**Considering audit script**

I think it might be helpful to run the audit script for this run as well. It could provide valuable insights or catch any issues I might’ve missed otherwise. Hmm, I wonder if it’ll slow things down or if it’ll just work smoothly alongside everything else. I really want to ensure that everything is running perfectly, so I’ll go ahead and include it in this process. Let's see how it all goes!


The core logic is now strictly validated in local-reference mode. I’m moving to the next production gate: the same worker path in `r2` reference mode, so we prove the startup download/cache behavior before touching Docker.

**Testing r2-mode startup**

Alright, I need to test the r2-mode startup since I have the reference uploaded. I’m thinking I might rerun the pilot using either the same three videos or just one video to validate the startup path. The user wants to ensure everything is finished and outputs are validated.

I should run one more pilot using the worker startup download from R2. Since the reference download is huge, downloading three videos should be fine, but maybe I’ll stick to just one to test the startup path efficiently.
**Planning R2 mode testing**

It seems that one video might be enough for the R2 mode test in local Docker, specifically using the video `CHUbGdCpT70`. I think I'll focus on staging A with this one video and use the same one for validation in stage B. I’ll need to set a distinct prefix like `pilot-final-export-r2-20260312` for the pilot outputs. Stage A is set to download quite a bit from R2, which is a large but manageable amount. I'll monitor in the background as the user wants to run a local Docker test after validation. It might also be helpful to perform the local non-Docker R2 mode first to validate the startup download.
**Setting up local Docker test**

I’m thinking about running a local Docker test, possibly starting with `docker build` using the `FINAL_EXPORT_STAGE=video` and enabling R2 mode with just one video. I might not need an actual queue or could use the same seeded queue, but I need to verify if Docker is even installed and available. After building the image, I could run a compactor, or maybe just execute `python -m` inside the container later. Before diving into the local R2 mode, I should patch for compile and lints after making changes, but I can save that for later. For now, let’s focus on the R2 mode pilot and create a seed queue for the one video.
**Setting up for video processing**

I'm setting up my environment with the appropriate reference prefix, bucket, and reference mode. Then I'll run Stage A with one video, which might take several minutes due to downloads. I'm considering whether to use the same workspace path /tmp/final_export_r2, but I need to ensure there's enough free disk space. I'll check that by running `df -h .` before proceeding. Okay, let’s do this!


I have enough free disk for the cache-heavy test, and the one-video R2-mode queue is seeded. I’m starting Stage A in R2 reference mode now; this run should first download the big reference snapshots into a local cache and then process the video from those cached copies.

**Preparing to run export stage**

I need to set some environment variables before running stage A, specifically using the shell command: `FINAL_EXPORT_REFERENCE_MODE=r2 FINAL_EXPORT_REFERENCE_BUCKET=1-cleaned-data FINAL_EXPORT_REFERENCE_PREFIX=final-export-reference/20260312-v1 python -m src.final_export_video_main ...`. It seems like I might use a local work root at `tmp/final_export_r2_test`. I’ll start this process in the background. Let's get started!


The R2-mode startup path is doing exactly what we wanted: it downloaded the uploaded manifest and has started pulling the `19G` canonical parquet into the worker cache. While that runs, I’m doing a final code sanity pass and checking whether Docker is available locally for the next phase.

**Planning Docker Build and Downloads**

I need to monitor something for a while and wonder if I could build a Docker image meanwhile. It might be a good idea to use the same references while preparing for the Docker build. I can start this after validating in r2 mode, but I also need to check if Docker is installed. Running lints again is necessary but can wait. It sounds feasible to build in parallel while downloading R2, but testing locally should wait until after validation.


Docker is available and the code still compiles/lints clean after the R2-mode patches. I’m starting the final-export image build now so the local container smoke test can begin as soon as the R2-mode worker run finishes.

**Considering Docker image build**

I think I might need to build the Docker image now while I wait. The Docker build could take just a few minutes, so I’m considering using Dockerfile.final_export. First, I should check that there are no running builds. If it's fine, I could run it in the background, but it might not be necessary since the build is likely quick—less than a few minutes. Alright, let’s go ahead with that!


The Docker image build is underway, and the R2-mode worker is in the expensive first-download stage. That’s expected; the key signal I’m watching for is the reference store reusing the cached snapshots after the first big transfer, because that’s what makes the production pattern viable across multiple claimed videos on the same worker.

**Planning Docker build process**

I'm realizing that the Docker build will take a while, so I might need to run a strict validator in r2 mode after the r2 worker is complete. It looks like I'll have to wait, and there's a possibility that running stage A could take several minutes due to downloads. I should definitely monitor the process and check periodically to ensure everything is running smoothly.


I'm considering how long to wait for the R2 worker since it could take over three minutes due to large downloads. It seems I might need to build progress indicators, which could help. I'll probably monitor the situation, but not too often. I think it might be a good idea to take a pause for around 90 seconds to see if that helps. Alright, let’s see how this goes!


The R2-mode worker and Docker build are both long-running as expected, so I’m checking for the two signals that matter: the worker advancing past reference download into actual video processing, and the Docker build crossing from system packages into Python deps and final image creation.


**Checking R2 worker status**

I need to finalize the R2 worker, but I'm not sure if I've included the exit lines in the log. It might be a good idea to check the tail for any exit messages. I think I should do this soon to ensure everything's wrapped up properly. It’s just one of those things I need to double-check to make sure everything is running smoothly!


The first R2-mode worker run completed successfully, including the full one-time snapshot download. I’m running one more one-video pass with the same worker cache path now to verify the production-critical behavior: snapshot reuse instead of re-downloading `40+ GB` every time the worker restarts.

**Considering video viability**

I think I need to make sure the viability is addressed. I might set up a second video with the same run ID and worker ID and adjust the max videos to one. Since the final export queue uses the video ID in a pending status, I’ll run it again and keep an eye out for logs indicating, “Reusing cached final export reference.” It sounds straightforward, but I want to ensure everything runs smoothly!


I found an important production detail from the R2-mode restart test: the worker is deleting its own local cache on shutdown, so a restarted worker re-downloads the giant snapshots instead of reusing them. I’m fixing that now by preserving the reference cache and only clearing per-video scratch data, then I’ll rerun the restart check.

**Considering video processing cleanup**

For local tests, I might clean up manually. I need to patch the video worker cleanup to avoid deleting the entire work root, focusing instead on removing temporary directories for each video after processing. Without cleanup, I risk filling up disk space in production. A better structure would involve having separate cache and work directories, like 'reference_cache' and 'jobs.' I can iterate through and remove job directories while keeping the final export reference in cleanup.
**Considering rerun process**

For this in-progress rerun, I might just let it complete, even if it means re-downloading. After the patch, I need to verify that no re-download occurs, which means running it again. Since the current run has already started, maybe I don't need to worry about it? However, it's re-downloading a hefty 19G, so perhaps killing it to save time is best. I should mention this to the user, noting the progress and confirming that killing the process is safe. I'll use PID 548764.
**Figuring out background processes**

I’m considering the idea of possibly terminating a background shell process from a file. It seems like the next logical step would be to patch things up after that. I might also need to queue a row for DCxl that’s currently claimed, and then think about resetting stale or reseeding. Okay, I’ll focus on killing that process first and see where it leads from there. I think I’ve got a plan!


I’m turning the restart/cache test into a cleaner two-step run: same `r2` run ID, same worker cache path, two videos processed in two separate worker invocations. That will let me verify both things at once: first startup downloads the snapshots, second startup reuses them, and the resulting outputs still validate normally.

**Validating video processing**

The user mentioned there shouldn’t be any bugs, so I think it’s vital to validate at least one run with the validator after the compactor. I’m considering running Stage A with two videos under the same run ID, followed by the compactor and validator.

The steps look like this:
1. Seed video A.
2. Run the worker in r2 mode and download references.
3. Seed video B using the same run ID.
4. Process B quickly while reusing cached references.
5. The compactor will follow for the same run ID.
6. Lastly, run the strict validator for final outputs. Great plan!
**Considering run ID usage**

I'm thinking I could use the run ID `pilot-final-export-r2cache-20260312`. It sounds like a good idea, but I should probably check if there's enough time to implement it. I might need to compile things quickly first before diving into using the new run ID. It’s a bit of a rush, but I'll see if I can manage it! Let's make sure everything is in order to proceed smoothly.


The cache-retention run is in the expected first-download phase. While it streams the snapshots, the remaining work after this is straightforward: confirm cache reuse on the second invocation, run one containerized smoke test from the built image, and then stop at a ready-to-deploy Vast dry run as you requested.

**Waiting for tasks to complete**

I need to wait for about two minutes, and I’m wondering if I should hold off on any more coding tasks for now. We still need to run a local Docker smoke test. While waiting, I might consider running a container to see if that helps. I think I need to wait for the first R2 run to complete before doing the smoke test or dry-run for the Vast deploy later. For now, it's all about waiting.


The first cache-retention run completed successfully and, importantly, it kept the downloaded references in place. I’m seeding one more video under the same run/worker cache path now; this second invocation is the proof point where we should see `Reusing cached final export reference ...` instead of multi-gigabyte downloads.


**Considering validation process**

I think it's a good idea to let everything complete first before deciding on using the compactor and validator for two videos. I’m leaning towards yes for running those tools later. Once everything is done, I’ll run the compactor and validator to check the r2-mode outputs. I should probably wait about 40 seconds or so to be sure everything is ready before taking those steps. Alright!


The cache reuse proof is good: the second R2-mode invocation skipped all multi-gigabyte reference downloads and went straight into processing. I’m compacting that two-video R2-backed run now and then I’ll validate its packaged outputs the same strict way as the local-reference pilot.


I found a real compactor bug during the R2-backed test: Stage B was claiming `pending` microshards by language globally, not filtering by `run_id`, so it can mix runs if multiple pilot prefixes exist. I’m stopping that run and patching Stage B to scope both language leasing and microshard claims to the active run before I continue.


**Patching claim query**

I'm thinking about needing to fix the placeholder in the claim query, especially with the language set to $3. I need to make sure that the methods like patch and compactor correctly call the run_id. There's also the idea of acquiring a lease with the language filter, which seems okay. Alright, let's move ahead and patch that! I want to ensure everything's running smoothly and effectively.


The run-scoping fix is patched. I’m doing a quick compile check and then re-running the compactor for the `r2cache` run so we can validate a clean, non-mixed Stage B result.


The previous aborted compactor left some `claimed` microshards behind, so the scoped compactor had nothing eligible to pick up yet. I’m forcing an immediate stale-claim reset for this test run and then re-running compaction so we can validate the clean scoped behavior end to end.


**Inspecting microshards**

I’m trying to figure out if the microshards are in a compacted status due to an aborted run. I think I need to inspect the database statuses for that run. The best way to do this is to use a query. I wonder if that’ll give me the answers I need. It feels important to clarify the current state so I can understand what’s going on with the microshards.


I checked the DB state for the `r2cache` run, and its own microshards are all marked `compacted`. Before I trust that run, I’m doing one extra contamination check: reading the final shard metadata to make sure those shards only contain the two expected videos from that run and didn’t inherit rows from the earlier mixed-run attempt.

**Querying final shards**

I want to ensure everything is clean, so I'll query the final shards for the current run and count distinct video IDs in the metadata rows. If CHUb shows up unexpectedly, that could be a problem. I might need to run a quick script manually. First, I need to gather the shard keys and I could use an audit script or a quick query with final_export_shards. I'll inspect those distinct video IDs in the final shards for the current run.


I hit a pooler quirk on that DB spot-check, not a pipeline issue. I’m rerunning it with prepared-statement caching disabled so we can finish the contamination check cleanly.


The mixed-run contamination check confirmed that the earlier unscoped compactor run did, in fact, pollute one pilot. The bug is fixed now, so I’m doing the correct thing: a fresh clean R2-backed run with a new run ID, then I’ll validate that clean run end to end and ignore the contaminated pilot.

**Considering cache reuse**

I'm thinking that I could use the same local work root, but the new run ID likely means a new cache path, which would result in additional downloads. It feels costly in terms of time, around two minutes. Ideally, I’d want to avoid that. Using the same run ID might allow cache reuse, but it seems tricky. Deleting rows in the database or remote objects seems tough, so a better option might be to create a new run ID. However, I wonder if there's any way to set the same run ID for cache reuse.
**Auditing videos and tests**

I need to apply a strict validator to those two videos and make sure to audit the final shards. Since I plan to run a local Docker test later, I think it’s a good idea to use the same references. This way, I can ensure everything aligns properly. Alright, let's proceed with those steps! It feels like I’m getting organized and ready to tackle the next phase.


I’m launching the clean scoped R2 run now. This is the one that matters for final validation after the compactor fix: new run ID, R2-backed references, and only the intended videos in scope.


**Waiting for the test**

I need to wait for about 3 minutes. After that, I might be able to run a local Docker test. I’m considering if I could run a Docker container while reusing the cache and perhaps mocking some things. But I think it's best to do this after I validate a clean run. So for now, I’ll just patiently wait and see what happens!


Stage A for the clean scoped R2-backed run is complete. I’m running the scoped compactor next, then the strict validator and manifest audit on that clean run so we have one fully valid `r2`-mode proof run with no mixed-run contamination.


The clean scoped R2 compactor finished. I’m doing the last three validation passes for this run now: manifest-side audit, strict segment-by-segment validation, and a shard-level contamination check to confirm the run contains only its intended videos.

**Running audits and checks**

I need to conduct an audit along with a strict validator for the run. I think it’s important to include a contamination check for distinct video IDs, just in case the validator misses any extras. Running both in parallel might be a good idea since they're independent, but I should consider that the strict validator could take some time. Using the same local final output makes sense as we proceed with the contamination check together.


The code path itself is now validated in both local-reference and clean R2-reference modes. I’m moving to the container smoke test next: one Stage A container run, one Stage B container run, then the same audit/validator checks on the Docker-produced outputs.

**Validating outputs for containers**

I need to ensure that the outputs from containerized Stage A are validated, probably after the compactor. I think doing a test run with one video in a Docker container might be enough for validation, maybe using CHUb. For Stage B, the container will produce shards, and I plan to run an audit with a strict validator for that run since the logic remains the same. Before running the container, I should check if user root access is needed, and I also need to seed the queue with a run ID and video, possibly using a local queue.


The Docker Stage A smoke test is live and has already reached the R2 reference download step inside the container, which confirms the image wiring and env propagation are correct. I’m waiting for it to finish so I can run the Stage B container and then validate the container-produced artifacts.

**Considering monitor necessity**

I'm thinking about whether I need a monitor—maybe I do, maybe I don't. Since the container is running, it might be a good idea to plan for a compactor container after it's finished. I'll take my time and wait to see how things progress before deciding. It's interesting how little decisions can seem so big sometimes!


The Docker Stage A smoke test completed successfully. I’m running the Stage B container now against the same run, and after that I’ll do the same audit plus strict validator so we know the container-built image produces correct packaged shards too.

**Considering container configurations**

I need to set up a Stage B container now. It could be helpful to reuse the cache since it's the same run ID and worker path. The compactor container might not require the reference cache, which is alright. I don't need a seed for this run. I plan to run the container with settings like FINAL_EXPORT_STAGE=compact and allow partial true, while considering a maximum of 20 shards. I'll also think about auditing and validation on one video. Let's move forward!


The Docker Stage B smoke test finished cleanly. I’m running the same audit and strict one-by-one validator against the Docker-produced run now, plus a quick shard video-ID check so we know the container outputs are clean too.

**Auditing with Docker**

I want to focus on running an audit and validator within Docker. I think we can utilize the same scripts as before, which is convenient. There's an idea of performing a contamination check, but for that, we might only need one video. Running everything in parallel could speed things up. I need to explore the best way to structure this process for efficiency and effectiveness. Let's keep it organized!


The Docker-produced outputs passed the manifest audit and the shard contamination check, but I hit a DB pooler quirk inside the validator script itself. I’m patching the validator to disable prepared-statement caching on its direct `asyncpg` connection, then I’ll rerun that final container-output validation.

**Analyzing validator patch**

I need to use `statement_cache_size=0` for the patch validator. Then, I’ll rerun the Docker validator to check if the earlier strict validator was successful and didn’t encounter issues. It’s weird; maybe the connection affects the statement cache too. I’m wondering if there’s a correlation with the fewer query counts. That feels odd. So, I'll patch to prevent this problem and rerun the Docker validator. Also, I should run the r2scoped validator for reassurance.


I’ve patched the validator itself. I’m rerunning just that last Docker-output validation now; if it passes, the local image smoke test is fully closed.

**Reassessing the process**

I think I need to run the docker validator again, as it might not be necessary to rerun the entire compile. I'm weighing whether r2scoped is actually needed, but I'm leaning towards just focusing on the docker validator for now. It's all about making sure I’m moving in the right direction without overcomplicating things. So, let's stick to the essentials and just rerun the docker validator!


Everything code-path related is now validated locally, including the container run. I’m doing the last non-destructive deployment prep step now: a Vast.ai dry run of the new deploy script, but I won’t create any instances until you explicitly signal.

**Updating tasks efficiently**

I'm thinking about how to update my to-do list without making it too verbose. It's easy to get carried away with details, so I'll try to keep it concise while still being clear. I want to ensure that I capture everything important without overwhelming myself or others. Balancing clarity and brevity can be a bit tricky, but I know it's essential for staying organized! Let's see how I can make these updates smoother.


I hit a trivial validation mistake of my own there: I asked Python to compile a shell script. I’m checking the deploy script with the right shell syntax checker now and then I’ll wrap up with the exact ready-state summary.

**Checking shell script syntax**

Oh no, I made a mistake thinking I could use py_compile on a shell script! I actually need to run `bash -n` to check the syntax instead. It’s a silly mistake, and I should have caught that earlier. I want to make sure I get this right, so let’s go ahead and check the shell script syntax properly. I'll take care of it now!


**Updating task status**

I might need to update my to-do list. I've completed the R2 flow, validator, and rerun for Docker. The user asked me to wait before deploying, so I can mark the task as completed with a note saying it’s just prepared, not actually run. I'll mention that the actual Vast deployment is pending the user's signal. I think it’s reasonable to mark it as completed but clarify that the actual creation isn’t done yet.


The pre-deploy work is finished. The pipeline is now in a state where the only thing I have not done is the real Vast.ai instance creation, because you asked me to stop before that.

## Ready State
I finished the remaining implementation work and closed the main correctness gaps:

- Inline variant fields are now read directly from `final_data/final_cleaned_segments_with_variants_rerouted_repetition_filtered.parquet`.
- Reference snapshots can now be uploaded to R2 once and downloaded by workers at startup into a local cache.
- Workers now preserve the reference cache across restarts and only clean per-video scratch data.
- The compactor is now scoped by `run_id`, so it cannot mix microshards from different runs.
- A strict validator now replays source audio and compares expected outputs against actual microshards and final shards one by one.
- A dedicated Docker image and runner were added for final export.
- A Vast deploy script was added and dry-run validated.

## What Was Validated
I validated three levels of execution.

`pilot-final-export-inline-20260312`
- Local-reference mode
- 3 real videos
- `114` expected kept segments
- Strict validator passed for both microshards and final shards
- Manifest audit passed for every shard

`pilot-final-export-r2scoped-20260312`
- Clean R2-reference mode
- 2 real videos
- `70` expected kept segments
- Strict validator passed for both microshards and final shards
- Manifest audit passed for every shard
- Shard contamination check passed: only the intended videos were present

`pilot-final-export-docker-20260312`
- Local Docker image
- Stage A container run + Stage B container run
- 1 real video
- `44` expected kept segments
- Strict validator passed for both microshards and final shards
- Manifest audit passed for every shard
- Shard contamination check passed

I also verified the startup cache behavior separately:
- First `r2` worker startup downloaded the full reference set from `final-export-reference/20260312-v1`
- Second startup with the same cache path reused the cached `canonical`, `raw_transcripts`, `validation`, and `youtube_meta` files instead of re-downloading them

## Bugs Found And Fixed
I did find real issues during validation and fixed them:

- Local-mode reference store failed because its DuckDB cache directory was not created before open.
- Worker cleanup was deleting the reference cache, which broke restart reuse.
- Compactor was claiming microshards across runs by language only; it now filters by `run_id`.
- Validator needed `statement_cache_size=0` for your pooled Postgres endpoint.

One earlier pilot run exposed the mixed-run compactor bug. That run is superseded by the clean scoped run above and should not be treated as authoritative.

## Deployment Prep
Reference snapshots are uploaded to R2 under:

- `s3://1-cleaned-data/final-export-reference/20260312-v1/`

The local image was built successfully as:

- `final-export-worker:local`

The new deployment assets are ready:

- `Dockerfile.final_export`
- `scripts/run_final_export_worker.sh`
- `scripts/deploy_final_export.sh`

I also ran a Vast dry run successfully. I did not create any real Vast instances, and I did not push the Docker image yet.

The next step is your signal to proceed with the real deployment sequence:
1. Tag and push the final export image.
2. Launch 1 Vast worker and observe.
3. Then scale to 5, 10, 20, and beyond based on ETA and utilization.