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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-tuning tutorial for Evo2: Adapt the 1b evo2 checkpoint for your hardware\n",
    "Deploy tutorial on brev.dev:\n",
    "[![ Click here to deploy.](https://brev-assets.s3.us-west-1.amazonaws.com/nv-lb-dark.svg)](https://console.brev.dev/launchable/deploy?launchableID=env-2uGqijcTiNxv3V8LZJxXAa7KlKC)\n",
    "\n",
    "### Background and motivation\n",
    "To motivate this tutorial, we have noticed that the public\n",
    "evo2 checkpoint in hugging face for the 1b model is sensitive to `--fp8` status in training, the zero shot inference\n",
    "task, as demonstrated in the zero shot BRCA-1 notebook, produces near random AUCs if you do not use `--fp8`. \n",
    "If you want to infer or score new data, you need FP8 enabled since it was trained that way. Interestingly the `7b` checkpoint does not suffer from this\n",
    "limitation and seems robust to FP8 being activated or not. The consequence of this is that if you have older GPUs with\n",
    "a compute capability less than 8.9, which do not support FP8, then the output that you get from scoring sequences with\n",
    "sensitive checkpoints may not be biologically meaningful. \n",
    "\n",
    "We plan on making\n",
    "a `1b` parameter evo2 checkpoint available soon that has been fine-tuned to be robust to FP8 or BF16 inference in bionemo\n",
    "on NGC, but in the meantime this notebook tutorial outlines the steps for fine-tuning. The only difference between this\n",
    "notebook and what we did in production was to run these steps on more data on a slurm cluster to increase the global\n",
    "batch size. That said, if you run this for enough steps to get loss on the 1b checkpoint to the 1.08 range, you should \n",
    "have good luck with downstream sequence scoring tasks. \n",
    "\n",
    "### Requirements\n",
    "\n",
    "This is a tutorial demonstrating how you can fine-tune Evo2 on new data and/or hardware. The tutorial should take \n",
    "slightly under 1 hour to run on an RTX A6000 in bf16 precision.\n",
    "\n",
    "As configured, this tutorial requires an NVIDIA GPU with approximately 45GB of ram. If you have multiple GPUs with less\n",
    "memory, or you are having trouble with CUDA OOM at the training step below, try reducing the `--micro-batch-size` and/or\n",
    "increasing the number of `--devices [int]` to match your setup and also setting `--tensor-parallel-size [int]` to\n",
    "the number of devices. This should split up most of the model evenly between your devices, which will require much less\n",
    "memory. When we train the 1b model in practice we typically have the micro batch size set to 8, and run without model \n",
    "parallelism on available devices to achieve the largest possible global batch size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "\n",
    "# This variable should be used in the notebooks to run a subset of the model layers or a smaller model/dataset\n",
    "FAST_CI_MODE: bool = os.environ.get(\"FAST_CI_MODE\", False)\n",
    "# Clean up any prior runs\n",
    "CLEANUP: bool = False\n",
    "if CLEANUP:\n",
    "    !rm -rf preprocessed_data\n",
    "    !rm -rf preatraining_demo\n",
    "    !rm -rf pretraining_demo\n",
    "    !rm -rf training_data_config.yaml\n",
    "    !rm -rf preprocess_config.yaml\n",
    "    !rm -f chr20.fa.gz\n",
    "    !rm -f chr21.fa.gz\n",
    "    !rm -f chr22.fa.gz\n",
    "    !rm -f chr20_21_22.fa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup training data\n",
    "Evo2 uses megatron style datasets behind the scenes with advanced support for randomly indexing into documents, and\n",
    "packing documents together into batches at scale. The file-formats backing these datasets is not a standard biological\n",
    "format like fasta for representing genomes. First we show how you can start from a fasta file and preprocess them into\n",
    "the required data format for downstream handling. High level the steps are as follows:\n",
    "1. Acquire fasta files locally, ideally in some shared cluster storage\n",
    "2. Write a config script defining how you want the processed files to be generated from the fasta files. This is where\n",
    "  you specify top level train/validation/test splitting decisions.\n",
    "3. Call the actual `preprocess_evo2` script to generate the results.\n",
    "\n",
    "The next 4 cells go through this process on a set of smaller human chromosomes. At least 3 fasta records need to be present,\n",
    "one for the train, validation, and test split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "import os\n",
    "\n",
    "\n",
    "concat_path = \"chr20_21_22.fa\"\n",
    "if not os.path.exists(concat_path):\n",
    "    !wget https://hgdownload.soe.ucsc.edu/goldenpath/hg38/chromosomes/chr20.fa.gz\n",
    "    !wget https://hgdownload.soe.ucsc.edu/goldenpath/hg38/chromosomes/chr21.fa.gz\n",
    "    !wget https://hgdownload.soe.ucsc.edu/goldenpath/hg38/chromosomes/chr22.fa.gz\n",
    "    !zcat chr20.fa.gz > chr20.fa\n",
    "    !zcat chr21.fa.gz > chr21.fa\n",
    "    !zcat chr22.fa.gz > chr22.fa\n",
    "    !cat chr20.fa chr21.fa chr22.fa > chr20_21_22.fa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_fasta_path = os.path.abspath(concat_path)\n",
    "output_dir = os.path.abspath(\"preprocessed_data\")\n",
    "output_yaml = f\"\"\"\n",
    "- datapaths: [\"{full_fasta_path}\"]\n",
    "  output_dir: \"{output_dir}\"\n",
    "  output_prefix: chr20_21_22_uint8_distinct\n",
    "  train_split: 0.9\n",
    "  valid_split: 0.05\n",
    "  test_split: 0.05\n",
    "  overwrite: True\n",
    "  embed_reverse_complement: true\n",
    "  random_reverse_complement: 0.0\n",
    "  random_lineage_dropout: 0.0\n",
    "  include_sequence_id: false\n",
    "  transcribe: \"back_transcribe\"\n",
    "  force_uppercase: false\n",
    "  indexed_dataset_dtype: \"uint8\"\n",
    "  tokenizer_type: \"Byte-Level\"\n",
    "  vocab_file: null\n",
    "  vocab_size: null\n",
    "  merges_file: null\n",
    "  pretrained_tokenizer_model: null\n",
    "  special_tokens: null\n",
    "  fast_hf_tokenizer: true\n",
    "  append_eod: true\n",
    "  enforce_sample_length: null\n",
    "  ftfy: false\n",
    "  workers: 1\n",
    "  preproc_concurrency: 100000\n",
    "  chunksize: 25\n",
    "  drop_empty_sequences: true\n",
    "  nnn_filter: false  # If you split your fasta on NNN (in human these are contigs), then you should set this to true.\n",
    "  seed: 12342  # Not relevant because we are not using random reverse complement or lineage dropout.\n",
    "\"\"\"\n",
    "with open(\"preprocess_config.yaml\", \"w\") as f:\n",
    "    print(output_yaml, file=f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!preprocess_evo2 --config preprocess_config.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 309M\n",
      "drwxr-xr-x 3 ubuntu ubuntu 4.0K Mar 10 22:17 chr20_21_22_uint8_distinct_byte-level_test\n",
      "-rw-r--r-- 1 ubuntu ubuntu  90M Mar 10 23:07 chr20_21_22_uint8_distinct_byte-level_test.bin\n",
      "-rw-r--r-- 1 ubuntu ubuntu   82 Mar 10 23:07 chr20_21_22_uint8_distinct_byte-level_test.idx\n",
      "drwxr-xr-x 3 ubuntu ubuntu 4.0K Mar 10 22:17 chr20_21_22_uint8_distinct_byte-level_train\n",
      "-rw-r--r-- 1 ubuntu ubuntu 123M Mar 10 23:06 chr20_21_22_uint8_distinct_byte-level_train.bin\n",
      "-rw-r--r-- 1 ubuntu ubuntu   82 Mar 10 23:07 chr20_21_22_uint8_distinct_byte-level_train.idx\n",
      "drwxr-xr-x 3 ubuntu ubuntu 4.0K Mar 10 22:17 chr20_21_22_uint8_distinct_byte-level_val\n",
      "-rw-r--r-- 1 ubuntu ubuntu  97M Mar 10 23:06 chr20_21_22_uint8_distinct_byte-level_val.bin\n",
      "-rw-r--r-- 1 ubuntu ubuntu   82 Mar 10 23:07 chr20_21_22_uint8_distinct_byte-level_val.idx\n"
     ]
    }
   ],
   "source": [
    "# There should be a collection of bin/idx files created in the preprocessed_data directory.\n",
    "!ls -lh preprocessed_data/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [Optional] specify or convert initial checkpoint\n",
    "The main difference between pre-training and fine-tuning is whether or not you decide to start training the model with\n",
    "weights from a prior training run. For this tutorial we want to tune a `1b` checkpoint from hugging face that is known\n",
    "(at the time of this writing) to be sensitive to GPU architecture so that it will work with your architecture. We have a\n",
    "script that will download and convert a savanna format evo2 checkpoint from hugging face, and output that into a NeMo2\n",
    "format checkpoint directory that can be used as the starting point for a fine-tuning run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "if not os.path.exists(\"nemo2_evo2_1b_8k\"):\n",
    "    !evo2_convert_to_nemo2 \\\n",
    "      --model-path hf://arcinstitute/savanna_evo2_1b_base \\\n",
    "      --model-size 1b --output-dir nemo2_evo2_1b_8k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure the training dataset\n",
    "The next step is to configure your training dataset, in this case configuring the simple single-file example we output\n",
    "two steps ago in this tutorial. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "\n",
    "output_pfx = str(Path(os.path.abspath(\"preprocessed_data\")) / \"chr20_21_22_uint8_distinct_byte-level\")\n",
    "output_yaml = f\"\"\"\n",
    "- dataset_prefix: {output_pfx}_train\n",
    "  dataset_split: train\n",
    "  dataset_weight: 1.0\n",
    "- dataset_prefix: {output_pfx}_val\n",
    "  dataset_split: validation\n",
    "  dataset_weight: 1.0\n",
    "- dataset_prefix: {output_pfx}_test\n",
    "  dataset_split: test\n",
    "  dataset_weight: 1.0\n",
    "\"\"\"\n",
    "with open(\"training_data_config.yaml\", \"w\") as f:\n",
    "    print(output_yaml, file=f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This next cell takes approximately 25 minutes to run on an RTX A6000 with `MAX_STEPS=100`. Each step takes about 9.5 seconds with the \n",
    "following configuration, so you can budget a desired number of max steps to try."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "MAX_STEPS: int = 10 if FAST_CI_MODE else 100\n",
    "val_check_interval = min(int(MAX_STEPS // 2), 50)\n",
    "warmup_steps = min(MAX_STEPS, 100)\n",
    "# For evo2 training and fine-tuning follow the same set of steps, so we use the same train_evo2 command.\n",
    "#  the big difference is the --ckpt-dir argument which points to a pre-existing checkpoint from some other training run.\n",
    "\n",
    "if FAST_CI_MODE:\n",
    "    model_subset_option = (\n",
    "        \"--num-layers 4 --hybrid-override-pattern SDH* --activation-checkpoint-recompute-num-layers 2\"\n",
    "    )\n",
    "else:\n",
    "    # By default do 5 layers of activation checkpointing\n",
    "    model_subset_option = \"--activation-checkpoint-recompute-num-layers 5\"\n",
    "train_cmd = f\"\"\"train_evo2 \\\n",
    "    -d training_data_config.yaml \\\n",
    "    --dataset-dir ./preprocessed_data \\\n",
    "    --result-dir pretraining_demo \\\n",
    "    --experiment-name evo2 \\\n",
    "    --model-size 1b \\\n",
    "    --devices 1 \\\n",
    "    --num-nodes 1 \\\n",
    "    --seq-length 8192 \\\n",
    "    --micro-batch-size 2 \\\n",
    "    --lr 0.000015 \\\n",
    "    --min-lr 0.0000149 \\\n",
    "    --warmup-steps {warmup_steps} \\\n",
    "    --grad-acc-batches 4 \\\n",
    "    --max-steps {MAX_STEPS} \\\n",
    "    --ckpt-dir nemo2_evo2_1b_8k \\\n",
    "    --clip-grad 250 \\\n",
    "    --wd 0.001 \\\n",
    "    --attention-dropout 0.01 \\\n",
    "    --hidden-dropout 0.01 \\\n",
    "    --val-check-interval {val_check_interval} \\\n",
    "    {model_subset_option} \\\n",
    "    --create-tensorboard-logger \\\n",
    "    --ckpt-async-save\"\"\"\n",
    "\n",
    "!{train_cmd}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plotting code is hidden in documentation for brevity. You can view the notebook on github, run it in jupyter-lab or launch the tutorial on brev.dev if you want to view the source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [
     "hide-input",
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import tensorboard.backend.event_processing.event_accumulator as event_accumulator\n",
    "\n",
    "\n",
    "# Function to extract data from TensorBoard event files and convert to DataFrame\n",
    "def tensorboard_to_dataframe(event_file):\n",
    "    \"\"\"Given a TensorBoard event file, return a pandas DataFrame with the training metrics.\"\"\"\n",
    "    # Load the event file\n",
    "    ea = event_accumulator.EventAccumulator(\n",
    "        event_file,\n",
    "        size_guidance={\n",
    "            event_accumulator.SCALARS: 0,  # 0 means load all\n",
    "        },\n",
    "    )\n",
    "    ea.Reload()\n",
    "\n",
    "    # Get list of all available tags\n",
    "    tags = ea.Tags()[\"scalars\"]\n",
    "\n",
    "    # First, find the union of all steps\n",
    "    all_steps = set()\n",
    "    for tag in tags:\n",
    "        events = ea.Scalars(tag)\n",
    "        steps = [event.step for event in events]\n",
    "        all_steps.update(steps)\n",
    "\n",
    "    # Sort steps for proper ordering\n",
    "    all_steps = sorted(all_steps)\n",
    "\n",
    "    # Initialize the dataframe with steps\n",
    "    df = pd.DataFrame({\"step\": all_steps})\n",
    "\n",
    "    # Add each metric as a column\n",
    "    for tag in tags:\n",
    "        events = ea.Scalars(tag)\n",
    "        # Create a dictionary mapping steps to values\n",
    "        step_to_value = {event.step: event.value for event in events}\n",
    "        # Add the values to the dataframe, using NaN for missing steps\n",
    "        df[tag] = df[\"step\"].map(step_to_value)\n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "# Example of creating a multi-metric plot with seaborn\n",
    "def plot_multiple_training_metrics(df, metrics_to_plot, figsize=(15, 10)):\n",
    "    \"\"\"Given a pandas DataFrame with the training metrics, plot the metrics.\"\"\"\n",
    "    n = len(metrics_to_plot)\n",
    "    fig, axes = plt.subplots(n, 1, figsize=figsize, sharex=True)\n",
    "\n",
    "    if n == 1:  # Handle the case of a single plot\n",
    "        axes = [axes]\n",
    "\n",
    "    sns.set_style(\"whitegrid\")\n",
    "\n",
    "    for i, metric in enumerate(metrics_to_plot):\n",
    "        if metric in df.columns:\n",
    "            sns.lineplot(x=\"step\", y=metric, data=df, ax=axes[i], linewidth=2.5, errorbar=\"sd\")\n",
    "            axes[i].set_title(metric, fontsize=14)\n",
    "            axes[i].set_ylabel(\"Value\", fontsize=12)\n",
    "    axes[-1].set_xlabel(\"Steps\", fontsize=14)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following figures show various training metrics per step.\n",
    "* `reduced_train_loss` captures the training loss. On larger runs you want to see the loss drop to about 1.08 consistently\n",
    "  for the 1b checkpoint.\n",
    "* `lr` shows the learning rate schedule for training. Typically we do a linear warmup schedule followed by an cosine decay.\n",
    "  this small notebook tutorial just goes through the initial warmup period.\n",
    "* `grad_norm` shows the gradient norm of the full model. As the model fits the data better you should see this value drop\n",
    "  down below 1.0 consistently. \n",
    "* `val_loss` shows the same kind of loss shown in `reduced_train_loss` but for a held-out set of validation samples. If you\n",
    "  ever train the model a very long time and see this start to go up while the training loss continues to drop that's a sign\n",
    "  of over-fitting. We have not yet seen this happen. Small fluctuations up and down are expected during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the TensorBoard event file for the training run\n",
    "log_dirs = !find pretraining_demo/evo2/dev -name \"events.out.tfevents*\"\n",
    "tf_event_file = log_dirs[0]\n",
    "\n",
    "# Extract data from your event file\n",
    "df = tensorboard_to_dataframe(tf_event_file)\n",
    "# You can uncomment and modify this to plot multiple metrics once you see what's available\n",
    "plot_multiple_training_metrics(df, [\"reduced_train_loss\", \"lr\", \"grad_norm\", \"val_loss\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you have a checkpoint that you can try out in place of the converted evo2 checkpoint in the BRCA-1 tutorial \n",
    "(the path is displayed in the next code cell). To test your checkpoint, please supply the following path to the saved \n",
    "checkpoint produced by this notebook as the `--ckpt-dir {checkpoint_path}`\n",
    "argument to the `predict_evo2` command in the zero shot BRCA tutorial. For the 1b checkpoint you should see AUC above\n",
    "0.73 if you successfully fine-tuned the checkpoint for your hardware, or to check that your hardware works with the \n",
    "converted checkpoint from hugging face as is.\n",
    "\n",
    "In our experience running this notebook for up to an hour on a single GPU is not sufficient to recover BF16 accuracy. We\n",
    "have more details about what did work in the Next Steps section below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pretraining_demo/default--val_loss=0.8664-epoch=0-consumed_samples=800.0-last'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_ckpt_paths = !ls -d pretraining_demo/evo2/checkpoints/*-last\n",
    "final_ckpt_path = final_ckpt_paths[-1]\n",
    "final_ckpt_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next steps\n",
    "On a small number of devices, or with the small demo fasta we provided in this tutorial, it's possible you are not at the needed\n",
    "1.08 loss level to get good downstream accuracy out of this checkpoint. You can try increasing the `MAX_STEPS` parameter in the training cell,\n",
    "or running a larger cluster with more GPUs. The following loss curve was generated with a global batch size of 256 at 8192 context or approximately\n",
    "2 million tokens per step. With that configuration we see a good loss of 1.08 after approximately 100 steps. The following figure shows our\n",
    "learning rate across the first 500 steps of fine-tuning with a global batch size of 256. Later on in this notebook we also show the slurm script\n",
    "to replicate this on your cluster.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 800
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the example loss curve from a larger training run\n",
    "from IPython.display import Image, display\n",
    "\n",
    "\n",
    "# Load and display the image\n",
    "display(Image(\"../assets/1b_finetuning_train_curve_500_steps_256gbs.png\", width=800))"
   ]
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    "#### How we fine-tuned the 1b checkpoint for bf16 accuracy\n",
    "An example of the full slurm script to run the above training curve on our infrastructure is as follows:\n",
    "\n",
    "First make a `~/.netrc` file with your wandb login info. You can also accomplish this by setting wandb ENV variables,\n",
    "assuming you want to log to wandb. If not you can pass the `--no-wandb` argument as part of the args to `train_evo2`:\n",
    "\n",
    "```ini\n",
    "machine api.wandb.ai\n",
    "  login user\n",
    "  password PASSWORD_HERE\n",
    "```\n",
    "\n",
    "Next, paste/edit the following sbatch script for your own configuration:\n",
    "\n",
    "```bash\n",
    "# TODO: You may need to add more SBATCH configuration here specific to your cluster.\n",
    "#SBATCH --nodes=4                       # number of nodes\n",
    "#SBATCH --gpus-per-node=8\n",
    "#SBATCH --ntasks-per-node=8                 # n tasks per machine (one task per gpu) <required>\n",
    "#SBATCH --time=04:00:00                     # wall time  (8 for batch, backfill, 2 for batch_short)\n",
    "#SBATCH --mem=0                             # all mem avail\n",
    "#SBATCH --exclusive\n",
    "set -x\n",
    "# You may want to edit this file and/or add your own version to your mounts.\n",
    "CONFIG_PATH_IN_CONTAINER=/workspace/bionemo2/sub-packages/bionemo-evo2/examples/configs/full_pretrain_shortphase_config.yaml\n",
    "# You can build a `.sqsh` file with enroot which may be faster to load on each node rather than pulling down from NGC\n",
    "IMAGE_PATH=nvcr.io/nvidia/clara/bionemo-framework:nightly\n",
    "WANDB_PROJECT_NAME= # Set you wandb project here, or leave blank and add --no-wandb to the image\n",
    "MODEL_SIZE=1b  # change this to 7b_arc_longcontext etc. This version is different.\n",
    "CP_SIZE=1\n",
    "TP_SIZE=1\n",
    "PP_SIZE=1\n",
    "MICRO_BATCH_SIZE=8\n",
    "GRAD_ACC_BATCHES=1\n",
    "SEQ_LEN=8192\n",
    "MAX_STEPS=580000 # 8T tokens given 1024 nodes and 8192 seq length\n",
    "VAL_CHECK=500\n",
    "CLIP_GRAD=250  # Arc trained without gradient clipping. Set to a large value so megatron still logs grad_norm.\n",
    "# The following arguments will remove the EOD/PAD tokens from the loss, unlike how the original Evo2 model was trained.\n",
    "#  this does not impact downstream accuracy in our experience and is more standard.\n",
    "EXTRA_ARGS=\"--enable-preemption --ckpt-async-save --overlap-grad-reduce --clip-grad $CLIP_GRAD --eod-pad-in-loss-mask\"\n",
    "LR=0.000015\n",
    "MIN_LR=0.0000015\n",
    "WU_STEPS=100\n",
    "SEED=1234 \n",
    "WD=0.001\n",
    "ADO=0.01\n",
    "HDO=0.01\n",
    "EXPERIMENT_NAME=fine_tune_evo2_1b_on_bf16\n",
    "# NCCL performance parameters\n",
    "# =========================\n",
    "export TORCH_NCCL_AVOID_RECORD_STREAMS=1\n",
    "\n",
    "# Mounts\n",
    "# =========================\n",
    "DATA_PATH= # PATH to the directory that stores your data that you want to mount into the container\n",
    "DATA_MOUNT=/workspace/bionemo2/data  # or if you configure your data with a different base dir in the config, use that here\n",
    "RESULTS_PATH_CLUSTER= # Where do you want the results to land on your shared cluster storage\n",
    "RESULTS_PATH_IMAGE=/results/\n",
    "CKPT_MOUNT_CLUSTER= # Path to shared location on your cluster where the checkpoint files can be found\n",
    "CKPT_MOUNT_IMAGE=/checkpoints/  # pragma: allowlist secret  (for some reason this line flags a high entropy string check in CI)\n",
    "NETRC_PATH=$HOME/.netrc\n",
    "NETRC_MOUNT=/root/.netrc\n",
    "# TODO either move your config to one of the mounted paths or add your own mount to a location with your configs\n",
    "\n",
    "mkdir -p $RESULTS_PATH_CLUSTER\n",
    "MOUNTS=${DATA_PATH}:${DATA_MOUNT},${RESULTS_PATH_CLUSTER}:${RESULTS_PATH_IMAGE},${NETRC_PATH}:${NETRC_MOUNT},${CKPT_MOUNT_CLUSTER}:${CKPT_MOUNT_IMAGE},$HOME/.cache:/root/.cache\n",
    "# Generate (or retrieve) a unique, shared ID per run to handle restarts in W&B and Tensorboard\n",
    "# =========================\n",
    "mkdir -p ${RESULTS_PATH_CLUSTER}\n",
    "if [ -f ${RESULTS_PATH_CLUSTER}/run.id ];\n",
    "then\n",
    "    RUN_ID=$(<${RESULTS_PATH_CLUSTER}/run.id)\n",
    "else\n",
    "    array=()\n",
    "    for i in {a..z} {A..Z} {0..9};\n",
    "    do\n",
    "    array[$RANDOM]=$i\n",
    "    done\n",
    "    RUN_ID=$(printf %s ${array[@]::8})\n",
    "    echo $RUN_ID > ${RESULTS_PATH_CLUSTER}/run.id\n",
    "fi\n",
    "# =========================\n",
    "read -r -d '' COMMAND <<EOF\n",
    "echo \"*******STARTING********\" \\\n",
    "&& echo \"---------------\" \\\n",
    "&& echo \"Starting training\" \\\n",
    "&&  \\\n",
    "train_evo2 \\\n",
    "    -d $CONFIG_PATH_IN_CONTAINER \\\n",
    "    --num-nodes=${SLURM_JOB_NUM_NODES} \\\n",
    "    --ckpt-dir $CKPT_MOUNT_IMAGE/nemo2_evo2_1b_8k \\\n",
    "    --devices=${SLURM_NTASKS_PER_NODE} \\\n",
    "    --grad-acc-batches $GRAD_ACC_BATCHES \\\n",
    "    --max-steps=$MAX_STEPS \\\n",
    "    --seed $SEED \\\n",
    "    ${EXTRA_ARGS} \\\n",
    "    --wandb-run-id $RUN_ID \\\n",
    "    --wandb-project $WANDB_PROJECT_NAME \\\n",
    "    --lr $LR \\\n",
    "    --wd $WD \\\n",
    "    --activation-checkpoint-recompute-num-layers 5 \\\n",
    "    --min-lr $MIN_LR \\\n",
    "    --warmup-steps $WU_STEPS \\\n",
    "    --attention-dropout $ADO \\\n",
    "    --hidden-dropout $HDO \\\n",
    "    --limit-val-batches=20 \\\n",
    "    --val-check-interval=${VAL_CHECK} \\\n",
    "    --result-dir=$RESULTS_PATH_IMAGE \\\n",
    "    --seq-length=${SEQ_LEN} \\\n",
    "    --tensor-parallel-size=${TP_SIZE} \\\n",
    "    --context-parallel-size=${CP_SIZE} \\\n",
    "    --pipeline-model-parallel-size=${PP_SIZE} \\\n",
    "    --workers 8 \\\n",
    "    --micro-batch-size=${MICRO_BATCH_SIZE} \\\n",
    "    --model-size=${MODEL_SIZE}\n",
    "EOF\n",
    "srun \\\n",
    "    --output ${RESULTS_PATH_CLUSTER}/slurm-%j.out \\\n",
    "    --error ${RESULTS_PATH_CLUSTER}/error-%j.out \\\n",
    "    --container-image=$IMAGE_PATH \\\n",
    "    --container-mounts ${MOUNTS} \\\n",
    "    bash -c \"${COMMAND}\"\n",
    "set +x\n",
    "\n",
    "```"
   ]
  }
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