- Added API support to run auto-annotation on jobs
- Added API schema definitions for offline lambda call endpoints

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([cvat-canvas](https://github.com/opencv/cvat/tree/develop/cvat-canvas#versioning),

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[cvat-data](https://github.com/opencv/cvat/tree/develop/cvat-data#versioning)
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This is the CLI equivalent of #5718.

Fixed x-forwarded-proto header (No need to set the header explicitly, as it is set by Traefik). (#6308)

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Manually with docker-compose.https.yml config enabled

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([cvat-canvas](https://github.com/opencv/cvat/tree/develop/cvat-canvas#versioning),

[cvat-core](https://github.com/opencv/cvat/tree/develop/cvat-core#versioning),

[cvat-data](https://github.com/opencv/cvat/tree/develop/cvat-data#versioning)
and

[cvat-ui](https://github.com/opencv/cvat/tree/develop/cvat-ui#versioning))

### License

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https://github.com/opencv/cvat/blob/develop/LICENSE) that covers the
project.
  Feel free to contact the maintainers if that's a concern.

---------
Co-authored-by: NNikita Manovich <nikita@cvat.ai>

The Docker image for this function includes Conda, and uses `conda run`
to run the Nuclio processor. Unfortunately, `conda run` buffers the
entire output of the child process until that process exits, and since
the processor never exits, its logs are never printed (and slowly
consume memory).

Conda is actually completely useless here, so just get rid of it, which
fixes the problem.

In addition, apply a few other improvements:

* Synchronize the PyTorch and TransT versions between the CPU and GPU
   variants.

* Replace `opencv-python` with `opencv-python-headless`, which has fewer
   dependencies.

* Use the `ADD` command instead of wget.

Altogether, these improvements shave ~780 MB off the size of the CPU
image (didn't check for the GPU one).

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Removed extra interpolation layer

### Motivation and context
Fixed #6250

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- [ ] I have increased versions of npm packages if it is necessary

([cvat-canvas](https://github.com/opencv/cvat/tree/develop/cvat-canvas#versioning),

[cvat-core](https://github.com/opencv/cvat/tree/develop/cvat-core#versioning),

[cvat-data](https://github.com/opencv/cvat/tree/develop/cvat-data#versioning)
and

[cvat-ui](https://github.com/opencv/cvat/tree/develop/cvat-ui#versioning))

### License

- [x] I submit _my code changes_ under the same [MIT License](
https://github.com/opencv/cvat/blob/develop/LICENSE) that covers the
project.
  Feel free to contact the maintainers if that's a concern.

The server part of #6039 

- Added support for Ground Truth jobs in a task
- Added support for job creation and removal (only Ground Truth jobs can
  be created or removed in a task)
- Added a component to autocompute quality metrics for a task
- Added tests
- Fixed https://github.com/opencv/cvat/issues/5971 (both parts - the
  outside problem and the manifest problem, the manifest part fix is also
  available in #6216)
Co-authored-by: Nklakhov <kirill.9992@gmail.com>
Co-authored-by: NRoman Donchenko <roman@cvat.ai>
Co-authored-by: NKirill Sizov <kirill.sizov@cvat.ai>

Closes: https://github.com/opencv/cvat/issues/6261

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### Motivation and context
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fixes an open
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This kind of annotations was already mentioned in docs, but it was not
implemented.

### How has this been tested?
<!-- Please describe in detail how you tested your changes.
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see how your change affects other areas of the code, etc. -->

I just deployed it with help of the docker-compose.dev.yml you provided.

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file
- [ ] I have updated the documentation accordingly. (it was already in
docs)
- [ ] I have added tests to cover my changes
- [X] I have linked related issues (see [GitHub docs](

https://help.github.com/en/github/managing-your-work-on-github/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword))
- [x] I have increased versions of npm packages if it is necessary

([cvat-canvas](https://github.com/opencv/cvat/tree/develop/cvat-canvas#versioning),

[cvat-core](https://github.com/opencv/cvat/tree/develop/cvat-core#versioning),

[cvat-data](https://github.com/opencv/cvat/tree/develop/cvat-data#versioning)
and

[cvat-ui](https://github.com/opencv/cvat/tree/develop/cvat-ui#versioning))

### License

- [X] I submit _my code changes_ under the same [MIT License](
https://github.com/opencv/cvat/blob/develop/LICENSE) that covers the
project.
  Feel free to contact the maintainers if that's a concern.

---------
Co-authored-by: NBoris Sekachev <boris.sekachev@yandex.ru>
Co-authored-by: NBoris Sekachev <sekachev.bs@gmail.com>

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- [ ] I have updated the documentation accordingly
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- [ ] I have increased versions of npm packages if it is necessary

([cvat-canvas](https://github.com/opencv/cvat/tree/develop/cvat-canvas#versioning),

[cvat-core](https://github.com/opencv/cvat/tree/develop/cvat-core#versioning),

[cvat-data](https://github.com/opencv/cvat/tree/develop/cvat-data#versioning)
and

[cvat-ui](https://github.com/opencv/cvat/tree/develop/cvat-ui#versioning))

### License

- [ ] I submit _my code changes_ under the same [MIT License](
https://github.com/opencv/cvat/blob/develop/LICENSE) that covers the
project.
  Feel free to contact the maintainers if that's a concern.
Co-authored-by: NBoris Sekachev <boris.sekachev@yandex.ru>

Fixes #5061, #4179

- Added a way to declare custom file ordering for the local task data
uploads via TUS protocol
- Added an option to use a manifest to support the `predefined` sorting
method
- This file is required for the `predefined` sorting mode with image
archives
- Fixed file ordering when tasks are created from SDK or CLI in the
`predefined` sorting mode
- Added more tests for task data uploading API

The uploading protocol is implemented:

The user specifies `sorting_method=predefined` if the task creation
request. Then the data is uploaded.

1. Client files uploading
1.1. The files are uploaded as separate files (using the TUS protocol)
or grouped files (using the `Upload-Multiple` requests).
1.2. The `Upload-Finish` request comes (or its unlabeled legacy
equivalent). The new optional field can be supplied: `upload_file_order`
- a list of strings. It allows to override the input file order, if
necessary, and is only valid with the `predefined` sorting method
specified.
1.2.1. If the field is empty or missing, the client files in the data
requests are considered ordered.
1.2.2. If the field is not empty, a list containing the file list in the
required order is expected in the `upload_file_order` field.
1.2.2.1. If there are `client_files` in the request, the files are
sorted
1.2.2.2. If file lists mismatch, an explanatory error is raised.

2. Data processing
2.1. At this point, all `*_files` are considered ordered as requested.
2.2. Require a metafile for zip uploads with predefined sorting. The
file is expected to accompany the uploaded zip file, not to be inside of
the archive.
2.3. If there is a metafile in the input data, files are ordered after
the metafile.
2.3.1. If the data is extracted from cloud, only the specified subset of
the files is kept in the manifest.
2.3.2. If the upload data doesn't exist in the metafile, an error is
raised.
2.3.3. A `job_file_mapping` has higher priority than metafile, if
specified.
Co-authored-by: NRoman Donchenko <roman@cvat.ai>

Fixed:
- wrong location of tmp file when importing job annotations
- ```Traceback (most recent call last):
File
"/home/maya/Documents/cvat/.env/lib/python3.8/site-packages/rq/worker.py",
line 795, in work
      self.execute_job(job, queue)
File "/home/maya/Documents/cvat/cvat/rqworker.py", line 37, in
execute_job
      return self.perform_job(*args, **kwargs)
File
"/home/maya/Documents/cvat/.env/lib/python3.8/site-packages/rq/worker.py",
line 1389, in perform_job
      self.handle_exception(job, *exc_info)
File
"/home/maya/Documents/cvat/.env/lib/python3.8/site-packages/rq/worker.py",
line 1438, in handle_exception
      fallthrough = handler(job, *exc_info)
File "/home/maya/Documents/cvat/cvat/apps/engine/views.py", line 2233,
in rq_exception_handler
      rq_job.exc_info = "".join(
  AttributeError: can't set attribute
  ```

Resolves https://github.com/opencv/cvat/issues/5773
Resolves https://github.com/opencv/cvat/issues/5563

- root causes of the issues: 
  - the annotation file was uploaded to the server by tus protocol and
rq job was created but no one next requests for checking status were not
made. (e.g. user closed the browser tab)
  - the annotation file was uploaded to the server by tus protocol but
rq job has not yet been created (e.g cvat instance restarted)
  - tasks/projects creation from backups with the same name at the
same time by different users
Co-authored-by: NRoman Donchenko <roman@cvat.ai>
Co-authored-by: NMaxim Zhiltsov <zhiltsov.max35@gmail.com>

Currently, we don't have validation of incoming annotations, as a
result, there is exist some cases when annotations successfully saved in
DB, but it's impossible to export them. In order to successfully export
a dataset with a skeleton track it's required that each track satisfy
the following condition:
` {frame number of track} == {frame number of parent track} == {frame
number of the first shape of the track}`

This PR adds an additional step during saving annotation in DB. This
additional step check that all these there "frame numbers" are equal and
try to automatically fix it's not true.

For function names, take the relative path, lowercase, join with dashes
and replace underscores with dashes.

For image names, take the relative path, lowercase and join with dots.

In a couple cases, rename the function directory instead of fixing the
config.

- Reduced rest_api testing time by ~25% in my environment: 430s vs 560s
- Enabled gzip compression
- Fixed webhook tests that not actually waiting for the required number
of delivered messages in response.
- Fixed `preview` tests

### Motivation and context
Resolved #6037
Related https://github.com/opencv/cvat/issues/4400
Related https://github.com/opencv/cvat/issues/6028


![image](https://user-images.githubusercontent.com/49038720/236890662-c44b578e-5808-4fde-a216-2dcab6e95ab0.png)
Co-authored-by: NBoris Sekachev <boris.sekachev@yandex.ru>
Co-authored-by: NBoris Sekachev <sekachev.bs@gmail.com>
Co-authored-by: NRoman Donchenko <roman@cvat.ai>
Co-authored-by: NNikita Manovich <nikita@cvat.ai>

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### Motivation and context
Resolved #5706
Resolved #6022

### How has this been tested?
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- [x] I have increased versions of npm packages if it is necessary

([cvat-canvas](https://github.com/opencv/cvat/tree/develop/cvat-canvas#versioning),

[cvat-core](https://github.com/opencv/cvat/tree/develop/cvat-core#versioning),

[cvat-data](https://github.com/opencv/cvat/tree/develop/cvat-data#versioning)
and

[cvat-ui](https://github.com/opencv/cvat/tree/develop/cvat-ui#versioning))

### License

- [x] I submit _my code changes_ under the same [MIT License](
https://github.com/opencv/cvat/blob/develop/LICENSE) that covers the
project.
  Feel free to contact the maintainers if that's a concern.

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file
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- [x] I have increased versions of npm packages if it is necessary

([cvat-canvas](https://github.com/opencv/cvat/tree/develop/cvat-canvas#versioning),

[cvat-core](https://github.com/opencv/cvat/tree/develop/cvat-core#versioning),

[cvat-data](https://github.com/opencv/cvat/tree/develop/cvat-data#versioning)
and

[cvat-ui](https://github.com/opencv/cvat/tree/develop/cvat-ui#versioning))

### License

- [x] I submit _my code changes_ under the same [MIT License](
https://github.com/opencv/cvat/blob/develop/LICENSE) that covers the
project.
  Feel free to contact the maintainers if that's a concern.

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- [ ] I have increased versions of npm packages if it is necessary

([cvat-canvas](https://github.com/opencv/cvat/tree/develop/cvat-canvas#versioning),

[cvat-core](https://github.com/opencv/cvat/tree/develop/cvat-core#versioning),

[cvat-data](https://github.com/opencv/cvat/tree/develop/cvat-data#versioning)
and

[cvat-ui](https://github.com/opencv/cvat/tree/develop/cvat-ui#versioning))

### License

- [ ] I submit _my code changes_ under the same [MIT License](
https://github.com/opencv/cvat/blob/develop/LICENSE) that covers the
project.
  Feel free to contact the maintainers if that's a concern.

Currently, this only happens when running in Kubernetes. This option
lets CVAT use Nuclio that's deployed to Kubernetes without being
deployed to Kubernetes itself, or just to use Nuclio that is deployed on
another machine.

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### Motivation and context
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It was broken for two reasons:

* Due to some changes on the PyTorch website, the old way of installing
the PyTorch packages now installs the ROCm version rather than the CPU
version (and it doesn't work doe to a missing dependency).

* The newest version of NumPy doesn't work with HRNet due to the
latter's usage of `np.int`.

Fix these problems, and in addition, rework the build recipe to avoid
installing unneeded packages. Altogether, the changes massively shrink
the Docker image size (from ~14 GB to ~2 GB).

I didn't update the GPU version, because a) the first issue doesn't
affect it, b) the second issue is already fixed in it, and c) I don't
have a GPU to test it on.

### How has this been tested?
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Include details of your testing environment, and the tests you ran to
see how your change affects other areas of the code, etc. -->
Manual testing with CVAT.

### Checklist
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- [x] I submit my changes into the `develop` branch
- [x] I have added a description of my changes into the
[CHANGELOG](https://github.com/opencv/cvat/blob/develop/CHANGELOG.md)
file
- ~~[ ] I have updated the documentation accordingly~~
- ~~[ ] I have added tests to cover my changes~~
- ~~[ ] I have linked related issues (see [GitHub docs](

https://help.github.com/en/github/managing-your-work-on-github/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword))~~
- ~~[ ] I have increased versions of npm packages if it is necessary

([cvat-canvas](https://github.com/opencv/cvat/tree/develop/cvat-canvas#versioning),

[cvat-core](https://github.com/opencv/cvat/tree/develop/cvat-core#versioning),

[cvat-data](https://github.com/opencv/cvat/tree/develop/cvat-data#versioning)
and

[cvat-ui](https://github.com/opencv/cvat/tree/develop/cvat-ui#versioning))~~

### License

- [x] I submit _my code changes_ under the same [MIT License](
https://github.com/opencv/cvat/blob/develop/LICENSE) that covers the
project.
  Feel free to contact the maintainers if that's a concern.
Co-authored-by: NBoris Sekachev <boris.sekachev@yandex.ru>

Currently, OpenVINO-based functions assume that a local directory will
be mounted into the container. In Kubernetes, that isn't possible, so
implement an alternate approach: create a separate base image and
inherit the function image from it.

In addition, implement some modernizations:

* Upgrade the version of OpenVINO to the latest (2022.3). Make the
necessary updates to the code. Note that 2022.1 introduced an entirely
new inference API, but I haven't switched to it yet to minimize changes.

* Use the runtime version of the Docker image as the base instead of the
dev version. This significantly reduces the size of the final image (by
~3GB).

* Replace the `faster_rcnn_inception_v2_coco` model with
`faster_rcnn_inception_resnet_v2_atrous_coco`, as the former has been
  removed from OMZ.

* Ditto with `person-reidentification-retail-0300` -> `0277`.

* The IRs used in the DEXTR function are not supported by OpenVINO
anymore (format too old), so rewrite the build process to create them
from the original code/weights instead.

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<!-- Provide a general summary of your changes in the Title above -->

### Motivation and context
Resolved #5984 
Resolved #6049
Resolved #6041

- Compatible only with ``sam_vit_h_4b8939.pth`` weights. Need to
re-export ONNX mask decoder with some custom model changes (see below)
to support other weights (or just download them using links below)
- Need to redeploy the serverless function because its interface has
been changed.

Decoders for other weights:
sam_vit_l_0b3195.pth:
[Download](https://drive.google.com/file/d/1Nb5CJKQm_6s1n3xLSZYso6VNgljjfR-6/view?usp=sharing)
sam_vit_b_01ec64.pth:
[Download](https://drive.google.com/file/d/17cZAXBPaOABS170c9bcj9PdQsMziiBHw/view?usp=sharing)

Changes done in ONNX part:
```
git diff scripts/export_onnx_model.py
diff --git a/scripts/export_onnx_model.py b/scripts/export_onnx_model.py
index 8441258..18d5be7 100644
--- a/scripts/export_onnx_model.py
+++ b/scripts/export_onnx_model.py
@@ -138,7 +138,7 @@ def run_export(

     _ = onnx_model(**dummy_inputs)

-    output_names = ["masks", "iou_predictions", "low_res_masks"]
+    output_names = ["masks", "iou_predictions", "low_res_masks", "xtl", "ytl", "xbr", "ybr"]

     with warnings.catch_warnings():
         warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
bsekachev@DESKTOP-OTBLK26:~/sam$ git diff segment_anything/utils/onnx.py
diff --git a/segment_anything/utils/onnx.py b/segment_anything/utils/onnx.py
index 3196bdf..85729c1 100644
--- a/segment_anything/utils/onnx.py
+++ b/segment_anything/utils/onnx.py
@@ -87,7 +87,15 @@ class SamOnnxModel(nn.Module):
         orig_im_size = orig_im_size.to(torch.int64)
         h, w = orig_im_size[0], orig_im_size[1]
         masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False)
-        return masks
+        masks = torch.gt(masks, 0).to(torch.uint8)
+        nonzero = torch.nonzero(masks)
+        xindices = nonzero[:, 3:4]
+        yindices = nonzero[:, 2:3]
+        ytl = torch.min(yindices).to(torch.int64)
+        ybr = torch.max(yindices).to(torch.int64)
+        xtl = torch.min(xindices).to(torch.int64)
+        xbr = torch.max(xindices).to(torch.int64)
+        return masks[:, :, ytl:ybr + 1, xtl:xbr + 1], xtl, ytl, xbr, ybr

     def select_masks(
         self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int
@@ -132,7 +140,7 @@ class SamOnnxModel(nn.Module):
         if self.return_single_mask:
             masks, scores = self.select_masks(masks, scores, point_coords.shape[1])

-        upscaled_masks = self.mask_postprocessing(masks, orig_im_size)
+        upscaled_masks, xtl, ytl, xbr, ybr = self.mask_postprocessing(masks, orig_im_size)

         if self.return_extra_metrics:
             stability_scores = calculate_stability_score(
@@ -141,4 +149,4 @@ class SamOnnxModel(nn.Module):
             areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1)
             return upscaled_masks, scores, stability_scores, areas, masks

-        return upscaled_masks, scores, masks
+        return upscaled_masks, scores, masks, xtl, ytl, xbr, ybr
```

### How has this been tested?
<!-- Please describe in detail how you tested your changes.
Include details of your testing environment, and the tests you ran to
see how your change affects other areas of the code, etc. -->

### Checklist
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- [x] I submit my changes into the `develop` branch
- [x] I have added a description of my changes into the
[CHANGELOG](https://github.com/opencv/cvat/blob/develop/CHANGELOG.md)
file
- [ ] I have updated the documentation accordingly
- [ ] I have added tests to cover my changes
- [x] I have linked related issues (see [GitHub docs](

https://help.github.com/en/github/managing-your-work-on-github/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword))
- [x] I have increased versions of npm packages if it is necessary

([cvat-canvas](https://github.com/opencv/cvat/tree/develop/cvat-canvas#versioning),

[cvat-core](https://github.com/opencv/cvat/tree/develop/cvat-core#versioning),

[cvat-data](https://github.com/opencv/cvat/tree/develop/cvat-data#versioning)
and

[cvat-ui](https://github.com/opencv/cvat/tree/develop/cvat-ui#versioning))

### License

- [x] I submit _my code changes_ under the same [MIT License](
https://github.com/opencv/cvat/blob/develop/LICENSE) that covers the
project.
  Feel free to contact the maintainers if that's a concern.

- Fixed completed task job count reporting in the task summary
- Added a test

Fixes #6098
Fixes #6034

<!-- Raise an issue to propose your change
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Read the [Contribution
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### Motivation and context
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fixes an open
issue, please link to the issue here. Describe your changes in detail,
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### How has this been tested?
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### Checklist
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- [x] I submit my changes into the `develop` branch
- [x] I have added a description of my changes into the
[CHANGELOG](https://github.com/opencv/cvat/blob/develop/CHANGELOG.md)
file
- [ ] I have updated the documentation accordingly
- [ ] I have added tests to cover my changes
- [ ] I have linked related issues (see [GitHub docs](

https://help.github.com/en/github/managing-your-work-on-github/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword))
- [x] I have increased versions of npm packages if it is necessary

([cvat-canvas](https://github.com/opencv/cvat/tree/develop/cvat-canvas#versioning),

[cvat-core](https://github.com/opencv/cvat/tree/develop/cvat-core#versioning),

[cvat-data](https://github.com/opencv/cvat/tree/develop/cvat-data#versioning)
and

[cvat-ui](https://github.com/opencv/cvat/tree/develop/cvat-ui#versioning))

### License

- [x] I submit _my code changes_ under the same [MIT License](
https://github.com/opencv/cvat/blob/develop/LICENSE) that covers the
project.
  Feel free to contact the maintainers if that's a concern.

The policy that mandated this is no longer relevant now that CVAT is no
longer developed by Intel. Moreover, the source code included was not
even complete (it didn't contain Python or NPM packages).

This saves ~1.6 GB in the unpacked image (and probably a bunch of build
time too, but I didn't measure it).

This originally started as a security update for redis-py (see
<https://github.com/redis/redis-py/releases/tag/v4.5.3>,
<https://github.com/redis/redis-py/releases/tag/v4.5.4>). However, I
also had to update other Redis-related components because of
incompatibilities.

* The old version of fakeredis is not compatible with the redis-py 4.x,
so I bumped it too. This also allowed me to remove the six workaround.

* redis-py 4.1.0 and newer don't support Redis < 5, so I bumped Redis
itself in `docker-compose.yml`. Note that the Helm chart is already
using Redis 7.0.x.

Obsoletes #5946.

Update CHANGELOG.md