Update bionemo test case + propose to subdirectories per orchastrator #396
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perifaws
approved these changes
Aug 6, 2024
| RUN git clone -b ${NCCL_VERSION} https://github.com/NVIDIA/nccl.git /opt/nccl \ | ||
| && cd /opt/nccl \ | ||
| && make -j $(nproc) src.build CUDA_HOME=/usr/local/cuda \ | ||
| NVCC_GENCODE="-gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_89,code=sm_89 -gencode=arch=compute_90,code=sm_90" |
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Perhaps add a link or quick description on what each arch is. Not required but good to have.
mhuguesaws
reviewed
Aug 7, 2024
| export DATASET_PATH=/fsx/ | ||
| ``` | ||
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| ## 1.4. Pull this github repo |
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Suggested change
| ## 1.4. Pull this github repo | |
| ## 1.4. Clone this github repo |
mhuguesaws
reviewed
Aug 7, 2024
| ```bash | ||
| cd /apps/ | ||
| git clone https://github.com/aws-samples/awsome-distributed-training.git | ||
| cp -r /apps/awsome-distributed-training/3.test_cases/14.bionemo/* ./apps/ |
mhuguesaws
reviewed
Aug 7, 2024
| ``` | ||
| # Miniconda is already installed if you are using the DLAMI but needs installation with Base AMI | ||
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| wget -O miniconda.sh "https://repo.anaconda.com/miniconda/${MINICONDA_INSTALLER}.sh" \ |
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Python virtual env is enough. No need to introduce conda here.
mhuguesaws
reviewed
Aug 7, 2024
| `3.test_cases/14.nemo-multimodal/0.Dockerfile` and we can build a image like below: | ||
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| ``` | ||
| docker build -t ${DOCKER_IMAGE_NAME}:${TAG} -f 0.Dockerfile . |
mhuguesaws
reviewed
Aug 7, 2024
| We will use the popular [UniRef50](https://www.uniprot.org/help/uniref) dataset for pretraining. We will use BioNemo's in-built functionality to download and pre-process data. To this end, we provide `prepare_uniref50.py` file to do so. You can edit the above to download and process [UniRef90]((https://www.uniprot.org/help/uniref)). To run the above python code on your slurm cluster in the BioNemo cluster execute the following: | ||
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| ```bash | ||
| sbatch 1.uniref50.slurm |
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rename the script
uniref50_download_preprocess.sbatch
mhuguesaws
reviewed
Aug 7, 2024
| Once the above image is pulled, you can run the container on the head node like below. This step could be used for troubleshooting purposes. Here we are running the container just to be able to copy launcher scripts on the host machine. If you need to run the container on the compute nodes, you would need to add `--gpus all` flag to the run command. It is recommended to have the docker run flags like below, as recommended by Nvidia PyTorch containers, otherwise you may potentially run into an error like [this](https://github.com/NVIDIA/Megatron-LM/issues/516) | ||
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| ``` | ||
| docker run -it nvcr.io/nvidia/clara/bionemo-framework:latest bash |
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