{
  "filename": "rollout-2026-01-30T13-43-01-019c0f24-95e2-70b1-b46e-99c38c5a062b.jsonl",
  "date": "2026-01-30",
  "session_id": "019c0f24-95e2-70b1-b46e-99c38c5a062b",
  "first_timestamp": "2026-01-30T13:43:01.861Z",
  "last_timestamp": "2026-01-30T13:52:10.951Z",
  "duration_seconds": 549.1,
  "model": "gpt-5.2-codex",
  "cli_version": "0.92.0",
  "approval_policy": "on-request",
  "effort": "xhigh",
  "raw_event_count": 65,
  "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": "2026-01-30T13:43:01.868Z"
    },
    {
      "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": "2026-01-30T13:43:01.868Z"
    },
    {
      "index": 2,
      "text": "<environment_context>\n  <cwd>/home/ubuntu/nikhil/cold-ranker</cwd>\n  <shell>bash</shell>\n</environment_context>",
      "timestamp": "2026-01-30T13:43:01.868Z"
    },
    {
      "index": 3,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/data_prep/data_prep.py\n\n## Active selection of the file:\nrecommend\n## Open tabs:\n- data_prep.py: col/src/data_prep/data_prep.py\n- driver.py: col/src/driver.py\n- meta.json: data/bn/test/shard-00000/meta.json\n- config.py: col/src/config/config.py\n- recommend.py: col/src/model/recommend.py\n\n## My request for Codex:\nexhausting permanent memory during write of data prep files - train, test, recommend\nwhat can we do? just tell dont edit code\n",
      "timestamp": "2026-01-30T13:43:01.936Z"
    },
    {
      "index": 4,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/data_prep/data_prep.py\n\n## Active selection of the file:\nrecommend\n## Open tabs:\n- data_prep.py: col/src/data_prep/data_prep.py\n- driver.py: col/src/driver.py\n- meta.json: data/bn/test/shard-00000/meta.json\n- config.py: col/src/config/config.py\n- recommend.py: col/src/model/recommend.py\n\n## My request for Codex:\nso i am not getting RAM OOM, i am exhausting permanent memory while writing the shards\n",
      "timestamp": "2026-01-30T13:46:46.480Z"
    },
    {
      "index": 5,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/data_prep/data_prep.py\n\n## Active selection of the file:\nrecommend\n## Open tabs:\n- data_prep.py: col/src/data_prep/data_prep.py\n- driver.py: col/src/driver.py\n- meta.json: data/bn/test/shard-00000/meta.json\n- config.py: col/src/config/config.py\n- recommend.py: col/src/model/recommend.py\n\n## My request for Codex:\ni save in .npy\n",
      "timestamp": "2026-01-30T13:47:27.056Z"
    },
    {
      "index": 6,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/utils/utils.py\n\n## Active selection of the file:\nnp.save(shard_dir / \"floats.npy\", floats)\n## Open tabs:\n- utils.py: col/src/utils/utils.py\n- data_prep.py: col/src/data_prep/data_prep.py\n- driver.py: col/src/driver.py\n- meta.json: data/bn/test/shard-00000/meta.json\n- config.py: col/src/config/config.py\n\n## My request for Codex:\ncan you give an example to read and write from one of theose compressed formates? \n\ncurrenty i do like np.save(shard_dir / \"floats.npy\", floats)\n",
      "timestamp": "2026-01-30T13:48:20.013Z"
    },
    {
      "index": 7,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/utils/utils.py\n\n## Active selection of the file:\nnp.save(shard_dir / \"floats.npy\", floats)\n## Open tabs:\n- utils.py: col/src/utils/utils.py\n- data_prep.py: col/src/data_prep/data_prep.py\n- driver.py: col/src/driver.py\n- meta.json: data/bn/test/shard-00000/meta.json\n- config.py: col/src/config/config.py\n\n## My request for Codex:\nwhich format is better?\n",
      "timestamp": "2026-01-30T13:48:54.910Z"
    },
    {
      "index": 8,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/utils/utils.py\n\n## Active selection of the file:\nnp.save(shard_dir / \"floats.npy\", floats)\n## Open tabs:\n- utils.py: col/src/utils/utils.py\n- data_prep.py: col/src/data_prep/data_prep.py\n- driver.py: col/src/driver.py\n- meta.json: data/bn/test/shard-00000/meta.json\n- config.py: col/src/config/config.py\n\n## My request for Codex:\nis there differnec time in loading and saving of npz and np\n",
      "timestamp": "2026-01-30T13:51:06.752Z"
    },
    {
      "index": 9,
      "text": "# Context from my IDE setup:\n\n## Active file: col/src/utils/utils.py\n\n## Active selection of the file:\nnp.save(shard_dir / \"floats.npy\", floats)\n## Open tabs:\n- utils.py: col/src/utils/utils.py\n- data_prep.py: col/src/data_prep/data_prep.py\n- driver.py: col/src/driver.py\n- meta.json: data/bn/test/shard-00000/meta.json\n- config.py: col/src/config/config.py\n\n## My request for Codex:\nhow to read and write npz\n",
      "timestamp": "2026-01-30T13:52:07.827Z"
    }
  ],
  "assistant_messages": [
    {
      "index": 0,
      "text": "Likely you’re holding too much in memory while preparing/saving each split. Common culprits are materializing full datasets (pandas/pyarrow), building huge lists/dicts before write, or keeping multiple splits in RAM at once. Here are practical directions without editing code:\n\n- Stream/iterate and write in chunks (row batches) instead of building full DataFrames/lists, then flush.\n- Avoid in‑memory concatenation; write partitioned shards incrementally (e.g., 10–100k rows per shard).\n- Use on‑disk formats that support streaming (JSONL/Parquet with batch writer) and avoid `.to_pandas()` or `.collect()` style calls.\n- Free references between splits; run GC after each split and after each shard write if Python.\n- Reduce peak memory: drop unused columns early, use categorical/typed arrays, avoid object dtype.\n- If preprocessing is heavy, cache intermediate shards to disk and resume rather than recomputing all in RAM.\n- Limit worker count if multiprocessing; each worker duplicates memory.\n\nIf you want more targeted advice, tell me:\n- how you’re currently building each split (pandas/pyarrow/datasets/torch?)\n- how big each split is (rows/GB)\n- the exact OOM message and when it happens (during transform vs write).",
      "timestamp": "2026-01-30T13:43:11.298Z"
    },
    {
      "index": 1,
      "text": "Got it — that sounds like **disk space**, not RAM. “Permanent memory” usually means the filesystem (disk/SSD) is full or you hit a quota while writing shards.\n\nQuick checks you can run:\n- `df -h` (disk free space)\n- `du -sh data/` (size of your dataset output)\n- If on a managed environment: check project/home quota usage.\n\nTypical fixes:\n- Delete old/duplicate shards before reruns (or write to a fresh empty dir).\n- Write fewer/larger shards (reduce shard count).\n- Enable compression (e.g., gzip/Parquet compression).\n- Write to a larger mount (set output dir to a disk with more space).\n- Ensure temp files aren’t going to a tiny `/tmp` (set `TMPDIR` to a larger disk).\n\nIf you can, paste the exact error message and the output path you’re writing to. That’ll tell us if it’s disk-full, quota, or temp-dir exhaustion.",
      "timestamp": "2026-01-30T13:46:55.862Z"
    },
    {
      "index": 2,
      "text": "`.npy` is **uncompressed**, so disk usage is basically:\n\n`bytes ≈ itemsize * prod(shape)` (plus tiny header)\n\nSo if you’re “exhausting permanent memory” while writing `.npy`, it’s just running out of disk/quota. Options without code edits:\n\n- **Use a smaller dtype** (e.g., float32 → float16 / int64 → int32) to halve size.\n- **Switch to compressed** format: `.npz` (`np.savez_compressed`) or **zarr** (chunked + compressed).\n- **Shard larger**, not smaller: fewer, bigger files reduces metadata overhead and temp files.\n- **Write to a bigger disk** (change output dir or mount).\n- **Avoid duplicate writes**: delete old shards before rerun.\n\nIf you can share:\n- array shape + dtype per shard\n- shard count\n- free disk (`df -h`)\nI can estimate expected total size and pinpoint the mismatch.",
      "timestamp": "2026-01-30T13:47:34.087Z"
    },
    {
      "index": 3,
      "text": "Here are two minimal examples for compressed storage (no code changes needed, just showing how):\n\n**1) `.npz` (NumPy compressed)**\n```python\n# write\nnp.savez_compressed(shard_dir / \"floats.npz\", floats=floats)\n\n# read\ndata = np.load(shard_dir / \"floats.npz\")\nfloats = data[\"floats\"]\n```\n\n**2) Zarr (chunked + compressed)**\n```python\nimport zarr\n\n# write\nzarr.save(shard_dir / \"floats.zarr\", floats=floats)  # auto-chunk + compression\n\n# read\nfloats = zarr.load(shard_dir / \"floats.zarr\")\n```\n\nIf you want, tell me which format you prefer and I can tailor options (e.g., compression level, chunk sizes, dtype).",
      "timestamp": "2026-01-30T13:48:24.299Z"
    },
    {
      "index": 4,
      "text": "It depends on how you read/write:\n\n- **.npz**: simplest drop‑in, single file, good portability. Best if you always read the whole array. Slower for huge arrays; no partial reads.\n- **Zarr**: best for big data, partial reads, parallel I/O, and cloud‑style storage. Faster for large datasets; needs extra dependency and creates a directory of chunks (many files).\n\nRule of thumb:\n- **Small/medium arrays, whole‑array reads → `.npz`**\n- **Large arrays, frequent partial reads / parallelism → Zarr**\n\nIf you tell me your typical shard size and read pattern, I’ll pick one.",
      "timestamp": "2026-01-30T13:49:01.312Z"
    },
    {
      "index": 5,
      "text": "Yes — there’s usually a time difference.\n\n- **`.npy`** (np.save / np.load): fastest save/load because it’s raw, uncompressed. Supports `mmap_mode` for fast, low‑RAM reads.\n- **`.npz`**:\n  - `np.savez` (uncompressed zip): a bit slower than `.npy` due to ZIP container overhead.\n  - `np.savez_compressed`: noticeably slower save/load because of compression, but uses less disk.\n\nRule of thumb:\n- **Need speed → `.npy`**\n- **Need smaller disk → `.npz` (compressed)**\n\nOne nuance: if disk is slow and arrays are huge, **compressed `.npz` can sometimes load faster overall** because you read fewer bytes, but CPU cost may still dominate.\n\nIf you want, share typical array sizes and I’ll estimate which is likely faster in your case.",
      "timestamp": "2026-01-30T13:51:16.102Z"
    },
    {
      "index": 6,
      "text": "Here’s the basic pattern.\n\nWrite:\n```python\nimport numpy as np\n\n# single array\nnp.savez_compressed(\"floats.npz\", floats=floats)\n\n# multiple arrays\nnp.savez_compressed(\"shard.npz\", floats=floats, ids=ids, labels=labels)\n```\n\nRead:\n```python\nimport numpy as np\n\ndata = np.load(\"floats.npz\")\nfloats = data[\"floats\"]\n\n# multiple arrays\nfloats = data[\"floats\"]\nids = data[\"ids\"]\nlabels = data[\"labels\"]\n```\n\nIf you want uncompressed `.npz`, use `np.savez(...)` instead of `np.savez_compressed(...)`.",
      "timestamp": "2026-01-30T13:52:10.950Z"
    }
  ],
  "tool_calls": [],
  "turn_count": 10,
  "files_touched": [],
  "summary_from_context": "auto",
  "metrics": {
    "outcome": "resolved",
    "session_type": "bug-fix",
    "iteration_count": 10,
    "struggle_score": 0,
    "prompt_specificity": 4,
    "time_of_day": "afternoon"
  }
}