# Migration to 2.0

> **Note:** Architecture 2.0 currently available in preview mode as transition not
> completed yet. There is no strict backward compatibility in preview mode
> (some changes/renaming may occur). It could be used for evaluation and
> prototyping, it's not recommended for production usage yet.

## Multiple ways to program media analytics pipeline

Starting release 2022.2, Deep Learning Streamer offers multiple ways to
program media analytics pipeline. The following table summarizes major
differences between programming models

  | Programming model: | GStreamer high-level (gva elements) | GStreamer low-level (low-level elements and processbin) | Direct programming |
|---|---|---|---|
| Why choice: | <br>Very easy to program - small or no C++/Python coding to build GStreamer pipeline<br>Out-of-box support for many typical use cases<br><br> | <br>Pretty easy to program<br>Allows to create custom C++ elements and mix with existent C++ elements in GStreamer pipeline<br><br> | <br>Full flexibility of C++/Python programming to any library/framework APIs:<br>FFmpeg, GStreamer, OpenVINO™, Level-Zero, OpenCL, OpenCV Mat, SYCL, VA-API<br>Application is responsible for pipeline management and data flow (queues, async execution, multi-stream, etc)<br>Application can use memory interop library and any C++ elements provided by Deep Learning Streamer<br><br> |
| Sample(s): | <br>GStreamer command-line: samples/gstreamer/gst_launch<br>GStreamer C++: samples/gstreamer/cpp<br>GStreamer Python: samples/gstreamer/python<br><br> | <br>For example, what if we should implement object classification (on ROI cropped images) with background removal?<br>Adding custom C++ element opencv_remove_background<br>in ~100 C++ lines allows to achieve that<br>We reused many existent low-level elements for pre-processing, inference, post-processing on segmentation and classification models.<br><br> | <br>FFmpeg+OpenVINO™: samples/ffmpeg_openvino<br>FFmpeg+DPCPP/SYCL: samples/ffmpeg_dpcpp<br><br> |

See [Samples 2.0](./samples_2.0.md) for table
with all samples.

## Backward compatibility between 1.0 and 2.0

Deep Learning Streamer sets the goal of backward compatible transition from
GStreamer gva* 1.0 elements to architecture 2.0 bin-elements, including
all elements and all properties except deprecated properties. After
transition is completed, all existent application and pipeline are
expected to continue working without any changes or with minor changes
replacing deprecated properties with new properties.

Many GStreamer high-level elements are now implemented as bin elements
with names different from gva* element. The table below lists mapping
from old to new names

| gva* element 1.0 | bin element 2.0 |
|---|---|
| gvainference | video_inference |
| gvadetect | object_detect |
| gvaclassify | object_classify |
| gvatrack | object_track |
| gvawatermark | meta_overlay |
| gvametaaggregate | meta_aggregate (WIP) |
| ... | ... |

We introduced environment variable to automatically redirect gva\*
elements `gvainference`, `gvadetect`, `gvaclassify` to corresponding bin
elements `video_inference`, `object_detect`, `object_classify`. Any
existent application automatically switches to architecture 2.0 elements
if the following environment variable is set:

``` none
export DLSTREAMER_GEN=2
```

One of 2023.x releases is expected to make such redirection from gva\*
element to bin elements the default behavior, essentially automatically
migrating all existent applications to architecture 2.0. After such
transition the same environment variable will allow to switch back to
gva\* elements and architecture 1.0 (for significant time period) via
setting:

``` none
export DLSTREAMER_GEN=1
```

## Deprecated properties not available in 2.0

The following table lists deprecated properties (not supported in 2.0)
and new property/value to set this parameter (supported in 2.0, and 1.0
as well for some properties).

| Element(s) | Deprecated property (supported in 1.0 only) | New property / element(s) | New property supported by both 1.0 and 2.0? |
|---|---|---|---|
| gvainference, gvadetect, gvaclassify | labels=LABELS_FILE.txt | labels-file=LABELS_FILE.txt | Yes |
| gvainference, gvadetect, gvaclassify | cpu-throughput-streams=N | ie-config=CPU_THROUGHPUT_STREAMS=N | Yes |
| gvainference, gvadetect, gvaclassify | gpu-throughput-streams=N | ie-config=GPU_THROUGHPUT_STREAMS=N | Yes |
| gvainference, gvadetect, gvaclassify | pre-process-config=VAAPI_FAST_SCALE_LOAD_FACTOR=1 | scale-method=fast | Yes |
| gvatrack | tracking-type= | object_track generate-objects= adjust-objects= spatial-feature= | Yes |
| gvatrack | device=GPU | object_track device=GPU | No |
| gvawatermark | device=GPU | meta_overlay device=GPU | No |