Hi @<1547028116780617728:profile|TimelyRabbit96>

Trying to do model inference on a video, so first step in

Preprocess

class is to extract frames.

Basically this depends on the RestAPI, usually would will be sending a link to data to be processed and returned Synchronously
What you should have a custom endpoint doing the extraction, send Raw data into another endpoint doing the model inference, basically think "pipeline" end points:
[None](https://github.com/allegro...

, but are you suggesting sending the requests to Triton frame-by-frame?

yes! trition backend will do the autobatching, and in an enterprise deployment the gRPC loadbalancer will split it across multiple GPU nodes 🙂

can we use a currently setup virtualenv by any chance?

You mean, if the cleamrl-agent needs to setup a new venv each time? are you running in docker mode ?
(by default it is caching the venv so the second time it is using a precached full venv, installing nothing)

notice that even inside docker the venv is cached on the host machine 🙂

no mention of STRING type ...

SmarmyDolphin68 What's the matplotlib version ? and python version?

Hi @<1547028116780617728:profile|TimelyRabbit96>
You are absolutely correct, we need to allow to override configuration
The code you want to change is here:
None
You can try:

channel = self._ext_grpc.aio.insecure_channel(triton_server_address, options=dict([('grpc.max_send_message_length', 512 * 1024 * 1024),  ('grpc.max_receive_message_len...

MysteriousBee56 , The agent is not running on the "server" it's running on its machine.
The server just reflects the fact he agent is up..
To actually take it down you need to SSH (or connect to that machine) and stop the actual trains-agent process.
What is exactly the scenario you had in mind?

Interesting question, should work and looks like an interesting combination, I'm curious what you come up with.
btw: grafana itself can already provide a lot of alerts for drift etc, this is basically their histogram delta feature

OK, I got it by modifying the .conf file and putting the credentials on node

Nice! 🙂

CrookedWalrus33 I found the issue, this is only failing with Python 3.6.
Let me check something

Thanks ReassuredTiger98 , yes that makes sense.
What's the python version you are using ?

Hi @<1570220858075516928:profile|SlipperySheep79>

Is there a way to specify the working dir from the decoratoe

not directly, but why would that change anything? I mean the coponent code will be created in the git root, and you can still access files inside the subfolders

from .subfolder import something

what am I missing?

SubstantialElk6 on the client side?

Interesting, do you think you could PR a "fixed" version ?
https://github.com/allegroai/clearml-web/blob/2b6aa6043c3f36e3349c6fe7235b77a3fddd[…]app/webapp-common/shared/single-graph/single-graph.component.ts

Hi @<1523703472304689152:profile|UpsetTurkey67>

I circumvented the problem by putting timestamp in task name, but I don't think this is necessary.

Just pass reuse_last_task_id=False to Task.init, it will never try to reuse them 🙂
None

You might need to update it to the latest Detection API 😞

MagnificentSeaurchin79 YEY!!!!
Very cool!

Do you feel like making it public, I have the feeling a lot of people will appreciate it, this is very useful 🙂

the second seems like a botocore issue :
https://github.com/boto/botocore/issues/2187

(But in venv mode is also hangs the same way)

Hmm this is strange, could it be you are running out of storage ?

suppose I have an S3 bucket where my data is stored and I wish to transfer it to ClearML file server.

Then you first have to download the entire bucket locally, then register the local copy.
Basically:

StorageManager.download_folder("

", "/target/folder")
# now register the local "/target/folder" with Dataset.add_files

if I encounter the need for that, I will adapt and open a PR 

Great!

Can you see it on the console ?

Releasing an RC

Okay, let's take a step back and I'll explain how things work.
When running the code (initially) and calling Task.init
A new experiment is created on the server, it automatically stores the git repo link, commit ID, and the local uncommitted changes . these are all stored on the experiment in the server.
Now assume the trains-agent is running on a different machine (which is always the case even if it is actually on the same machine).
The trains-agent will create a new virtual-environmen...

Hi SteadyFox10
Yes we changed the Web UI, to something more intuitive (but after you get used to the original design , I guess not that obvious).
After selecting a bunch of experiment, right click one of them, you will be able to archive them all (it will display the number of experiments you are about to archive)

try:
None

docker_install_opencv_libs: true

SoggyBeetle95 the question is, where does clearml stores these arguments, and the answer is on the Task object (from there the agent will take them and apply to the docker execution). Now since all users see all the tasks, they also see these arguments. Wdyt?

My main query is do I wait for it to be a sufficient batch size or do I just send each image as soon as it comes to train

This is usually a cost optimization issue, generally speaking if GPU up time is not an issue that the process is stochastic anyhow, so waiting for a batch or not is not the most important factor (unless you use batchnorm layer, in that case this is basically a must)

I would not be able to split the data into train test splits, and that it would be very expensiv...

?