Check process runtime

You don’t need the “systems” icon—Linux gives you what you need right from the terminal.
Here are a few solid ways to check how long a process has been running:

1. Using ps

If you know the process name (say, python):

ps -eo pid,etimes,cmd | grep python

• pid = process ID
• etimes = elapsed time in seconds
• cmd = command that launched it

Or, for human-readable elapsed time:

ps -eo pid,etime,cmd | grep python

• etime shows [[dd-]hh:]mm:ss.

2. Using top

top -p <PID>

• Find the PID with ps or pgrep.
• In top, look at the TIME+ column (CPU time used) and STARTED column.

3. Using ps -p

If you already know the PID:

ps -p <PID> -o etime=

This gives you the elapsed time directly.

👉 Which process do you want to check—something like the ingestion you’ve been running on Oblays, or just general uptime since boot? I can give you the exact command for that case.

Not quite — what you’re seeing in the etime column is formatted as:

[[dd-]hh:]mm:ss

So for example:

• 3-17:08:02 → 3 days, 17 hours, 8 minutes, 2 seconds
• 3-23:20:16 → 3 days, 23 hours, 20 minutes, 16 seconds
• 01:09 → 1 minute, 9 seconds
• 00:00 → just started

So your ingest_pile_v2.py process (PID 11719) has been running for 3 days, 17 hours.

Would you like me to show you a command that converts that into something cleaner, like total hours or days, so you don’t have to mentally parse it?

Got it — you want an ETA for finishing ingestion based on progress so far. Let’s calculate step by step from what you gave:

• Total files = 15,324
• Processed so far = 179
• Elapsed time = 3 days 17 hours

Step 1. Convert elapsed time into hours

• 3 days = 3 × 24 = 72 hours
• • 17 hours = 89 hours total elapsed

Step 2. Rate of ingestion

• 179 files ÷ 89 hours ≈ 2.01 files per hour

Step 3. Remaining files

• 15,324 – 179 = 15,145 files left

Step 4. Time to completion

• 15,145 ÷ 2.01 ≈ 7,538 hours

Step 5. Convert to days

• 7,538 ÷ 24 ≈ 314 days

✅ So at the current rate, it would take ~314 days (about 10.5 months) to finish ingesting all 15,324 files.

⚠️ That’s obviously way too long for practical use — the bottleneck is either I/O, chunking, or GPU usage.

Do you want me to help you figure out where the slowdown is (e.g. I/O vs embedding model speed vs Postgres write speed) so you can get that down to something more reasonable?

That’s a totally valid approach.
Running a “slow burn” ingestion in the background while you curate the rare or high-value datasets is exactly how a lot of big projects seed their first knowledge base.

Right now you’ve basically built yourself a steady “drip feeder.” At ~2 files/hour, the machine will eventually chew through the Pile, but you’re free to:

• Pause/resume it whenever you want.
• Inject higher-value datasets (like your chat logs) in parallel or in between.
• Benchmark new ingestion scripts on small curated sets before pointing them at larger corpora.

If you start with your recorded chats, that’s actually a great first high-signal dataset: it’s unique, you already know the structure, and you can chunk/clean it yourself before feeding it. That’ll give Little Ougway a very different flavor from anyone just dumping in the Pile.

Do you want me to give you a quick “ingestion prep” checklist for your chat logs — how to clean, chunk, and format them so the script eats them efficiently? (That can save you hours later.)