Home > AI Solutions > Artificial Intelligence > White Papers > Training Models Made Easy with Dell Enterprise Hub > Security considerations
Select a model from the catalog that fits your platform and business needs. Training processes can be GPU-intensive, so it is important to select a model that fits your hardware specifications.
On opening the model card, you will find that a training model has two cards: train and deploy. Training the model consists of three tasks: load the model, train the model on selected data, and then redeploy the model using the new training.
Perform the following steps:
A model card opens that is similar to the sample card shown in the following figure:
Column mapping links a corresponding input and output so that the model knows how to process the input data and what to expect from the output. For more information, see Understanding Column Mapping. All the files must be in the CSV format, but the formatting of each dataset should be different based on your use case. Depending on the trainer, the data must have the following columns and names:
Regardless of which trainer you are using, you must ensure accurate mapping for correct training. To do this:
The following code snippet shows a sample Kubernetes deployment for an SFT training model:
apiVersion: batch/v1
kind: Job
metadata:
name: autotrain-dell-sft
spec:
template:
metadata:
name: autotrain-dell
labels:
app: autotrain-dell
hf.co/model: meta-llama-meta-llama-3.1-8b
spec:
nodeSelector:
kubernetes.io/hostname: node032
# nvidia.com/gpu.product: NVIDIA-L40S
containers:
- name: trl-container
image: registry.dell.huggingface.co/enterprise-dell-training-meta-llama-meta-llama-3.1-8b
args:
- "--model=/app/model"
- "--project-name=fine-tune"
- "--data-path=/app/data"
- "--text-column=text"
- "--trainer=sft"
- "--epochs=3"
- "--mixed_precision=bf16"
- "--batch-size=2"
- "--peft"
- "--quantization=int4"
- "--merge_adapter"
env:
- name: ACCELERATE_LOG_LEVEL
value: "INFO"
- name: TRANSFORMERS_LOG_LEVEL
value: "INFO"
- name: TQDM_POSITION
value: "-1"
resources:
requests:
nvidia.com/gpu: 2
limits:
nvidia.com/gpu: 2
volumeMounts:
- mountPath: /dev/shm
name: dshm
- name: data-mount
mountPath: /app/data
readOnly: true
- name: output-mount
mountPath: /app/autotrain
readOnly: false
volumes:
- name: dshm
emptyDir:
medium: Memory
sizeLimit: 32Gi
- name: data-mount
nfs:
server: f600-21.ai.lab
path: /ifs/data/huggingface/phase-2/autotrain-example-datasets
- name: output-mount
nfs:
server: f600-21.ai.lab
path: /ifs/data/huggingface/phase-2/fine_tunned_model/llama-3.1-8b/760xa/2xL40S
restartPolicy: "Never"
Because AutoTrain is embedded in the container, no additional configuration is required. When the model is deployed, it is immediately ready for training. The Dell AI Solutions team applied the alpacas dataset from Hugging Face.
The model will begin training and provide summarized accuracy and precision results.
Return to the model page and click Deploy Fine-tuned at the top of the page.