.. _: Weight Streaming Appliance Mode Workflow ======================================== The appliance mode is a workflow that simplifies running large models across the Cerebras Wafer-Scale Cluster as if it were a single device. Perform the following steps to run the appliance: 1. Ensure that the admin setup is complete. Check with your Sysadmin. 2. Follow the first-time user setup procedure. 3. Run the scripts to train or evaluate your model. Admin setup ----------- Your admin should have set up the following parameters: - Kubernetes is set up. - Cluster management is already running on the appliance and is ready to interact with the user node. - TLS certificate is generated, and location known. - Python 3.7 is available. - The path to the Cerebras packages is available. First-time user setup ---------------------- The first time you use this mode, you must set it up as shown below for weight streaming execution. .. Note:: Make sure that you have the TLS Certificate available from your sysadmin. You will need this to communicate between the user node and the Wafer-Scale Cluster. Your admin will have shared the path to this file during the setup. 1. Set up the Python virtual environment using Python 3.7. Create the environment named ``venv_appliance`` using the following command: .. code-block:: bash python3.7 -m venv venv_appliance 2. There are three main sets of packages available. There is the ``cerebras_appliance`` software package, the ``cerebras_tensorflow`` package if you wish to use TensorFlow, and the ``cerebras_pytorch`` package if you wish to use PyTorch. 3. Enter the following commands on the user node (make sure to execute the commands in this order to install the appliance wheel first): .. code-block:: bash source venv_appliance/bin/activate pip install /cerebras_appliance-___-py3-none-any.whl --find-links= pip install /cerebras_pytorch-___-py3-none-any.whl --find-links= .. Note:: With the ``find-links`` command, it will find the correct cerebras-appliance version if you place all the wheels in the same directory. If you are only doing a PyTorch pipeline for the release, then you shouldn't need ``cerebras_pytorch`` because the ``csrun`` scripts are packaged in ``cerebras_appliance``. Running the scripts to compile, train, or evaluate your model ------------------------------------------------------------- The steps to train your model are as follows. 1. Enter the Python environment using the following command: .. code-block:: bash source venv_appliance/bin/activate In the Model Zoo, you find ``run-appliance.py`` for TensorFlow models supported in the weight streaming appliance mode. For example, for the GPT-2 model, navigate to the following directory in the Model Zoo: .. code-block:: bash cd modelzoo/transformers/tf/gpt2 2. Within this directory, run the following command that performs the initial stage of compilation to get feedback about whether your model can be lowered. .. code-block:: bash python run-appliance.py --params params.yaml --num_csx=1 --model_dir model_dir --validate_only --mode train --credentials_path= --mount_dirs --python_paths This process should be considerably faster running than a full compile. 3. This step runs the full compile. The artifacts from this run are used in the training run. .. code-block:: bash python run-appliance.py --params params.yaml --num_csx=1 --model_dir model_dir --compile_only --mode train --credentials_path= --mount_dirs --python_paths This compile can take longer, depending on the size and complexity of the model (30 minutes to two hours). 4. This is the training step. - If you are running one CS-2, enter the following: .. code-block:: bash python run-appliance.py --params params.yaml --num_csx=1 --model_dir=model_dir --num_steps=10 --mode train --credentials_path= --mount_dirs --python_paths - If you are running multiple CS-2s, enter the following. Note that ``csx=2`` refers to the number of appliances you are using. In this case, you are running two appliances. .. code-block:: bash python run-appliance.py --params params.yaml --num_csx=2 --model_dir=model_dir --num_steps=10 --mode train --credentials_path= --mount_dirs --python_paths The output log is as follows: .. code-block:: bash INFO root:start_utils.py:519 # 1. Start Coordinator on separate process INFO root:start_utils.py:534 # 2. Begin Run INFO root:start_utils.py:545 # 3. Start Workers on separate processes INFO root:start_utils.py:554 # 4. Start Chief on separate processes INFO root:start_utils.py:564 # 5. Start WS Runtime servers (i.e. ws-srv) on separate processes INFO root:cs_estimator_app.py:274 Loaded global step 0 INFO root:cs_estimator_app.py:817 Output activation tensors: ['truediv_3_1'] INFO root:cluster_client.py:217 Initiating a new compile wsjob against the cluster server. INFO root:cluster_client.py:220 Compile job initiated INFO root:appliance_manager.py:135 Creating a framework GRPC client: localhost:50065, None, INFO root:appliance_manager.py:359 Compile successfully written to cache directory: cs_10097974384330522877 INFO root:cluster_client.py:243 Initiating a new execute wsjob against the cluster server. INFO root:cluster_client.py:246 Execute job initiated INFO root:appliance_manager.py:149 Removing a framework GRPC client INFO root:cs_estimator_app.py:940 final generation of weights: 9 INFO cerebras_appliance.appliance_client:appliance_client.py:435 Input fn serialized: 80036374657374732e77732e6d696c6573746f6e655f6d6f64656c732e74662e646174610a746f795f696e7075745f666e0a71002e INFO root:appliance_manager.py:135 Creating a framework GRPC client: localhost:50066, None, INFO root:appliance_manager.py:282 About to send initial weights INFO root:tf_appliance_manager.py:85 Dropping tensor: 'good_steps' INFO root:appliance_manager.py:284 Finished sending initial weights INFO root:cs_estimator_app.py:482 global step 2: loss = 0.0 (0.37 steps/sec) INFO root:cs_estimator_app.py:482 global step 4: loss = 0.0 (0.74 steps/sec) INFO root:cs_estimator_app.py:388 Taking checkpoint at step: 5 INFO root:cs_estimator_app.py:437 saving last set of weights: 9 INFO root:cs_estimator_app.py:482 global step 6: loss = 0.0 (1.06 steps/sec) INFO root:cs_estimator_app.py:482 global step 8: loss = 0.0 (1.41 steps/sec) INFO root:cs_estimator_app.py:388 Taking checkpoint at step: 10 INFO root:cs_estimator_app.py:391 Taking final checkpoint INFO root:cs_estimator_app.py:437 saving last set of weights: 9 INFO root:cs_estimator_app.py:482 global step 10: loss = 0.0 (1.69 steps/sec) INFO root:cs_estimator_app.py:489 Training complete. Completed 640 sample(s) in 5.9104249477386475 seconds INFO root:start_utils.py:587 Wait for server completion INFO root:start_utils.py:599 Servers Completed 5. To run an eval job, run the following command: .. code-block:: bash python run-appliance.py --params params.yaml --num_csx=1 --model_dir=model_dir -–mode eval –-eval_steps=10 --credentials_path= --mount_dirs --python_paths .. Note:: Cerebras only supports one CS-2 for eval mode. Contents of ``run-appliance.py`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ For your reference, the contents of ``run_appliance.py`` is as shown in `Cerebras Model Zoo `_. Output files and artifacts ~~~~~~~~~~~~~~~~~~~~~~~~~~ The output files and artifacts of the model directory (``model_dir``) contain all the results and artifacts of the latest run, including: - Checkpoints - Tensorboard event files - ``yaml`` files Checkpoints *********** Checkpoints are stored in ``model_dir>/model-ckpt*``. Tensorboard event files *********************** Tensorboard event files are stored in the ```` directory.