# Release jobs are very basic. They don't use any caching or RBE,
# but they do upload logs to resultstore.
# IMPORTANT: trailing slash is required on GCS URIs, as it tells gcloud to
# pretend the path is a directory.
TFCI_ARTIFACT_FINAL_GCS_ENABLE=1
TFCI_ARTIFACT_FINAL_GCS_SA_PATH="${KOKORO_KEYSTORE_DIR}/73361_tensorflow_release_binary_uploader_service_account"
TFCI_ARTIFACT_FINAL_GCS_URI="gs://tensorflow/versions/"

# IMPORTANT: trailing slash is required on GCS URIs, as it tells gcloud to
# pretend the path is a directory.
# 1. Upload nightlies
TFCI_ARTIFACT_FINAL_GCS_ENABLE=1
TFCI_ARTIFACT_FINAL_GCS_SA_PATH="${KOKORO_KEYSTORE_DIR}/73361_tensorflow_release_binary_uploader_service_account"
TFCI_ARTIFACT_FINAL_GCS_URI="gs://tensorflow/nightly/"

# Request Files

You can define files to be uploaded by the client using `File`.

!!! info
    To receive uploaded files, first install <a href="https://github.com/Kludex/python-multipart" class="external-link" target="_blank">`python-multipart`</a>.

    E.g. `pip install python-multipart`.

    This is because uploaded files are sent as "form data".

## Import `File`

Import `File` and `UploadFile` from `fastapi`:

=== "Python 3.9+"

    vcsRootId = gradlePromotionBranches
) {
    init {
        id("Promotion_SnapshotFromQuickFeedbackStepUpload")
        name = "Nightly Snapshot (from QuickFeedback) - Upload"
        description = "Builds and uploads the latest successful changes on '${branch.branchName}' from Quick Feedback as a new distribution"

        steps {
            buildStep(

#    See full examples below for more details on these. Some other modifiers are:
#      Ex. versions_upload -- for TF official release versions
#      Ex. nightly_upload -- for TF nightly official builds; changes version numbers
#      Ex. no_upload      -- Disable all uploads, usually for temporary CI issues

# Recommended: use a local+remote cache.
#

# Disable ALL uploads of any kind.
TFCI_ARTIFACT_FINAL_GCS_ENABLE=
TFCI_ARTIFACT_FINAL_GCS_SA_PATH=
TFCI_ARTIFACT_FINAL_GCS_URI=
TFCI_ARTIFACT_FINAL_PYPI_ARGS=
TFCI_ARTIFACT_FINAL_PYPI_ENABLE=
TFCI_ARTIFACT_LATEST_GCS_URI=
TFCI_ARTIFACT_STAGING_GCS_ENABLE=
TFCI_ARTIFACT_STAGING_GCS_URI=

## Enabling Parquet Format

Parquet is DISABLED by default since hostile crafted input can easily crash the server.

If you are in a controlled environment where it is safe to assume no hostile content can be uploaded to your cluster you can safely enable Parquet.
To enable Parquet set the environment variable `MINIO_API_SELECT_PARQUET=on`.

## Example using Python API

### 1. Prerequisites

        listen       9000;
        listen  [::]:9000;
        server_name  localhost;

        # To allow special characters in headers
        ignore_invalid_headers off;
        # Allow any size file to be uploaded.
        # Set to a value such as 1000m; to restrict file size to a specific value
        client_max_body_size 0;
        # To disable buffering
        proxy_buffering off;
        proxy_request_buffering off;

        listen       9000;
        listen  [::]:9000;
        server_name  localhost;

        # To allow special characters in headers
        ignore_invalid_headers off;
        # Allow any size file to be uploaded.
        # Set to a value such as 1000m; to restrict file size to a specific value
        client_max_body_size 0;
        # To disable buffering
        proxy_buffering off;
        proxy_request_buffering off;

```

### Step 3: Test with Thumbnailer

We used [Thumbnailer](https://github.com/minio/thumbnailer) to listen for MinIO notifications when a new JPEG file is uploaded (HTTP PUT). Triggered by a notification, Thumbnailer uploads a thumbnail of new image to MinIO server. To start with, download and install Thumbnailer.

```
git clone https://github.com/minio/thumbnailer/
npm install
```