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Running Small to Medium Models (Pipelined Execution)

Running Small to Medium Models (Pipelined Execution)

This reference guide is for the original workflow that utilizes Slurm as the orchestrating software running on our CPU nodes to mediate communication between the CS system and the original Cerebras support-cluster.

Note

This is no longer the recommended workflow. Slurm/Singularity is supported only on the original Cerebras installation while the latest Cerebras Wafer-Scale Clusters only support a Kubernetes-based workflow. Refer to Running Small to Medium Models (Pipelined Execution) for more information.

Prerequisites

System Setup Confirmation

Before you can start using the CS system, check with your system administrator and go over the following prerequisites first.

  1. The Singularity software is installed on all the nodes, including the chief and the worker nodes, and can launch Cerebras container that consists of the Cerebras Graph Compiler (CGC) and other necessary libraries.

  2. The Slurm orchestrator software is installed and is running on all the nodes. The orchestrator software performs the coordination between the CS system and the nodes in the CS cluster.

  3. You have the hostnames of the chief and the worker nodes. You log in to the chief node and perform all your work on the chief node. You need hostnames of the worker nodes for debugging.

  4. You have the IP address and the port number of the network attached CS system accelerator. You pass this IP address and port number to the --cs_ip flag of your runtime scripts during compiling and running your models.

  5. Steps to log in to the chief node of the CS system cluster. Logging into the chief node is done by using ssh.

Clone the reference samples repository

  1. Log in to your CS system cluster.

  2. Clone the reference samples repository to your preferred location in your home directory.

    git clone https://github.com/Cerebras/modelzoo/tree/master/modelzoo

  3. In the reference samples directory, there are a few models for PyTorch and TensorFlow. In this guide, we use the FC-MNIST model.

  4. Navigate to the fc_mnist model directory.

    cd modelzoo/fc_mnist/tf/

Compile on CPU

Cerebras recommends that you first compile your model successfully on a support cluster CPU node before running it on the CS system.

You can run in validate_only mode that runs a fast, light-weight verification. In this mode, the compilation only runs through the first few stages, up until kernel library matching.

After a successful validate_only run, you can run full compilation with compile_only mode.

This section of the quick start shows how to execute these steps on a CPU node.

Tip

The validate_only step is very fast, enabling you to rapidly iterate on your model code. Without needing access to the CS system wafer scale engine, you can determine in this ``validate_only `` step if you are using any TensorFlow layer or functionality that is unsupported by either XLA or CGC.

Follow these steps:

  1. Navigate to the model directory.

    cd modelzoo/fc_mnist/tf/

  2. Run the compilation in validate_only mode.

    csrun_cpu python run.py --mode train --validate_only
...
XLA Extraction Complete
=============== Starting Cerebras Compilation ===============
Cerebras compilation completed: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:02s,  1.23s/stages]
=============== Cerebras Compilation Completed ===============

Note

The validate_only mode checks the kernel compatibility of your model. When your model passes this mode, run the full compilation with compile_only to generate the CS system executable.

  1. Run the full compilation process in compile_only mode.

This step runs the full compilation through all stages of the Cerebras software stack to generate a CS system executable.

csrun_cpu python run.py --mode train --compile_only --cs_ip <specify your CS_IP>
...
XLA Extraction Complete
=============== Starting Cerebras Compilation ===============
Cerebras compilation completed: |                    | 17/? [00:18s,  1.09s/stages]
=============== Cerebras Compilation Completed ===============

When the above compilation is successful, the model is guaranteed to run on the CS system. You can also use validate-only mode to run pre-compilations of many different model configurations offline so you can more fully utilize the allotted CS system cluster time.

Note

The compiler detects whether a binary already exists for a particular model config and skips compiling on the fly during training if it detects one.

Train and evaluate on CPU

To train and eval on CPU follow these steps:

  1. Navigate to the model directory.

    cd modelzoo/fc_mnist/tf/

  2. Train and evaluate the model on the CPU.

    # train on CPU
csrun_cpu python run.py --mode train

# run eval on CPU
csrun_cpu python run.py --mode eval  --eval_steps 1000

Note

The max_steps and other parameters such as save_checkpoints_steps can also be set in the params.yaml file.

The above command trains the FC-MNIST model for 100,000 steps by executing on the CS system at the IP address specified in the --cs_ip flag. When the command executes, you see an output similar to what is shown below:

srun: job 5834 queued and waiting for resources
srun: job 5834 has been allocated resources
...
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into model_dir/model.ckpt.
INFO:tensorflow:Programming CS system fabric. This may take a couple of minutes - please do not interrupt.
INFO:tensorflow:Fabric programmed
INFO:tensorflow:Coordinator fully up. Waiting for Streaming (using 0.97% out of 301600 cores on the fabric)
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
...
INFO:tensorflow:Training finished with 25600000 samples in 187.465 seconds, 136558.69 samples / second
INFO:tensorflow:Saving checkpoints for 100000 into model_dir/model.ckpt.
INFO:tensorflow:global step 100000: loss = 1.901388168334961e-05 (532.0 steps/sec)
INFO:tensorflow:global step 100000: loss = 1.901388168334961e-05 (532.0 steps/sec)
INFO:tensorflow:Loss for final step: 1.9e-05.

Run the model on the CS system

The below csrun_wse command compiles the code if no existing compile artifacts are found, and then runs the compiled executable on the CS system.

csrun_wse python run.py --mode train \
--cs_ip <YOUR-CS1-IP-ADDRESS> \
--max_steps 100000

Note

The max_steps and other parameters such as save_checkpoints_steps can also be set in the params.yaml file.

The above command trains the FC-MNIST model for 100,000 steps by executing on the CS system at the IP address specified in the --cs_ip flag. When the command executes, you see an output similar to what is shown below:

srun: job 5834 queued and waiting for resources
srun: job 5834 has been allocated resources
...
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into model_dir/model.ckpt.
INFO:tensorflow:Programming CS system fabric. This may take a couple of minutes - please do not interrupt.
INFO:tensorflow:Fabric programmed
INFO:tensorflow:Coordinator fully up. Waiting for Streaming (using 0.97% out of 301600 cores on the fabric)
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
...
INFO:tensorflow:Training finished with 25600000 samples in 187.465 seconds, 136558.69 samples / second
INFO:tensorflow:Saving checkpoints for 100000 into model_dir/model.ckpt.
INFO:tensorflow:global step 100000: loss = 1.901388168334961e-05 (532.0 steps/sec)
INFO:tensorflow:global step 100000: loss = 1.901388168334961e-05 (532.0 steps/sec)
INFO:tensorflow:Loss for final step: 1.9e-05.

Output files and artifacts

The output files and artifacts include a model directory (model_dir), which contains all the results and artifacts of the latest run, including:

  • Compile directory (cs_<checksum>)

  • performance.json file

  • Checkpoints

  • Tensorboard event files

  • yaml files

Compile dir – The directory containing the cs_<checksum>

The cs_<checksum> dir (also known as cached compile directory), contains the .elf, which is used to program the system.

Output of compilation indicates whether the compile passed or failed; if failed, then the logs show at which stage compilation failed.

performance.json file and its parameters

There is a performance directory that should contain the performance.json <model_dir>/performance/performance.json. This contains information as listed below:

  • compile_time - The amount of time that it took to compile the model to generate the Cerebras executable.

  • ``est_samples_per_sec ``- The estimated performance in terms of samples per second based on the Cerebras compile. Note that this number is theoretical and actual performance may vary.

  • programming_time - This is the time taken to prepare the system and load with the model that is compiled.

  • samples_per_sec - The actual performance of your run execution; i.e., the number of samples processed on the WST per second.

  • suspected_input_bottleneck - This is a beta feature. It indicates whether you are input-starved and need more input workers to feed the Cerebras system.

  • total_samples - The total gross samples that were iterated during the execution.

  • total_time - The total time it took to complete the total samples.

Checkpoints

Checkpoints are stored in <model_dir>; for example, <model_dir>/model-ckpt-0.index, <model_dir>/model-ckpt-0.meta, and <model_dir>/model-ckpt-1.data-00000-of-00001. They are saved with the frequency specified in the runconfig file.

Tensorboard event files

Ternsorboard event files are also stored in the <model_dir>.

yaml files content after the run

The yaml file is stored in the train directory. This yaml file contains information about the specifics of the run, such as model specific configuration (e.g., dropout, activation_fn), optimizer type and optimizer parameters, input data configuration, such as batch_size, and shuffle and run configuration, such as max_steps, checkpoint_steps, and num_epochs.

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The run.py Template