EnviousStarfish54 we just fixed an issue that relates to "installed packages" on windows.
RC is due to be release in the upcoming days, I'll keep you posted
Yep that will fi it, nice one!!
BTW I think we should addtge ability to continue aborted datasets, wdyt?
PompousParrot44 Enterprise licensing pricing usually custom tailored to the size of the company and based on usage. If you are interested feel free to leave details in the "contact us" form on the website, and someone from sales will contact you shortly after.
JitteryCoyote63
are the calls from the agents made asynchronously/in a non blocking separate thread?
You mean like request processing on the apiserver are multi-threaded / multi-processed ?
@<1540142651142049792:profile|BurlyHorse22> do you mean the one refereed in the video ? (I think this is the raw data in kaggle)
and then?
The thing is programmatically this is not easy to do as API, because at the end the "function" (i.e. LCI) never leaves, it connects to the SSH and stays
But you can query the Task it creates, the project is known, the user is known and it is of special type/tag
BTW: is this on the community server or self-hosted (aka docker-compose)?
Can you see that the environment is actually being passed ?
. It is not possible to specify the full output destination right?
Correct 😞
Oh found it:temp.linux-aarch64-cpython-39this is Arm?!
Thanks @<1694157594333024256:profile|DisturbedParrot38> !
Nice catch.
Could you open a github issue so that at least we output a more informative error?
BTW: 0.14.3 solved the issue you are referring to, so you can import trains before / parsing the args without an issue. Regrading passing project/name as parameters. A few thoughts: (1) you can always rename / move projects from the UI (2) If you are running it with trains-agent there is no meaning to these arguments, as by definition the Task was already created... Maybe we should give an option to exclude a few arguments from argparser, I think this topic came up a few times... What d...
My apologies, let me rephrase:
if you are using pip ans package manager and not running in docker-mode, trains-agent cannot touch the cuda/cuddn drivers (actually .so) library.
If you want to verify you can check echo $LD_LIBRARY_PATH
Hi @<1545216070686609408:profile|EnthusiasticCow4>
Many of the dataset we work with are generated by SQL query.
The main question in these scenarios is, are those DB stable.
By that I mean, generally speaking DB serve applications, and from time to time they undergo migration (i.e. change in schema, more/less data etc).
The most stable way is to create a script that runs the SQL query, and creates a clearml dateset from it (that script becomes part of the Dataset, to have full tracta...
Oh you achieve exactly the same with plotly and te restapi/python interface.
Basically pull data from tasks , create visualization and log it on one if the Task or on a new one
Yep 🙂
Basically:
` task = Task.get_task(task_id='aaaa')
while task.status not in ('completed', 'stopped',):
do something ?
sleep(15) `(Notice task.status / task.get_status() will refresh the Task status on every call)
Hi GrievingTurkey78
I think the main issue is the lack of support for jsonargparse , is that correct ?
(vanilla pytorch lightning is using argpraser, which seems to work out of the box)
task = Task.init(project_name='debug', task_name='test tqdm cr cl') print('start') for i in tqdm.tqdm(range(100), dynamic_ncols=True,): sleep(1) print('done')This code snippet works as expected (console will show the progress at the flush interval without values in between). What's the difference ?!
VexedCat68
a Dataset is published, that activates a Dataset trigger. So if every day I publish one dataset, I activate a Dataset Trigger that day once it's published.
From this description it sounds like you created a trigger cycle, am I missing something ?
Basically you can break the cycle by saying, trigger only on New Dataset with a specific Tag (or create the auto dataset in a different project/sub-project).
This will stop your automatic dataset creation from triggering the "orig...
each of it gets pushed as a separate Model entity right?
Correct
But there’s only one unique model with multiple different version of it
Do you see multiple lines in the Model repository ? (every line is an entity) basically if you store it under the same local file, it will override the model entry (i.e. reuse it and upgrade the file itself), otherwise you are creating a new model, "version" will be progress in time ?
WickedGoat98 the agent itself can be executed on bare metal, no need to setup a docker for it (although fully supported)
Specifically the docker compose has the docker running in services mode, i.e. for CPU light weight tasks such as running pipelines .
If the agent running on GPU, the easiest way to is run on bare metal
Hi MortifiedCrow63
saw
, ...
By default ClearML will only log the exact local place where you stored the file, I assume this is it.
If you pass output_uri=True to the Task.init it will automatically upload the model to the files_server and then the model repository will point to the files_server (you can also have any object storage as model storage, e.g. output_uri=s3://bucket )
Notice yo...
HandsomeCrow5 Seems like the right place would be in the artifacts, as a summary of the experiment (as opposed to on going reporting), is that the case?
If it is then in the Artifacts tab clicking on the artifact should open another tab with your summary, which sounds like what you were looking for (with the exception of the preview thumbnail 🙂
Hi @<1523702969063706624:profile|PoisedShark13>
However, INSTALLED PACKAGES of my task is misses many of installed packages (any idea why?)
It automatically detects the directly imported packages, literally analyzing your code base and looking for imports
The derivative packages (i.e. the one that any of the "main" packages need, will be listed after the first time the agent installs everything)
If something specific is missing, you can manually add it with:
Task.add_requiremen...Hi @<1564785037834981376:profile|FrustratingBee69>
It's the previous container I've used for the task.
Notice that what you are configuring is the Default container, i.e. if the Task does not "request" a specific container, then this is what the agent will use.
On the Task itself (see Execution Tab, down below Container Image) you set the specific container for the Task. After you execute the Task on an Agent, the agent will put there the container it ended up using. This means that ...
let's call it an applicative project which has experiments and an abstract/parent project, or some other name that group applicative projects.
That was my way of thinking, the guys argued it will soon "deteriorate" into the first option :)
DeliciousBluewhale87 fyi, the new version of the pipeline (hopefully pushed towards the end of this week), will allow you to more easily write steps as functions (not only as Tasks, as the current implementation)
Also check the new Trigger and Scheduler both intended to trigger these pipelines:
https://github.com/allegroai/clearml/blob/fe3c481c37e70881c44d67c1cf9bbce00a84747e/clearml/automation/scheduler.py#L457
https://github.com/allegroai/clearml/blob/fe3c481c37e70881c44d67c1cf9bbce00a8...
Where exactly are the model files stored on the pod?
clearml cache folder, usually under ~/.clearml
Currently I encounter the problem that I always get a 404 HTTP error when I try to access the model via the...
How are you deploying it? I would start by debugging and runnign everything in the docker-compose (single machine) make sure you have everything running, and then deploy to the cluster
(becuase on a cluster level, it could be a general routing issue, way before getting t...