DepressedChimpanzee34 Have you noticed the "Show n experiments selected" button on the bottom bar? This effectively toggles your view between whatever is currently sorted/filtered and the current item selection.
To address the scenario you describe: Switch to "Show selected experiments", remove the redundant items, and switch back to the original view: "Show all experiments"
Thoughts?
SharpDove45 you can programmatically control the configured server using https://allegro.ai/clearml/docs/rst/references/clearml_python_ref/task_module/task_task.html?highlight=set_credentials#clearml.task.Task.set_credentials
WittyOwl57 I just used a couple of the experiments in the https://app.community.clear.ml/projects/764d8edf41474d77ad671db74583528d/ of the free tier server.
@<1628927672681762816:profile|GreasyKitten62> When you have specific display considerations, you can implement them through report_table's 'extra_layout' and 'extra_data' parameters
CooperativeSealion8 For future reference, notice there's a configuration reference available at https://allegro.ai/docs/references/trains_ref/
OutrageousSheep60 You can see https://github.com/allegroai/clearml/issues/724 a discussion on the topic.
TL;DR:
Currently the containing project is available in the UI as a tooltip to the dataset name An alternate "Project view" to the datasets page is in the works
Hi DefeatedCrab47 ,
The examples folder has just been restructured: Find the example here:
https://github.com/allegroai/trains/blob/master/examples/services/hyper-parameter-optimization/hyper_parameter_optimizer.py
MysteriousBee56 would providing Trains with an "import mode" (say, via environment or command line variable), which means that it should create a draft server entry, populate all the execution/environment info and exit before it actually starts employing the ML infrastructure address your use case?
DefeatedCrab47 For the most part, mlflow can serve basic ML models using scikit-learn. In contrast, Trains was designed with more general purpose ML/DL workflows in mind, for which there's no "generic" way to serve models as different scenarios can use different input encoding, models results would be represented in a variety of forms, etc.
Consider also, that creating an HTTP endpoint for model inference is quite a breeze: there are multiple examples of Flask on top of any DL/ML framework w...
RotundHedgehog76 Thanks for the spot - seems like docs are wrong, and CLI help is correct: '--skip-docker-network' will NOT pass '--network host' to the docker.
WittyOwl57 Is that information available for you on each of the compared experiments when you view them individually?
DepressedChimpanzee34 Experience has shown that some mechanisms for mitigating large sets impact on browser performance are required.
Your 2nd suggestion for adding an in-app search tool for such sections seems to be completely in line with ClearML's behaviour in other UI sections (e.g. console logs) - It'd be great if you can https://github.com/allegroai/clearml/issues/new/choose
@<1523709410411548672:profile|NuttyFox2> Since the default server user configuration does not require authentication, I'm assuming your use case calls for some users being authenticated where others are not?
Such mixed access mode is currently not on the near term roadmap for the OSS server - You should create a feature request to help push it into the development plan.
DepressedChimpanzee34 Thanks for clarifying where the current debug images display falls short for your use case - Extending the filtering to liken the behaviour of the scalars sound like a great idea 🙂
@<1559349204206227456:profile|BeefyStarfish55> try checking out the general overview on pipelines here , and info on the pipelines UI here .
Each step's arguments (and results) should appear in the steps details panel (which you could then follow to the underlying task for complete, in-depth, details).
@<1523701157564780544:profile|TenseOstrich47> The storage in question here is what's available on the machine hosting the ClearML server's docker containers (specifically, the ES one).
HappyDove3 you can get some more insight on the different configuration methods and how to use theme https://clear.ml/docs/latest/docs/fundamentals/hyperparameters
There's an example here to get you going @<1645597514990096384:profile|GrievingFish90> .
We'll definitely look into finding a place for this info in the ClearML docs.
DepressedChimpanzee34 ClearML tries to conserve storage by limiting the history length for debug images (see sdk.metrics.file_history_size https://clear.ml/docs/latest/docs/configs/clearml_conf#sdk-section ), though the history can indeed grow large by setting a large value or using a metric/variant naming scheme to circumvent this limit.
Does your use case call for accessing a specific iteration for all images or when looking at a specific image? Note that the debug image viewer (wh...
@<1523701157564780544:profile|TenseOstrich47> This is typically indicative of insufficient server disk space causing ES to go into read-only mode or turn active shards into inactive or unassigned (see FAQ ).
The disk watermarks controlling the ES free-disk constraints are defined by default as % of the disk space (so it might look to you like you still have plenty of space, but ES thinks otherwise). You can configure di...
From the https://github.com/allegroai/trains-server/releases/tag/0.13.0 :
Reports average load metrics per day (CPU/memory) Reports average workload per day (amount and average duration of queues, agents and experiments)
ExcitedFish86 You can https://clear.ml/docs/latest/docs/webapp/webapp_exp_table#adding-metrics-and--or-hyperparameters to include any parameter/metric column that helps your analysis (and subsequently filter the table on those columns).
There's not yet the equivalent of a parameter importance visualization, though such insight visualizations are definitely in our sights.
Sure appreciate if you can https://github.com/allegroai/clearml/issues/new on the subject :)
Take a look at https://clear.ml/docs/latest/docs/pipelines/pipelines_sdk_tasks#running-the-pipeline ;
By default pipelines are enqueued for execution by a ClearML Agent. You can explicitly change this behaviour in your code.
DepressedChimpanzee34 Always appreciated
GentleSwallow91 For more information, look at what ClearML logs for your experiments: https://docs-testing.allegro.ai/docs/latest/docs/fundamentals/task#logging-task-information
Hi JuicyOtter4
The GUI search returns all experiments in the project that have your search string in their task id, name, description or any of their models' names.
You can use regex with the '.*' button in the search bar.
SmarmySeaurchin8 Following up on ColossalDeer61 's hint, notice https://allegroai-trains.slack.com/archives/CTK20V944/p1597248476076700?thread_ts=1597248298.075500&cid=CTK20V944 not-too-old thread on reusing globally installed packages.
JitteryCoyote63 Not currently there, but certainly sounds like something to add to the list - Care to https://github.com/allegroai/clearml/issues/new/choose ?