# 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
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.ccv.brown.edu/oscar/gpu-computing/installing-frameworks-pytorch-tensorflow-jax/installing-tensorflow.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.