
      "text": "# AGENTS.md instructions for /home/ubuntu/nikhil/cold-ranker\n\n<INSTRUCTIONS>\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.\n### Available skills\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)\n### How to use skills\n- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.\n- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.\n- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.\n- How to use a skill (progressive disclosure):\n  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.\n  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.\n  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.\n  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.\n- Coordination and sequencing:\n  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.\n  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.\n- Context hygiene:\n  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.\n  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.\n  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.\n- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.\n</INSTRUCTIONS>",
      "timestamp": "2026-01-31T06:30:50.424Z"
    },
    {
      "index": 2,
      "text": "<environment_context>\n  <cwd>/home/ubuntu/nikhil/cold-ranker</cwd>\n  <shell>bash</shell>\n</environment_context>",
      "timestamp": "2026-01-31T06:30:50.424Z"
    },
    {
      "index": 3,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/driver.py\n\n## Active selection of the file:\n()\n## Open tabs:\n- driver.py: col/src/driver.py\n- model.py: col/src/model/model.py\n- recommend.py: col/src/model/recommend.py\n\n## My request for Codex:\ncan recommend.py be made faster? how?\n",
      "timestamp": "2026-01-31T06:30:50.558Z"
    },
    {
      "index": 4,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/driver.py\n\n## Active selection of the file:\n()\n## Open tabs:\n- driver.py: col/src/driver.py\n- model.py: col/src/model/model.py\n- recommend.py: col/src/model/recommend.py\n\n## My request for Codex:\nwhich is the biggest win? do that\n",
      "timestamp": "2026-01-31T06:34:15.749Z"
    },
    {
      "index": 5,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/driver.py\n\n## Active selection of the file:\n()\n## Open tabs:\n- driver.py: col/src/driver.py\n- model.py: col/src/model/model.py\n- recommend.py: col/src/model/recommend.py\n\n## My request for Codex:\n 2479MiB / 15360MiB\nnvidia-smi\n",
      "timestamp": "2026-01-31T06:39:29.480Z"
    },
    {
      "index": 6,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/driver.py\n\n## Active selection of the file:\n()\n## Open tabs:\n- driver.py: col/src/driver.py\n- model.py: col/src/model/model.py\n- recommend.py: col/src/model/recommend.py\n\n## My request for Codex:\nyes\n",
      "timestamp": "2026-01-31T06:39:52.653Z"
    },
    {
      "index": 7,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/driver.py\n\n## Active selection of the file:\n()\n## Open tabs:\n- driver.py: col/src/driver.py\n- model.py: col/src/model/model.py\n- recommend.py: col/src/model/recommend.py\n\n## My request for Codex:\ndo what you recommend\n",
      "timestamp": "2026-01-31T06:40:24.852Z"
    },
    {
      "index": 8,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/model/recommend.py\n\n## Active selection of the file:\noutput_path\n## Open tabs:\n- recommend.py: col/src/model/recommend.py\n- driver.py: col/src/driver.py\n- model.py: col/src/model/model.py\n\n## My request for Codex:\n3609MiB / 15360MiB\n",
      "timestamp": "2026-01-31T06:42:14.601Z"
    },
    {
      "index": 9,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/model/recommend.py\n\n## Active selection of the file:\nuser_batch_size=32,\n## Open tabs:\n- recommend.py: col/src/model/recommend.py\n- driver.py: col/src/driver.py\n- model.py: col/src/model/model.py\n\n## My request for Codex:\nGenerating recommendations:   0%|                    | 1024/1362277 [00:24<9:06:17, 41.53it/s]\n 3611MiB / 15360MiB\n\nthe it/s or total time are not decreasing even with increasing \nuser_batch_size=32,\n",
      "timestamp": "2026-01-31T06:44:39.090Z"
    },
    {
      "index": 10,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/model/recommend.py\n\n## Active selection of the file:\nREC_SERIES_BATCH_SIZE\n## Open tabs:\n- recommend.py: col/src/model/recommend.py\n- driver.py: col/src/driver.py\n- model.py: col/src/model/model.py\n\n## My request for Codex:\nremove the auto‑scaling so series_batch_size stays large,\n",
      "timestamp": "2026-01-31T06:46:11.049Z"
    },
    {
      "index": 11,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/model/recommend.py\n\n## Open tabs:\n- recommend.py: col/src/model/recommend.py\n- driver.py: col/src/driver.py\n- model.py: col/src/model/model.py\n\n## My request for Codex:\nstill Generating recommendations:   0%|                     | 896/1362277 [00:22<9:26:06, 40.08it/s]\n",
      "timestamp": "2026-01-31T06:48:30.894Z"
    }
  ],
  "assistant_messages": [
    {
      "index": 0,
      "text": "Yes — the hot path is `score_user` doing a full series pass per user with lots of tiny tensor allocations and Python overhead. There are several concrete ways to speed it up:\n\n1) Batch users to amortize per‑user overhead  \nScore `U` users at a time against each series batch: build user tensors of shape `(U, …)` and broadcast with a series batch of size `S` (flatten to `(U*S, …)` for the model). This reduces Python loop overhead and improves GPU utilization. You can cap `U` to fit memory.\n\n2) Pre‑tensorize user features once  \nRight now, each user call builds `torch.tensor` and `torch.from_numpy` repeatedly. Pre‑stack user features into arrays and move them to device once, then slice for each user/batch. This alone can be a big CPU win if you have many users.\n\n3) Avoid per‑batch branching + re‑allocs  \nMove the `\"target_series_id\" in series_meta_int_idx` check outside the loop and precompute `series_ids_all` and other per‑series tensors once. Then just slice: `series_ids = series_ids_all[start:end]`, etc.\n\n4) Enable faster math on GPU  \nIf precision is OK for ranking:\n- `torch.backends.cuda.matmul.allow_tf32 = True`\n- `with torch.autocast(device_type=\"cuda\", dtype=torch.float16/bfloat16):`\nThis often speeds inference significantly.\n\n5) Consider `torch.compile` for the model  \n`model = torch.compile(model)` can reduce overhead for repeated inference loops (PyTorch 2.x), especially with fixed shapes.\n\n6) Reduce JSONL overhead  \nIf this is large-scale, use `orjson` and buffer writes (e.g., collect rows for a batch and write once) to cut serialization and I/O time.\n\nIf you want, I can implement a user‑batching version and/or pre‑tensorization in `recommend.py`. Which direction do you prefer?",
      "timestamp": "2026-01-31T06:31:27.730Z"
    },
    {
      "index": 1,
      "text": "I implemented the biggest win: batching users so the model scores multiple users per forward and top‑k is updated in a vectorized way, cutting per‑user overhead and improving throughput while keeping per‑forward work roughly constant by scaling `series_batch_size`. \n\nDetails\n- Added `score_users_batch` and switched `driver()` to batch users, build batch tensors once, and compute top‑k per user in a single pass. `col/src/model/recommend.py`\n- Added `user_batch_size` (default `4`) and auto‑scales `series_batch_size` by `// user_batch_size` to keep memory similar to the previous single‑user setting. `col/src/model/recommend.py`\n\nNo tests run.\n\nIf you want, I can tune defaults for your hardware. Suggested next steps:\n1) Run `recommend.driver(user_batch_size=8)` (or higher) and watch memory/throughput.\n2) If you have GPU headroom, set a larger `series_batch_size` and increase `user_batch_size` together for bigger gains.",
      "timestamp": "2026-01-31T06:37:55.890Z"
    },
    {
      "index": 2,
      "text": "GPU looks mostly free: 2479 MiB / 15360 MiB used, GPU util 99% by PID 7695 (python). Driver 535.146.02, CUDA 12.2, Tesla T4. Let me know if you want me to increase `user_batch_size` or `series_batch_size` based on this headroom.",
      "timestamp": "2026-01-31T06:39:44.068Z"
    },
    {
      "index": 3,
      "text": "Got it. Which knob do you want me to change?\n\n1) Increase `user_batch_size` (recommended first; big win, modest memory)\n2) Increase `series_batch_size` (only if memory allows)\n3) Set both (I can pick a safe combo for a 16 GB T4)\n\nTell me 1/2/3, or give a target GPU memory limit to stay under.",
      "timestamp": "2026-01-31T06:39:56.820Z"
    },
    {
      "index": 4,
      "text": "Set the recommended defaults to batch users more aggressively: `user_batch_size=8`, which auto-scales `series_batch_size` to keep per-forward memory roughly constant. File updated: `col/src/model/recommend.py`.\n\nIf you want to push harder, next step is trying `user_batch_size=16` and watching GPU memory/utilization.",
      "timestamp": "2026-01-31T06:40:32.557Z"
    },
    {
      "index": 5,
      "text": "Nice — still lots of headroom. I’d bump `user_batch_size` to 16 next. Want me to set that default?",
      "timestamp": "2026-01-31T06:42:17.601Z"
    },
    {
      "index": 6,
      "text": "That’s expected with the current logic: I **auto‑scale `series_batch_size` down** when you increase `user_batch_size`, so the total work per forward stays about the same. You’re still doing the same number of (user, series) pairs per step, so `it/s` (users/sec) doesn’t improve much. With GPU util already ~99%, overhead isn’t the bottleneck.\n\nIf you want real speedup, you need to **increase total pairs per step** (or make each pair cheaper). Two best options:\n\n1) **Keep `series_batch_size` fixed and raise `user_batch_size`**  \nThis increases total work per forward, which can improve throughput if you still have memory headroom.  \nExample: set `user_batch_size=16` *and* keep `series_batch_size=2**17` (or bump it).  \nRight now the auto‑scaling cancels that benefit.\n\n2) **Use mixed precision / TF32**  \nThis can cut per‑pair cost substantially on a T4:\n- `torch.backends.cuda.matmul.allow_tf32 = True`\n- Wrap the forward in `torch.autocast(device_type=\"cuda\", dtype=torch.float16)` (or `bfloat16`)\n\nIf you want, I can:\n- remove the auto‑scaling so `series_batch_size` stays large, **or**\n- add an `auto_scale_series_batch=False` flag, **and**\n- enable mixed precision in `score_users_batch`.\n\nPick one:\n1) Disable auto‑scaling (keep `series_batch_size` large)  \n2) Enable mixed precision  \n3) Both",
      "timestamp": "2026-01-31T06:44:52.989Z"
    },
    {
      "index": 7,
      "text": "Removed auto‑scaling so `series_batch_size` stays large. Updated `col/src/model/recommend.py`.\n\nWant me to add mixed precision next?",
      "timestamp": "2026-01-31T06:46:18.472Z"
    },
    {
      "index": 8,
      "text": "You’re compute‑bound now (GPU util ~99%), so `it/s` won’t improve just by changing batching—each (user, series) pair still costs the same. To move the needle, you need to make each forward cheaper.\n\nBiggest next win: mixed precision + TF32.\n- `torch.backends.cuda.matmul.allow_tf32 = True`\n- Wrap the forward in `torch.autocast(device_type=\"cuda\", dtype=torch.float16)` (or `bfloat16`)\n\nI can wire that into `score_users_batch` so it’s a one‑line toggle. Want me to add it?",
      "timestamp": "2026-01-31T06:48:39.073Z"
    }
  ],
  "tool_calls": [
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "Exit code: 101\nWall time: 0 seconds\nOutput:\n\nthread 'main' (7383) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\nerror running landlock: Sandbox(LandlockRestrict)\nnote: run with `RUST_BACKTRACE..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to list repository files; sandboxed command failed due to landlock restricti...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ncol\ndata\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubunt..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,220p' col/src/model/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to inspect recommend.py to suggest p...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport json\nimport os\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\nfrom model.model import CTRModelWithCosineSignals\nfrom utils.utils import load_pic..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '220,520p' col/src/model/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Continue inspecting recommend.py for pe...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n            user_id_b = uid_t.expand(n)\n            gender_b = gender_t.expand(n)\n            age_b = age_t.expand(n, 1)\n            total_interactions_b = to..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,260p' col/src/model/model.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect model details to identify performance...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nclass CTRModelWithCosineSignals(nn.Module):\n    def __init__(\n        self,\n        in..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"REC_.*BATCH\\\" -n col/src/config/config.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Check config for existing batch si...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n96:REC_SERIES_BATCH_SIZE = 2**17\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profi..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' col/src/driver.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect driver.py to ensure changes integrate with...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nfrom datetime import datetime\nos.environ[\"AWS_DEFAULT_REGION\"] = \"ap-south-1\"\nos.environ[\"OPENAI_API_KEY\"] = \"sk-proj-qRoswT4wSHS3R4i7ZcIbP3FW5XT0xI..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' col/src/config/config.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect config to understand embedding dims...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nfrom openai import OpenAI\n\nLANGUAGE=\"hindi\"\nLANGUAGE_KEY=\"hi\"\n\nMODE='prod'\n\ndef _env_flag(name: str, default: bool = False) -> bool:\n    value = os...."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"nvidia-smi\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"User asked to run nvidia-smi to check GPU usage; requires escalated due t...",
      "output_summary": "Exit code: 0\nWall time: 0.2 seconds\nOutput:\nSat Jan 31 06:39:42 2026       \n+---------------------------------------------------------------------------------------+\n| NVIDIA-SMI 535.146.02           ..."
    }
  ],
  "turn_count": 12,
  "files_touched": [],
  "summary_from_context": "auto",
  "metrics": {
    "outcome": "resolved",
    "session_type": "bug-fix",
    "iteration_count": 12,
    "struggle_score": 2,
    "prompt_specificity": 5,
    "time_of_day": "morning"
  }
}