We provide GPU versions of various frameworks such as `tensorflow`, `keras`, `theano`, via [modules](../../modules).
However, sometimes you may need additional libraries or packages that are not available as part of these modules.
In this case, you will need to create your own GPU Anaconda environment.
To do this, you need to first clone one of our GPU modules to a new Anaconda environment, and then install the desired packages in this new environment.
The reason for this is that the GPU modules we support are built using the specific CUDA drivers our GPU nodes have.
If you just create custom GPU environment without cloning the module, your code will not utilize the GPUs correctly.
For example, if you want to use `tensorflow` with additional packages, first do:
{{% panel theme="info" header="Cloning GPU module to a new Anaconda environment" %}}
This will create a new `tensorflow-gpu-1.14-custom` environment in your home directory that is a copy of the `tensorflow-gpu` module.
Then, you can install the additional packages you need in this environment.
{{% panel theme="info" header="Install new packages in the currently active environment" %}}
{{<highlightbash>}}
module load anaconda
conda activate tensorflow-gpu-1.14-custom
conda install <packages>
{{</highlight>}}
{{% /panel %}}
Next, whenever you want to use this custom GPU Anaconda environment, you need to add these two lines in your submit script:
{{<highlightbash>}}
module load anaconda
conda activate tensorflow-gpu-1.14-custom
{{</highlight>}}
{{% notice info %}}
If you have custom GPU Anaconda environment please only use the two lines from above and **DO NOT** load the module you have cloned earlier.
Using `module load tensorflow-gpu/py36/1.14` and `conda activate tensorflow-gpu-1.14-custom` in the same script is **wrong** and may give you various errors and incorrect results.
{{% /notice %}}
### Using an Anaconda Environment in a Jupyter Notebook on Crane
It is not difficult to make an Anaconda environment available to a
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@@ -185,7 +232,7 @@ Jupyter Notebook. To do so, follow the steps below, replacing
2. Using the command-line environment, load the target conda