Closed simonw closed 1 year ago
Then: https://replicate.com/docs/reference/http#predictions.get
GET https://api.replicate.com/v1/predictions/{prediction_id}
Returning:
{
"id": "rrr4z55ocneqzikepnug6xezpe",
"version": "be04660a5b93ef2aff61e3668dedb4cbeb14941e62a3fd5998364a32d613e35e",
"urls": {
"get": "https://api.replicate.com/v1/predictions/rrr4z55ocneqzikepnug6xezpe",
"cancel": "https://api.replicate.com/v1/predictions/rrr4z55ocneqzikepnug6xezpe/cancel"
},
"created_at": "2022-09-13T22:54:18.578761Z",
"started_at": "2022-09-13T22:54:19.438525Z",
"completed_at": "2022-09-13T22:54:23.236610Z",
"source": "api",
"status": "succeeded",
"input": {
"prompt": "oak tree with boletus growing on its branches"
},
"output": [
"https://replicate.com/api/models/stability-ai/stable-diffusion/files/9c3b6fe4-2d37-4571-a17a-83951b1cb120/out-0.png"
],
"error": null,
"logs": "Using seed: 36941...",
"metrics": {
"predict_time": 4.484541
}
}
I'm going to put these in a replicate_predictions
table.
It's not clear to me if I can get the crucial model name from this. The version
on its own is no good without the model - see https://replicate.com/docs/reference/http#models.versions.get which needs this:
GET https://api.replicate.com/v1/models/{model_owner}/{model_name}/versions/{version_id}
This is blocked on:
I may ship it anyway since it's still useful, especially for users who don't use many different models on Replicate.
I could even have LLM take a guess at the model name by looking up the version ID in its replicate JSON files.
I will store that as _model_guess
to avoid clashing with any fields they add tot their JSON in the future.
Another way to guess: look for URLs in the output
field:
"output": [
"https://replicate.com/api/models/stability-ai/stable-diffusion/files/9c3b6fe4-2d37-4571-a17a-83951b1cb120/out-0.png"
]
I'm going to put _model_guess
after id
before saving to the DB.
Looks like guessing based on output
doesn't work any more because they redesigned those URLs to look like this:
https://replicate.delivery/mgxm/e2b8944a-7aaa-4b19-b9a6-180fe6fa6ca6/out-0.png
Here's the code I had written that doesn't work:
def guess_model(data, version_to_model):
version = data["version"]
if version in version_to_model:
return version_to_model[version]
# Try to guess from the output
output = data.get("output") or []
if isinstance(output, list) and output:
first_url = output[0]
if (
first_url.startswith("https://replicate.com/api/models/")
and "/files/" in first_url
):
# https://replicate.com/api/models/stability-ai/stable-diffusion/files/...
parts = first_url.split("/")
owner = parts[5]
name = parts[6]
return "{}/{}".format(owner, name)
return None
I have 140 predictions right now, which means 142 API calls (2 to paginate through the list, 140 because I have to fetch details for each one).
I don't think the Replicate API has a rate limit on this, but in case it does I should make it so it doesn't attempt to fetch the same prediction twice, that way you can run the command again if it hits a rate limit error.
A progress bar would be really nice here.
Could I have a feature where it starts where you last left off? That depends on the order that the predictions endpoint returns - if it starts from most recent I could always fetch the first page but only fetch the second page if there are predictions on the first page I haven't seen before.
If it starts at the beginning then I'll always need to fetch every page of predictions. I can still save on the prediction details calls though.
The other case to consider is that sometimes a prediction is "pending" or "running" in which case I should fetch it again on subsequent runs of the command. Only predictions that have completed_at
of null should be fetched each time (unless they have status failed I think).
Moving to a PR.
I just spotted https://replicate.com/docs/reference/http#predictions.list in their docs: