Data Preparation

User Guide (Latest Version)

The data_preparation configuration in conf/config.yaml specifies the data preparation configuration file that will be used. Its default value is download_gpt3_pile, which corresponds to the file conf/data_preparation/download_gpt3_pile.yaml.

The configurations in the data preparation configuration file are used to download, extract, and preprocess the Pile dataset for GPT models. Modify these configurations to control data preparation tasks and to specify where to store the datasets, vocabulary, and merge files.

To download a reduced portion of the dataset to run tests, you can set the file_numbers configuration to download only one of the shards by changing “0-29” to “0.” The value must be a series of numbers separated by hyphens (’‑‘) or commas ‘,’). For example, this configuration value would download and prepare files 0, 3, 5, 6, and 7:

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file_numbers="0,3,5-7"

First, ensure that the cluster configuration in conf/cluster/bcm.yaml is correct. Set the cluster and cluster_type configurations in conf/config.yaml to bcm. Then make any required changes to time_limit and other configurations related to the job in download_gpt3_pile.yaml for GPT models.

You can parallelize data preparation by using up to 30 nodes to download all 30 files in parallel.

Example

To run only the data preparation pipeline and not the training, evaluation or inference pipelines, set the stages section of conf/config.yaml to:

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stages: - data_preparation

Then enter:

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python3 main.py

To run the data preparation script on Base Command Platform, set the cluster_type configuration in conf/config.yaml to bcp. This configuration can be overridden from the command line using hydra.

By default, the data preparation script downloads the data into the data/ directory. NVIDIA recommends that you set the data_dir configuration to a workspace, making the data visible across multiple jobs later on. Store the vocabulary and merge files in the same workspace for later use.

You must launch the data preparation code in a multi-node job. To speed up dataset preparation, you can parallelize it to use between 2 and 30 nodes.

You can download the 700+ GB dataset once and share it with multiple users in the same ACE by setting the nemo_megatron_data_ws workspace’s permissions.

To run the data preparation pipeline for GPT models, enter:

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python3 /opt/NeMo-Framework-Launcher/launcher_scripts/main.py stages=<data_preparation> \ cluster_type=bcp launcher_scripts_path=/opt/NeMo-Framework-Launcher/launcher_scripts data_dir=/mount/data/the_pile_gpt3 \ base_results_dir=/mount/results data_preparation.file_numbers='0-29' \ data_preparation.vocab_save_dir=/mount/data/bpe data_preparation.merges_save_dir=/mount/data/bpe >> /results/data_gpt3_log.txt 2>&1

The command above assumes that you want to prepare the entire dataset (files 0-29), and that you mounted the data workspace in /mount/data, and the results workspace in /mount/results. stdout and stderr are redirected to the file /results/data_gpt3_log.txt, which you can download from NGC. You can add any other configuration required to modify the command’s behavior.

To run data preparation on a kubernetes cluster, set both the cluster and cluster_type parameters to k8s in conf/config.yaml. Additionally, set the launcher_scripts_path parameter to the location where the launcher scripts are located on the NFS filesystem. This must be the same path on all nodes in the cluster. Ensure the stages parameter is set to data_preparation and data_preparation in the defaults section points to the intended data preparation script.

The conf/config/k8s.yaml file also needs to be updated with the kubernetes container registry secret if created earlier (pull_secret), the shm_size to determine how much local memory to put in each pod, and the NFS server and path to where the launcher scripts are saved. These can all be overridden from the command line using hydra as well.

Once all of the config files are updated, the data preparation can be launched from the controller node with:

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python main.py

This will generate and launch a job via Helm in the default namespace which can be viewed with helm show or kubectl get pods. The logs can be followed with kubectl logs <pod-name> for the first pod deployed for the job.

Set the data preparation job’s configuration for GPT models in the YAML file:

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run: name: download_gpt3_pile results_dir: ${base_results_dir}/${.name} time_limit: "4:00:00" dependency: "singleton" node_array_size: 30 array: ${..file_numbers} bcp_preproc_npernode: 2 # 2 should be safe to use and x2 times faster. dataset: pile download_the_pile: True # Whether to download the pile dataset from the internet. the_pile_url: "https://huggingface.co/datasets/monology/pile-uncopyrighted/tree/main/train/" # Source URL to download The Pile dataset from. file_numbers: "0-29" # The pile dataset consists of 30 files (0-29), choose which ones to download. preprocess_data: True # True to preprocess the data from a jsonl file, False otherwise. download_vocab_url: "https://huggingface.co/gpt2/resolve/main/vocab.json" # URL to download the vocab from. download_merges_url: "https://huggingface.co/gpt2/resolve/main/merges.txt" # URL to download the merges from. vocab_save_dir: ${data_dir}/bpe merges_save_dir: ${data_dir}/bpe tokenizer_type: GPT2BPETokenizer rm_downloaded: True # Extract script will remove downloaded zst after extraction rm_extracted: True # Preprocess script will remove extracted files after preproc.

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© | | | | | | |. Last updated on May 30, 2024.