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https://github.com/Grench6/RX580-rocM-tensorflow-ubuntu20.4-guide

Install guide of ROCm and Tensorflow on Ubuntu for the RX580
https://github.com/Grench6/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: 

> 



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.