Ray tune pytorch lightning. Ran on a 2-node AWS cluster of m5.

Defaults to “checkpoint”. The CLI reporter only ever shows the trials as PENDING, and they never change to RUNNING. Below, we define a function that trains the Pytorch model for multiple epochs. Would it be possible to get PyTorch Lightning modules working with the Jan 13, 2021 · Saved searches Use saved searches to filter your results more quickly Ray Tune includes the latest hyperparameter search algorithms, integrates with TensorBoard and other analysis libraries, and natively supports distributed training through Ray’s distributed machine learning engine. filename: Filename of the checkpoint within the checkpoint directory. Sep 7, 2023 · How severe does this issue affect your experience of using Ray? High: It blocks me to complete my task. testcode:: from typing import Dict, List, Optional from ray. All of the output of your script will show up on your console. Tune will report on experiment status, and after the experiment finishes, you can inspect the results. Ray Tune is a Python Ray Tune: Hyperparameter Tuning. sample_from () function makes it possible to define your own sample methods to obtain hyperparameters. My problem: only, seemingly random, trials each with full training and validation epochs terminate. 0. 3/12. Sep 21, 2023 · The suggested config parameters are passed on to my lightning trainer n… The new ray 2. report() not being recognized or causing unexpected behavior. 0 Optuna allows you to define the kinds and ranges of hyperparameters you want to tune directly within your code using the trial object. Ray Tune currently offers two lightweight integrations for Weights & Biases. Environment variables used by Ray Tune. Optuna is a hyperparameter optimization library. g. We will just use the latter in this example so that we can retrieve the saved model later. Callback) Callback Interface. get_context() methods which assume that you’re executing within a trainer. utilities. best_checkpoint, "checkpoint") ) Feb 18, 2021 · Ray Tune’s implementation of optimization algorithms like Population Based Training (shown above) can be used with PyTorch for more performant models. DeepSpeed is an open-source deep learning optimization library for PyTorch. Therefore I can’t use a Ray search space for my dataloader’s batch_size parameter. with_resources(train_model, {'cpu':10, 'gpu': 1}): tuner = tune. Fine-tune a vicuna-13b text generation model with Ray Tune includes the latest hyperparameter search algorithms, integrates with TensorBoard and other analysis libraries, and natively supports distributed training through Ray’s distributed machine learning engine. The search space, search algorithm, scheduler, and Trainer are passed to a Tuner, which runs the hyperparameter tuning workload by evaluating We would like to show you a description here but the site won’t allow us. 2xlarge head node, within 2 minutes. Jan 22, 2021 · I found that Ray Tune does not work properly with DDP PyTorch Lightning. yaml tune_script. In this example, we will demonstrate how to perform full fine-tuning for a vicuna-13b-v1. In fact, the following points from the official website summarize its wide range of capabilities quite well. A platform for freely expressing thoughts and ideas through writing on various topics. dev0. Advanced. py onto the head node, and run python tune_script localhost:6379, which is a port opened by Ray to enable distributed execution. trial_resources. 3 GiB Using AsyncHyperBand: num_stopped=0 Bracket Jul 14, 2022 · Hi, I am running ray tune 1. Apr 27, 2023 · Updated LightningTrainer: Third, in the broader deep learning space, we’re introducing the LightningTrainer, allowing you to scale your PyTorch Lightning on Ray. Weirdly, I’m getting the following error: lightning_lite. import os import torch from ray. In this tutorial, we will show you how to integrate Ray Tune into your PyTorch training workflow. You can tune your favorite machine learning framework ( PyTorch, XGBoost, TensorFlow and Keras, and more) by running state of the art algorithms such as Population Based Training (PBT) and HyperBand/ASHA . In particular, it follows three steps: Preprocess the CoLA dataset with Ray Data. xgboost) LightGBM (tune. 0 with the pytorch lightning (1. In the non-academic world Example #. I let 8 trials run, I set the hyperparams to the same value so every trial should do the same work Ray Tune comes with two XGBoost callbacks we can use for this. How to Enable Fault Tolerance in Ray Tune; Using Callbacks and Metrics; Getting Data in and out of Tune; Analyzing Tune Experiment Results; A Guide to Population Based Training with Tune. I use “DistributedTrainableCreator” this How to Configure Persistent Storage in Ray Tune; How to Enable Fault Tolerance in Ray Tune; Using Callbacks and Metrics; Getting Data in and out of Tune; Analyzing Tune Experiment Results; A Guide to Population Based Training with Tune. Easily scale up. It demonstrates how to train a basic neural network on the MNIST dataset with distributed data parallelism. Lightning project seed. fit() call. Mar 4, 2021 · Ray Libraries (Data, Train, Tune, Serve) Ray Tune. . Configure a dataloader to shard data across the workers and place data on the correct CPU or GPU device. freeze()x=some_images_from_cifar10()predictions=model(x) We used a pretrained model on imagenet, finetuned on CIFAR-10 to predict on CIFAR-10. Each model is trained with PTL. 3. Ray Tune Examples. You can run multiple PyTorch Lightning training runs in parallel, each with a different hyperparameter configuration, and each training run parallelized by itself. Of course, you can also use PyTorch Lightning or other libs as well . (3 learning rates, 2 clusters of NYC taxi locations). However when building Ray’s LightningConfigBuilder, I have to pass an instance of my data-module for &hellip; Oct 5, 2023 · How severe does this issue affect your experience of using Ray? None: Just asking a question out of curiosity Low: It annoys or frustrates me for a moment. fit() / tuner. Hi @amogkam! I missed that in the pytorch-lightning Ray tune tutorial. Ray Tune Examples — Ray 2. Ran on a 2-node AWS cluster of m5. 1. Launch a multi-node distributed hyperparameter sweep in less than 10 lines of code. The goal here is to improve readability and reproducibility. I’m starting to use Raytune with my pytorch-lightning code and even though I’m reading documentation and stuff I’m still having a lot of trouble wrapping my Analyzing Tune Experiment Results. Open in app. For example, if the grid contains two hyperparameter combinations, and trains each of the two networks for 500 ray_lightning also integrates with Ray Tune to provide distributed hyperparameter tuning for your distributed model training. You can refer to this example for more details: Using PyTorch Lightning with Tune — Ray 3. DataFrame, labels: pd. It features an imperative (“how” over “what” emphasis), define-by-run style user API. Recommended Lightning Project Layout ¶. However, in our distributed training setup, we call init_process_group ourselves, and it seems this part is handled by Ray Sep 21, 2023 · I am using Ray Tune to perform HP search with my PyTorch Lightning project. Framework. In contrast to other libraries, it employs define-by-run style hyperparameter definitions. Tuner(. . Launch distributed training with Ray Train’s TorchTrainer. Video on how to refactor PyTorch into PyTorch Lightning. It is very popularin the machine learning and data science community for its superb visualizationtools. The TuneReportCallback just reports the evaluation metrics back to Tune. Dec 27, 2021 · Although we will be using Ray Tune for hyperparameter tuning with PyTorch here, it is not limited to only PyTorch. Tune can retry failed trials automatically, as well as entire experiments; see How to Define Stopping Criteria for a Ray Tune Experiment. vblagoje August 27, 2021, 9:09am 1. best_checkpoint But I am unable to restore my pytorch lightning model with it. The first way is to ask lightning to save the values anything in the __init__ for you to the checkpoint. Ray Train provides support for many frameworks: PyTorch Ecosystem. fit_params. rliaw March 4, 2021, 7:48pm 2. Learn how to: Configure the Lightning Trainer so that it runs distributed with Ray and on the correct CPU or GPU device. 2. External library integrations for Ray Tune. Oct 21, 2021 · I have a ray tune analysis object and I am able to get the best checkpoint from it: analysis = tune_robert_asha(num_samples=2) best_ckpt = analysis. Aug 31, 2023 · I am using Ray Tune to perform HP search with my PyTorch Lightning project. Jun 18, 2023 · Ray Tune is a framework that implements several state-of-the-art hyperparameter tuning algorithms. Ray is a fast and scalable framework for distributed computing in Python. Examples using Ray Tune with ML Sep 2, 2021 · Pytorch-lightning: Provides a lot of convenient features and allows to get the same result with less code by adding a layer of abstraction on regular PyTorch code. Researchers love it because it reduces… Mar 4, 2021 · I know the essence of Ray is that, given n nodes, you assign a single “head” node and n-1 “worker” nodes, and then supposedly Ray takes care of the rest. The problem arises from a mismatch between the &hellip; Step 4: Run the trial with Tune. Here are the main benefits of Ray Lightning: Simple setup. As part of our continued effort for seamless integration and ease of use, we have enhanced and replaced our existing ray_lightning integration, which was widely adopted, with the Nov 29, 2022 · I have read this guide. In this example, the l1 and l2 parameters should be powers of 2 between 4 and 256, so either 4, 8, 16, 32, 64, 128, or 256. train. which will let your model know about the new resources assigned. grid_search(values:Iterable)→Dict[str,Iterable][source] #. Image from Deepmind. Tune is a Python library for experiment execution and hyperparameter tuning at any scale. GeoffNN December 22, 2022, 7:22pm 3. I have one machine with 80 CPU cores and 2 GPUs. Common Use Cases ¶. It supports multiple types of ML frameworks, including pytorch, pytorch-lightning, jax and tensorflow. Visualizing and Understanding PBT; Deploying Tune in the Cloud; Tune Architecture; Scalability Benchmarks Aug 27, 2021 · Distributed training in PyTorch and init_process_group. Ray Lightning is a simple plugin for PyTorch Lightning to scale out your training. Is there a way around this? I know I could move my dataloaders inside You can either create a model from pre-trained weights or reload the model checkpoint from a previous run. model=ImagenetTransferLearning. utils. On a cluster with a GPU is failing. Using the DistributedDataParallel of PyTorch Lightning. Whether you have large models or large datasets, Ray Train is the simplest solution for distributed training. Thanks for the link – I fixed my code by adding tune. They will look something like this. Visualizing and Understanding PBT; Deploying Tune in the Cloud; Tune Architecture; Scalability Benchmarks; Ray Tune Examples. PyTorch Lightning is a framework which brings structure into training PyTorch models. 4) integration and wandb mixin as integrated per ray tune documentation. Fine-tune a Llama-2 text generation models with DeepSpeed and Hugging Face Accelerate. load_from_checkpoint( os. schedulers import PopulationBasedTraining from Feb 2, 2021 · The package introduces 2 new Pytorch Lightning accelerators for both DDP and Horovod training on Ray for quick and easy distributed training. set_state`. tune import Callback from ray. Defaults to "checkpoint". Define a training function with PyTorch Lightning. Medium severity. Train a Pytorch Lightning Image Classifier. Specifically, we’ll leverage early stopping and Bayesian Optimization via HyperOpt to do so. config import ScalingConfig model¶ (LightningModule) – Model to tune. pytorch_lightning) XGBoost (tune. A set of hyperparameters you want to tune in a search space. run(). I try: MyLightningModel. For instance, I receive errors indicating that the specified metrics Aug 20, 2019 · Tune supports PyTorch, TensorFlow, XGBoost, LightGBM, Keras, and others. 6. utils import ( download_data, build_compute_metrics_fn, ) from ray. 2. The CLI reporter output always stays the same, looking like this: == Status == Memory usage on this node: 1. 1. Hi @veydan , the best way is to use TorchTrainer + Tuner. 0001 and 0. Where there is at least 5 . Keras Example; PyTorch Example; PyTorch Lightning Example; Ray RLlib Example; XGBoost Example; LightGBM Example; Horovod Example; Hugging Face Transformers Example; Tune Experiment Tracking Examples. Values specified in a grid search are guaranteed to be sampled. If I set max_t parameter of ASHAScheduler very high, does the training the best model continue. A search algorithm to effectively optimize your parameters and optionally use a scheduler to stop searches early and speed up your experiments. This also makes those values available via self. This saves the Mar 4, 2024 · Is this expected, and are there plans to fully support Lightning in Ray? To work around the issue, I rewrote most of the ray. In this guide, we’ll walk through some common workflows of what analysis you might want to perform after running your Tune experiment with tuner. Lightning, DeepSpeed. High: It blocks me to complete my task. Computing cluster (SLURM) Child Modules. First, you define the hyperparameters you want to tune in a search space and pass them Aug 18, 2019 · $ ray submit tune-default. This tutorial will walk you through the process of setting up a Tune experiment. Tune Callbacks (tune. Step 5: Inspect results. #. E. Tip. Use Ray Tune to optimize Pytorch Lightning hyperparameters in 30 lines of code! Aug 18, 2020. Runtime less than 2 minutes total. You can write the same code for 1 GPU, and change 1 parameter to scale to a large cluster. lightning import LightningTrainer, LightningConfigBuilder from ray import air, tune from ray. You should not have to modify anything here. Hi I am trying to train a NeuralForecast model with their AutoNHits library, which Feb 10, 2023 · My python script is adapted from Using PyTorch Lightning with Tune; when run on a local CPU machine, it works perfectly. But I have to downgrade my requirements, e. Dec 21, 2022 · GeoffNN December 21, 2022, 1:42am 1. Accelerate, DeepSpeed, Hugging Face. The main abstraction of PyTorch Lightning is the LightningModule class, which should be Other Integrations. It also takes care of distributed training in a multi-device setting. filename – Filename of the checkpoint within the checkpoint directory. This demo introduces how to fine-tune a text classifier on the CoLA (The Corpus of Linguistic Acceptability) dataset using a pre-trained BERT model. In essence, Tune has six crucial components that you need to understand. 3 model using Ray Train PyTorch Lightning integrations with the DeepSpeed ZeRO-3 strategy. pbt_transformers. join(analysis. This function will be executed on a separate Ray Actor (process) underneath the hood, so we need to communicate the performance of the model back to Tune (which is on the main Python process). Here is my SLURM script: #!/bin/bash. In this tutorial we introduce BayesOpt, while running a simple Ray Tune experiment. experiment PyTorch Lightning 101 class. Trainer. 4xlarge worker nodes and one m5. If this is a dict, each key will be the name reported to Tune and the respective value will be the metric key reported to PyTorch Lightning. MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. Example. This Searcher is a thin wrapper around Optuna’s search algorithms. """ import os import ray from ray import tune from ray. import-antigravity: Hi, I have a bit of experience running simple SLURM jobs on my school’s HPCC. #SBATCH -N 2 -n 8. You can also obtain the current trial resourcesby calling Trainable. You can also learn more about Ray's features and libraries, such as data processing, machine learning, and reinforcement learning, by exploring the related webpages. I was able to get this script to run by commenting out these lines: trainer = pl. The tune. tune import CLIReporter from ray. copying over response from another thread: Ray Tune includes the latest hyperparameter search algorithms, integrates with TensorBoard and other analysis libraries, and natively supports distributed training through Ray’s distributed machine learning engine. It aims to avoid boilerplate code, so you don’t have to write the same training loops all over again when building a new model. save_checkpoints – If True (default), checkpoints will be saved and reported to Ray. Configure scaling and CPU or GPU resource model=ImagenetTransferLearning()trainer=Trainer()trainer. py: Here is where all Ray Tune is written. Ray-tune: Hyper parameter tuning library for advanced tuning strategies at any scale. Jul 2, 2021 · I’m trying to run a hyperparameter search with PyTorch Lightning, but it doesn’t seem like any of the trials are ever actually started. The trials start normally and proceed for a while until some of them just stop. If multiple grid search variables are defined, they are combined with the combinatorial product. Configure training function to report metrics and save checkpoints. Examples using Ray Tune with ML In this example, we will demonstrate how to perform full fine-tuning for a vicuna-13b-v1. Hi! I’m trying to use Ray tune for hyperparameter search. I am running this on windows 10 with 2 GPUs (RTX 2080 ti and a Quadro P1000) and a Xeon E5-2630 v4 CPU with 64gb of RAM I am experiencing incredibly long run times with this setup compared to previous with ray tune and just pytorch. It currently offers four components, including MLflow Tracking to record and query experiments, including code, data, config, and results. Looking at the dashboard, it seems that the trials get stuck in ray. Dec 3, 2022 · I’m using Centos 7, Pytorch Lightning and try to implement a hyperparameter tuning pipeline with Ray Tune, seems simple enough to follow the Guide. save_hyperparameters() and I’ve managed to save the hparams but I’ve not found a way to pass metrics properly. Weights & Biases Example; MLflow Example; Aim Example; Comet Example This tutorial walks through the process of converting an existing PyTorch Lightning script to use Ray Train. Sep 26, 2023 · The issue you’re facing now is the same as in the original post – you shouldn’t run this function outside the scope of the TorchTrainer, since the provided lightning callbacks/plugins use train. Apr 5, 2020 · This post uses pytorch-lightning v0. I’m using both ASHAScheduler from Ray and EarlyStopping from PyTorch Lightning. 16-bit training. PyTorch Lightning (tune. Medium: It contributes to significant difficulty to complete my task, but I can work around it. See below. Upon :ref:`Tune experiment restoration <tune-experiment-level-fault-tolerance>`, callback state will be restored via :meth:`~ray. hparams. import os import numpy as np import Aug 19, 2021 · Introducing Ray Lightning. With Optuna, a user has the ability to dynamically construct the search spaces for the hyperparameters. In this guide, for each hyperparameter combination, it seems like Tune uses the metrics obtained by the network weights at the end of its training. pytorch_lightning module using Lightning imports instead. path. This example introduces how to train a Pytorch Lightning Module using Ray Train TorchTrainer. Works with Jupyter Notebook. This webpage provides instructions on how to install Ray on different platforms and environments. Using Weights & Biases with Tune#. integration. DataLoader or a LightningDataModule specifying training samples. Loading Tune experiment results from a directory. Trainer: Sep 27, 2021 · I want to tune my hyper-parameters using ray-tune. Mar 5, 2024 · Hello, I am currently working with Ray and PyTorch Lightning to train a Language Model, and I’m facing a strange issue when attempting to load a checkpoint after training. MisconfigurationException: No supported gpu backend found! The distributed hparam search works on CPU, and training without Ray works Getting Started with Ray Tune #. If you consider switching to PyTorch Lightning to get rid of some of your boilerplate training code, please know that we also have a walkthrough on how to use Tune with PyTorch Lightning models. May 16, 2022 · None: Just asking a question out of curiosity. It’s designed to reduce computing power and memory usage, and to train large distributed Ray Tune includes the latest hyperparameter search algorithms, integrates with TensorBoard and other analysis libraries, and natively supports distributed training through Ray’s distributed machine learning engine. train import Checkpoint # Option 1: Initialize model with pretrained weights def initialize_model(): # Load pretrained model params model = models. torch import TorchConfig from ray. Let’s quickly walk through the key concepts you need to know to use Tune. No changes to existing training code. This tutorial walks through the process of converting an existing PyTorch script to use Ray Train. py: A subclass of the standard LightningCLI class. save_checkpoints: If True (default), checkpoints will be saved and reported to Ray. Trainer( max_epochs=5, check_val_every_n_epoch=2, log_every_n_steps=100, # devices=4 Running Tune experiments with BayesOpt#. 7,pytorch lightning 1. I’ve tried the pytorch lightning function This is called automatically by Tune to periodically checkpoint callback state. To get started, we take a PyTorch model and show you how to leverage Ray Tune to optimize the hyperparameters of this model. Hey guys, I can run single-node distributed training in the PyTorch toy example. ! pip install "torchmetrics>=0. 6". You can pass any Optuna sampler, which will be used to generate hyperparameter suggestions. load_from_checkpoint(PATH)model. Stack trace of one of the errors I’ve encountered when using TuneReportCheckpointCallback with a Lightning. I believe that natively PyTorch Lightning will use multiprocessing, which in fact will not work with Tune. Tune’s Search Algorithms integrate with BayesOpt and, as a result, allow you to seamlessly scale up a BayesOpt optimization process - without sacrificing performance. I’m running several ML experiments with Python/PyTorch on a shared server with several GPUs and CPUs. Ray Tune: Hyperparameter Tuning. I’m using Ray Tune with PyTorch Lightning and am a little confused about how the early stopping rules combine. train_dataloaders ¶ ( Union [ Any , LightningDataModule , None ]) – A collection of torch. Ray 1. DataFrame, tokenizer: BertTokenizer, max_token_len: int = 512 ): self Similar to Ray Tune, Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. How to Configure Persistent Storage in Ray Tune; How to Enable Fault Tolerance in Ray Tune; Fine-tune vicuna-13b with DeepSpeed and PyTorch Lightning. fit(). Callback. Let’s take a Oct 15, 2020 · Scaling up PyTorch Lightning hyperparameter tuning with Ray Tune PyTorch Lightning has been touted as the best thing in machine learning since sliced bread. Fine-tune a GPT-J-6B text generation model with DeepSpeed and Hugging Face Transformers. So my code looks like a adapted version of it. get_trial_resources()inside the training function. If you are using the functional API for tuning, get the current trial resources obtained by calling. Sometimes your init might have objects or other parameters you might not want to save. Ray Libraries (Data, Train, Tune, Serve) Ray Tune. dev0, pytorch 1. 8. data. From PyTorch to PyTorch Lightning. What changes do I need to make to my code to fit Tuning parameters for Batch size and say learning rate Here is my code step by step. Dec 11, 2020 · On the tensorboard page it states “If using TF2, Tune also automatically generates TensorBoard HParams output, as shown below:” Is it possible to get this to work when using pytorch (specifically pytorch lightning), I’ve tried self. However when building Ray’s LightningConfigBuilder, I have to pass an instance of my data-module for the . the pink line has done only 2 steps. py --start \--args=”localhost:6379” This will launch your cluster on AWS, upload tune_script. Please check it out, and would love to hear any feedback. class SRDataset(Dataset): def __init__( self, data: pd. from ray. I have my dataloaders in a PTL DataModule. Specify a grid of values to search over. Fine-tune a vicuna-13b text generation model with MNIST PyTorch Example #. Jan 24, 2023 · Screenshot Ray Tune Trial Status while tuning six PyTorch Forecasting TemporalFusionTransformer models. If you want to see practical tutorials right away, go visit our user guides . Lastly, the batch size is a choice Jan 25, 2023 · Thanks for sharing! I was able to narrow it down to devices=4, accelerator='cpu' in the constructor of pl. Examples using Ray Tune with ML Frameworks. Aug 31, 2023 · What would be the best way to tune batch size when using lightning data modules? Ray code based on this tutorial: Using PyTorch Lightning with Tune — Ray 2. This means every possible combination of values will be sampled. My specific situation is as follows. Setting up a Tuner for a Training Run with Tune#. Key Concepts of Ray Tune. air. 9" "pytorch_lightning>=1. Aug 31, 2020 · Running a hyperparameter search with Ray Tune is as simple as defining a search space and calling tune. I want to use Ray Tune to carry out 1 trial, which requires 10 CPU cores and 2 GPUs. 7 update provided a solution to this problem where the data module can be included in the training function! This solves my problem, so this issue can be closed. resnet50(pretrained=True) # Replace the original classifier Ray Train allows you to scale model training code from a single machine to a cluster of machines in the cloud, and abstracts away the complexities of distributed computing. Hugging Face, DeepSpeed. Model development: Pytorch lightning If this is a dict, each key will be the name reported to Tune and the respective value will be the metric key reported to PyTorch Lightning. Weights & Biases(Wandb) is a tool for experimenttracking, model optimizaton, and dataset versioning. previous. Get Started with Distributed Training using PyTorch. However, I would like to use the network weights which yield the lowest validation score throughout training. main. 32. exceptions. I am solving multi-label classification using BERT model. It’s designed to reduce computing power and memory usage, and to train large distributed Using PyTorch Lightning with Tune. The TuneReportCheckpointCallback also saves checkpoints after each evaluation round. It also integrates with Ray Tune for distributed hyperparameter tuning. Mar 30, 2024 · I am a new user to ray tune I’ve been encountering multiple issues while attempting to use Ray Tune for hyperparameter tuning in my PyTorch project. fit(model) And use it to predict your data of interest. If you already have your custom implementation of CLI, just make this implementation be a subclass of yours. xwjiang2010 February 13, 2023, 4:37pm 2. examples. 13. tune. Learn how to: Configure a model to run distributed and on the correct CPU/GPU device. The lr (learning rate) should be uniformly sampled between 0. cli. tune. """ This example is uses the official huggingface transformers `hyperparameter_search` API. Using MLflow with Tune. import os import numpy as np import Dec 22, 2022 · Ray Libraries (Data, Train, Tune, Serve) Ray Tune. However I’m not sure how the parallelization in Ray is supposed to interact with the parallelization in pytorch and lightning. , fewer trials and epochs, etc in that case. Feb 21, 2024 · config=param_space, num_samples=1, ) yunxuanx February 23, 2024, 10:33pm 2. lightgbm) Tune Internals. Despite following the official documentation and examples, I’m running into errors primarily related to tune. Basic experiment-level analysis: get a quick overview of how trials performed. fr wm pz vc oz cj yj ja ku id