嘎啦線上看(2024)完整版HD.1080P.高清电影
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For more information of how the evaluator works, please refer the [template's reference page](https://gitlab.aicrowd.com/aicrowd/evaluator-templates/tree/master/predictions-evaluator).
The starter kit for this template is available at [https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit).
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2. Participant adds their code to the [starter kit](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit), commits it and creates a git tag (manually or using the helper scripts).
4. AIcrowd sets up the software runtime needed to run the participant's code. For more information on how participants can specify their software runtime please refer to [this post](https://discourse.aicrowd.com/t/how-to-specify-runtime-environment-for-your-submission/2274).
5. AIcrowd runs the participant's code on a validation dataset. The logs during this phase are exposed to the participants so that they can debug any code errors.
6. AIcrowd runs the participant's code on the test dataset. The logs during this phase are generally not exposed to the participants to prevent data leaks. However, the logs can be exposed by settings `logs: true` in `aicrowd.yaml` (file inside evaluator code repo).
7. AIcrowd runs evaluator code (written by the organizer) that will aggregate the results generated by the participant's code and return a score.
1. Submission [starter kit](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit) (submitted by the participant).
The [starter kit](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit) should contain an `aicrowd.json` file with `challenge_id` attribute pointing to the challenge. For example, if the challenge page is https://www.aicrowd.com/challenges/my-challenge, then the contents of `aicrowd.json` should look similar to this.
For the remainder of the [starter kit](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit), we recommend that it be structured similar to the example [starter kit](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit) containing
1. An [`evaluation_utils`](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit/-/tree/master/evaluation_utils) directory with scripts for local evaluation. The files in the [starter kit](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit) are only for participants' reference. These files can be ignored/replaced during evaluation. Explained in [writing launcher scripts](#launcher-scripts).
3. A [`local_evaluation.py`](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit/-/blob/master/local_evaluation.py) that participants can use to run their code locally.
4. A [`run.py`](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit/-/blob/master/run.py) where participants can add their code in a well-defined class interface.
a. It is recommended that organizers add class interface in this file with as much inline documentation as possible for the participants.
b. It is recommended to avoid adding logical code from organizer's side to this file. Any evaluation related code or utility functions can remain as a part of [`evaluation_utils`](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit/-/tree/master/evaluation_utils) package.
5. Runtime setup files to run the starter kit code. For a list of supported runtime configuration, please refer to [this post](https://discourse.aicrowd.com/t/how-to-specify-runtime-environment-for-your-submission/2274).
Please use the [example starter kit](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit) as a starting point when creating your own starter kit.
2. Launcher scripts (scripts that will start the participant code; placed in the [`data`](data) directory).
`AIcrowdEvaluator` can be a simple wrapper around your existing evaluation scripts. The class needs to implement an `evaluate` method.
# Refer example evaluator.py at https://gitlab.aicrowd.com/aicrowd/mnist-code-evaluator/-/tree/master/evaluator.py
**Note:** Please refer [this article](https://wiki.aicrowd.com/share/efa47829-ef4e-452e-b75e-c5ac3fb24290) for how to use `scores` dictionary. You can add multiple metrics, private scores, media using the `scores` dictionary.
You can find more information on how the evaluation scripts are invoked at [[Evaluation Flow]](https://gitlab.aicrowd.com/aicrowd/evaluator-templates/-/tree/master/predictions-evaluator#evaluation-flow).
The `AIcrowdEvaluator(...).render_current_status_as_markdown()` is invoked in a new process and will keep running as long as the evaluation is in progress. You can use this method to return some markdown content that we will use to display on the GitLab issue page for the participants. You can show the evaluation progress, live scores, and other interesting information that can improve the submission experience for the participant. You can also display images, videos and audios. You can upload the media files to s3 and insert the link in your markdown content. If you need help with uploading the files to s3 (or any file hosting provider), please reach out to us.
These are the scripts that are used to start the evaluation using participants' code. Typically, these include
1. Entrypoint scripts (a python script that imports the participant's prediction class and starts the evaluation loop and a bash script to call the python script)
For example, let us consider the [MNIST starter kit](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit). It has a [`local_evaluation.py`](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit/-/blob/master/local_evaluation.py) and [`evaluation_utils`](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit/-/tree/master/evaluation_utils) directory. During the evaluation, we use a [`predict.py`](data/predict.py) that comes from the evaluator repo to start the evaluation instead of the `local_evaluation.py`. We also replace the files in the [`evaluation_utils` directory in the starter kit](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit/-/tree/master/evaluation_utils) using the [files from the evaluator repo](data/evaluation_utils). This will drop any changes that participants might have made and also gives the flexibility to add some hidden functions as needed.
**Note:** Only the files placed in the [`data`](data) directory of the evaluator repo can be placed in the participant's code during evaluations. Even among these files, the files that need to be mounted should be explicitly defined in `aicrowd.yaml`. [[refer]](aicrowd.yaml#L46)
- AIcrowd systems reads [`aicrowd.yaml`](aicrowd.yaml#L56) for mountable files (defined under `evaluation.global.files`).
* [`evaluation_utils/base_predictor.py`](data/evaluation_utils/base_predictor.py) at `/home/aicrowd/evaluation_utils/base_predictor.py`
* [`evaluation_utils/mnist_evaluator.py`](data/evaluation_utils/mnist_evaluator.py) at `/home/aicrowd/evaluation_utils/mnist_evaluator.py`
+ Please note that these files should exist inside [`data`](data) directory of the evaluator repository.
+ **The participant code is available at `/home/aicrowd` (`/home/aicrowd` acts as the participant's repo root).**
**Note:** The `base_predictor.py` from the [starter kit](https://gitlab.aicrowd.com/aicrowd/mnist-starter-kit/-/tree/master/evaluation_utils/base_predictor.py) and the [evaluator repo](data/evaluation_utils/base_predictor.py) showcase a simple use case of needing only two methods to be filled by the participants -- a setup method and a prediction method. We encourage you to modify this class as per your needs.
The orchestration for the evaluation is handled using the values defined in your `aicrowd.yaml` file. The file has several inline comments to guide you through different options available. For more details on how the orchestration works, please refer [https://gitlab.aicrowd.com/aicrowd/evaluator-templates/-/tree/master/predictions-evaluator](https://gitlab.aicrowd.com/aicrowd/evaluator-templates/-/tree/master/predictions-evaluator).
You can specify the dataset in `aicrowd.yaml` under the `dataset` section. We recommend that you upload your dataset as a zip file having the following file structure.
`debug_test_data` | `AICRODW_DATASET_DIR` | `evaluation.debug_run` | **Yes** | Ideally some validation data to check if the participant's code is bug free. We generally expose the logs for the `debug_run` phase so that it is easier for participants to debug their code.
`ground_truth_data` | `AICROWD_GROUND_TRUTH_DIR` | `evaluation.scoring` | No | Ground truth data needed for the evaluator code to score the submissions.
`test_data` | `AICROWD_DATASET_DIR` | `evaluation.runs[]` | **Yes** | Test data consumed by the participant's code to generate predictions.
In some cases, you might want to split the dataset into multiple sections and run multiple instances of the inference to speed up the predictions. For example, let say you have a dataset containing 100,000 images and the evaluation is expected to take 4 hours. We recommend that you split your dataset into subsets so that each set can be evaluated in ~1 hour. We can run multiple instances of the participant's code in parallel to speed up the evaluation time. In this case you can restructure your dataset as
This configuration mounts the respective directories inside `data.zip` at `AICROWD_DATASET_PATH` during the inference.
An example implementation is available in the [`dataset-split`](https://gitlab.aicrowd.com/aicrowd/mnist-code-evaluator/-/tree/dataset-split) branch.