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RX580-rocM-tensorflow-ubuntu20.4-guide

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Install guide of ROCm and Tensorflow on Ubuntu for the RX580

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# Installation of ROCm and TensorFlow on Ubuntu 20.4 LTS for Radeon RX580 
This guide will show you how to set up your **fresh Ubuntu 20.4 LTS** OS to be ready to run **TensorFlow** projects, using **ROCm** to take advantage of the power of your **RX580 graphics card (or any gfx803)** in a tested, easy and fast way.
The majority of the content is a resume of the [official guide by AMD](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html) and the [unofficial guide by Mathieu Poliquin](https://www.videogames.ai/Install-ROCM-Machine-Learning-AMD-GPU). I highly recommend to check those links, in order to understand what you are doing and why.

**In this guide we stick to ROCm 3.5.1**, and there are two reasons for this:
1. There are some bugs in posterior versions which haven't been fixed at all (bugs: [1](https://github.com/RadeonOpenCompute/ROCm/issues/1269), [2](https://github.com/RadeonOpenCompute/ROCm/issues/1265))
2. AMD dropped official support for the RX580 ([3](https://github.com/RadeonOpenCompute/ROCm/issues/1353))

So... lets get started!
## Remove ROCm
**You can skip this step if you haven't installed ROCm yet**

It's preferrable to do a fresh Ubuntu reinstall instead of removing ROCm;
if for some reason you still need to remove ROCm, follow the steps bellow:
1. Run ```sudo apt autoremove rocm-dkms```
2. Make sure that all packages are removed under */opt/rocm-x*
3. Remove any other additional packages you installed explicitly with ```sudo apt remove --purge```. To find relevant packages, you can use `sudo dpkg -l | grep -E 'hsa|hip|llvm|rocm|rock'`.
4. Reboot the system

## Install the kernel
ROCm requieres the kernel version 5.4 (you can check your currently running kernel version with `uname -r`).

1. Install the requiered kernel:

```
   sudo apt install linux-headers-5.4.0-54-generic \
   linux-image-5.4.0-54-generic \
   linux-modules-5.4.0-54-generic \
   linux-modules-extra-5.4.0-54-generic  
   ```

2. Reboot your computer. In the GRUB menu choose *"Additional options for Ubuntu"* and select *"Boot with kernel 5.4.0-x-generic"*

3. Remove the other kernels: `sudo apt remove --purge linux-generic-hwe-20.04 linux-oem-20.04 linux-hwe-* linux-oem-* linux-modules-5.1* linux-modules-5.8.0-* linux-modules-5.6.0-* `

- [x] **Check your progress:** You should see something like the following as the output of `uname -r`:
> 5.4.0-x-generic

## Install ROCm
```
sudo apt update
sudo apt dist-upgrade
sudo apt install libnuma-dev
sudo reboot
```

Add the repo and install rocm-dkms:
```
wget -q -O - https://repo.radeon.com/rocm/rocm.gpg.key | sudo apt-key add -
echo 'deb [arch=amd64] http://repo.radeon.com/rocm/apt/3.5.1/ xenial main' | sudo tee /etc/apt/sources.list.d/rocm.list
sudo apt update
sudo apt install rocm-dkms && sudo reboot
```
Set user permissions to access video card features:
```
sudo usermod -a -G video $LOGNAME
sudo usermod -a -G render $LOGNAME
sudo reboot
```
Add rocm to PATH:
```
echo 'export PATH=$PATH:/opt/rocm/bin:/opt/rocm/profiler/bin:/opt/rocm/opencl/bin' | sudo tee -a /etc/profile.d/rocm.sh
sudo ldconfig
sudo reboot
```
- [x] **Check your progress:** At this point you should be able to enter `rocminfo` into a terminal without getting any error. You should also see your video card name in the command output (something like this):
> Name:                    gfx803                             
> Uuid:                    GPU-XX                             
> Marketing Name:          Ellesmere [Radeon RX 470/480/570/570X/580/580X/590]

You are half the way now!
## Install Tensorflow

```
sudo apt install python3 python3-pip
pip uninstall protobuf && pip install protobuf==3.20.*
pip uninstall numpy && pip install numpy==1.19.5
```

You must install a Python version between 3.5 and 3.8. If a different version is installed, check [this](https://www.linuxcapable.com/how-to-install-python-3-8-on-ubuntu-22-04-lts/).

```
sudo apt install rocm-libs miopen-hip
pip3 install -Iv tensorflow-rocm==2.2.0
sudo apt install rccl
sudo apt install libtinfo5
sudo reboot
```
- [x] **Check your progress:** At this point you should be able to import Tensorflow in Python, make a simple operation, and exit without any error. Try the following in a Python interactive session:
```python
import tensorflow as tf
tf.add(2,5)
exit()
```
In case everything is OK, you should see something like this: 
> <tf.Tensor: shape=(), dtype=int32, numpy=7>

Otherwise, you may get an error similar to this:
>  Could not load dynamic library 'libhip_hcc.so'; dlerror: libhip_hcc.so: cannot open shared object file: No such file or directory

In that case, here is a workaroud extracted from [this link](https://github.com/RadeonOpenCompute/ROCm/issues/1163). Don't forget [make the change permanent](https://askubuntu.com/questions/887442/how-to-permanently-set-an-environment-variable) if it works for you:
```
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/rocm/hip/lib
sudo ldconfig
```

Congrats! Your machine is now ready to use tensorflow-rocm! You should still consider testing it with something more complete, like a benchmark.
## Test with a benchmark
```
sudo apt install git
git clone https://github.com/tensorflow/benchmarks
cd ./benchmarks/scripts/tf_cnn_benchmarks
python3 tf_cnn_benchmarks.py --num_gpus=1 --batch_size=32 --model=resnet50
```
Expect it to take some time (5-10 minutes). If you see something like the output bellow (numbers may vary a lot, someone got 36.56), then go and get yourself a taco 🌮, you did it!
> total images/sec: 87.92

***Have fun! :D***

-------
# Additional stuff
## HIP Installation
**HIP** allows developers to convert CUDA code to portable C++. The same source code can be compiled to run on NVIDIA or AMD GPUs. HIP code can be developed either on AMD ROCm platform using HIP-Clang compiler, or a CUDA platform with NVCC installed. **HIP-Clang** is the compiler for compiling HIP programs on AMD platform (**Clang** as front end, **LLVM** as its back end).

If you want to compile aplications/samples using HIP or if you are having issues with Clang (not being installed, or being incorrectly installed), then [check this link](https://rocmdocs.amd.com/en/latest/Installation_Guide/HIP-Installation.html#amd-platform) for detailed instructions on installing/building HIP-Clang.

RX580-rocM-tensorflow-ubuntu20.4-guide

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