multi GPU runs

 

Gemini: 
 your script is correctly configured for Single-Node, Multi-GPU (8 GPUs) training using PyTorch Distributed Data Parallel (DDP) and SLURM.

You have correctly implemented the "Gradient Coordination" concepts we discussed. Here is a breakdown of why this code works and a few small things to watch out for as you scale.

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Why this script is correct:

1. Orchestration via SLURM: Using srun to launch the Python script is the best practice on clusters. It automatically assigns SLURM_PROCID and SLURM_LOCALID to each process, which you then use to initialize the process group.

2. The NCCL Backend: You used backend="nccl", which is the high-performance library designed by NVIDIA for the "All-Reduce" gradient synchronization we talked about.

3. Correct Device Pinning: torch.cuda.set_device(local_rank) ensures that Rank 0 uses GPU 0, Rank 1 uses GPU 1, and so on. Without this, all 8 processes might try to crowd onto GPU 0, leading to an "Out of Memory" (OOM) error.

4. Data Sharding: By using DistributedSampler, you ensure that each GPU receives a different subset of your 50 records. Without this, every GPU would train on the exact same data, wasting 7/8ths of your compute power.

5. DDP Wrapper: model = DistributedDataParallel(model, device_ids=[local_rank]) is the magic line. This wraps your model so that during the .backward() call, the GPUs automatically trigger the All-Reduce algorithm to average gradients across the 8 cards.

6. Clean Saving: You used model.module.state_dict(). This is important because DDP adds a .module. prefix to all parameter names; saving model.module ensures your weights can be loaded later into a normal, non-DDP model.

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How the Gradients are Coordinated in this Code

While your code is running, the coordination follows this invisible "choreography":

1. Compute: Each of the 8 tasks calculates gradients for its local batch of data.

2. Trigger: As soon as the gradients are ready, torch.nn.parallel.DistributedDataParallel catches the signal.

3. All-Reduce: The 8 GPUs communicate over the NVLink (the internal high-speed bridge on the A100-8 node). They sum their gradients and divide by 8.

4. Update: Only after all 8 GPUs have the exact same averaged gradient does the optimizer.step() move the weights forward.

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Small Suggestions for Improvement

• The "Smoke Test" Math: You have 50 records and 8 GPUs. With a batch_size=8 per GPU, your "Global Batch Size" is 64. Since $50 < 64$, your code will only run for one single step per epoch, and some GPUs might even receive empty batches depending on how the sampler divides the remainder. For a smoke test, this is fine, but for real training, ensure Total Records >> (Batch Size * Num GPUs).

• Data Loading Efficiency: Currently, every one of the 8 processes reads the JSONL files from the disk:

  Python

  records = read_jsonl_records(paths) # Every process does this
  

  For 50 records, this is instant. If you scale to 50 million records, 8 processes hitting the hard drive at once can cause a "bottleneck." Usually, you want to use a WebDataset or a library like HuggingFace Datasets that streams the data.

• Multi-Node Scaling: If you ever move from 8 GPUs (1 node) to 16 GPUs (2 nodes), you will need to change #SBATCH --ntasks-per-node=8 and add #SBATCH --nodes=2. You will also need to ensure MASTER_ADDR is set to the hostname of the first node specifically, rather than just $(hostname) on every node.

Would you like me to show you how to modify the MASTER_ADDR logic so this script works across multiple physical servers (nodes)?