about: Use this template for reporting Lite ops you are using or missing
labels: 'comp:lite'

---

**System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installed from (source or binary):
- TensorFlow version (or github SHA if from source):


**Provide the text output from tflite_convert**

```
# Copy and paste here
```

**Standalone code to reproduce the issue** 

export MINIO_CI_CD=1
export MINIO_ROOT_USER=minio
export MINIO_ROOT_PASSWORD=minio123
minio server /mnt/data
```

### 5. Test with a regular IAM user

Ensure that `mc` is installed and the configured with the above server with the alias `myminio`.

```sh
# 1. Create a bucket and a user, and upload a file. These operations will succeed.
mc mb myminio/test
mc admin user add myminio foo foobar123

## Prerequisites

- Prometheus and MinIO configured as explained in [document here](https://github.com/minio/minio/blob/master/docs/metrics/prometheus/README.md).
- Grafana installed as explained [here](https://grafana.com/grafana/download).

## MinIO Grafana Dashboard

-   type: dropdown id: source attributes: label: Source description: Tensorflow
    installed from options: - source - binary validations: required: true

-   type: input id: tfversion attributes: label: Tensorflow Version description:
    placeholder: ex,. tf 2.8 validations: required: true

Chroot allows user based namespace isolation on many standard Linux deployments.

## 1. Prerequisites

- Familiarity with [chroot](http://man7.org/linux/man-pages/man2/chroot.2.html)
- Chroot installed on your machine.

## 2. Install MinIO in Chroot

```sh
mkdir -p /mnt/export/${USER}/bin
wget https://dl.min.io/server/minio/release/linux-amd64/minio -O /mnt/export/${USER}/bin/minio

import javax.net.ssl.SSLSocketFactory
import javax.net.ssl.TrustManagerFactory
import javax.net.ssl.X509TrustManager
import okhttp3.Protocol

/**
 * Platform using OpenJSSE (https://github.com/openjsse/openjsse) if installed as the first
 * Security Provider.
 *
 * Requires org.openjsse:openjsse >= 1.1.0 on the classpath.
 */
class OpenJSSEPlatform private constructor() : Platform() {
  private val provider: Provider =

   
      * Try Google Colab to use TensorFlow.
         * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install```  to install any other preferred TF version.

    int ERROR_NO_DATA = 232;
    /** No process is on the other end of the pipe */
    int ERROR_PIPE_NOT_CONNECTED = 233;
    /** More data is available */
    int ERROR_MORE_DATA = 234;
    /** The service is not installed */
    int ERROR_SERVICE_NOT_INSTALLED = 2184;
    /** The list of servers for this workgroup is not currently available */
    int ERROR_NO_BROWSER_SERVERS_FOUND = 6118;

    /** Array of Windows error codes */

import javax.net.ssl.X509TrustManager
import okhttp3.Protocol
import org.bouncycastle.jsse.BCSSLSocket
import org.bouncycastle.jsse.provider.BouncyCastleJsseProvider

/**
 * Platform using BouncyCastle if installed as the first Security Provider.
 *
 * Requires org.bouncycastle:bctls-jdk15on on the classpath.
 */
class BouncyCastlePlatform private constructor() : Platform() {

  fi
fi
# VERY basic check to ensure the [and-cuda] package variant is installable.
# Checks TFCI_BAZEL_COMMON_ARGS for "gpu" or "cuda", implying that the test is
# relevant. All of the GPU test machines have CUDA installed via other means,
# so I am not sure how to verify that the dependencies themselves are valid for
# the moment.
if [[ "$TFCI_BAZEL_COMMON_ARGS" =~ gpu|cuda ]]; then