> For the complete documentation index, see [llms.txt](https://docs.ccv.brown.edu/oscar/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.ccv.brown.edu/oscar/gpu-computing/installing-frameworks-pytorch-tensorflow-jax/installing-tensorflow.md).

# Installing TensorFlow

## Apptainer Using NGC Containers (Our #1 Recommendation)

There are multiple ways to install and run TensorFlow. Our recommended approach is via NGC containers. The containers are available via [NGC Registry](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorflow). In this example we will pull TensorFlow NGC container

1. Build the container:

   ```bash
   apptainer build tensorflow-24.03-tf2-py3.simg docker://nvcr.io/nvidia/tensorflow:24.03-tf2-py3
   ```

   This will take some time, and once it completes you should see a .simg file.

   <div data-gb-custom-block data-tag="hint" data-style="info" class="hint hint-info"><p>For your convenience, the pre-built container images are located in directory:</p><pre class="language-bash"><code class="lang-bash">/oscar/runtime/software/external/ngc-containers/tensorflow.d/x86_64/
   </code></pre><p>You can choose either to build your own or use one of the pre-downloaded images.</p></div>

   <div data-gb-custom-block data-tag="hint" data-style="danger" class="hint hint-danger"><p>Working with Apptainer images requires lots of storage space. By default Apptainer will use ~/.apptainer as a cache directory which can cause you to go over your Home quota.</p><pre class="language-bash"><code class="lang-bash">export APPTAINER_CACHEDIR=/tmp
   export APPTAINER_TMPDIR=/tmp
   </code></pre></div>
2. Once the container is ready, request an interactive session with a GPU

   ```bash
   interact -q gpu -g 1 -f ampere -m 20g -n 4
   ```
3. Run a container wih GPU support

   ```bash
   export APPTAINER_BINDPATH="/oscar/home/$USER,/oscar/scratch/$USER,/oscar/data"
   # Run a container with GPU support
   apptainer run --nv tensorflow-24.03-tf2-py3.simg
   ```

   <div data-gb-custom-block data-tag="hint" data-style="success" class="hint hint-success"><p>the --nv flag is important. As it enables the NVIDA sub-system</p></div>
4. Or, if you're executing a specific command inside the container:

   ```bash
   # Execute a command inside the container with GPU support
   apptainer exec --nv tensorflow-24.03-tf2-py3.simg nvidia-smi
   ```
5. Make sure your Tensorflow image is able to detect GPUs

   ```bash
   python
   ```

   ```python
   >>> import tensorflow as tf
   >>> tf.test.is_gpu_available(cuda_only=False, min_cuda_compute_capability=None)
   True
   ```
6. If you need to install more custom packages, the containers itself are non-writable but we can use the `--user` flag to install packages inside `.local` Example:

   ```bash
   Apptainer> pip install <package-name> --user
   ```

## Slurm Script:

Here is how you can submit a SLURM job script by using the srun command to run your container. Here is a basic example:

```bash
#!/bin/bash
#SBATCH --nodes=1               # node count
#SBATCH -p gpu --gres=gpu:1     # number of gpus per node
#SBATCH --ntasks-per-node=1     # total number of tasks across all nodes
#SBATCH --cpus-per-task=1       # cpu-cores per task (>1 if multi-threaded tasks)
#SBATCH --mem=40G               # total memory (4 GB per cpu-core is default)
#SBATCH -t 01:00:00             # total run time limit (HH:MM:SS)
#SBATCH --mail-type=begin       # send email when job begins
#SBATCH --mail-type=end         # send email when job ends
#SBATCH --mail-user=<USERID>@brown.edu

module purge
unset LD_LIBRARY_PATH
export APPTAINER_BINDPATH="/oscar/home/$USER,/oscar/scratch/$USER,/oscar/data"
srun apptainer exec --nv tensorflow-24.03-tf2-py3.simg python examples/tensorflow_examples/models/dcgan/dcgan.py
```


---

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