zhiminy/EvalEng huggingface dataset:
Repository: Giskard-AI/giskard-oss
Issue: Dataset deleted from backend but still displayed on frontend
GitHub labels: yt

p:blocker

[GSK-297](https://giskard.youtrack.cloud/issue/GSK-297)



Dataset has been deleted from the backend but is still displayed on the frontend .

 To replicate the issu : <https://dev.giskard.ai/main/projects/191/datasets>



![](image.png){width=70%}



Project: Giskard
Reporter: princy
Created: 2022-09-28
Repository: Giskard-AI/giskard-oss
Issue: httpretty conflict with other libraries needing connection
Root cause: External Dependency Breakage

[GSK-395](https://giskard.youtrack.cloud/issue/GSK-395)







Project: Giskard
Reporter: rabah
Created: 2022-12-02

This was only relevant in the case of model upload unit tests. For instance, `httpretty` interferes with `h2o.init()`. But eventually we decided that there was no need to invoke httpretty for these specfic unit-tests.
Repository: Giskard-AI/giskard-oss
Issue: Class based model upload
Root cause: Unimplemented Feature Gap

[GSK-336](https://giskard.youtrack.cloud/issue/GSK-336)







Project: Giskard
Reporter: aavtomonov
Created: 2022-11-07

List of PRs:
- https://github.com/Giskard-AI/giskard/pull/376

List of daughter issues:
- https://github.com/Giskard-AI/giskard/issues/374
- https://github.com/Giskard-AI/giskard/issues/398
- https://github.com/Giskard-AI/giskard/issues/402
- https://github.com/Giskard-AI/giskard/issues/405
Repository: Giskard-AI/giskard-oss
Issue: remove feature_names in the upload of model
GitHub labels: new-upload-api

yt

p:normal

[GSK-390](https://giskard.youtrack.cloud/issue/GSK-390)



Problem with unifai, column_names has variable



**Feedback by unifai**

'fatearesnames" is redudundant with "feature names"

"people forget to put metadata column in column-names"



Project: Giskard
Reporter: jmjm
Created: 2022-11-29
Repository: Giskard-AI/giskard-oss
Issue: Impossible to delete datasets that are already in use in Giskard
GitHub labels: yt

p:blocker

yt_client:citibeats

[GSK-284](https://giskard.youtrack.cloud/issue/GSK-284)



To reproduce the problem, you can try to delete the datasets here : <https://dev.giskard.ai/main/projects/191/datasets>



Project: Giskard
Reporter: jmjm
Created: 2022-09-13
Repository: Giskard-AI/giskard-oss
Issue: Make LIME categories searcheable
GitHub labels: yt

p:normal

yt_client:unifai

[GSK-394](https://giskard.youtrack.cloud/issue/GSK-394)



Max from Unifai:

Ah et autre feedback d’UI \<@U02GW359JDV\>: quand je veux tester un autre label pour le lime, par exemple le actual au lieu du predicted, et que j’ai beaucoup de labels, je suis obligé de scroller pour le trouver.

ça serait pas mal une petite search, là j’en ai 697, et pour trouver le bon code c’est rock’n’roll



![](image.png){width=70%}



Project: Giskard
Reporter: aavtomonov
Created: 2022-12-02
Repository: Giskard-AI/giskard-oss
Issue: Error in Explanation of a pytorch NLP model
GitHub labels: yt

p:normal

[GSK-393](https://giskard.youtrack.cloud/issue/GSK-393)



I'm currently getting the following error when attempting to explain a NLP model:

```
Traceback (most recent call last):
  File "/Users/rak/Documents/giskard/python-client/giskard/ml_worker/core/model_explanation.py", line 67, in explain_text
    text_explainer.fit(text_document, prediction_function)
  File "/Users/rak/Documents/giskard/python-client/.venv/lib/python3.7/site-packages/eli5/lime/lime.py", line 265, in fit
    random_state=self.rng_
  File "/Users/rak/Documents/giskard/python-client/.venv/lib/python3.7/site-packages/eli5/lime/lime.py", line 367, in _train_local_classifier
    random_state=rng)
  File "/Users/rak/Documents/giskard/python-client/.venv/lib/python3.7/site-packages/eli5/lime/utils.py", line 32, in fit_proba
    random_state=random_state,
  File "/Users/rak/Documents/giskard/python-client/.venv/lib/python3.7/site-packages/eli5/lime/utils.py", line 111, in expanded_X_y_sample_weights
    sample_weight
  File "/Users/rak/Documents/giskard/python-client/.venv/lib/python3.7/site-packages/eli5/lime/utils.py", line 145, in expand_dataset
    for label in rng.choice(classes, size=factor, p=probs):
  File "mtrand.pyx", line 937, in numpy.random.mtrand.RandomState.choice
ValueError: probabilities are not non-negative

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/rak/Documents/giskard/python-client/giskard/ml_worker/utils/error_interceptor.py", line 43, in wrapper
    res = behavior(request, context)
  File "/Users/rak/Documents/giskard/python-client/giskard/ml_worker/server/ml_worker_service.py", line 182, in explainText
    html_response = explain_text(model, input_df, text_column, text_document, n_samples)._repr_html_()
  File "/Users/rak/Documents/giskard/python-client/giskard/ml_worker/utils/logging.py", line 74, in wrap
    result = fn(*args, **kw)
  File "/Users/rak/Documents/giskard/python-client/giskard/ml_worker/core/model_explanation.py", line 71, in explain_text
    raise Exception("Failed to create text explanation") from e
Exception: Failed to create text explanation
```



Project: Giskard
Reporter: rabah
Created: 2022-11-30
Repository: Giskard-AI/giskard-oss
Issue: HuggingFace POC
GitHub labels: yt

p:normal

[GSK-271](https://giskard.youtrack.cloud/issue/GSK-271)





* [x] deploy it on HF side



Project: Giskard
Reporter: alex
Created: 2022-09-08

https://huggingface.co/spaces/giskardai/giskard

Hi @thomasabou01,

There is a problem with the explanation API; could you look into it?
<img width="1429" alt="CleanShot 2022-12-13 at 15 14 16@2x" src="https://user-images.githubusercontent.com/4739848/207356792-5d7a0906-b3a8-45cb-aa07-b4dd3d1d2a72.png">

Thanks! 

hey @alexcombessie. I think this behaviour really depends of the state of machine behind our space. Even before that I spotted different issues with the space feature of HF like huge building time differences of two exact same images.
I tried to reproduce this behaviour several time with 500 samples. Some times it's working, some time not. In addition, with the exact same image I tried to reproduce on local and couldn't spot the bug.
To stabilize the application, I decreased the number of samples to 20.

That's a good workaround; let's do that

Alex Combessie
Co-founder & CEO @ *Giskard*
*Eliminate risks of AI biases*
Website: giskard.ai ( https://bit.ly/364GAGi )
*⭐️ Star us on G itHub ( https://bit.ly/37aWG1y )*
( https://bit.ly/37aWG1y )

On Wed, Dec 14, 2022 at 09:58:22, Thomas Abou < ***@***.*** > wrote:

> 
> 
> 
> hey @ alexcombessie ( https://github.com/alexcombessie ). I think this
> behaviour really depends of the state of machine behind our space. Even
> before that I spotted different issues with the space feature of HF like
> huge building time differences of two exact same images.
> I tried to reproduce this behaviour several time with 500 samples. Some
> times it's working, some time not. In addition, with the exact same image
> I tried to reproduce on local and couldn't spot the bug.
> To stabilize the application, I decreased the number of samples to 20.
> 
> 
> 
> —
> Reply to this email directly, view it on GitHub (
> https://github.com/Giskard-AI/giskard/issues/390#issuecomment-1350667043 )
> , or unsubscribe (
> https://github.com/notifications/unsubscribe-auth/ABEFGCHHCMIH7PQIFUODWZ3WNGD25ANCNFSM6AAAAAASUDXKBI
> ).
> You are receiving this because you were mentioned. Message ID: <Giskard-AI/giskard/issues/390/1350667043
> @ github. com>
> 
> 
>

@alexcombessie did you have anything else in mind to do for this card ?

@andreybavt we have to wait for 2 blockers before release
- for us to remove user access management
- for HF to add volume persistency to their Spaces

Also we need to write some product marketing content - FYI @jmsquare 
- documentation on huggingface github
- documentation on our page
- product launch article
- demo video
Repository: Giskard-AI/giskard-oss
Issue: get rid of Lime html generation
Root cause: Architectural Constraint

[GSK-355](https://giskard.youtrack.cloud/issue/GSK-355)



currently we call `text_explainer.show_prediction` to generate Lime explanations. It requires us to install ipython just to be able to generate HTML on top of raw Lime values.

Instead of adding ipython dependency we should visualize lime values on the frontend



Project: Giskard
Reporter: aavtomonov
Created: 2022-11-14
Repository: Giskard-AI/giskard-oss
Issue: test_diff* tests fail with zero division if there are no correct predictions for the dataset
Root cause: Validation Gap

[GSK-391](https://giskard.youtrack.cloud/issue/GSK-391)



instead of throwing an exception a test should fail with a human-readable message



Project: Giskard
Reporter: aavtomonov
Created: 2022-11-29
Repository: Giskard-AI/giskard-oss
Issue: AWS AMI is not working anymore
Root cause: External Dependency Breakage

[GSK-389](https://giskard.youtrack.cloud/issue/GSK-389)







Project: Giskard
Reporter: jmjm
Created: 2022-11-29

TODO: invesigate how we can get AMI back, for now we decided to focus on `docker compose` installation
old AMI to be removed
Repository: Giskard-AI/giskard-oss
Issue: Improve the error log of `_validate_model_is_pickleable`
GitHub labels: new-upload-api

yt

p:normal

[GSK-387](https://giskard.youtrack.cloud/issue/GSK-387)



Expanding the model upload error to:

```
ValueError: Unable to pickle or unpickle model on Giskard -- check if the uploaded model is supported by Giskard: https://docs.giskard.ai/verified-models for more info
```

related to: <https://github.com/Giskard-AI/giskard/issues/374>

and: <https://giskard.youtrack.cloud/agiles/121-12/current?issue=GSK-336>



Project: Giskard
Reporter: rabah
Created: 2022-11-28

@rabah-khalek is this one still in progress?

@andreybavt yes. This is a daughter issue of https://github.com/Giskard-AI/giskard/issues/384. I can take care of linking the related issues to the model-API if you prefer.
Repository: Giskard-AI/giskard-oss
Issue: H2O models unable to unpickle on Giskard
Root cause: Unimplemented Feature Gap

[GSK-388](https://giskard.youtrack.cloud/issue/GSK-388)



github-issue: <https://github.com/Giskard-AI/giskard/issues/374>



Project: Giskard
Reporter: rabah
Created: 2022-11-28

just to be clear: H2O models serialization won't be supported for giskard v2.0
Repository: Giskard-AI/giskard-oss
Issue: "Email Classification Model logistic_regression" demo model doesn't work
Root cause: Unimplemented Feature Gap

[GSK-383](https://giskard.youtrack.cloud/issue/GSK-383)





![](Screenshot 2022-11-22 at 20.10.38.png){width=70%}



Project: Giskard
Reporter: aavtomonov
Created: 2022-11-22
Repository: Giskard-AI/giskard-oss
Issue: tensorflow-text doesnt work unless imported in grpc_mapper.py
Root cause: Architectural Constraint

[GSK-286](https://giskard.youtrack.cloud/issue/GSK-286)



tensorflow-text is an important library in NLP and it requires to be imported in the file where the model is unpickled which requires us to add tensorflow in giskard package which would increase the docker image size. 



Here  is the notebook to reproduce the error <https://colab.research.google.com/drive/1SBitRY5Fc_fwwHBhZwqE0jwUntavi3Qy>



Project: Giskard
Reporter: princy
Created: 2022-09-16
Repository: Giskard-AI/giskard-oss
Issue: Unable to pickle or unpickle model on Giskard (tensorflow==2.11.0)
Root cause: External Dependency Breakage

[GSK-382](https://giskard.youtrack.cloud/issue/GSK-382)



linked to: <https://giskard.youtrack.cloud/agiles/121-12/current?issue=GSK-347>

![](Screenshot 2022-11-22 at 14.41.21.png){width=70%}

I'm getting this error when trying to inspect






Project: Giskard
Reporter: rabah
Created: 2022-11-22

This issue turns out not to be related to the python version, rather the tensorflow one.
we're having currently the above errors with any python using tensorflow==2.11.
the issue dissapears when using tensorflow==2.9.2 (compatible also with python3.10).

solution: https://github.com/Giskard-AI/giskard-examples/pull/6, indicate 2.9.2 version for tensorflow in the examples notebook.

The corresponding unit-test is: https://github.com/Giskard-AI/giskard/blob/GSK-336_model-API/python-client/tests/model_upload/test_tensorflow.py

This should be solved by the Giskard model-API as we'll be using internal saving method from 2.11.
Repository: Giskard-AI/giskard-oss
Issue: Change the error message: doc link is not updated anymore
GitHub labels: bug

good first issue

yt

p:critical

d:easy

[GSK-386](https://giskard.youtrack.cloud/issue/GSK-386)



the configuration page changes in the doc. Instead of "To install it, refer to...", put "To have more information on ML Worker, please see: <https://docs.giskard.ai/start/guides/installation/ml-worker>



![](image.png){width=70%}



Project: Giskard
Reporter: jmjm
Created: 2022-11-24
Repository: Giskard-AI/giskard-oss
Issue: In GCP workbench, !pip install zstandard==0.15.2 does not work
Root cause: External Dependency Breakage

[GSK-385](https://giskard.youtrack.cloud/issue/GSK-385)



giskard is not installable in GCP workbench "managed notebook". It is only installable with "user managed notebooks". Here is the error:

![](image.png){width=70%}



The problem is coming from zstandard==0.15.2. Maybe upgrade to 0.19



Project: Giskard
Reporter: jmjm
Created: 2022-11-24
Repository: Giskard-AI/giskard-oss
Issue: TFAutoModel has issues while unpickling
Root cause: External Dependency Breakage

[GSK-285](https://giskard.youtrack.cloud/issue/GSK-285)



TFAutomodel does not have the .bert function of TFBertModel which solves the known unpicking issue (<https://github.com/huggingface/transformers/issues/3627>) forcing the users to use only TFBertModel instead of TFAutomodel. This is NOT OUR BUG but we will face integration issues in future. Creating card to keep track of the issue.



Project: Giskard
Reporter: princy
Created: 2022-09-16

I have same issue unpickling the model, but I found solved for model.save() and load_model. if the [link](https://github.com/huggingface/transformers/issues/3627) helps
Repository: Giskard-AI/giskard-oss
Issue: Give an explicit error message for get_project when the projet-id does not exist
GitHub labels: good first issue

yt

p:normal

[GSK-313](https://giskard.youtrack.cloud/issue/GSK-313)



Today when we execute get_project when the project id is not created it returns the following error message

![](image.png){width=70%}



Project: Giskard
Reporter: jmjm
Created: 2022-10-10
Repository: Giskard-AI/giskard-oss
Issue: Error in metamorphic decreasing number of perturbed row show wrong results
Root cause: Algorithmic Error

[GSK-352](https://giskard.youtrack.cloud/issue/GSK-352)



![](image.png){width=70%}



Project: Giskard
Reporter: jmjm
Created: 2022-11-14

@jmsquare , could you elaborate a bit on this?

It could happen that the perturbation doesn't change the prediction except for 3 entries among many.

Which project is that one?
Repository: Giskard-AI/giskard-oss
Issue: Email Classification Demo  Logistic Regression Model throws error while running on docker
Root cause: Environment Incompatibility

[GSK-384](https://giskard.youtrack.cloud/issue/GSK-384)



![](image.png){width=70%}



Project: Giskard
Reporter: princy
Created: 2022-11-23
Repository: Giskard-AI/giskard-oss
Issue: Manual Test Plan: python 3.10
GitHub labels: yt

p:normal

[GSK-347](https://giskard.youtrack.cloud/issue/GSK-347)



https://www.notion.so/giskard/Manual-Test-Plan-1416550d02594764baa0c12f2bd3abe6



Project: Giskard
Reporter: aavtomonov
Created: 2022-11-08
Repository: Giskard-AI/giskard-oss
Issue: Make all the tests that are automatically generated from demo projects work
Root cause: Unimplemented Feature Gap

[GSK-342](https://giskard.youtrack.cloud/issue/GSK-342)



Especially metamophic tests (tests for citibeats for example) from automatic testing are not working for ENRON Demo project. See comments in GSK-321



Project: Giskard
Reporter: jmjm
Created: 2022-11-08
Repository: Giskard-AI/giskard-oss
Issue: update notebooks with the external ml-worker implementation
Root cause: Documentation Deficiency

[GSK-322](https://giskard.youtrack.cloud/issue/GSK-322)



* [ ] from giskard import GiskardClient
* [ ] giskard worker start -d



Project: Giskard
Reporter: princy
Created: 2022-10-17
Repository: Giskard-AI/giskard-oss
Issue: Add more unit tests to cover error scenarios in giskard-client
GitHub labels: yt

p:normal

[GSK-301](https://giskard.youtrack.cloud/issue/GSK-301)



add more unit tests to cover error scenarios in giskard-client



Project: Giskard
Reporter: princy
Created: 2022-09-30
Repository: Giskard-AI/giskard-oss
Issue: Create a 80% rule (Disparate impact) as a test (Pareto rule)
Root cause: Unimplemented Feature Gap

[GSK-278](https://giskard.youtrack.cloud/issue/GSK-278)



If the average probability between men and female is different than 0.1, make the test fail

Check if the process of <https://www.notion.so/giskard/Contributing-a-test-in-Giskard-repo-8280ad1b921a4088bd31e751400987b1> is working



Project: Giskard
Reporter: jmjm
Created: 2022-09-12
Repository: Giskard-AI/giskard-oss
Issue: Triggered an assert when reduced dataset of ENRON
Root cause: Validation Gap

[GSK-362](https://giskard.youtrack.cloud/issue/GSK-362)



When we reduced the datasets of ENRON down to 10, it triggered the following assert:

```python
            assert not len(
                non_declared_categories
            ), f'Predicted classes don\'t exist in the dataset "{actual_slice.target}" column: {non_declared_categories}'
```

giving:

```text
  File "/Users/rak/Documents/giskard-client/giskard/ml_worker/testing/performance_tests.py", line 61, in test_auc
    ), f'Predicted classes don\'t exist in the dataset "{actual_slice.target}" column: {non_declared_categories}'
AssertionError: Predicted classes don't exist in the dataset "Target" column: {'INTERNAL'}
```

that's because the reduced dataset's target didn't cover all the possible outputs that exist in the full dataset, and more specifically that are predicted by the model.



This causes only the AUC test to fail.

Best solution: we should be more careful in the way we reduce the dataset and redo the exercise by picking entries that have all the possible labels.

linked to <https://giskard.youtrack.cloud/issue/GSK-342/Make-all-the-tests-that-are-automatically-generated-from-demo-projects-work>



Project: Giskard
Reporter: rabah
Created: 2022-11-15
Repository: Giskard-AI/giskard-oss
Issue: Reduce default number of lime samples for enron project
Root cause: Configuration Error

[GSK-356](https://giskard.youtrack.cloud/issue/GSK-356)



Actually, running lime by default on enron with the pytorch model is pretty long (~30mn). We will switch from 500 to 5 repetitions



Project: Giskard
Reporter: thomasabou
Created: 2022-11-15
Repository: Giskard-AI/giskard-oss
Issue: Remove "delete test suite" button from test screen
GitHub labels: yt

p:normal

yt_client:cgi

[GSK-353](https://giskard.youtrack.cloud/issue/GSK-353)



C’est deux boutons identiques sont dangereux. Y a moyen pour cette release de demander à Thomas de supprimer le bouton “detete test suite”



Project: Giskard
Reporter: aavtomonov
Created: 2022-11-14
Repository: Giskard-AI/giskard-oss
Issue: Change the location of the RUN button in the test creation window
GitHub labels: yt

p:normal

[GSK-374](https://giskard.youtrack.cloud/issue/GSK-374)



Le bouton RUN du test est pas accessible , il faut scroller



Project: Giskard
Reporter: jmjm
Created: 2022-11-17
Repository: Giskard-AI/giskard-oss
Issue: Check if we can exectute test and explanation at the same time
Root cause: Resource Mishandling

[GSK-373](https://giskard.youtrack.cloud/issue/GSK-373)



Quand on execute un test,  alors que l'explanation travaille. Il y a un message d'erreur. Put Lime with a big sample value and check if tet are executable



Project: Giskard
Reporter: jmjm
Created: 2022-11-17
Repository: Giskard-AI/giskard-oss
Issue: ML Worker -d option not working on Amazon Sagemaker with Huggingface transformers (sentiment analysis notebook)
Root cause: Validation Gap

[GSK-359](https://giskard.youtrack.cloud/issue/GSK-359)



````2022-11-15 10:55:35,321 pid:21137 MainThread grpc._cython.cygrpc ERROR    Unexpected [PermissionError] raised by servicer method [/worker.MLWorker/runModelForDataFrame]
Traceback (most recent call last):
  File "src/python/grpcio/grpc/_cython/_cygrpc/aio/server.pyx.pxi", line 682, in grpc._cython.cygrpc._handle_exceptions
  File "src/python/grpcio/grpc/_cython/_cygrpc/aio/server.pyx.pxi", line 796, in _handle_rpc
  File "src/python/grpcio/grpc/_cython/_cygrpc/aio/server.pyx.pxi", line 547, in _handle_unary_unary_rpc
  File "src/python/grpcio/grpc/_cython/_cygrpc/aio/server.pyx.pxi", line 411, in _finish_handler_with_unary_response
  File "/home/ec2-user/anaconda3/envs/amazonei_pytorch_latest_p37/lib/python3.7/concurrent/futures/thread.py", line 57, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/home/ec2-user/anaconda3/envs/amazonei_pytorch_latest_p37/lib/python3.7/site-packages/giskard/ml_worker/server/ml_worker_service.py", line 193, in runModelForDataFrame
    predictions = model.run_predict(ds)
  File "/home/ec2-user/anaconda3/envs/amazonei_pytorch_latest_p37/lib/python3.7/site-packages/giskard/ml_worker/core/model.py", line 41, in run_predict
    raw_prediction = self.prediction_function(df)
  File "/home/ec2-user/anaconda3/envs/amazonei_pytorch_latest_p37/lib/python3.7/site-packages/giskard/client/project.py", line 418, in &lt;lambda&gt;
    return lambda df: prediction_function(df[feature_names])
  File "/tmp/ipykernel_16299/3628593170.py", line 10, in predict
  File "/home/ec2-user/anaconda3/envs/amazonei_pytorch_latest_p37/lib/python3.7/site-packages/transformers/trainer.py", line 424, in __init__
    os.makedirs(self.args.output_dir, exist_ok=True)
  File "/home/ec2-user/anaconda3/envs/amazonei_pytorch_latest_p37/lib/python3.7/os.py", line 223, in makedirs
    mkdir(name, mode)
PermissionError: [Errno 13] Permission denied: 'tmp_trainer'```
````

Reproduced on AWS Sagemaker notebook connected to the SoYouStart instance
<http://ns3031812.ip-188-165-228.eu:19000/main/projects/42/inspection/107>



Project: Giskard
Reporter: alex
Created: 2022-11-15
Repository: Giskard-AI/giskard-oss
Issue: Add terraform step to create/delete ARM EC2 instance for each build
GitHub labels: yt

p:major

[GSK-371](https://giskard.youtrack.cloud/issue/GSK-371)



currently ARM builds are done on a `c6g.xlarge` EC2 machine which is $0.136/hour

machines with less RAM fail at frontend build



Project: Giskard
Reporter: aavtomonov
Created: 2022-11-17
Repository: Giskard-AI/giskard-oss
Issue: Do not predict the full dataset when creating an inspection
Root cause: Unimplemented Feature Gap

[GSK-370](https://giskard.youtrack.cloud/issue/GSK-370)



Today when we create an inspection we score the full dataset. This can take a long time and thus should be a background task. In this case at inspection creation time we could take first N rows.

The full prediction is needed to make filters work. In case of partial dataset scroring the filter could be disabled by default and user would trigger full scoring to enable it when needed.



Project: Giskard
Reporter: aavtomonov
Created: 2022-11-16
Repository: Giskard-AI/giskard-oss
Issue: Avoid cloning the repo to run GIskard
Root cause: Unimplemented Feature Gap

[GSK-369](https://giskard.youtrack.cloud/issue/GSK-369)



The downsides of the current installation process:

* [ ] the necessity to clone the whole repo, not just docker-compose.yml
* [ ] while cloning the repo, users get the `main` version, but `latest` image tag points to the latest release git tag. There's a chance of mismatch between images and docker-compose.yml
* [ ] implement `giskard backend start` to run `docker compose up -d` 






Project: Giskard
Reporter: aavtomonov
Created: 2022-11-16