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16 × Eureka!AttributeError: 'PosixPath' object has no attribute 'loc'
SarcasticSquirrel56 I'm assuming the artifacts is pandas and you forgot to either import before or add as requirement for the Task π
This is causing the artifact .get()
method to revert to returning the local path to the artifact, instead of actually de-serializing
(We should print a warning though, I'll make sure we do π )
EDIT: basically clearml failed to realize you also need pandas because it was never imported ...
What's the trains-server version?
Also there was a truck that worked in the previous big, could you zoom out in the browser, and see if you suddenly get the plot?
@<1597762318140182528:profile|EnchantingPenguin77> can you provide the full log?
GCP is being released to the SaaS and then should work its way to the open-source
Azure is till being worked on (only in beta at the moment)
Hey WickedGoat98
I found the bug, it is due to the fact the numpy (passed to plotly) contains both datetime and nan, and plotly.js does not like it. I'll make sure this is fixed, in the meantime you can just remove the first row (it contains the nan):df = pd.concat([tickerDf.Close, tickerDf_Change.Close_pcent], axis=1) df = df[1:]
I think it fails because it tries to install trains twice. Could you remove the trains package, and test? I'm also curious how do you have both installed?!
Also, I just wanted to say thanks for the tool! I'm managing a small data science practice and it's going to be really nice to have a view of all of the experiments we've got and know our GPU utilization, all without having to give every data scientist access to each box where the workflows are run. Incredibly stoked.
β₯ β€ β₯
User/pass should be enough,
Could it be the specific commit ID is not pushed?
I think this is the issue, it was search and replaced . The thing is I'm not sure the helm chart is updated to clearml. Let me check
Can you see all the agent in the UI (that basically means they are configured correctly and can connect to the server)
I think the limit is a few GB, I'm not sure, I'll have to check
And yes the oldest experiments will be deleted first (with the exception of published experiments, they will be deleted last)
WickedGoat98 sure that will not be complicated:
try something along the lines of :agent: networks: - backend container_name: clearml-agent image: allegroai/clearml-agent:latest restart: unless-stopped privileged: true environment: CLEARML_HOST_IP: ${CLEARML_HOST_IP} CLEARML_WEB_HOST: ${CLEARML_WEB_HOST:-} CLEARML_API_HOST:
`
CLEARML_FILES_HOST: ${CLEARML_FILES_HOST:-}
CLEARML_API_ACCESS_KEY: ${CLEARML_API_ACCESS_KEY:-}
...
First that is awesome to hear PanickyFish98 !
Can you send the full exception? You might be on to something...
2. Actually we thought of it, but could not find a use case, can you expand?
3. I'm not sure I follow, do you mean you expect the first execution to happen immediately?
Okay we have located the issue, thanks guys! We will push a patch release hopefully later today
Hi AstonishingWorm64
Is this the same ?
https://github.com/allegroai/clearml-serving/issues/1
(I think it was fixed on the later branch, we are releasing 0.3.2 later today with a fix)
Can you try:pip install git+
Sure. JitteryCoyote63 so what was the problem? can we fix something?
I presume is via theΒ
project_name
Β andΒ
task_name
Β parameters.
You are correct in your assumption, it only happens when you call Task.init but two distinctions:
ArgParser arguments are overridden (with trains-agent) even before Task.init is called Task.init when running under trains-agent will totally ignore the project/task name, it receives a pre-made task id, and uses it. So the project name and experiment are meaningless if you are running the tas...
Hmm I just tested on the community version and it seems to work there, Let me check with frontend guys. Can you verify it works for you on https://app.community.clear.ml/ ?
SmilingFrog76 this is not a weird mechanism at all , this is proper HPC scheduler πtrains-agent
is not actually aware of other nodes, it is responsible for launching a Task on its own hardware (with whatever configuration it was set). What can be done is to use the trains-agent
inside a 3rd party scheduler and have the scheduler allocate the node and trains-agent spin the experiment. There is a k8s example here: basically pulling jobs for the trains-server queue and pushing ...
we have some other parts, and for some cases we get initialization time can be about 10 times the experiment time
Before I dive into some agent in agent hacking, I would consider "caching" this preprocessing on an auxiliary Task as an artifact. Basically add another argument for the auxiliary Task, and fetch the data from it (obviously you will need to run it once before the optimizer launches the first experiment).
Now that is out of the way (which really would be the preferred engin...
Okay yes, that's exactly the reason!! Cross origin blocks the file link
Hmm could it be this is on the "helper functions" ?
Hi @<1523702000586330112:profile|FierceHamster54>
Nope π nothing to worry about.
That said do notice the open-source file-server is not secure, this does not mean it will spill data on the server, but it does mean that you should probably put it behind a VPN or use S3/GCP/Azure if this is open to the public internet